The Federal Reserve accomplished what many thought was unattainable in 2024: a rare economic soft landing, reducing inflation without triggering a recession. Yet, as 2025 approaches, stubborn inflation and incoming policy shifts under President-elect Donald Trump are creating new uncertainties for the U.S. economy. FED Chair Jerome Powell’s cautious optimism is underscored by persistent price pressures, cautious rate cuts, and questions about the future impact of tariffs, tax changes, and geopolitical instability.

The Federal Reserve closed 2024 on a note of measured success, achieving an economic soft landing that avoided the recession many had feared. Elevated interest rates guided inflation downward while maintaining economic growth and relatively stable unemployment rates. This rare balance allowed the FED to begin cutting rates for the first time in over four years.

FED Chair Jerome Powell expressed satisfaction with the year’s results at a December press conference: “I think it’s pretty clear we have avoided a recession. The path down has been better than many predicted.” However, persistent inflation remains a concern, as it continues to hover above the FED’s 2% target, with policymakers projecting it will not reach that goal until 2027.

Rate cuts with caution

The FED enacted three rate cuts in 2024, starting with a significant 50-basis-point reduction in September, followed by smaller cuts later in the year. These moves signaled the central bank’s confidence in the economy’s stability while addressing inflation.

Yet, these actions were not without dissent. FED Governor Michelle Bowman opposed the September cut, arguing that inflation goals had not been fully achieved. “A larger policy action could be interpreted as a premature declaration of victory,” she warned. Cleveland FED President Beth Hammack also dissented in December, preferring a pause to ensure inflation was resuming its downward path.

Inflation and Trump policies

As President-elect Donald Trump prepares to take office, his proposals—ranging from tax cuts to tariffs—are expected to complicate the FED’s fight against inflation. Powell acknowledged the challenge, noting that FED officials have begun incorporating potential policy impacts into their forecasts. However, the exact implications of these policies remain unclear, as details on tariffs and other measures are still developing.

Despite these uncertainties, Powell emphasized the FED’s commitment to data-driven decisions. “We are not done cutting rates,” he said, “but we will proceed cautiously.”

The FED’s cautious stance on inflation has influenced market behavior. Gold prices, for example, fell sharply following Powell’s press conference, reflecting concerns over the slower pace of rate cuts and persistent inflation. Spot gold traded at $2,592 per ounce, marking a 2.05% daily decline.

Powell also addressed broader economic concerns, including geopolitical risks and the strain of high consumer prices. He highlighted that while inflation persists in sectors like housing, the U.S. economy has outperformed expectations. “The U.S. economy is performing very, very well, substantially better than our global peer group,” he said.

Looking ahead, the FED faces a complex 2025. Policymakers must navigate the dual challenges of persistent inflation and the potential economic shifts brought by the new administration. As Powell summed up; the outlook is pretty bright for US economy, but caution remains essential.

The United Arab Emirates (UAE) has found itself at the center of global tech competition, particularly as it balances relations with both the US and China.

Despite pressure from Washington to limit cooperation with Chinese firms, especially Huawei, which helped install the UAE’s 5G infrastructure in 2019, the UAE is pursuing a strategic “tech hedging” strategy to diversify its options in artificial intelligence (AI) and other emerging technologies.

Huawei and the 5G controversy

Since 2019, Huawei has been a key player in building the UAE’s 5G network, cementing the tech giant’s presence in the Middle East. However, this relationship has drawn scrutiny from the United States, which has raised concerns about potential national security risks posed by Chinese telecom infrastructure.

The tensions escalated when the US alleged that China was constructing an intelligence facility at Abu Dhabi’s Port Khalifa, operated by Cosco Shipping Ports, a Chinese firm. The UAE, after investigating these claims, rejected them as unfounded.

These geopolitical frictions contributed to the UAE’s decision to withdraw from a US$23 billion arms deal, which would have included the purchase of advanced F-35 stealth jets and MQ-9 Reaper drones from the US.

This move signaled a broader intention by the UAE to assert its independence in technology and defense policies, rather than fully aligning with either superpower.

Building AI data centers across Asia

While navigating its relationship with China and the US, the UAE is positioning itself as a future tech powerhouse. In a strategic move to expand its influence in AI, Abu Dhabi-based tech conglomerate G42 announced plans to establish AI data centers in Asia.

These centers, which will be built in countries like India, Indonesia, Malaysia, and the Philippines, are set to play a pivotal role in the UAE’s ambition to lead the Global South in technological advancements.

On September 18, G42 committed to building AI data centers in India, with a planned power generation capacity of up to 2GW—doubling the country’s current capacity. These data centers will house powerful supercomputers, further advancing AI infrastructure in the region.

The role of global partnerships

To support its growing AI ambitions, the UAE is forming key international partnerships. G42’s involvement in the Global AI Infrastructure Investment Partnership, a consortium that includes major US corporations like Microsoft, BlackRock, and Global Infrastructure Partners, exemplifies this strategy.

The consortium aims to invest up to US$100 billion in AI data centers and energy infrastructure, leveraging Nvidia’s expertise in AI chip design. In the short term, the partnership is expected to raise US$30 billion in private equity, accelerating AI developments across the Middle East, Africa, and Central Asia.

Despite this growing collaboration with US firms, the UAE has maintained a careful distance from fully joining the US-led chips and AI coalition, which includes nations like Japan and South Korea. Analysts suggest that the UAE’s leadership is not keen on being locked into any one geopolitical camp.

UAE’s tech hedging

According to political analyst Ahmed Aboudouh, the UAE’s AI strategy can be seen as a “tech hedging” approach. This means the UAE is actively diversifying its technology partnerships to avoid overreliance on either the US or China. Aboudouh describes the UAE’s goal as becoming the “Taiwan of the Global South“—a tech hub known for innovation and independence.

The UAE is charting its own path, developing industrial and technological sectors while ensuring its long-term strategic interests are protected.

While the recent partnership with the US represents a significant step in AI cooperation, the UAE is unlikely to completely sever its ties with China, particularly in areas of neutral interest like renewable energy and biotech.

US and China’s role in the Middle East

The evolving tech landscape in the Middle East reflects the broader global competition between the US and China. As Robert Mogielnicki, a senior scholar at the Arab Gulf States Institute, notes, both countries are vying for influence in the region, offering different types of partnerships. While the US contributes technological expertise and robust regulatory frameworks, China brings its own economic and infrastructure investments to the table.

In this complex web of alliances, the UAE’s “tech hedging” strategy appears to be a calculated effort to maximize its opportunities without being overly dependent on one global power. This allows Abu Dhabi to continue developing cutting-edge AI technologies and infrastructure while navigating the geopolitical pressures of the US-China rivalry.

As the UAE forges ahead with its ambitious AI agenda, it is skillfully managing its relationships with both the US and China. By strategically partnering with US firms while maintaining selective cooperation with Chinese companies, the UAE is positioning itself as a global tech leader. With plans to establish AI data centers across Asia and strengthen its AI infrastructure, the UAE’s vision of becoming the “Taiwan of the Global South” is steadily coming into focus.

Featured image credit: Emre Çıtak/Ideogram AI

Kamala Harris’ policies are set to take center stage as she emerges as a potential Democratic nominee following President Joe Biden’s decision to step down from the race.

With President Joe Biden stepping down from the race, Vice President Kamala Harris emerges as a potential Democratic nominee, garnering significant attention for her stance on technology policies. Biden has expressed his unequivocal support for Harris, endorsing her as the nominee for the party, while Harris has declared her intent to secure the nomination. However, it remains uncertain if she will face competition from other Democratic leaders at an open convention or through alternative selection processes.

Harris, with her deep-rooted connections in the Bay Area—having been born in Oakland—and extensive experience in the tech industry, stands out as a candidate. Her career spans roles as San Francisco’s district attorney, California’s attorney general, and a U.S. senator since 2016. Her early political supporters included venture capitalists like John Doerr and Ron Conway, and she quickly received an endorsement from LinkedIn co-founder Reid Hoffman during her presidential campaign. Nevertheless, other tech industry leaders, such as Netflix co-founder Reed Hastings, have been more reserved, suggesting a preference for an open convention.

Critics argue that during her tenure as attorney general, Harris did not sufficiently address the expanding influence of tech giants. Conversely, she has also demonstrated a willingness to hold tech CEOs accountable and advocate for stricter regulations. As a senator, Harris challenged major social networks on the spread of misinformation. When questioned during the 2020 presidential campaign about the possibility of breaking up companies like Amazon, Google, and Facebook—a stance championed by rival Elizabeth Warren—Harris advocated for stringent regulations to safeguard consumer privacy instead.

Kamala Harris’ policies focused around tech

In her role as vice president, Kamala Harris’ policies have included discussions on the need for AI regulation, emphasizing that she and President Biden reject the notion that public protection and innovation advancement are mutually exclusive. Biden’s executive order urging companies to establish new AI development standards aligns with Harris’ views, with her stating that these voluntary commitments are merely the beginning. She underscored the necessity for robust government oversight to prevent tech companies from prioritizing profits over consumer welfare, community safety, and democratic stability.

Concerns about overregulation of AI have been cited by venture capitalists Marc Andreessen and Ben Horowitz as reasons for their support of Donald Trump, highlighting a significant point of contention in the tech industry. On another front, Harris addressed national security concerns related to TikTok, indicating that while there are issues with its parent company, ByteDance, there is no current intention to ban the app.

Harris has remained relatively silent on cryptocurrency issues but is expected to back the Biden Administration’s regulatory stance on crypto. Kamala Harris’ policies, balancing regulation with innovation, will likely be a focal point as she navigates her potential candidacy.

The focus on Kamala Harris’ policies has intensified following President Joe Biden’s announcement that he will not seek reelection, opting instead to concentrate on his presidential duties for the remainder of his term. Biden made this declaration in a letter posted on social media platforms, expressing his full support and endorsement for Harris as the Democratic nominee.

Record-breaking donations on ActBlue following Harris campaign launch

In an impressive display of support, small-dollar donors contributed nearly $47 million via ActBlue in the seven hours following Vice President Kamala Harris’s presidential campaign launch on Sunday afternoon, as reported by the Democratic fundraising platform. This surge in donations marks the largest single-day fundraising effort of the 2024 election cycle, highlighting the enthusiasm of grassroots supporters.

“As of 9pm ET, grassroots supporters have raised $46.7 million through ActBlue following Vice President Kamala Harris’ campaign launch. This has been the biggest fundraising day of the 2024 cycle. Small-dollar donors are fired up and ready to take on this election,” ActBlue announced on the social media platform X.

This fundraising total reflects contributions to various races, not just Harris’s campaign. The record-breaking day came shortly after President Biden announced his decision to step aside from the presidential race, endorsing Harris as his successor.

ActBlue also noted that within the first five hours of Harris’s campaign, over $27.5 million was raised through small donor contributions. The New York Times reported that Sunday was the single biggest day for online Democratic donations since the 2020 election, with the previous record set the day after Supreme Court Justice Ruth Bader Ginsburg’s death in September 2020, when ActBlue raised approximately $73.5 million.

Prominent Democratic donors are now backing Harris following Biden’s decision to step down. This comes after weeks of diminishing fundraising for Biden, with big-dollar donors freezing around $90 million in response to calls for him to exit the race.

Biden’s decision comes amid growing calls from Democrats and the media for him to step aside, citing concerns about his age and ability to defeat former President Donald Trump. The pressure mounted after Biden appeared confused during a recent debate, leading to widespread speculation about his health.

pic.twitter.com/RMIRvlSOYw

— Joe Biden (@JoeBiden) July 21, 2024

In his announcement, Biden highlighted the achievements of his administration, including economic growth, healthcare expansion, and climate legislation. Despite these accomplishments, he acknowledged that stepping down was in the best interest of the party and the country. He praised Harris for her partnership and emphasized his trust in her ability to lead the nation.

Harris, decades younger than Biden at 59, now stands poised to potentially become the first female and first South Asian American president. Her candidacy brings renewed attention to her tech-related policies and her long-standing relationship with the tech industry, stemming from her roots in the Bay Area. As the election approaches, the focus on how Harris navigates issues like AI regulation, tech industry accountability, and national security concerns related to technology will be crucial in shaping her campaign and the future of Democratic tech policy.

Featured image credit: The White House

The pile dataset has become a hot topic in AI circles, sparking debates about how data is used and the ethics involved. This massive collection of text has been used by big tech companies to train their AI models.

However, the way this data was gathered and used raises questions about consent, ownership, and the limits of harvesting online content.

For AI to get smarter, it needs lots of data to learn from. The pile dataset, put together by the non-profit AI research group EleutherAI, has become a go-to resource for this. It’s got all sorts of stuff in it – YouTube video subtitles, European Parliament documents, and even old Enron emails. Big names like Apple, Nvidia, and Salesforce have been using it to teach their AIs new tricks.

But here’s where things get sticky: YouTube doesn’t allow people to scrape content from its platform without permission. They even demanded answers on Sora’s training data back then.

Yet, the investigation by Wired found that subtitles from tons of popular creators and institutions were used without them knowing or agreeing to it.

What is the pile dataset?

The pile dataset is a massive collection of text data used for training artificial intelligence models. It’s become a hot topic in tech circles due to its size, diversity, and the controversy surrounding its content sources.

The pile dataset has a wide variety of text from across the internet. It’s designed to provide AI models with a broad range of human-generated content to learn from, helping them understand and generate more natural language.

One of the key features of the pile dataset is its sheer variety. It contains subtitles from over 48,000 YouTube channels, including popular creators like MrBeast, as well as content from educational institutions like MIT and Harvard.

Beyond YouTube content, the dataset also includes material from:

• European Parliament documents
• English Wikipedia articles
• Scientific papers and technical reports
• Online forums and discussion boards
• News articles and blog posts

This diverse mix of content types and sources is what makes the pile dataset so valuable for AI training. It exposes AI models to a wide range of writing styles, topics, and formats, helping them become more versatile and capable.

How is Big Tech using the pile dataset?

Big tech companies have been quietly tapping into the pile dataset to power their AI advancements. This massive collection of digital content has become a key resource for training sophisticated language models and other AI systems.
Companies like Apple, Nvidia, Salesforce, and Anthropic have openly admitted to using the pile dataset in their AI development processes.

These tech powerhouses are leveraging this vast trove of information to enhance their AI capabilities across various applications and services.
The appeal of the pile dataset lies in its diversity and scale.

With content ranging from YouTube subtitles to academic papers and even old corporate emails, it provides a rich tapestry of human-generated text for AI models to learn from. This breadth of data helps AI systems better understand and generate human-like language in various contexts.

Web Scraping Tools Under Regulatory Threat but AI Could Save SMEs

Putting together the pile dataset is a tricky business, balancing tech progress with doing the right thing. While everyone wants AI to improve, the way this data was collected has raised some eyebrows. The dataset includes stuff from all over – universities, entertainment channels, you name it – showing just how much info AI needs to learn.

One of the biggest issues with the pile dataset is how it uses YouTube subtitles. Content creators often spend a lot of time and money on these transcripts. Using them without asking not only goes against YouTube’s rules but also makes creators wonder about their rights in the digital space.

To make things even more complicated, there are companies that scrape data and sell it to tech firms. This creates a sort of buffer between the original creators and the companies using their work. It lets big tech companies like Apple say they’re not directly responsible for where the data came from.

Content creators are not really pleased by it

When content creators found out about the pile dataset, it caused quite a stir. Big YouTubers like Marques Brownlee aren’t happy about their work being used without their say-so, especially since they invest a lot in making good transcripts, stating:

“AI has been stealing my videos, and this is going to be a problem for creators for a long time”

In an Instagram post, followed by this post on X:

Apple has sourced data for their AI from several companies

One of them scraped tons of data/transcripts from YouTube videos, including mine

Apple technically avoids "fault" here because they're not the ones scraping

But this is going to be an evolving problem for a long time https://t.co/U93riaeSlY

— Marques Brownlee (@MKBHD) July 16, 2024

The fact that major tech companies are using this dataset also brings up questions about whether they should be more careful about where their data comes from. Companies like Anthropic say using the dataset isn’t the same as directly using YouTube, but to creators whose work was used without them knowing, that might not make much difference.

This whole situation with the pile dataset also touches on bigger issues about AI ethics and how data should be managed. As AI keeps getting more advanced, we need clearer rules about how data can be collected and used. What’s happening now shows how hard it is to balance pushing technology forward while also protecting people’s and companies’ rights.

Looking ahead, this controversy might lead to changes in how data is gathered and used for AI training. It shows we need more openness in AI development and might result in stricter rules about where training data comes from. It could also make us rethink how content creators, platforms, and AI developers work together, maybe leading to new ways of paying creators or working with them.

To wrap it up, the pile dataset shows how complicated things can get when you mix tech progress with ethical questions in the AI world. As the debate goes on, it’s clear that finding a middle ground between innovation and respecting creators’ rights will be key in shaping how AI develops and how content is created in the future.

Featured image credit: Freepik

In the United States, where gun ownership is deeply ingrained in the cultural fabric, a new trend is emerging that blends convenience with controversy: AI-powered ammo vending machines. These automated dispensers, created by American Rounds, allow individuals over the age of 21 to purchase ammunition with the ease of using an ATM, are touted for their convenience but have raised profound concerns about safety, regulation, and the broader implications for gun control. Let’s get to know these machines before we delve deeper into the discussion.

How do American Rounds’ AI-powered ammo vending machines work?

American Rounds has introduced vending machines across grocery stores in states like Oklahoma, Alabama, and Texas. These machines allow individuals over 21 years old to purchase ammunition easily, akin to withdrawing cash from an ATM. They are operational round-the-clock, offering buyers flexibility outside traditional store hours. Here’s how they work:

• Identification and age verification: The vending machines are equipped with AI algorithms that integrate facial recognition software and card scanning capabilities. When a customer approaches the machine, they are prompted to scan a valid identification card (typically a driver’s license or state ID). The AI analyzes the facial features from the scanned ID to verify the buyer’s identity and age.

• Compliance with age restrictions: Federal law in the United States mandates specific age requirements for purchasing ammunition: individuals must be at least 21 years old to purchase ammunition for long guns (such as rifles and shotguns) and at least 21 years old for ammunition used in handguns. The artificial intelligence technology ensures that only individuals meeting these age criteria are permitted to proceed with the purchase.
• Transaction process: Once the buyer’s identity and age are verified through the AI system, they can select the type and quantity of ammunition they wish to purchase from the vending machine’s interface. The transaction typically involves payment through methods accepted by the machine, which may include credit/debit cards or other electronic payment options.
• Security features: To prevent unauthorized access and ensure safety, the vending machines are designed with robust security measures. These may include tamper-resistant enclosures, surveillance cameras, and alarm systems to deter theft or misuse.
• Accessibility and convenience: American Rounds emphasizes the convenience of their vending machines, which are accessible 24/7. This accessibility is particularly beneficial in areas where traditional firearm supply stores may have limited operating hours or are geographically distant.
• Monitoring and maintenance: The company likely employs a system to monitor machine operations remotely, including inventory levels and operational status. This allows for timely restocking of ammunition and maintenance to ensure uninterrupted service.
• Legal compliance: American Rounds ensures that their vending machines comply with federal and local regulations governing firearm and ammunition sales. This includes adhering to zoning requirements and obtaining necessary permits and approvals from relevant authorities.

In summary, American Rounds’ AI-powered ammo vending machines combine AI technology, facial recognition software, and rigorous identification checks to facilitate legal and secure transactions for firearm ammunition. While they offer convenience and accessibility, their deployment has sparked debates regarding safety, regulation, and the broader implications for gun control in the United States.

Legal and regulatory challenges

Despite their convenience, these machines have faced scrutiny. In Tuscaloosa, Alabama, for example, a machine was recently removed from a grocery store amid legal questions raised at a city council meeting. The legality of such machines hinges on meeting local zoning requirements, with ongoing concerns about public safety and accessibility to ammunition.

American Rounds CEO, Grants Magers, defends the machines as promoting responsible gun ownership. He argues that traditional retail and online platforms may inadvertently sell to minors or face high theft rates, which their machines mitigate through robust ID verification processes.

Public reaction and debate

The introduction of ammo vending machines has sparked varied reactions across communities and social media. Proponents appreciate the convenience, likening it to other automated services. Conversely, critics, including gun control advocates, express concerns about potential implications for public safety and the broader discourse on gun control in the United States.

Future prospects

Despite initial skepticism, American Rounds reports increasing demand with requests from over 200 stores across nine states. The company plans further expansion, suggesting growing acceptance of this new retail model for ammunition.

As discussions continue, the presence of ammo vending machines highlights ongoing tensions between convenience and regulation in firearms and ammunition sales in America.

In summary, while ammo vending machines represent a technological leap in retail convenience, their introduction also underscores significant questions about regulation, safety, and the broader implications for gun control policies in the United States.

Featured image credit: American Rounds

Think product success comes from the most innovative idea or a flashy marketing campaign? Think again. What truly separates thriving products from those that fade away is a data-driven approach.

Using data to guide every decision transforms good ideas into big wins. In today’s competitive world, relying on data can mean the difference between a product that succeeds and one that never takes off.

My journey to embracing data

When I started out as a Database Administrator, I didn’t immediately understand the importance of data in the bigger picture. My role was to manage high-volume databases and analyze vast amounts of numbers to inform business decisions. Initially, it felt purely technical, and I didn’t grasp how impactful those data sets could be.

Then came a project that changed my perspective. We were tasked with optimizing shipping logistics. By analyzing the data, we found inefficiencies that had been costly. Implementing data-driven solutions not only saved money but also revealed the true power of data. This was more than a technical exercise; it was transformative.

That experience showed me that data wasn’t just numbers on a screen. It was a tool capable of driving real-world impact. As I transitioned into product management, I realized that data could be used not only to cut costs but also to drive revenue growth and refine product offerings.

By analyzing sales trends and customer behavior, we improved our strategies and achieved substantial results. This formed the foundation of my belief: data isn’t just a nice-to-have; it’s transformative, giving product managers the confidence to make informed, impactful decisions.

Using data to drive product success

When I moved into product management, I faced new challenges requiring strategic thinking. One project involved designing features to improve user experience, balancing various needs and constraints.

We began with research: conducting user interviews, testing feature concepts, and gathering insights. Once we had that foundation, data became our guide.

We used continuous A/B testing to refine our features, carefully analyzing the outcomes. Each piece of data provided new insights, allowing us to pivot quickly if needed. This level of agility meant our updates didn’t just boost metrics but genuinely enhanced the user experience.

The key lesson? Data isn’t just helpful; it’s essential for building features that resonate with users. It allows you to be responsive and make decisions that are backed by evidence, making a world of difference in product development.

Why data-driven decisions are so effective

What makes data-driven decision-making so impactful for product managers? Here’s why:

Less risk, more certainty

Making decisions based solely on intuition is risky. Data reduces that risk, providing evidence to confirm or challenge assumptions. Instead of hoping a product or feature will succeed, data shows if you’re on the right path. This minimizes blind spots and gives you a clearer direction, reducing the risk of failure.

For example, if you’re considering launching a new feature for a specific segment, data analysis will often reveal insights you hadn’t considered, like the potential impact of a different approach.

It’s a huge advantage to have data on your side. With data guiding your steps, you can avoid costly missteps and build with more confidence.

Better user experience

Creating products people love requires understanding their needs. Data provides that understanding, showing how users interact with your product, what frustrates them, and what delights them. Sometimes, it’s about finding balance—one group may want more control while another prefers simplicity. A/B testing and data analysis can help strike the right balance.

By listening to users and validating ideas with data, you can improve engagement and trust. Data-driven product design ensures your decisions are thoughtful and effective, essential in today’s competitive landscape.

This user-centric approach isn’t just a best practice; it’s crucial for long-term success. Understanding how your audience behaves and acting on that information leads to products that people don’t just use but genuinely enjoy.

Smarter strategies and roadmaps

Data also plays a crucial role in long-term planning. By analyzing trends, you can identify growth opportunities and anticipate shifts in user behavior.

Understanding how different segments engage with your product helps prioritize development and resource allocation. Data might reveal which features provide the most value, influencing your strategy.

Additionally, data can guide privacy and compliance efforts, ensuring your product remains trustworthy. A well-thought-out, data-informed roadmap sets up a product for ongoing success, making product managers better equipped to adapt to market changes.

It’s about making strategic decisions that will pay off over time, ensuring your product continues to meet user needs as they evolve.

Balancing data and intuition

Data is powerful, but it’s not everything. Sometimes, product managers need to trust their instincts, especially when data is unclear. The best managers know how to balance data with intuition.

For example, designing accessibility features requires empathy and creativity. Data can highlight the need, but the design process demands a human-centered approach.

In these cases, data provides clarity, but intuition adds a human touch. Knowing when to rely on data and when to trust your gut is what separates good product managers from great ones.

The best solutions often emerge from a mix of both. It’s important to remember that data offers direction, but human creativity and intuition bring ideas to life in ways data alone cannot achieve.

The future of product management

As technology evolves, the role of data in product management grows. AI and machine learning are making data collection and analysis more efficient. Product managers have unprecedented insights that shape the future of their products. These advancements provide new opportunities but also come with challenges.

However, this power comes with responsibility. Privacy concerns are at the forefront, and users need to trust that their data is being handled responsibly.

Transparency and ethical practices are not just legal necessities; they are vital to maintaining trust. Companies that balance data use with user protection will lead the way. Data ethics will become even more important as technology continues to advance.

The future of product management will depend on how well we harness data responsibly. Those who use data effectively while prioritizing user privacy and ethical considerations will set the standard.

As data becomes more central to product development, the emphasis will be on striking a balance between leveraging insights and ensuring user protection.

Wrapping it up

Reflecting on my career, I’m continually amazed at how transformative data has been. From saving costs to boosting engagement, data has helped me make better decisions and turn challenges into opportunities. It’s the foundation of every successful product decision I’ve made. However, data alone isn’t enough.

The true magic happens when data is combined with human intuition, empathy, and creativity. Product managers must learn to balance both, understanding the story data tells and using instincts to bring that story to life.

This combination creates products that don’t just function but leave a meaningful impact on people’s lives. The most effective product managers are those who can blend the best of both worlds.

One of the most important ongoing challenges for companies that develop AI is integrating vast amounts of enterprise data within their AI models.

This data is the lifeblood of many AI applications, but its management can be a complex and time-consuming process. Snorkel Flow, a recent update to the Snorkel AI platform, aims to streamline this process for businesses looking to leverage Llama 3, a powerful AI model from Meta AI, and Gemini AI, another advanced AI model by Google.

Why is managing enterprise data crucial?

Enterprise data encompasses a wide range of information collected by businesses during their daily operations. This can include customer data, financial records, marketing campaign results, sensor data from machinery, and much more. Effectively managing this data is crucial for several reasons.

First, it allows businesses to identify trends and patterns that might otherwise be missed. For instance, by analyzing customer purchase history, a company can discover which products are frequently bought together, allowing them to tailor promotions and product placement strategies.

Second, enterprise data can be used to improve decision-making. For example, a financial institution might analyze historical loan data to develop more accurate risk assessment models. Finally, enterprise data is essential for training AI models. These models require massive amounts of labeled data to learn and perform tasks effectively.

However, managing this data can be a significant challenge. Enterprise data often resides in various formats and locations, making it difficult to access and integrate. The process of labeling data for AI training can also be expensive and time-consuming.

Here’s where Snorkel Flow comes in.

Taming the data deluge

Snorkel Flow is an update to the Snorkel AI platform designed to simplify the integration of enterprise data with AI models, particularly Llama 3 and Gemini AI. Snorkel uses a technique called weak labeling, which allows users to leverage unlabeled data for training purposes. This is achieved by defining heuristics, or “labeling functions” that can automatically assign labels to data points based on specific criteria.

For example, imagine a company that wants to train an AI model to identify customer support tickets that require urgent attention. A labeling function could be created to identify tickets containing specific keywords or phrases, such as “urgent” or “critical.” While these labels might not be perfect, they can still be valuable for training the AI model.

Snorkel Flow builds upon this concept by introducing a streamlined workflow for managing the data labeling process. It allows users to define labeling functions, manage data sources, and monitor the quality of the generated labels. This can significantly reduce the time and resources required to prepare enterprise data for AI training.

Expanded LLM and data source integrations

In a blog post, Snorkel AI explained in detail the innovations they brought to Snorkel Flow. Here are the features of the renewed Snorkel Flow:

• LLM Integrations: Snorkel Flow now supports fine-tuning not only established models but also Google’s Gemini family and Meta’s Llama 3. This broadens the options for businesses to choose the LLM best suited for their needs.
• Data source întegrations: New integrations with Databricks Unity Catalog, Vertex AI, and Microsoft Azure Machine Learning streamline data access for labeling, curation, and development purposes. Businesses can leverage their existing data infrastructure within Snorkel Flow.

Multimodal data support (Beta)

• Image processing: Snorkel Flow introduces programmatic labeling functions for images (currently in beta). This allows businesses to leverage image data alongside text data for LLM training. Businesses can use this feature to extract insights from visual data and integrate it with their AI solutions.

Enhanced security and accessibility

• Role-Based Access Control (RBAC): This feature grants admins granular control over data access within Snorkel Flow. This ensures sensitive information is protected by restricting access to specific users and data sources.
  Improved Document Processing:
• Foundation Model (FM)-powered PDF Workflow: Snorkel Flow now includes a dedicated PDF prompting UI for labeling PDFs. This leverages advanced foundation models to streamline the process of extracting valuable insights from complex documents.

Simplified LLM integration:

• Enhanced SDK: The upgraded SDK allows easier integration with various custom LLM services, providing businesses with more flexibility in their AI development process.
• Databricks integration: Seamless compatibility with Databricks Unity Catalog allows effortless deployment of models within existing workflows. Similar integration is available with Vertex AI and Azure Machine Learning.

Streamlined data annotation

• Multi-task Annotation (R2 Release Preview): This feature, currently in preview, allows SMEs (subject matter experts) to annotate data for multiple tasks within a single project. This improves efficiency by reducing project setup time and streamlining workflows.

Integration with Llama 3 and Gemini AI

Snorkel Flow specifically integrates with Llama 3 and Gemini AI, two powerful AI models. Llama 3, developed by Meta AI, is a factual language model, trained on a massive dataset of text and code. This allows it to understand and respond to complex queries in an informative way. Gemini AI, on the other hand, is a generative language model, capable of creating different creative text formats, like poems, code, scripts, musical pieces, email, letters, etc.

By integrating Snorkel Flow with these models, businesses can leverage the power of AI to extract insights from their enterprise data and automate various tasks. For instance, Llama 3 could be used to analyze customer reviews and identify common themes or complaints. Gemini AI, meanwhile, could be used to generate creative marketing copy or product descriptions based on existing data.

By simplifying the data labeling process and offering compatibility with powerful models like Llama 3 and Gemini AI, Snorkel Flow has the potential to unlock new possibilities for businesses looking to leverage the power of AI.

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the United States and the United Kingdom have joined forces to address one of our most pressing challenges: ensuring AI’s safety. This historic partnership, announced today, signifies a pivotal moment in the global effort to navigate the complex landscape of AI technologies.

The Memorandum of Understanding (MOU) signed between the two nations initiates a collaborative effort to develop comprehensive tests for advanced AI models. Under the leadership of U.S. Commerce Secretary Gina Raimondo and UK Technology Secretary Michelle Donelan, both countries will pool their resources to align scientific approaches, conduct joint research, and provide guidance on AI safety.

Deep dive into the partnership

The goal of the partnership between the United States and the United Kingdom on AI safety is to advance the development and implementation of robust safety measures for artificial intelligence (AI) technologies. This collaboration aims to address the emerging risks associated with AI systems while harnessing the transformative potential of this groundbreaking technology. Here is what you need to know about the partnership :

• Alignment of scientific approaches: The U.S. and UK will work together to align their scientific methodologies and approaches to AI safety. By harmonizing their efforts, both countries can develop comprehensive frameworks for evaluating AI models, systems, and agents.
• Joint research and development: Through the U.S. and UK AI Safety Institutes, collaborative research initiatives will be undertaken to explore various aspects of AI safety. This includes the development of standardized testing protocols, the identification of emerging risks, and the formulation of guidance for AI developers and policymakers.
• Information sharing and expert exchanges: The partnership will facilitate the exchange of vital information and expertise between the U.S. and UK. This exchange will enable both countries to leverage each other’s strengths and insights, accelerating progress in AI safety research and implementation.
• Shared capabilities and resources: By pooling their resources and capabilities, the U.S. and UK aim to enhance their collective capacity to address AI safety challenges effectively. This includes conducting joint testing exercises on AI models and systems to identify vulnerabilities and mitigate risks.
• Promotion of global collaboration: Recognizing AI safety as a global issue, the partnership also seeks to promote international collaboration on AI safety initiatives. By sharing knowledge, best practices, and resources with other countries, the U.S. and UK aim to establish a common scientific foundation for addressing AI risks worldwide.

Risks and benefits of artificial intelligence

The partnership between the United States and the United Kingdom is a bold step towards ensuring the safe and responsible development of artificial intelligence (AI). Together, they aim to lead the way in AI safety through collaborative research, information exchange, and global cooperation. Their goal: to harness the benefits of AI while minimizing risks, setting a precedent for a secure AI-powered future.

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To cut back on relying too much on China for crucial AI hardware, big U.S. tech players like Nvidia, Amazon, Google, and Microsoft are changing where they make things. They’ve asked Foxconn, a major supplier, to produce more in Mexico instead of China.

Why Mexico?

The allure of Mexico stems from various factors. Firstly, the escalating technological and national security tensions between the United States and China have spurred a reevaluation of supply chain dynamics. With both nations imposing regulations and bans on the export of sensitive technologies, companies are actively seeking alternative manufacturing solutions.

Moreover, the implementation of the US-Mexico-Canada Agreement (USMCA) in 2020 has catalyzed this shift. The agreement, aimed at bolstering free trade among the signatory nations, has incentivized manufacturers to diversify their operations away from China and towards Mexico.

Foxconn’s Role

You might know Foxconn for making iPhones, but they also make a lot of AI hardware. They’ve listened to the tech giants and invested heavily in Mexico—around $690 million in the last four years. They even bought land in Jalisco for $27 million to ramp up AI production this February.

Making friends nearby

The trend towards “friendshoring” or “nearshoring” is gaining traction, particularly in the geopolitical landscape. Tech companies are strategically aligning their supply chains with countries that maintain close political partnerships with the United States. By shifting production to Mexico, these firms aim to reduce their dependence on China, a geopolitical rival of the U.S.

Impact on trade

Mexico’s emergence as a lucrative investment destination is already reshaping global trade dynamics. Recent data from the U.S. Census Bureau indicates that, for the first time in two decades, U.S. imports from Mexico have surpassed those from China. This trend underscores the significant impact of the strategic realignment of production facilities in response to evolving geopolitical and trade dynamics.

In summary, moving the production of AI hardware to Mexico is a savvy move by U.S. tech giants to reduce risks and seize new trade opportunities. As the world evolves, decisions like these will shape the future of tech manufacturing and international trade.

What if Big Tech completes the transition from China to Mexico?

If Big Tech completes the transition from China to Mexico for AI hardware production, it could significantly reshape global manufacturing dynamics, with Mexico emerging as a key hub for technology production. This transition could also lead to greater stability in supply chains, reduced geopolitical risks, and potentially foster closer economic ties between the United States and Mexico. However, it may also pose challenges such as adapting to new regulatory environments, addressing labor concerns, and managing potential disruptions in existing supply chains.  Overall, the completion of this transition could have far-reaching implications for both the technology industry and international trade relations.

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Recent news from the United States Department of Justice (DOJ) and FBI has uncovered something alarming: a sophisticated network of cyber operations coming from China. According to the US State Department, seven Chinese hackers behind this web. Now, they’re offering a whopping $10 million reward to anyone who can help catch them.

Want to find out how it works, who it affects, what’s being done to stop it, and who these hackers are? We’ve explained everything known so far about this cybersecurity threat.

Cracking the code

The Chinese hacking web, as revealed by recent disclosures from the United States Department of Justice (DOJ) and FBI, represents a sophisticated and extensive network of cyber operations orchestrated by Chinese nationals.

Here’s a detailed breakdown of its components and implications:

How it works

This cyber operation is not a one-time thing; it’s been going on for a long time, stretching over 14 years. It’s not just a few individuals working independently either; there’s evidence suggesting that the Chinese government might be involved or supporting these activities.

These hackers are sneaky. They use tricks like sending fake emails to trick people into giving away important information or downloading harmful files. Once they get in, they use advanced software to steal data or mess with computer systems. And they’re not just randomly targeting anyone – they go after specific people, like government officials, critics of China, and big business leaders.

Who gets hurt

Their reach isn’t limited to one place; they’ve targeted people all over the world. The damage they cause is serious. People’s personal information can be stolen, leading to identity theft or fraud. Businesses can lose valuable ideas and inventions, making it harder for them to compete globally. Sometimes, they even demand money from their victims to stop the attacks or to keep stolen information secret.

Why they do it

Governments and organizations are fighting back. They’re publicly naming and shaming the hackers, putting pressure on them to stop. The US State Department published names and photos of suspected attackers in a statement. The defendants are Ni Gaobin (倪高彬), 38; Weng Ming (翁明), 37; Cheng Feng (程锋), 34; Peng Yaowen (彭耀文), 38; Sun Xiaohui (孙小辉), 38; Xiong Wang (熊旺), 35; and Zhao Guangzong (赵光宗), 38. All are believed to reside in the PRC.

The seven individuals reportedly dispatched more than 10,000 “malicious emails, affecting thousands of victims worldwide,” according to the Justice Department, labeling it a “highly active global hacking campaign” supported by the Chinese government. The US State Department unveiled a bounty of up to $10 million (£8 million) for any leads on the whereabouts or identities of the seven individuals.

People are also stepping up their online security, using better passwords, and being careful about what they click on.

What’s being done

In response to these cyber threats, governments and organizations are implementing countermeasures such as:

• Public indictments: The public disclosure of indictments aims to identify and hold accountable those responsible for cyber attacks.
• Enhanced cybersecurity measures: Governments and businesses are bolstering their cybersecurity defenses, including implementing stronger authentication mechanisms, regularly updating software, and conducting comprehensive security assessments.

Future

The Chinese hacking web represents a sophisticated and pervasive cyber threat with far-reaching implications for individuals, businesses, and governments worldwide. Understanding its structure, tactics, and motives is crucial for developing effective countermeasures and safeguarding against future attacks.

Collaboration between nations, organizations, and cybersecurity experts is essential in combating this evolving threat landscape and ensuring a secure digital environment for all.

Featured image credit: Eray Eliaçık/Bing

The recent revelation of a Chinese hackers cyber attack, highlighted by FBI Director Christopher Wray, has thrust U.S. Want to learn FBI’s concerns, counteraction efforts, the suspect—Volt Typhoon, and the imperative for collective defense? We explained everything known about the alleged Chinese hackers cyber attack.

Chinese hackers cyber attack: 6 things you need to know

According to FBI Director Christopher Wray, the Chinese hackers cyber attack represents a significant and multifaceted threat to U.S. national security. Here are the key aspects of this cyber attack:

• Targeted sectors: The hackers are focusing their efforts on critical U.S. infrastructure. This includes water treatment plants, the electric grid, oil and natural gas pipelines, and transportation hubs. By targeting these vital sectors, the hackers aim to cause “real-world harm” and potentially disrupt essential services for Americans.

• State-sponsored operations: Wray emphasized that these cyber operations are state-sponsored, indicating a coordinated effort by the Chinese government to infiltrate and compromise U.S. systems.
• Civilian infrastructure vulnerability: Unlike traditional cyber threats that primarily target political and military entities, these hackers strategically position themselves across civilian infrastructure.
• FBI’s concerns: FBI Director Wray has consistently highlighted China’s broader efforts to undermine the U.S. through espionage campaigns, intellectual property theft, and cyberattacks. The analogy used by Wray, comparing the situation to placing bombs on American infrastructure in cyberspace, underscores the gravity of the threat and the potential for widespread damage.
• Counteraction and disruption: The U.S. government, in response to these cyber threats, has launched a significant operation. The Justice Department and FBI have been granted legal authorization to disrupt aspects of the alleged Chinese hackers cyber attack.
• The suspect: The hacking group at the center of recent activities is known as Volt Typhoon. Intelligence officials believe it is part of a larger effort to compromise Western critical infrastructure. The group’s tactics, such as taking control of vulnerable digital devices worldwide to hide downstream attacks into more sensitive targets, exemplify the sophisticated methods employed by state-sponsored hackers.

In conclusion, the alleged Chinese hackers cyber attack represents a complex and evolving threat that requires a concerted effort to safeguard critical infrastructure, uphold national security, and navigate the intricate landscape of international cyber relations.

What does “real world” harm mean?

Hacking critical infrastructure, encompassing water treatment plants, the electric grid, oil and gas pipelines, and transportation hubs, can have dire consequences. Disruptions may lead to compromised water supply, widespread power outages, environmental hazards, transportation chaos, and a significant economic impact.

Beyond immediate effects, there are risks to national security and public safety and long-term consequences such as a loss of public trust and increased cybersecurity regulations. Successful attacks may encourage further cyber threats, contributing to a deterioration of overall cybersecurity and potentially escalating geopolitical tensions on the global stage. Safeguarding critical infrastructure is imperative for public well-being, economic stability, and national security.

What is Volt Typhoon?

Volt Typhoon is a Chinese hacking group that has garnered attention for its alleged involvement in cyber-espionage activities, particularly targeting Western critical infrastructure. The group, subjected to a recent U.S. government operation, has raised concerns due to its potential impact on global cybersecurity and geopolitical tensions.

Operating stealthily, Volt Typhoon utilizes botnets, controlling vulnerable devices worldwide to disguise downstream attacks on sensitive targets. Despite mounting evidence, China denies any involvement, and experts suggest the group’s interest in operational security aims to evade public scrutiny. The focus on disrupting critical infrastructure has broader implications for international cybersecurity and stability.

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Stumbled upon a legal hold and don’t know why that happened to you? You will find out exactly why soon.

Navigating the complex terrain of legal matters, particularly in the face of litigation, investigations, or internal challenges, demands a strategic approach to safeguarding crucial evidence. At the forefront of this strategy is the concept of a legal hold—a powerful directive ensuring the preservation of pertinent documents, data, and evidence.

This proactive measure acts as a stop sign, compelling individuals and organizations to retain information, preventing its inadvertent destruction or alteration. Beyond mere compliance, a legal hold serves as a critical player in data integrity, impacting fairness in legal proceedings, preventing spoliation of evidence, and aiding in meeting legal obligations.

But why do we need it and why is it so important for data integrity and security? Let us explain.

What is a legal hold?

A legal hold is a court-ordered or legally mandated directive to preserve specific documents, data, or other evidence that is relevant to a legal case or investigation.

The purpose of a legal hold is to ensure that relevant evidence is not destroyed or altered during the pendency of a legal proceeding, and to prevent the spoliation of evidence.

The scope of a legal hold can be broad or narrow, depending on the specific circumstances of the case. It may apply to all documents and data related to a particular subject matter, or it may be limited to specific types of evidence that are relevant to the case. For example, it may be issued to preserve all emails and documents related to a particular project, or it may be limited to preserving only those documents that are relevant to a specific issue or event.

The duration of a legal hold is typically the duration of the legal case or investigation, and it may be lifted once the case is resolved or the investigation is completed. However, the hold may be extended or renewed if necessary, depending on the progress of the case or investigation.

Your data can have a digital fingerprint

A legal hold may be issued by a court, a government agency, or a party to a legal case or investigation. It may be verbal or written, and it may be directed to individuals, organizations, or third-party vendors. Recipients of a legal hold must comply with its terms, which may include specific requirements for preserving evidence, such as saving emails, documents, or other data in their original form and not deleting or modifying them.

Monitoring and enforcement of a legal hold may be included in the terms of the hold, to ensure compliance and prevent spoliation of evidence. This may involve regular reports or audits to ensure that all relevant evidence has been preserved and that no evidence has been altered or destroyed. Failure to comply with it can result in sanctions, including fines, penalties, or even dismissal of a case.

In addition to preserving evidence, a legal hold may also include provisions for the handling and protection of sensitive or confidential information. This may include requirements for encryption, access controls, or other security measures to prevent unauthorized access or disclosure of the preserved evidence.

How is a legal hold triggered?

Imagine a company stumbles upon trouble. It could be a lawsuit, a government investigation, or even an internal issue like employee complaints. This is like a warning sign that something’s not right.

That’s when a legal hold comes in. It’s like a big STOP sign for information. It tells everyone in the company to hold onto any documents, emails, messages, or anything else that might be related to the trouble.

Why do we do this? Because when things get messy in court, having all the relevant information is crucial. It helps the company tell its side of the story and protects it from accusations of hiding evidence.

But not every bump needs a legal hold. Only when the trouble seems serious and likely to lead to a fight do we raise the STOP sign. We also consider how much information needs protection and whether it’s worth the effort.

So, it is like a safety net, catching important information before it gets lost or destroyed. It’s a smart precaution that helps companies navigate tricky legal situations and avoid getting into bigger trouble.

What’s the difference between a litigation hold and a legal hold?

Litigation hold and legal hold are often used interchangeably, but there is a subtle difference between the two.

Litigation hold refers specifically to a court order or directive to preserve evidence in a legal case. It is a court-ordered hold that is issued during the course of a lawsuit, and it is typically issued by a judge or court clerk. The purpose of a litigation hold is to ensure that all relevant evidence is preserved and available for use in the case.

Legal hold, on the other hand, is a broader term that refers to any directive or order to preserve evidence in a legal context. It can include not only court orders, but also internal company directives or policies to preserve evidence in anticipation of a legal dispute or investigation. It may be issued by a court, a government agency, or a party to a legal case or investigation.

In other words, a litigation hold is a specific type of legal hold that is issued by a court during the course of a lawsuit. A legal hold, on the other hand, is a more general term that encompasses all directives or orders to preserve evidence in a legal context, regardless of whether they are issued by a court or another party.

Here are some key differences between litigation holds and legal holds:

• Scope: A litigation hold is typically limited to the specific case or legal proceeding in which it is issued, while a legal hold may be broader in scope and apply to multiple cases or legal proceedings
• Purpose: The purpose of a litigation hold is to ensure that all relevant evidence is preserved for use in a specific legal case, while the purpose of a legal hold may be to preserve evidence for potential future legal proceedings or to comply with legal or regulatory requirements
• Issuing authority: A litigation hold is typically issued by a court, while a legal hold may be issued by a court, a government agency, or a party to a legal case or investigation
• Duration: A litigation hold is typically in effect for the duration of the specific legal case or proceeding, while a legal hold may be in effect for a longer period of time, depending on the specific circumstances

A key player in data integrity

Legal hold plays a critical role in ensuring the integrity and availability of potentially relevant data within a company, especially when facing litigation, investigations, or internal issues.

Here’s why it’s so important for data integrity:

Preserving evidence for legal matters

• Fairness and transparency: A proper legal hold ensures both parties in a legal dispute have access to the same potentially relevant information, promoting a fair and transparent legal process
• Preventing spoliation of evidence: Accidental or intentional destruction of relevant data can have drastic consequences, including financial penalties, adverse jury instructions, or even case dismissal. Legal hold prevents such spoliation, protecting the company from unnecessary legal trouble
• Meeting legal obligations: Various laws and regulations, like the Federal Rules of Civil Procedure or industry-specific regulations, mandate data preservation in certain situations. A legal hold helps companies comply with these legal requirements and avoid potential fines or litigation

Enhancing company reputation

• Demonstrating good faith: By proactively implementing a legal hold and diligently preserving relevant data, a company demonstrates good faith and cooperation in legal matters, potentially influencing judges and juries favorably
• Mitigating risks: Failing to adhere to legal hold requirements can lead to serious consequences, including financial penalties, reputational damage, and loss of investor confidence. A comprehensive legal hold strategy effectively mitigates these risks
• Protecting employees and assets: Legal holds implemented in response to internal issues, like employee complaints or potential misconduct, can help protect the company from liability and ensure fair investigation and resolution

Streamlining eDiscovery and investigations

• Accessibility of relevant data: A well-organized legal hold ensures relevant information is readily available and easily accessible for internal legal teams, external lawyers, and forensic investigators, streamlining the eDiscovery process and reducing associated costs
• Maintaining chain of custody: By carefully documenting and tracking the preservation process, legal holds ensure the authenticity and admissibility of evidence in court, strengthening the company’s legal position
• Saving time and resources: Implementing a systematic approach to legal holds through efficient technology and protocols can save the company valuable time and resources during investigations and litigation

So, legal hold goes beyond mere compliance. It’s a proactive risk management strategy that protects companies from costly legal and reputational consequences, fosters fair legal proceedings, and protects crucial evidence for investigation and resolution. Companies must prioritize establishing robust legal hold procedures and training employees to ensure adherence, safeguarding their future in the face of potential legal challenges.

The gold of our era

Data security is absolutely crucial in today’s digital world. Just like you wouldn’t leave your wallet lying on the sidewalk, you can’t afford to let your data lie unprotected.

Your data is like your digital footprint, it contains sensitive information like financial details, medical records, and even embarrassing photos. Breaches can lead to identity theft, financial loss, and reputational damage.

Companies hold valuable data about customers, employees, and intellectual property. Leaks can damage customer trust, disrupt business operations, and give competitors an edge.

And thanks to proactive measures like this, we can live safely without any question marks within our heads.

Featured image credit: kues1/Freepik.

Today, big data has already become an everyday phenomenon in the gaming virtual worlds across the globe. The industry is experiencing innovativeness, personalization, and immersion in demand. Further, the advent of FC 24, which is currently considered the recent addition to the football game category, confirms that the contribution made by big data in the gaming world is at its peak level now. Data analytics has now come into the FC 24 universe whereby how one plays and interacts with this revered soccer simulator is nothing but sheer chance. The use of big data by FC 24 will be investigated in this article to comprehend its role in remaking a field where this gaming craze takes place.

It is the power of big data that will drive FC24

The large amount of data known as “Big data” is generated by players’ activities and interactions inside FC 24. The game’s developers and publishers have a goldmine of useful information here. Let’s uncover how it’s changing the way we experience FC 24:

1. Enhanced gameplay profiling: With big data, FC 24’s development team can build player profiles on how they behave, what they prefer, and their in-game interactions. This is an invaluable insight that informs how to design a specialized gaming experience. Take FC 24 as an example, its big data might result into in-game challenges tailored toward your favorite team or playing style.
2. Revolutionized personalization: The gameplay in FC 24 is not generic, one size fits all. With big data, your FC 24 is truly one of a kind. This ranges from player attributes and team dynamics to match results as well as strategy effectiveness; all of which are customized for every player included in a given lineup.
3. Predictive gameplay analysis: Big data-driven predictive analytics is revolutionizing FC 24. Analyzing player data would help FC 24 predict in-game activity and consequently produce appropriate intelligent responses. # In case you are finding it hard in this one-match game the game may provide you with hints or it can change the opponent’s tactics making sure that you remain enthusiastic about your football quest.
4. Dynamic football storytelling: FC 24 adopts dynamic storytelling. The game’s narrative is heavily influenced by your decisions and actions. These are the decisions that it analyzes on the story line so as to change and adapt to every gameplay giving you a kind experience of playing football all the time.
5. Optimized game balance: The game balance in FC 24 is determined by big data. Analyzing player data helps developers identify overpowered teams, underused players, and unbalanced game outcomes. It facilitates an equitable and exciting match setting for all.

Where to supercharge your FC 24 experience

To take your FC 24 journey to the next level, consider the option of buying FC 24 coins. These in-game coins provide you with valuable resources to enhance your gameplay, from unlocking new players to acquiring powerful equipment for your team. If you’re interested in acquiring FC 24 coins to boost your gaming experience, check out https://skycoach.gg/fc-24-boost/fc-24-coins. SkyCoach offers a reliable and secure platform for obtaining FC 24 coins, ensuring you’re always at the top of your game.

Big data and the future to expect of FC 24

As big data’s role in FC 24 continues to expand, we can anticipate even more exciting developments:

1. Hyper-realistic football environments: Hyper-realistic gaming environment of the FC 24. Everything will change to what is happening on the field including player animations and crowd behavior.
2. Seamless multiplayer experiences: Multiplayer gaming will also be improved upon with big data in FC 24. With this, you will be paired with gamers whose skill level suits yours for fun matches and exciting rivalries.
3. AI-enhanced opponents: Your matches against non-playable sides will become tougher as they will be smart and unpredictable thus, making FC 24 a true game.
4. Health-conscious gaming: FC 24 would also contribute towards ensuring that your gaming was not excessive thereby helping in maintaining balance in life as well. Big data is capable of keeping track of in-game habits and indicating when to take a break.

Conclusion

It is redefining our perception of this virtual football field, as it combines FC 24 with big data. Indeed, FC 24 will have the most personalized football matches and hyper-realistic stadiums in the future. The FC 24 gaming has become an extension of your distinctiveness as your moments in the virtual field are fascinating all the time for you. However, as big data keeps growing, the blurred line between the real and virtual football World creates whole new opportunities for the player.

No matter whether you’re a casual FC 24 player or a hard fan, the incorporation of big data into this phenomenon is destined for exhilarating developments. Therefore, gear up, ladies, and let the matches commence.

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Artificial intelligence is no longer fiction and the role of AI databases has emerged as a cornerstone in driving innovation and progress. An AI database is not merely a repository of information but a dynamic and specialized system meticulously crafted to cater to the intricate demands of AI and ML applications. With the capacity to store, organize, and retrieve data efficiently, AI databases provide the scaffolding upon which groundbreaking AI models are built, refined, and deployed.

As the complexity of AI and ML workflows deepens, the reliance on large volumes of data, intricate data structures, and sophisticated analysis techniques becomes more pronounced. Herein lies the crux of the AI database’s significance: it is tailored to meet the intricate requirements that underpin the success of AI and ML endeavors. No longer confined to traditional databases, AI databases are optimized to accommodate a spectrum of data types, each uniquely contributing to the overarching goals of AI—learning, understanding, and predictive analysis.

But which AI database tools can you rely on for your artificial journey into today’s technology? Let’s take the first step of a successful AI initiative together.

What is an AI database?

An AI database is a specialized type of database designed to support the storage, management, and efficient retrieval of data used in artificial intelligence (AI) and machine learning (ML) applications. These databases are engineered to accommodate the unique requirements of AI and ML workflows, which often involve large volumes of data, complex data structures, and sophisticated querying and analysis.

AI databases are optimized to handle various types of data, including structured, semi-structured, and unstructured data, that are essential for training and deploying AI models. The types of data mentioned in the context of AI databases refer to different formats in which information is stored and organized. These formats play a significant role in how data is processed, analyzed, and used to develop AI models.

Structured data is organized in a highly organized and predefined manner. It follows a clear data model, where each data entry has specific fields and attributes with well-defined data types.

Examples of structured data include data stored in traditional relational databases, spreadsheets, and tables. In structured data, the relationships between data points are explicitly defined, making it easy to query and analyze using standardized methods. For AI applications, structured data can include numerical values, categorical labels, dates, and other well-defined information.

Semi-structured data is more flexible than structured data but still has some level of organization. Unlike structured data, semi-structured data doesn’t adhere to a strict schema, meaning that different entries can have different sets of attributes. However, there is usually some consistency in the way the data is organized.

Semi-structured data is often represented using formats like JSON (JavaScript Object Notation), XML (eXtensible Markup Language), or key-value pairs. This type of data is common in web data, sensor data, and data obtained from APIs. In AI, semi-structured data might include text with associated metadata or data with varying levels of structure.

Unstructured data lacks a predefined structure or format. It is typically more complex and challenging to process than structured or semi-structured data. Unstructured data includes text, images, audio, video, and other data types that don’t neatly fit into rows and columns.

In AI applications, unstructured data can be vital for tasks such as natural language processing, image recognition, and sentiment analysis. Analyzing unstructured data often involves using techniques like machine learning to extract meaningful patterns and insights from the raw information.

What makes AI databases different from traditional databases?

They provide the foundation for data preprocessing, feature extraction, model training, and inference.

Several key features set AI databases apart from traditional databases:

• Scalability: AI databases are designed to scale horizontally and vertically, enabling them to handle the substantial amounts of data required for training complex models. They often leverage distributed computing techniques to manage and process data efficiently
• Data diversity: AI databases can handle a wide variety of data types, including text, images, audio, video, and sensor data. This versatility is crucial for training models that require multi-modal data sources
• Complex queries: AI databases support advanced querying capabilities to enable complex analytical tasks. This may involve querying based on patterns, relationships, and statistical analysis required for ML model development
• Parallel processing: Given the computational demands of AI and ML tasks, AI databases are optimized for parallel processing and optimized query execution
• Integration with ML frameworks: Some AI databases offer integration with popular machine learning frameworks, allowing seamless data extraction and transformation for model training
• Feature engineering: AI databases often provide tools for data preprocessing and feature engineering, which are crucial steps in preparing data for ML tasks
• Real-time data ingestion: Many AI applications require real-time or near-real-time data processing. AI databases are equipped to handle streaming data sources and provide mechanisms for timely ingestion and analysis
• Metadata management: Managing metadata related to data sources, transformations, and lineage is crucial for ensuring data quality and model reproducibility
• Security and privacy: AI databases need to ensure robust security mechanisms, particularly as AI applications often involve sensitive data. Features like access controls, encryption, and anonymization may be implemented

What are the top 10 AI databases in 2023?

The selection of a suitable AI database is a crucial consideration that can significantly impact the success of projects.

The diverse options of available databases offer a range of options, each tailored to meet specific requirements and preferences.

Redis

Redis stands out as an open-source, in-memory data structure that has gained recognition for its versatility and robust feature set. It boasts the ability to support various data types, ranging from simple strings to more complex data structures, enabling developers to work with diverse data formats efficiently.

Furthermore, Redis encompasses a rich spectrum of functionalities, including support for transactions, scripting capabilities, and data replication, which enhances data durability and availability.

PostgreSQL

As an open-source object-relational AI database system, PostgreSQL has earned its reputation for its unwavering commitment to data integrity and advanced indexing mechanisms. Its support for various data types makes it a versatile choice, accommodating a wide array of data structures.

With a strong emphasis on ACID compliance (Atomicity, Consistency, Isolation, Durability), PostgreSQL is well-equipped to handle complex data workloads with the utmost security and reliability.

MySQL

MySQL, a renowned open-source relational AI database management system, has maintained its popularity for its strong security measures, scalability, and compatibility. It seamlessly accommodates structured and semi-structured data, making it adaptable to a diverse range of applications.

MySQL’s reliability and performance have made it a favored choice in various industries, and its open-source nature ensures a thriving community and continuous development.

Apache Cassandra

Apache Cassandra has emerged as a highly scalable NoSQL database, favored by major platforms like Instagram and Netflix. Its automatic sharding and decentralized architecture empower it to manage vast amounts of data efficiently.

This makes it particularly suitable for applications requiring high levels of scalability and fault tolerance, as it effortlessly accommodates the demands of modern data-driven initiatives.

Couchbase

Couchbase is an open-source distributed engagement database that offers a potent combination of high availability and sub-millisecond latencies. Beyond its performance merits, Couchbase also integrates Big Data and SQL functionalities, positioning it as a multifaceted solution for complex AI and ML tasks.

This blend of features makes it an attractive option for applications requiring real-time data access and analytical capabilities.

Elasticsearch

Elasticsearch, built on the foundation of Apache Lucene, introduces a distributed search and analytics engine that facilitates the extraction of real-time data insights. Its capabilities prove invaluable in applications demanding rapid data retrieval and analytics, enabling informed decision-making.

With its real-time querying prowess, Elasticsearch contributes significantly to enhancing AI and ML workflows.

Google Cloud Bigtable

Google Cloud Bigtable distinguishes itself as a distributed NoSQL AI database offering robust scalability, low latency, and data consistency. These features make it particularly adept at handling high-speed data access requirements.

However, it’s worth noting that while Google Cloud Bigtable excels in performance, its pricing complexity may require careful consideration during implementation.

See how Google Cloud Bigtable works in the video by Google Cloud Tech below.

MongoDB

MongoDB‘s prominence lies in its flexible, document-oriented approach to data management. This attribute, coupled with its scalability capabilities, makes it an attractive choice for handling unstructured data.

Developers seeking to manage complex data structures and accommodate the dynamic nature of AI and ML projects find MongoDB’s features well-aligned with their needs.

Amazon Aurora

Amazon Aurora, a high-performance relational database, offers compatibility with MySQL and PostgreSQL. Its ability to scale seamlessly and robust security features and automatic backup mechanisms position it as a compelling option for AI and ML applications.

Organizations leveraging Amazon Aurora benefit from its efficient handling of complex data workloads.

Chorus.ai

Chorus.ai takes a specialized approach by targeting client-facing and sales teams. It provides an AI assistant designed to enhance note-taking processes. As businesses strive to streamline interactions and gather insights from customer engagements, Chorus.ai’s AI assistant plays a pivotal role in capturing vital information and fostering efficient communication.

How to choose the right AI database for your needs

The key to selecting the right AI database lies in aligning the database’s features and capabilities with the specific requirements of the project at hand. By carefully evaluating factors such as scalability, security, data consistency, and support for different data types and structures, developers can make accurate decisions that contribute to the success of their AI and ML endeavors.

To choose the right AI database, start by clearly defining your project’s requirements. Consider factors such as the volume of data you’ll be dealing with, the complexity of your data structures, the need for real-time processing, and the types of AI and ML tasks you’ll be performing.

Once you have decided your requirements for the selection of an AI database, evaluate the types of data you’ll be working with—structured, semi-structured, or unstructured. Ensure that the AI database you choose can efficiently handle the variety of data your project requires.

Don’t forget to consider the scalability needs of your project. If you expect your data to grow significantly over time, opt for a database that offers horizontal scaling capabilities to accommodate the increased load.

Assess the performance metrics of the AI database. For real-time applications or high-speed data processing, choose a database that offers low latency and high throughput.

Once you have done that, review the querying and analytical capabilities of the database. Depending on your project’s requirements, you may need advanced querying features to extract insights from your data.

If you’re planning to use specific machine learning frameworks, consider databases that offer integration with those frameworks. This can streamline the process of data extraction and transformation for model training.

Data security is also paramount, especially if your project involves sensitive information. Ensure the AI database you are going to choose offers robust security features, including access controls, encryption, and compliance with relevant regulations.

Evaluate the user-friendliness of the database. An intuitive interface and user-friendly management tools can simplify data administration and reduce the learning curve.

Make sure that you also consider the size and activity of the user community surrounding the database. A strong community often indicates ongoing development, support, and a wealth of resources for troubleshooting.

Also, look for case studies and examples of how the AI database has been successfully used in projects similar to yours. This can provide insights into the database’s effectiveness in real-world scenarios.

By carefully considering these factors and conducting thorough research, you can identify the AI database that best aligns with your project’s needs and goals. Remember that selecting the right database is a crucial step in building a solid foundation for successful AI and ML endeavors.

Featured image credit: Kerem Gülen/Midjourney.

Influencer marketing has been in existence for about a decade now. During this time, this brand promotion tactic has become increasingly popular among businesses of various sizes due to its effectiveness in reaching a wider audience and increasing brand awareness.

No wonder, that the global influencer marketing platform market was worth $7.36 billion in 2021 and will reach the value of $69.92 billion by 2029, according to Data Bridge Market Research. However, it’s very likely that by 2029 influencer marketing will be somehow different from what it is today.

As an instrument, it has been constantly evolving since day one and it keeps changing with new social platforms, technologies, and trends emerging every now and then. This evolution is currently taking a new turn with the introduction of AI tools and big data analytics to the niche.

Combined or used separately, these technologies are set to make influencer marketing more efficient for both businesses and influencers by improving the processes of setting and executing influencer marketing campaigns. They are already being implemented here and there, and the results are impressive.

It’s just a matter of time until AI and big data analytics will be used all over, for each new influencer marketing campaign by every brand. Let’s look at how these technologies can be implemented on various stages of a campaign.

Influencer scouting

For influencer marketing purposes, big data can be collected from various open sources, including social media platforms, blogs, forums, and other websites, for AI algorithms to analyze it. AI can go through all that information to identify the key insights with enormous speed that no human can achieve.

It allows businesses to gain understanding of the behavior of consumers, their preferences, and opinions. With this data, it’s possible to identify the most relevant influencers to a brand’s target audience. It allows companies to spend marketing budgets with greater efficiency, saving money and getting the best possible results.

In addition, it can help brands to avoid fraud and ensure that they are working with legitimate influencers. By analyzing data, it’s possible to identify influencers who have fake followers or engage in fraudulent activities. This saves brands from wasting resources on inefficient campaigns too.

Moreover, AI-powered big data analytics can be used to discover not yet well-known influencers who have the potential to reach a large audience later. Collaborations with such micro- and nano-influencers allow brands to tap into new audiences and expand their reach.

Besides, when these influencers’ audience grows, they are going to be more loyal to brands that noticed them first. So, in the future it may be much easier to approach them with new campaigns.

Automating repetitive tasks

Even though marketing is a creative field, there are still numerous mundane repetitive tasks that come with it. It’s good that a lot of them can be delegated to AI. We already know that marketers don’t have to search social media platforms manually to find suitable influencers because AI-powered big data analytics tools can do that for them, but there’s more.

For instance, writing emails and messages to a bunch of influencers when approaching them with a new campaign. ChatGPT and other AI chatbots can assist you in writing those, personalizing each message based on data on the influencers from your list.

Chatbots can also help brands manage their influencer campaigns more efficiently by automating tasks such as scheduling posts, sending reminders, and tracking performance metrics. After a campaign is completed, AI can assemble a presentation, showcasing its results. There are already AI services for presentations, like Beautiful.ai and Kroma.ai, and some influencer marketing platforms have similar built-in tools.

Content creation

AI can help brands and influencers create more efficient campaigns by analyzing social media content of the target platform to identify the best performing trends of the moment or for that particular influencer. This includes analyzing the language used in posts, recognizing popular hashtags, and even predicting which types of content will perform best with a specific audience.

By leveraging this information, brands and bloggers can create more targeted campaigns that are more likely to resonate with their audience, building stronger relationships and driving more engagement. In addition, AI can easily help with content drafts and ideas, be it texts or visuals.

Measuring efficiency

Another benefit of big data analytics and AI in influencer marketing is the ability to measure effectiveness of campaigns automatically. By analyzing data on engagement rates, conversions, and other metrics, brands can determine which influencers and campaigns are driving the most ROI (return on investment) and adjust their strategies accordingly.

Also, as it was mentioned above, AI can easily turn this information into a presentation for stakeholders. In addition, it can use it to produce recommendations for the next campaigns to boost their efficiency.

Overall, the integration of AI and big data analytics into influencer marketing is revolutionizing the industry in many ways, making it a more organized and approachable industry. By leveraging the power of data analysis, chatbots, and other AI tools, brands can create more effective campaigns, build stronger relationships with influencers, and drive more ROI from their advertising efforts.

The internet is filled with lots of information, and this information is not accessible on the internet until the end of time thanks to data deprecation.

When we use the internet, we leave a trail of data behind us. This data tells a story about us – what we like, what we do, and how we behave online. That’s why companies love collecting this data as it helps them understand their customers better. They can use it to show us personalized ads and make their products or services more appealing to us. It’s like they’re trying to get to know us so they can offer us things we’re interested in.

But there’s a problem. Sometimes our data is not kept private, and it can be misused. We might not even know what companies are doing with our information. This has made people worried about their privacy and how their data is being handled.

To address these concerns, a concept called data deprecation has come up. Data deprecation means putting limits on how companies can use our data for advertising. It includes things like restricting the use of cookies, which are small files that track our online activities, and making sure companies get our permission before collecting and using our data.

Data deprecation affects everyone – companies, individuals like us, and even the people who make the rules about data privacy. It’s about finding a balance between using data to improve our online experiences and making sure our privacy is respected.

As a result, companies need to rethink how they collect and use our data. They have to be more transparent about what they’re doing and give us more control over our information. It’s all about treating our data with care and making sure we feel comfortable using the internet.

What is data deprecation?

Data deprecation means restricting how advertisers can use platforms to show ads. It’s mainly about limits set by web browsers and operating systems, like changes to cookies or mobile ad IDs.

But it’s not just that. It also includes actions taken by individuals to protect their privacy, as well as closed data systems like Google or Amazon.

Data deprecation is also influenced by privacy laws such as GDPR and ePrivacy, which affect how advertisers can track and store user data in different parts of the world.

To understand the impact of data deprecation better, let’s break it down and look at each aspect separately.

There are restrictions

The main part of data deprecation is about the restrictions imposed by operating systems and web browsers. One of the things being restricted is the use of third-party cookies. But what are they?

Third-party cookies are little trackers that websites put on your browser to collect data for someone other than the website owner. These cookies are often used by ad networks to track your online actions and show you targeted ads later on.

A study found that around 80% of US marketers rely on third-party cookies for digital advertising. However, these cookies will be restricted soon, and other methods that require your consent will be used instead.

Similar restrictions will also apply to mobile ad IDs. The Identifier for Advertisers (IDFA), which provides detailed data for mobile advertising, will also be phased out.

Moreover, the growing popularity of privacy-focused web browsers will have a big impact on how marketers target users based on their identities. More and more people are choosing to block third-party cookies and prevent the collection of their sensitive data, making privacy a top priority.

Privacy is a growing concern

According to a study conducted in January 2021, around 66% of adults worldwide believe that tech companies have excessive control over their personal data.

To counter this control, individuals are taking privacy measures such as using ad blockers or regularly clearing their web browser history. These actions aim to reduce the influence that tech companies and other businesses have over personal data, and they contribute to the overall impact of data deprecation.

There is a growing emphasis on customer consent and choice in the digital landscape. Users are increasingly opting out of allowing their data to be stored and tracked by third parties. This shift is happening at much higher rates than ever before. While the vast amount of data generated by online users can be beneficial for advertising, it also places a significant responsibility on data managers.

Unfortunately, data managers have often failed to meet this responsibility in the past. Personally identifiable information, which includes sensitive data, deserves special attention. Numerous consumer data breaches in recent years and the reporting of cyber incidents as a significant risk by 43% of large enterprise businesses have heightened consumer concerns about how their data is stored, used, and shared.

As a result of these factors, we are now witnessing changes that prioritize consent management. Brands that currently rely on third-party tracking data will need to seek alternative solutions to adapt and survive in the post-cookie era.

We are also “protected” by law

In addition to individual users taking steps to protect their privacy, countries worldwide have enacted data protection and privacy laws, such as the General Data Protection Regulation (GDPR) and the California Privacy Rights Act (CPRA).

These laws require all companies to comply with the new regulations, which means businesses globally must ensure their data privacy practices meet the standards to avoid significant fines or legal consequences.

For instance, GDPR was implemented in 2018 across the EU and EEA region. It grants citizens greater control over their personal data and provides increased assurances of data protection.

GDPR applies to all businesses operating in the EU, and affected businesses are advised to appoint a data protection officer to ensure compliance with the rigorous standards.

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Similar regulations have been enacted in various parts of the world, and brands need to ensure they comply with the new data protection laws, even if it means limited access to valuable online identity data.

Interestingly, Epsilon reports that 69% of US marketers believe that the elimination of third-party cookies and the Identifier for Advertisers (IDFA) will have a more significant impact compared to regulations like GDPR or the California Consumer Privacy Act (CCPA).

What are the causes of data deprecation?

Data deprecation occurs due to various factors. One major reason is people’s growing concerns about their privacy. They want more control over how their personal information is used by companies.

In addition, new regulations have been introduced. These rules, such as GDPR and CCPA, require businesses to handle data more responsibly and give users greater rights.

Changes made by web browsers and operating systems also play a role. They are putting restrictions on things like third-party cookies and tracking technology, which impacts how companies collect data.

Furthermore, individuals are taking action to safeguard their privacy. They use tools like ad blockers or regularly clear their browsing history to limit data tracking.

The market is also evolving. Consumers now value privacy more, and businesses need to adapt to meet their expectations.

Lastly, data breaches and security concerns have raised awareness about the risks associated with personal data. This puts pressure on companies to enhance data security measures and demonstrate responsible data management practices.

When to expect data deprecation?

Data deprecation doesn’t follow a set schedule. It can happen at different times depending on various factors.

Changes in web browsers and operating systems are already occurring. They’re limiting third-party cookies and tracking technologies, which means data deprecation might already be taking place.

Data protection and privacy regulations like GDPR and CCPA have specific deadlines for compliance. Companies must adapt their data practices within those timeframes.

Different industries and businesses will adopt data deprecation at their own pace. Some may be quicker than others due to competition, customer demands, and industry-specific considerations.

User behavior and preferences also influence data deprecation. As people become more aware of privacy issues, they may take steps to protect their data. This can accelerate the overall process.

Is there a way to counter data deprecation for your company?

It can seem overwhelming and unsettling, can’t it? Until recently, brands had easy access to consumer data, using it with varying degrees of caution. But things have changed. Consumers now recognize the value of their personal data and are determined to protect it.

So, what’s the next step? How can brands establish a relationship with their audience that respects privacy and complies with regulations?

Here are four actions to navigate data deprecation with confidence:

1. Evaluate your current data collection strategy: Take a close look at the data you’re collecting. Are you utilizing all of it effectively? Is your data well-organized or scattered across different systems? Consider your integrations with solution providers in your marketing technology stack. Ask yourself these important questions about your organization.
2. Ensure compliance with data privacy: Are you obtaining explicit consent from your audience to collect and use their data? Do they understand how their data is stored and utilized? Remember, third-party data will soon become obsolete, so it’s crucial to align your strategy with a privacy-first approach.
3. Emphasize first-party and zero-party data: These types of data are invaluable in the context of data deprecation. By collecting first-party and zero-party data, brands can have consented and actionable data at their disposal. Consumers willingly share their data with trusted brands to improve their brand experience. They no longer want irrelevant messages but desire targeted and personalized communication. Consider the advantages of a virtual call center to enhance communication retention.
4. Explore innovative data collection methods: Experiment with interactive marketing and interaction-based loyalty programs. These approaches help you gain a deeper understanding of your audience’s needs and expectations. By doing so, you can provide personalized experiences, reward them for engaging with your brand, and offer relevant content.

Remember, adapting to data deprecation is about building trust, respecting privacy, and delivering tailored experiences to your audience. It may feel challenging at first, but by taking these proactive steps, brands can forge stronger connections with their customers while staying compliant with evolving data regulations.

Featured image: Photo by Jason Dent on Unsplash.

Making the right decisions in an aggressive market is crucial for your business growth and that’s where decision intelligence (DI) comes to play. As each choice can steer the trajectory of an organization, propelling it towards remarkable growth or leaving it struggling to keep pace. In this era of information overload, utilizing the power of data and technology has become paramount to drive effective decision-making.

Decision intelligence is an innovative approach that blends the realms of data analysis, artificial intelligence, and human judgment to empower businesses with actionable insights. Decision intelligence is not just about crunching numbers or relying on algorithms; it is about unlocking the true potential of data to make smarter choices and fuel business success.

Imagine a world where every decision is infused with the wisdom of data, where complex problems are unraveled and transformed into opportunities, and where the path to growth is paved with confidence and foresight. Decision intelligence opens the doors to such a world, providing organizations with a holistic framework to optimize their decision-making processes.

At its core, decision intelligence harnesses the power of advanced technologies to collect, integrate, and analyze vast amounts of data. This data becomes the lifeblood of the decision-making process, unveiling hidden patterns, trends, and correlations that shape business landscapes. But decision intelligence goes beyond the realm of data analysis; it embraces the insights gleaned from behavioral science, acknowledging the critical role human judgment plays in the decision-making journey.

Think of decision intelligence as a synergy between the human mind and cutting-edge algorithms. It combines the cognitive capabilities of humans with the precision and efficiency of artificial intelligence, resulting in a harmonious collaboration that brings forth actionable recommendations and strategic insights.

From optimizing resource allocation to mitigating risks, from uncovering untapped market opportunities to delivering personalized customer experiences, decision intelligence is a guiding compass that empowers businesses to navigate the complexities of today’s competitive world. It enables organizations to make informed choices, capitalize on emerging trends, and seize growth opportunities with confidence.

What is decision intelligence?

Decision intelligence is an advanced approach that combines data analysis, artificial intelligence algorithms, and human judgment to enhance decision-making processes. It leverages the power of technology to provide actionable insights and recommendations that support effective decision-making in complex business scenarios.

At its core, decision intelligence involves collecting and integrating relevant data from various sources, such as databases, text documents, and APIs. This data is then analyzed using statistical methods, machine learning algorithms, and data mining techniques to uncover meaningful patterns and relationships.

In addition to data analysis, decision intelligence integrates principles from behavioral science to understand how human behavior influences decision-making. By incorporating insights from psychology, cognitive science, and economics, decision models can better account for biases, preferences, and heuristics that impact decision outcomes.

AI algorithms play a crucial role in decision intelligence. These algorithms are carefully selected based on the specific decision problem and are trained using the prepared data. Machine learning algorithms, such as neural networks or decision trees, learn from the data to make predictions or generate recommendations.

The development of decision models is an essential step in decision intelligence. These models capture the relationships between input variables, decision options, and desired outcomes. Rule-based systems, optimization techniques, or probabilistic frameworks are employed to guide decision-making based on the insights gained from data analysis and AI algorithms.

Human judgment is integrated into the decision-making process to provide context, validate recommendations, and ensure ethical considerations. Decision intelligence systems provide interfaces or interactive tools that enable human decision-makers to interact with the models, incorporate their expertise, and assess the impact of different decision options.

Continuous learning and improvement are fundamental to decision intelligence. The system adapts and improves over time as new data becomes available or new insights are gained. Decision models can be updated and refined to reflect changing circumstances and improve decision accuracy.

At the end of the day, decision intelligence empowers businesses to make informed decisions by leveraging data, AI algorithms, and human judgment. It optimizes decision-making processes, drives growth, and enables organizations to navigate complex business environments with confidence.

How does decision intelligence work?

Decision intelligence operates by combining advanced data analysis techniques, artificial intelligence algorithms, and human judgment to drive effective decision-making processes.

Let’s delve into the technical aspects of how decision intelligence works.

Data collection and integration

The process begins with collecting and integrating relevant data from various sources. This includes structured data from databases, unstructured data from text documents or images, and external data from APIs or web scraping. The collected data is then organized and prepared for analysis.

Data analysis and modeling

Decision intelligence relies on data analysis techniques to uncover patterns, trends, and relationships within the data. Statistical methods, machine learning algorithms, and data mining techniques are employed to extract meaningful insights from the collected data.

This analysis may involve feature engineering, dimensionality reduction, clustering, classification, regression, or other statistical modeling approaches.

Behavioral science integration

Decision intelligence incorporates principles from behavioral science to understand and model human decision-making processes. Insights from psychology, cognitive science, and economics are utilized to capture the nuances of human behavior and incorporate them into decision models.

This integration helps to address biases, preferences, and heuristics that influence decision-making.

AI algorithm selection and training

Depending on the nature of the decision problem, appropriate artificial intelligence algorithms are selected. These may include machine learning algorithms like neural networks, decision trees, support vector machines, or reinforcement learning.

The chosen algorithms are then trained using the prepared data to learn patterns, make predictions, or generate recommendations.

Decision model development

Based on the insights gained from data analysis and AI algorithms, decision models are developed. These models capture the relationships between input variables, decision options, and desired outcomes.

The models may employ rule-based systems, optimization techniques, or probabilistic frameworks to guide decision-making.

Human judgment integration

Decision intelligence recognizes the importance of human judgment in the decision-making process. It provides interfaces or interactive tools that enable human decision-makers to interact with the models, incorporate their expertise, and assess the impact of different decision options. Human judgment is integrated to provide context, validate recommendations, and ensure ethical considerations are accounted for.

Continuous learning and improvement

Decision intelligence systems often incorporate mechanisms for continuous learning and improvement. As new data becomes available or new insights are gained, the models can be updated and refined.

This allows decision intelligence systems to adapt to changing circumstances and improve decision accuracy over time.

Decision execution and monitoring

Once decisions are made based on the recommendations provided by the decision intelligence system, they are executed in the operational environment. The outcomes of these decisions are monitored and feedback is collected to assess the effectiveness of the decisions and refine the decision models if necessary.

How is decision intelligence different from artificial intelligence?

AI, standing for artificial intelligence, encompasses the theory and development of algorithms that aim to replicate human cognitive capabilities. These algorithms are designed to perform tasks that were traditionally exclusive to humans, such as decision-making, language processing, and visual perception. AI has witnessed remarkable advancements in recent years, enabling machines to analyze vast amounts of data, recognize patterns, and make predictions with increasing accuracy.

On the other hand, Decision intelligence takes AI a step further by applying it in the practical realm of commercial decision-making. It leverages the capabilities of AI algorithms to provide recommended actions that specifically address business needs or solve complex business problems. The focus of Decision intelligence is always on achieving commercial objectives and driving effective decision-making processes within organizations across various industries.

To illustrate this distinction, let’s consider an example. Suppose there is an AI algorithm that has been trained to predict future demand for a specific set of products based on historical data and market trends. This AI algorithm alone is capable of generating accurate demand forecasts. However, Decision intelligence comes into play when this initial AI-powered prediction is translated into tangible business decisions.

In the context of our example, Decision intelligence would involve providing a user-friendly interface or platform that allows a merchandising team to access and interpret the AI-generated demand forecasts. The team can then utilize these insights to make informed buying and stock management decisions. This integration of AI algorithms and user-friendly interfaces transforms the raw power of AI into practical Decision intelligence, empowering businesses to make strategic decisions based on data-driven insights.

By utilizing Decision intelligence, organizations can unlock new possibilities for growth and efficiency. The ability to leverage AI algorithms in the decision-making process enables businesses to optimize their operations, minimize risks, and capitalize on emerging opportunities. Moreover, Decision intelligence facilitates decision-making at scale, allowing businesses to handle complex and dynamic business environments more effectively.

Below we have prepared a table summarizing the difference between decision intelligence and artificial intelligence:

How can decision intelligence help with your business growth?

Decision intelligence is a powerful tool that can drive business growth. By leveraging data-driven insights and incorporating artificial intelligence techniques, decision intelligence empowers businesses to make informed decisions and optimize their operations.

Strategic decision-making is enhanced through the use of decision intelligence. By analyzing market trends, customer behavior, and competitor activities, businesses can make well-informed choices that align with their growth goals and capitalize on market opportunities.

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Optimal resource allocation is another key aspect of decision intelligence. By analyzing data and using optimization techniques, businesses can identify the most efficient use of resources, improving operational efficiency and cost-effectiveness. This optimized resource allocation enables businesses to allocate their finances, personnel, and time effectively, contributing to business growth.

Risk management is critical for sustained growth, and decision intelligence plays a role in mitigating risks. Through data analysis and risk assessment, decision intelligence helps businesses identify potential risks and develop strategies to minimize their impact. This proactive approach to risk management safeguards business growth and ensures continuity.

Market insights are invaluable for driving business growth, and decision intelligence help businesses uncover those insights. By analyzing data, customer behavior, and competitor activities, businesses can gain a deep understanding of their target market, identify emerging trends, and seize growth opportunities. These market insights inform strategic decisions and provide a competitive edge.

Personalized customer experiences are increasingly important for driving growth, and decision intelligence enable businesses to deliver tailored experiences. By analyzing customer data and preferences, businesses can personalize their products, services, and marketing efforts, enhancing customer satisfaction and fostering loyalty, which in turn drives business growth.

Agility is crucial in a rapidly changing business landscape, and decision intelligence supports businesses in adapting quickly. By continuously monitoring data, performance indicators, and market trends, businesses can make timely adjustments to their strategies and operations. This agility enables businesses to seize growth opportunities, address challenges, and stay ahead in competitive markets.

There are great companies that offer decision intelligence solutions your business need

There are several companies that offer decision intelligence solutions. These companies specialize in developing platforms, software, and services that enable businesses to leverage data, analytics, and AI algorithms for improved decision-making.

Below, we present you with the best decision intelligence companies out there.

• Qlik
• ThoughtSpot
• DataRobot
• IBM Watson
• Microsoft Power BI
• Salesforce Einstein Analytics

Qlik

Qlik offers a range of decision intelligence solutions that enable businesses to explore, analyze, and visualize data to uncover insights and make informed decisions. Their platform combines data integration, AI-powered analytics, and collaborative features to drive data-driven decision-making.

ThoughtSpot

ThoughtSpot provides an AI-driven analytics platform that enables users to search and analyze data intuitively, without the need for complex queries or programming. Their solution empowers decision-makers to explore data, derive insights, and make informed decisions with speed and simplicity.

DataRobot

DataRobot offers an automated machine learning platform that helps organizations build, deploy, and manage AI models for decision-making. Their solution enables businesses to leverage the power of AI algorithms to automate and optimize decision processes across various domains.

IBM Watson

IBM Watson provides a suite of decision intelligence solutions that leverage AI, natural language processing, and machine learning to enhance decision-making capabilities. Their portfolio includes tools for data exploration, predictive analytics, and decision optimization to support a wide range of business applications.

Microsoft Power BI

Microsoft Power BI is a business intelligence and analytics platform that enables businesses to visualize data, create interactive dashboards, and derive insights for decision-making. It integrates with other Microsoft products and offers AI-powered features for advanced analytics.

While you can access Power BI for a fixed fee, with the giant company’s latest announcement, Microsoft Fabric, you can access all the support your business needs with this service in a pay-as-you-go pricing form.

Salesforce Einstein Analytics

Salesforce Einstein Analytics is an AI-powered analytics platform that helps businesses uncover insights from their customer data. It provides predictive analytics, AI-driven recommendations, and interactive visualizations to support data-driven decision-making in sales, marketing, and customer service.

These are just a few examples of companies offering decision intelligence solutions. The decision intelligence market is continuously evolving, with new players entering the field and existing companies expanding their offerings.

Organizations can explore these solutions to find the one that best aligns with their specific needs and objectives to achieve business growth waiting for them on the horizon.

Microsoft has made good on its promise to deliver a simplified and more efficient Microsoft Fabric price model for its end-to-end platform designed for analytics and data workloads. Based on the total compute and storage utilized by customers, the company’s new pricing structure eliminates the need for separate payment for compute and storage buckets associated with each of Microsoft’s multiple services.

This strategic move lights up the competition with major rivals like Google and Amazon, who offer similar analytics and data products but charge customers multiple times for various discrete tools employed on their respective cloud platforms.

Microsoft Fabric price is about to be announced

Although we do not have official Microsoft Fabric price data, which will be shared tomorrow, VentureBeat shared the average prices that Microsoft will charge for this service, and it is as follows:

As you can see in the table, the Microsoft Fabric price is shaped to deliver the service your company needs with minimum expenditure by choosing a way that you will pay as much as you use according to the quantity of SKU and CU you will use and not on a fixed price of the service you receive.

Especially for small businesses, we think that this kind of payment plan is much more accurate and a good step to ensure equality in the market because similar services in the market are not very accessible, especially on a low budget.

Microsoft’s unified pricing model for the Fabric suite marks a significant advancement in the analytics and data market. With this model, customers will be billed based on the total computing and storage they utilize.

This eliminates the complexities and costs associated with separate billing for individual services. By streamlining the pricing process, Microsoft is positioning itself as a formidable competitor to industry leaders such as Google and Amazon, who have repeatedly charged customers for different tools employed within their cloud ecosystems.

It is a fact that the Microsoft Fabric price will differentiate it from other tools in the industry because, normally, when you buy such services, you are billed for several services that you do not really use. The pricing Microsoft offers your business is a bit unusual.

All you need in one place

So is the Microsoft Fabric price the tech giant’s only plan to stay ahead of the data game? Of course not!

Microsoft Fabric suite integration brings together six different tools into a unified experience and data architecture, including:

• Azure Data Factory
• Azure Synapse Analytics
  • Data engineering
  • Data warehouse
  • Data science
  • Real-time analytics
• Power BI

This consolidation within the Microsoft Fabric price you will pay allows engineers and developers to seamlessly extract insights from data and present them to business decision-makers.

Microsoft’s focus on integration and unification sets Fabric apart from other vendors in the market, such as Snowflake, Qlik, TIBCO, and SAS, which only offer specific components of the analytics and data stack.

This integrated approach provides customers with a comprehensive solution encompassing the entire data journey, from storage and processing to visualization and analysis.

The contribution of Power BI

The integration of Microsoft Power BI and Microsoft Fabric offers a powerful combination for organizations seeking comprehensive data analytics and insights. Together, these two solutions work in harmony, providing numerous benefits:

• Streamlined analytics workflow: Power BI’s intuitive interface and deep integration with Microsoft products seamlessly fit within the Microsoft Fabric ecosystem, enabling a cohesive analytics workflow.
• Unified data storage: Fabric’s centralized data lake, Microsoft OneLake, eliminates data silos and provides a unified storage system, simplifying data access and retrieval.
• Cost efficiency: Power BI can directly leverage data stored in OneLake, eliminating the need for separate SQL queries and reducing costs associated with data processing.
• Enhanced insights through AI: Fabric’s generative AI capabilities, such as Copilot, enhance Power BI by enabling users to use conversational language to create data flows, build machine learning models, and derive deeper insights.
• Multi-cloud support: Fabric’s support for multi-cloud environments, including shortcuts that virtualize data lake storage across different cloud providers, allows seamless incorporation of diverse data sources into Power BI for comprehensive analysis.
• Flexible data visualization: Power BI’s customizable and visually appealing charts and reports, combined with Fabric’s efficient data storage, provide a flexible and engaging data visualization experience.
• Scalability and performance: Fabric’s robust infrastructure ensures scalability and performance, supporting Power BI’s data processing requirements as organizations grow and handle larger datasets.
• Simplified data management: With Fabric’s unified architecture, organizations can provision compute and storage resources more efficiently, simplifying data management processes.
• Data accessibility: The integration allows Power BI users to easily access and retrieve data from various sources within the organization, promoting data accessibility and empowering users to derive insights.

This combination enables organizations to unlock the full potential of their data and make data-driven decisions with greater efficiency and accuracy.

Centralized data lake for all your data troubles

At the core of Microsoft Fabric lies the centralized data lake, known as Microsoft OneLake. OneLake is designed to store a single copy of data in a unified location, leveraging the open-source Apache Parquet format.

This open format allows for seamless storage and retrieval of data across different databases. By automating the integration of all Fabric workloads into OneLake, Microsoft eliminates the need for developers, analysts, and business users to create their own data silos.

This approach not only improves performance by eliminating the need for separate data warehouses but also results in substantial cost savings for customers.

Flexible compute capacity

One of the key advantages of Microsoft Fabric is its ability to optimize compute capacity across different workloads. Unused compute capacity from one workload can be utilized by another, ensuring efficient resource allocation and cost optimization. Microsoft’s commitment to innovation is evident in the addition of Copilot, Microsoft’s chatbot powered by generative AI, to the Fabric suite.

Copilot enables developers and engineers to interact in conversational language, simplifying data-related tasks such as querying, data flow creation, pipeline management, code generation, and even machine learning model development.

Moreover, Fabric supports multi-cloud capabilities through “Shortcuts,” allowing virtualization of data lake storage in Amazon S3 and Google Cloud Storage, providing customers with flexibility in choosing their preferred cloud provider.

Why should your business use Microsoft Fabric?

Microsoft Fabric offers numerous advantages for businesses that are looking to enhance their data and analytics capabilities.

Here are compelling reasons why your business should consider using Microsoft Fabric:

• Unified data platform: Microsoft Fabric provides a comprehensive end-to-end platform for data and analytics workloads. It integrates multiple tools and services, such as Azure Data Factory, Azure Synapse Analytics, and Power BI, into a unified experience and data architecture. This streamlined approach eliminates the need for separate solutions and simplifies data management.
• Simplified pricing: The Microsoft Fabric price is based on total compute and storage usage. Unlike some competitors who charge separately for each service or tool, Microsoft Fabric offers a more straightforward pricing model. This transparency helps businesses control costs and make informed decisions about resource allocation.
• Cost efficiency: With Microsoft Fabric, businesses can leverage a shared pool of compute capacity and a single storage location for all their data. This eliminates the need for creating and managing separate storage accounts for different tools, reducing costs associated with provisioning and maintenance. This is one of the most important features that make the Microsoft Fabric price even more accessible.
• Improved performance: Fabric’s centralized data lake, Microsoft OneLake, provides a unified and open architecture for data storage and retrieval. This allows for faster data access and eliminates the need for redundant SQL queries, resulting in improved performance and reduced processing time.
• Advanced analytics capabilities: Microsoft Fabric offers advanced analytics features, including generative AI capabilities like Copilot, which enable users to leverage artificial intelligence for data analysis, machine learning model creation, and data flow creation. These capabilities empower businesses to derive deeper insights and make data-driven decisions.
• Multi-cloud support: Fabric’s multi-cloud support allows businesses to seamlessly integrate data from various cloud providers, including Amazon S3 and Google storage. This flexibility enables organizations to leverage diverse data sources and work with multiple cloud platforms as per their requirements.
• Scalability and flexibility: Microsoft Fabric is designed to scale with the needs of businesses, providing flexibility to handle growing data volumes and increasing analytics workloads. The platform’s infrastructure ensures high performance and reliability, allowing businesses to process and analyze large datasets effectively.
• Streamlined workflows: Fabric’s integration with other Microsoft products, such as Power BI, creates a seamless analytics workflow. Users can easily access and analyze data stored in the centralized data lake, enabling efficient data exploration, visualization, and reporting.
• Simplified data management: Microsoft Fabric’s unified architecture and centralized data lake simplify data management processes. Businesses can eliminate data silos, provision resources more efficiently, and enable easier data sharing and collaboration across teams.
• Microsoft ecosystem integration: As part of the broader Microsoft ecosystem, Fabric integrates seamlessly with other Microsoft services and tools. This integration provides businesses with a cohesive and comprehensive solution stack, leveraging the strengths of various Microsoft offerings.

When we take the Microsoft Fabric price into account, bringing all these features together under a pay-as-you-go model is definitely a great opportunity for users.

How to try Microsoft Fabric for free

Did you like what you saw? You can try this platform that can handle all your data-related tasks without even paying the Microsoft Fabric price.

To gain access to the Fabric app, simply log in to app.fabric.microsoft.com using your Power BI account credentials. Once logged in, you can take advantage of the opportunity to sign up for a free trial directly within the app, and the best part is that no credit card information is needed.

In the event that the account manager tool within the app does not display an option to initiate the trial, it is possible that your organization’s tenant administration has disabled access to Fabric or trials. However, don’t worry, as there is still a way for you to acquire Fabric. You can proceed to purchase Fabric via the Azure portal by following the link conveniently provided within the account manager tool.

Microsoft Fabric price and its impact on competitors

The move on the Microsoft Fabric price, which offers a unified approach, poses a significant challenge to major cloud competitors like Amazon and Google, who have traditionally charged customers separately for various services.

By providing a comprehensive and integrated package of capabilities, Fabric also puts pressure on vendors that offer only specific components of the analytics and data stack. For instance, Snowflake’s reliance on proprietary data formats and limited interoperability raises questions about its ability to compete with Microsoft’s holistic solution.

Let’s see if Microsoft can once again prove why it is a leading technology company and usher in a new era of data management.

Data engineers play a crucial role in managing and processing big data. They are responsible for designing, building, and maintaining the infrastructure and tools needed to manage and process large volumes of data effectively. This involves working closely with data analysts and data scientists to ensure that data is stored, processed, and analyzed efficiently to derive insights that inform decision-making.

What is data engineering?

Data engineering is a field of study that involves designing, building, and maintaining systems for the collection, storage, processing, and analysis of large volumes of data. In simpler terms, it involves the creation of data infrastructure and architecture that enable organizations to make data-driven decisions.

Data engineering has become increasingly important in recent years due to the explosion of data generated by businesses, governments, and individuals. With the rise of big data, data engineering has become critical for organizations looking to make sense of the vast amounts of information at their disposal.

In the following sections, we will delve into the importance of data engineering, define what a data engineer is, and discuss the need for data engineers in today’s data-driven world.

Job description of data engineers

Data engineers play a critical role in the creation and maintenance of data infrastructure and architecture. They are responsible for designing, developing, and maintaining data systems that enable organizations to efficiently collect, store, process, and analyze large volumes of data. Let’s take a closer look at the job description of data engineers:

Designing, developing, and maintaining data systems

Data engineers are responsible for designing and building data systems that meet the needs of their organization. This involves working closely with stakeholders to understand their requirements and developing solutions that can scale as the organization’s data needs grow.

Collecting, storing, and processing large datasets

Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

Implementing data security measures

Data security is a critical aspect of data engineering. Data engineers are responsible for implementing security measures that protect sensitive data from unauthorized access, theft, or loss. They must also ensure that data privacy regulations, such as GDPR and CCPA, are followed.

Ensuring data quality and integrity

Data quality and integrity are essential for accurate data analysis. Data engineers are responsible for ensuring that the data collected is accurate, consistent, and reliable. This involves creating data validation rules, monitoring data quality, and implementing processes to correct any errors that are identified.

Creating data pipelines and workflows

Data engineers create data pipelines and workflows that enable data to be collected, processed, and analyzed efficiently. This involves working with various tools and technologies, such as ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) processes, to move data from its source to its destination. By creating efficient data pipelines and workflows, data engineers enable organizations to make data-driven decisions quickly and accurately.

How does workflow automation help different departments?

Challenges faced by data engineers in managing and processing big data

As data continues to grow at an exponential rate, it has become increasingly challenging for organizations to manage and process big data. This is where data engineers come in, as they play a critical role in the development, deployment, and maintenance of data infrastructure. However, data engineering is not without its challenges. In this section, we will discuss the top challenges faced by data engineers in managing and processing big data.

Data engineers are responsible for designing and building the systems that make it possible to store, process, and analyze large amounts of data. These systems include data pipelines, data warehouses, and data lakes, among others. However, building and maintaining these systems is not an easy task. Here are some of the challenges that data engineers face in managing and processing big data:

• Data volume: With the explosion of data in recent years, data engineers are tasked with managing massive volumes of data. This requires robust systems that can scale horizontally and vertically to accommodate the growing data volume.
• Data variety: Big data is often diverse in nature and comes in various formats such as structured, semi-structured, and unstructured data. Data engineers must ensure that the systems they build can handle all types of data and make it available for analysis.
• Data velocity: The speed at which data is generated, processed, and analyzed is another challenge that data engineers face. They must ensure that their systems can ingest and process data in real-time or near-real-time to keep up with the pace of business.
• Data quality: Data quality is crucial to ensure the accuracy and reliability of insights generated from big data. Data engineers must ensure that the data they process is of high quality and conforms to the standards set by the organization.
• Data security: Data breaches and cyberattacks are a significant concern for organizations that deal with big data. Data engineers must ensure that the data they manage is secure and protected from unauthorized access.

Volume: Dealing with large amounts of data

One of the most significant challenges that data engineers face in managing and processing big data is dealing with large volumes of data. With the growing amount of data being generated, organizations are struggling to keep up with the storage and processing requirements. Here are some ways in which data engineers can tackle this challenge:

Impact on infrastructure and resources

Large volumes of data put a strain on the infrastructure and resources of an organization. Storing and processing such vast amounts of data requires significant investments in hardware, software, and other resources. It also requires a robust and scalable infrastructure that can handle the growing data volume.

Solutions for managing and processing large volumes of data

Data engineers can use various solutions to manage and process large volumes of data. Some of these solutions include:

• Distributed computing: Distributed computing systems, such as Hadoop and Spark, can help distribute the processing of data across multiple nodes in a cluster. This approach allows for faster and more efficient processing of large volumes of data.
• Cloud computing: Cloud computing provides a scalable and cost-effective solution for managing and processing large volumes of data. Cloud providers offer various services such as storage, compute, and analytics, which can be used to build and operate big data systems.
• Data compression and archiving: Data engineers can use data compression and archiving techniques to reduce the amount of storage space required for large volumes of data. This approach helps in reducing the costs associated with storage and allows for faster processing of data.

Velocity: Managing high-speed data streams

Another challenge that data engineers face in managing and processing big data is managing high-speed data streams. With the increasing amount of data being generated in real-time, organizations need to process and analyze data as soon as it is available. Here are some ways in which data engineers can manage high-speed data streams:

Impact on infrastructure and resources

High-speed data streams require a robust and scalable infrastructure that can handle the incoming data. This infrastructure must be capable of handling the processing of data in real-time or near-real-time, which can put a strain on the resources of an organization.

Solutions for managing and processing high velocity data

Data engineers can use various solutions to manage and process high-speed data streams. Some of these solutions include:

• Stream processing: Stream processing systems, such as Apache Kafka and Apache Flink, can help process high-speed data streams in real-time. These systems allow for the processing of data as soon as it is generated, enabling organizations to respond quickly to changing business requirements.
• In-memory computing: In-memory computing systems, such as Apache Ignite and SAP HANA, can help process high-speed data streams by storing data in memory instead of on disk. This approach allows for faster access to data, enabling real-time processing of high-velocity data.
• Edge computing: Edge computing allows for the processing of data at the edge of the network, closer to the source of the data. This approach reduces the latency associated with transmitting data to a central location for processing, enabling faster processing of high-speed data streams.

Variety: Processing different types of data

One of the significant challenges that data engineers face in managing and processing big data is dealing with different types of data. In today’s world, data comes in various formats and structures, such as structured, unstructured, and semi-structured. Here are some ways in which data engineers can tackle this challenge:

Impact on infrastructure and resources

Processing different types of data requires a robust infrastructure and resources capable of handling the varied data formats and structures. It also requires specialized tools and technologies for processing and analyzing the data, which can put a strain on the resources of an organization.

Solutions for managing and processing different types of data

Data engineers can use various solutions to manage and process different types of data. Some of these solutions include:

• Data integration: Data integration is the process of combining data from various sources into a single, unified view. It helps in managing and processing different types of data by providing a standardized view of the data, making it easier to analyze and process.
• Data warehousing: Data warehousing involves storing and managing data from various sources in a central repository. It provides a structured and organized view of the data, making it easier to manage and process different types of data.
• Data virtualization: Data virtualization allows for the integration of data from various sources without physically moving the data. It provides a unified view of the data, making it easier to manage and process different types of data.

Veracity: Ensuring data accuracy and consistency

Another significant challenge that data engineers face in managing and processing big data is ensuring data accuracy and consistency. With the increasing amount of data being generated, it is essential to ensure that the data is accurate and consistent to make informed decisions. Here are some ways in which data engineers can ensure data accuracy and consistency:

Impact on infrastructure and resources

Ensuring data accuracy and consistency requires a robust infrastructure and resources capable of handling the data quality checks and validations. It also requires specialized tools and technologies for detecting and correcting errors in the data, which can put a strain on the resources of an organization.

Solutions for managing and processing accurate and consistent data

Data engineers can use various solutions to manage and process accurate and consistent data. Some of these solutions include:

• Data quality management: Data quality management involves ensuring that the data is accurate, consistent, and complete. It includes various processes such as data profiling, data cleansing, and data validation.
• Master data management: Master data management involves creating a single, unified view of master data, such as customer data, product data, and supplier data. It helps in ensuring data accuracy and consistency by providing a standardized view of the data.
• Data governance: Data governance involves establishing policies, procedures, and controls for managing and processing data. It helps in ensuring data accuracy and consistency by providing a framework for managing the data lifecycle and ensuring compliance with regulations and standards.

Security: Protecting sensitive data

One of the most critical challenges faced by data engineers in managing and processing big data is ensuring the security of sensitive data. As the amount of data being generated continues to increase, it is essential to protect the data from security breaches that can compromise the data’s integrity and reputation. Here are some ways in which data engineers can tackle this challenge:

Impact of security breaches on data integrity and reputation

Security breaches can have a significant impact on an organization’s data integrity and reputation. They can lead to the loss of sensitive data, damage the organization’s reputation, and result in legal and financial consequences.

Solutions for managing and processing data securely

Data engineers can use various solutions to manage and process data securely. Some of these solutions include:

• Encryption: Encryption involves converting data into a code that is difficult to read without the proper decryption key. It helps in protecting sensitive data from unauthorized access and is an essential tool for managing and processing data securely.
• Access controls: Access controls involve restricting access to sensitive data based on user roles and permissions. It helps in ensuring that only authorized personnel have access to sensitive data.
• Auditing and monitoring: Auditing and monitoring involve tracking and recording access to sensitive data. It helps in detecting and preventing security breaches by providing a record of who accessed the data and when.

In addition to these solutions, data engineers can also follow best practices for data security, such as regular security assessments, vulnerability scanning, and threat modeling.

Cyberpsychology: The psychological underpinnings of cybersecurity risks

Best practices for overcoming challenges in big data management and processing

To effectively manage and process big data, data engineers need to adopt certain best practices. These best practices can help overcome the challenges discussed in the previous section and ensure that data processing and management are efficient and effective.

Data engineers play a critical role in managing and processing big data. They are responsible for ensuring that data is available, secure, and accessible to the right people at the right time. To perform this role successfully, data engineers need to follow best practices that enable them to manage and process data efficiently.

Adopting a data-centric approach to big data management

Adopting a data-centric approach is a best practice that data engineers should follow to manage and process big data successfully. This approach involves putting data at the center of all processes and decisions, focusing on the data’s quality, security, and accessibility. Data engineers should also ensure that data is collected, stored, and managed in a way that makes it easy to analyze and derive insights.

Investing in scalable infrastructure and cloud-based solutions

Another best practice for managing and processing big data is investing in scalable infrastructure and cloud-based solutions. Scalable infrastructure allows data engineers to handle large amounts of data without compromising performance or data integrity. Cloud-based solutions offer the added benefit of providing flexibility and scalability, allowing data engineers to scale up or down their infrastructure as needed.

In addition to these best practices, data engineers should also prioritize the following:

• Data Governance: Establishing data governance policies and procedures that ensure the data’s quality, security, and accessibility.
• Automation: Automating repetitive tasks and processes to free up time for more complex tasks.
• Collaboration: Encouraging collaboration between data engineers, data analysts, and data scientists to ensure that data is used effectively.

Leveraging automation and machine learning for data processing

Another best practice for managing and processing big data is leveraging automation and machine learning. Automation can help data engineers streamline repetitive tasks and processes, allowing them to focus on more complex tasks that require their expertise. Machine learning, on the other hand, can help data engineers analyze large volumes of data and derive insights that might not be immediately apparent through traditional analysis methods.

Implementing strong data governance and security measures

Implementing strong data governance and security measures is crucial to managing and processing big data. Data governance policies and procedures can ensure that data is accurate, consistent, and accessible to the right people at the right time. Security measures, such as encryption and access controls, can prevent unauthorized access or data breaches that could compromise data integrity or confidentiality.

Establishing a culture of continuous improvement and learning

Finally, data engineers should establish a culture of continuous improvement and learning. This involves regularly reviewing and refining data management and processing practices to ensure that they are effective and efficient. Data engineers should also stay up-to-date with the latest tools, technologies, and industry trends to ensure that they can effectively manage and process big data.

In addition to these best practices, data engineers should also prioritize the following:

• Collaboration: Encouraging collaboration between data engineers, data analysts, and data scientists to ensure that data is used effectively.
• Scalability: Investing in scalable infrastructure and cloud-based solutions to handle large volumes of data.
• Flexibility: Being adaptable and flexible to changing business needs and data requirements.

Conclusion

Managing and processing big data can be a daunting task for data engineers. The challenges of dealing with large volumes, high velocity, different types, accuracy, and security of data can make it difficult to derive insights that inform decision-making and drive business success. However, by adopting best practices, data engineers can successfully overcome these challenges and ensure that data is effectively managed and processed.

In conclusion, data engineers face several challenges when managing and processing big data. These challenges can impact data integrity, accessibility, and security, which can ultimately hinder successful data-driven decision-making. It is crucial for data engineers and organizations to prioritize best practices such as adopting a data-centric approach, investing in scalable infrastructure and cloud-based solutions, leveraging automation and machine learning, implementing strong data governance and security measures, establishing a culture of continuous improvement and learning, and prioritizing collaboration, scalability, and flexibility.

By addressing these challenges and prioritizing best practices, data engineers can effectively manage and process big data, providing organizations with the insights they need to make informed decisions and drive business success. If you want to learn more about data engineers, check out article called: “Data is the new gold and the industry demands goldsmiths.”

Data silos are a common problem for organizations, as they can create barriers to data accessibility, data integrity, and data management. Data silos occur when different departments or teams within an organization have their databases or systems for storing data, and there is no central repository for all of the data. This can make it difficult to get a complete picture of the data or to use the data effectively for business purposes.

What are data silos?

A data silo is an isolated repository of data that is not easily accessible or shareable with other systems or departments within an organization. Data silos can occur when different departments or teams within an organization have their own databases or systems for storing data, and there is no central repository for all of the data. This can create problems with data accessibility, data integrity, and data management, as it can be difficult to get a complete picture of the data or to use the data effectively for business purposes.

Data silos can also hinder the ability of an organization to make data-driven decisions, as the data may not be easily accessible or may be difficult to integrate with other data sources. To address these issues, organizations may implement data integration and data management strategies to break down data silos and facilitate the sharing and use of data across departments and teams.

Breaking down data silos

Breaking down data silos is an important step in improving an organization’s data management and enabling the effective use of data for business purposes. There are several strategies that organizations can use to break down data silos and facilitate the sharing and use of data across departments and teams.

One approach is to implement a centralized data repository or a data warehouse, which is a single, comprehensive source of data that is accessible to all departments and teams within the organization. This can help to improve data accessibility and make it easier to integrate data from multiple sources, as all of the data is stored in a single location.

Another strategy is to implement data integration and data management practices, such as data governance and data management policies. Data governance involves establishing a set of rules and procedures for managing and using data within an organization, while data management policies outline the standards and best practices for storing, organizing, and using data. These practices can help to ensure that data is properly managed and used in a consistent and controlled manner, which can help to break down data silos and improve data sharing and integration.

In addition to these technical approaches, it is also important to consider the cultural and organizational factors that may contribute to data silos. For example, departments or teams may be hesitant to share data if they do not see the value in doing so or if they are concerned about losing control of their data. To address these issues, organizations can encourage a culture of data sharing and collaboration and provide training and resources to help teams understand the benefits of sharing data and how to do so effectively.

Why are data silos problematic?

Data silos can be problematic for a number of reasons:

Data accessibility

Data silos make it difficult for users to access data from other departments or systems, as the data is isolated and not easily sharable. This can hinder the ability of an organization to make data-driven decisions and to effectively use data for business purposes.

Data integrity

Data silos can lead to problems with data integrity, as it can be difficult to ensure that the data is accurate and up-to-date. This is especially true if the data is not properly managed or if different departments or teams are using different standards for storing and organizing data.

Data management

Managing data within data silos can be time-consuming and resource-intensive, as it requires maintaining multiple systems and databases. This can also make it difficult to get a complete picture of the data or to integrate data from different sources.

Decision-making

Data silos can hinder the ability of an organization to make informed decisions, as the data may not be easily accessible or may be difficult to integrate with other data sources.

Collaboration

Data silos can also create barriers to collaboration and hinder the ability of teams to work together effectively, as it can be difficult to share data and insights across departments and systems.

Overall, data silos can create significant challenges for organizations and hinder their ability to effectively use data to drive business success. Breaking down data silos and improving data management and integration is an important step in enabling organizations to leverage the power of data.

Why do data silos exist?

There are several reasons why data silos can exist within an organization:

Departmentalization

Data silos can occur when different departments or teams within an organization have their own databases or systems for storing data. This can happen if each department is responsible for managing its own data and there is no central repository for all of the data.

Transforming your business with data observability in the era of digitization

Technological barriers

Data silos can also be caused by technological barriers, such as differences in software or hardware platforms, which can make it difficult to share data across departments or systems.

Lack of standardization

Data silos can arise if different departments or teams are using different standards or formats for storing and organizing data, making it difficult to integrate data from different sources.

Organizational culture

Data silos can also be a result of organizational culture, as some departments or teams may be hesitant to share data due to concerns about losing control of their data or not seeing the value in sharing.

Overall, data silos can be caused by a combination of technological, organizational, and cultural factors. To address data silos and improve data management and integration, organizations may need to consider both technical and non-technical approaches, such as implementing a centralized data repository, implementing data governance and data management practices, and fostering a culture of data sharing and collaboration.

How to get rid of data silos?

There are several strategies that organizations can use to get rid of data silos and facilitate the sharing and use of data:

Implement a centralized data repository

One approach is to create a single, comprehensive source of data that is accessible to all departments and teams within the organization. This could be in the form of a data warehouse or a data lake, which is a large, centralized repository of structured and unstructured data.

Use data integration and data management practices

Implementing data governance and data management policies can help to ensure that data is properly managed and used in a consistent and controlled manner. Data governance involves establishing a set of rules and procedures for managing and using data within an organization, while data management policies outline the standards and best practices for storing, organizing, and using data.

DataOps as a holistic approach to data management

Foster a culture of data sharing and collaboration

Encouraging a culture of data sharing and collaboration can help to overcome resistance to sharing data and can facilitate the sharing of insights and ideas across departments and teams.

Invest in data integration and management tools

There are a number of tools and technologies that can help to facilitate data integration and management, such as data integration platforms, data management platforms, and data governance software. These tools can help to automate data integration and management processes, making it easier to share and use data across the organization.

Provide training and resources

Providing training and resources to help teams understand the benefits of sharing data and how to do so effectively can also be an important step in breaking down data silos and improving data management and integration.

So, getting rid of data silos requires a combination of technical and non-technical approaches, including implementing a centralized data repository, implementing data governance and data management practices, fostering a culture of data sharing and collaboration, and investing in data integration and management tools.

Conclusion

In conclusion, data silos can have significant negative impacts on an organization, including reduced productivity, inefficient data management, inaccurate or outdated data, limited data-driven decision-making, and difficulty collaborating. To address these challenges and unlock the full potential of data for business success, organizations must take a proactive approach to breaking down data silos and improving data management and integration. By implementing a centralized data repository, implementing data governance and data management practices, and fostering a culture of data sharing and collaboration, organizations can overcome the barriers to data integration and effectively use data to drive business success.

TikTok data practices on EU citizens and ads catering to kids are under investigation by the European Union. Allegations include that the social media giant sent European users’ data to China. Following the US, the EU started investigating TikTok’s data practices and its compliance with General Data Protection Regulation (GDPR) requirements. 

US politicians have strong data privacy concerns about TikTok, and just recently, $92 million TikTok data privacy settlement payments started. However, it appears that not only the US authorities are alarmed, and TikTok may face additional fines in the future. Is it as bad as it sounds? Let’s take a closer look at everything you need to know about the TikTok data practices investigations…

TikTok data practices under investigation by EU: What is happening to Europeans’ data?

There are numerous ongoing investigations against TikTok data practices, according to the head of the European Commission’s executive body of the European Union. The investigations focus on whether EU citizens’ data sent to China and children are getting targeted ads on the platform. TikTok is being investigated to see if its data practices comply with the General Data Protection Regulation (GDPR).

The President of the European Commission, Ursula von der Leyen, reacted to the concerns expressed by members of the European Parliament on the possibility of Chinese public agencies having access to TikTok’s data of EU citizens. Investigations concentrate on EU citizens’ data, its alleged transfer to China, and the targeted ads for kids.

The General Data Protection Regulation (GDPR), which strongly emphasizes data protection, has been the subject of ongoing investigations by the European Union into several digital businesses. Many businesses have already faced penalties imposed by EU courts. TikTok, one of the targets of these investigations, is a short-form video hosting service owned by the Chinese company ByteDance. The platform quickly gained immense popularity, becoming one of the most-used social networks worldwide in just a few years.

The EU has been investigating the app’s data practices for some time. In response to a lawsuit alleging that TikTok had violated EU consumer laws earlier this year, the parent company ByteDance agreed to apply specific restrictions around advertisements and branded content. But it seems like the cautions taken by the internet giant were not enough to convince the European regulators.

The European Commission stated in a letter that TikTok appears to have provided false or misleading information, including claiming TikTok does not trace its users’ locations in the United States. U.S. authorities also acted on these claims by the European Commission. TikTok had previously appeared before a bipartisan committee in the US and made statements about allegations. Members of the U.S. Congress are now demanding further explanations and evidence from TikTok.

Do you know the TikTok vulnerability that Microsoft discovered

TikTok is in trouble on both sides of the Atlantic

It should be highlighted that concerns over security and privacy on social networks have been widespread, including on both sides of the Atlantic. Many questions have been raised, particularly about the belief that the Chinese government employs a program created by TikTok’s parent company ByteDance to obtain user information, and it effectively controls the social network’s algorithm. Last month, TikTok refuted claims that its parent company from China uses TikTok to track the whereabouts of U.S. citizens worldwide.

TikTok’s all traffic in the United States is currently routed through Oracle Cloud Infrastructure. The social media giant stores its European users’ data in a third-party data center in Dublin, Ireland.

Concerns didn’t stop advertisers

TikTok has been subjected to heated debates since it gained explosive popularity during the pandemic. Politicians and authorities recently demanded a “ban” on the social network, referring to it as “possible malware.” U.S. FBI director said TikTok poses national security concerns.

Marketers continue to pour money into the platform despite the raised privacy and national security concerns. TikTok is anticipated to generate close to $10 billion in advertising income, with a 155% increase over 2021.

Check out the Snapchat lawsuit details

What type of data does TikTok collect and share with advertisers?

Even if you haven’t created a TikTok account, the app is designed in a way to collect data about you. When you click a funny TikTok video sent from a friend, TikTok generates an anonymous data ID that links that specific video to information like:

• Your device,
• Your location,
• Your IP address,
• Your search history,
• New content you viewed after the initial one,
• The app you used right before you viewed TikTok content.

Therefore, this anonymous shadow ID creates a profile of the things you’ve liked, even if you don’t have a TikTok profile. Your past watch history generates fresh recommendations when the TikTok algorithm finds you again.

TikTok can extrapolate other information from the video you watch. This includes your age range, gender, interests determined by the content you have watched, and biometric data like your voice and facial details.

AI art spices up TikTok: Reverse AI Art filter TikTok trend

How to stop TikTok data collection?

Considering how technically focused TikTok is on data collection and the authorities’ claims, it seems difficult for a user to prevent it completely. However, the app does offer some tools and methods for privacy. However, there is a trade-off here. When TikTok’s location and data storage features are turned off, the social network’s experience significantly diminishes. Users have the option to enable or disable tailored advertisements with the following steps:

• Tap ‘Me‘ and then go to ‘Settings.’
• Go to Privacy > Safety > Personalize.
• Toggle the ‘Data’ feature to be off.

You can also request the data that TikTok has gathered on you. Here’s how to view your TikTok data profile:

• Go to the TikTok app and tap ‘Profile.’
• Choose the ‘Settings.’
• Navigate to Privacy > Personalize > Data.
• Hit ‘Download TikTok data.’

It’s key to understanding the roles of big data and artificial intelligence in our data-driven world. Before anyone knew big data existed, it had already taken over the globe. Big data had amassed an enormous amount of stored information by the time the term was coined. If properly examined, it might provide insightful knowledge about the sector to which that particular data belonged.

The task of sorting through all of that data, parsing it (turning it into a format more easily understood by a computer), and analyzing it to enhance commercial decision-making processes was quickly found to be too much for human minds to handle. Writing algorithms with artificial intelligence would be necessary to complete the challenging task of extracting knowledge from complex data.

As businesses expand their big data and artificial intelligence capabilities in the upcoming years, data professionals and individuals with a master’s in business analytics or data analytics are anticipated to be in high demand. The goal is to keep up with and use the volume of data that all our computers, mobile smartphones and tablets, and Internet of Things (IoT) devices are producing.

Understanding big data and artificial intelligence

Big data and artificial intelligence are powered by several technological advancements that have defined the current digital environment and Industry 4.0. These two developments aim to maximize the value of the substantial data generated today.

Big data is the term used to describe the processing and storing of enormous amounts of structured, semi-structured, and unstructured data that have the potential to be organized and extracted into useful information for businesses and organizations.

On the other hand, artificial intelligence uses a variety of algorithms with the goal of building machines that mimic human functions (such as learning, reasoning, and making decisions). Let’s now explore these cutting-edge technologies.

What is big data?

The management of massive amounts of data from many sources is the focus of the field of “big data.” Big data is used when the amount of data is too great for conventional data management techniques to be useful. Long ago, businesses began gathering enormous volumes of data about customers, prices, transactions, and product security. However, finally, the data volume proved too great for humans to evaluate manually.

“Big data requires a new processing mode in order to have stronger decision-making, insight, and process optimization capabilities to adapt to massive, high growth rate and diversification of information assets.”

Gartner

This idea conveyed a very key significance. Big data is now valued as a resource for information. We require new processing methods in the big data era to process these information assets because the original processing method cannot handle these data in a timely or accurate manner.

Five V’s of big data

The traits of large data are used to summarize another idea. Massive data scale, rapid data flow, a variety of data types, and low-value density were listed by McKinsey as the four characteristics of big data. That is what we typically refer to as the big data 4V characteristic. The definition of big data, which is the 5V features of big data that are reasonably prevalent in the industry, was created by IBM after adding the fifth characteristic afterward. Let’s examine each of the so-called 5V traits individually.

Everything you should know about big data services

Volume

The first V is the volume. That means in the big data era, a lot of data needs to be processed. Currently, this magnitude is frequently utilized for terabyte-scale data analytics and mining.

Variety

The second trait is referred to as multiple forms of data. Before most of the data that we could process was structured, that is, presented in two-dimensional tables. But in the age of big data, a wider range of data kinds must be processed, including structured, unstructured, and semi-structured data. Big data technology must process these data independently or perhaps together.

Value

Low data value density is the third attribute. Although there is a huge amount of data, not much of it is useful to us. The value density of these data is rather low because they are drowned in the large ocean of data. Therefore we must filter and mine through hundreds of millions of data, but we might only find a few dozen or a few hundred useful data.

Velocity

Fast processing speed is the fourth quality. The process of processing data to produce results used to take weeks, months, or even longer, but now we need the results in a shorter amount of time, like minutes or even seconds.

Veracity

The fifth quality is connected to the third quality. Veracity asserted that the value of commercial value is high or more real, that is, the value of the mined data is very high, whether or not it directly influences our decision-making, provides us with new information or helps us improve our processes. It is, therefore, simpler.

These 5V characteristics of big data inform us that the term “big data” in use today includes both data and a number of processing methods. In order to make decisions or optimize for the work, we must quickly locate and mine the portion of data from a vast amount of data that is useful to our work. The entire procedure is known as big data.

Big data analytics

The often challenging process of analyzing large amounts of data to find information that might assist businesses in making wise decisions about their operations, such as hidden patterns, correlations, market trends, and customer preferences, is known as big data analytics.

Organizations can analyze data sets and gain new insights using data analytics technology and processes. Basic inquiries regarding business performance and operations are addressed by business intelligence (BI) queries.

Advanced analytics, which includes aspects like predictive models, statistical algorithms, and what-if analysis powered by analytics systems, is a subset of big data analytics.

What is artificial intelligence?

The creation and use of computer systems that are capable of logic, reasoning, and decision-making are known as artificial intelligence (AI). This self-learning technology analyzes data and produces information more quickly than human-driven methods by using visual perception, emotion detection, and language translation.

You probably already work with AI systems on a daily basis. Artificial intelligence is used in the user interfaces of some of the biggest businesses in the world, including Amazon, Google, and Facebook. Personal assistants like Siri, Alexa, and Bixby are all powered by AI, which also enables websites to suggest goods, movies, or articles that may be of interest to you. These focused recommendations are the outcome of artificial intelligence; they are not a coincidence.

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AI and big data analytics

Although gathering data has long been a crucial aspect of business, modern digital tools have made it simpler than ever. It’s practically difficult for anyone or a company to effectively use the data they’re collecting because data sets are growing exponentially. That’s why comprehending big data, and artificial intelligence is vital.

Applications with AI capabilities may quickly process any data set, whether derived from a database or gathered in real time. AI solutions are being used by businesses to boost productivity, create personalized experiences, support decision-making, and cut costs.

Analytics and automation are frequently enhanced with data and AI, assisting organizations in transforming their operations.

Analytics technologies, such as Microsoft Azure Synapse, assist organizations in anticipating or identifying trends that guide decisions regarding workflows, product development, and other areas. Your data will also be arranged into readable dashboard visualizations, reports, charts, and graphs.

Meanwhile, corporate processes can be automated when big data and artificial intelligence solutions are created. For instance, AI can enhance the manufacturing sector’s safety checks, predictive maintenance, and inventory tracking. Any company can utilize AI to evaluate documents, conduct document searches, and handle customer service inquiries.

Due to how AI analyzes visual, textual, and auditory representations, even though it hasn’t yet equaled or surpassed human intellect, technology is becoming easier to adopt and integrate into many commercial activities.

While it might seem like big data and artificial intelligence have endless potential, the technology has limitations. Let’s go over five areas where AI shines so you can get a full idea of how you may use it in your company:

• AI may be taught to organize data, make suggestions, and aid in semantic search. These tools will enhance the user experience of your digital products by providing beneficial information that satisfies their needs. Additionally, since your application AI will keep improving its skills based on historical data, you may optimize the utility of both current and future data.
• AI can be trained to analyze, recognize, and search images using computer vision, a class of algorithms designed to comprehend and react to images and video. AI with vision training can store and caption documents and support IoT sensor arrays. Many sectors are using visual tracking to boost productivity and effectiveness.

• Customers demand current search engines’ accuracy and speed, but it might be challenging to match those high standards with your own tools. With AI, you can improve the search capabilities of your digital tools and enable them to analyze webpages, photos, videos, and more to provide consumers with the exact results they’re looking for. 
• By turning speech to text and text to speech, AI technology is frequently used to engage customers. You can simply review recorded customer conversations with annotated transcripts for studying customer behavior or instructing personnel. You can also create speech-based assistants like Siri or Alexa in your applications.
• Natural Language Processing makes it possible to converse with our technology in entire phrases, the way people naturally converse and receive meaningful responses (NLP). You can integrate NLP into your applications or bots to better serve user demands or create customer support tools that can have voice or text conversations. These big data and artificial intelligence perks can also be used to recognize and translate languages.

Big data vs artificial intelligence

At this point, big data is unquestionably here to stay, and artificial intelligence (AI) will continue to be in high demand. AI is meaningless without data, yet mastering data is impossible without AI. Therefore data and AI are melding into a synergistic connection.

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By fusing the two disciplines, we may start to recognize and forecast future trends in business, technology, commerce, entertainment, and everything in between.

Big data is the initial, unprocessed input that must be cleaned, organized, and integrated before it can be used; artificial intelligence is the final, intelligent product of data processing. The two are hence fundamentally different.

Artificial intelligence is a type of computer that enables robots to carry out cognitive tasks, such as acting or responding to input, in a manner that is analogous to that of humans. Traditional computing apps also respond to data, but all of these activities need hand-coding. The program is unable to respond if a curveball of any kind, such as an unexpected result, is thrown. As a result, big data and artificial intelligence systems continually refine their responses and adjust their behavior to account for new information.

A machine with AI capabilities is built to analyze and interpret data, solve problems or deal with problems depending on those interpretations. With machine learning, the computer first learns how to behave or respond to a certain result and then understands to act in the same way going forward.

Big data only search for results rather than acting on them. It describes incredibly vast quantities of data as well as data that can be exceedingly diverse. Structured data, like transactional data in a relational database, can be found in big data sets, and less structured or unstructured data, such as photographs, email data, sensor data, and so on.

They differ in how they are used as well. Gaining insight is the main goal of using big data. How does Netflix come up with recommendations for movies and TV series based on what you watch? Because it considers the purchasing patterns and preferences of other consumers and infers that you would feel the same way.

AI is about making decisions and improving upon those decisions. AI is performing jobs previously performed by humans but more quickly and with fewer mistakes, whether it is self-tuning software, self-driving automobiles, or analyzing medical samples. These are mainly the differences between big data and artificial intelligence technologies.

Big data and artificial intelligence are still indispensable twins

Despite their stark differences, big data and artificial intelligence nonetheless complement one another effectively. This is so because machine learning, in particular, needs data to develop its intelligence. For example, a machine learning picture identification program studies thousands of images of an airplane to determine what makes one so it can identify them in the future.

Big data is the starting point, but in order to train the model, it must be sufficiently structured and integrated for computers to spot useful patterns in the data consistently.

Big data collects enormous volumes of data, but before anything useful can be done with it, the wheat must be separated from the chafe. The unwanted, redundant, and useless data that is used in AI and ML has already been “cleaned” and deleted. So that’s the significant first step.

AI can then prosper after that. The data required to train the learning algorithms can be provided by big data. There are two sorts of data learning: routinely collected data and initial training, which acts as a kind of priming of the pump. Once they have completed their initial training, AI programs never stop learning. They keep acquiring fresh information, and as the data evolves, they adapt their course of action accordingly. Data is, therefore, initially and continuously required.

Pattern recognition is used in both computer paradigms, but they do so in distinct ways. Big data analytics uses sequential analysis to discover patterns in data that have occasionally been collected in the past, or “cold data.”

Machine learning continuously gathers data and learns from it. Your self-driving car continuously gathers data, learns new skills, and improves operations. New data is constantly being received and used. This indicates that big data and artificial intelligence are in a mutual relationship.

The future of big data and artificial intelligence

The rapid use of the Internet of Things digitizes data across the economy, making it now possible for AI systems to process or analyze it. As a result, AI is becoming more prevalent in various industries and companies. Some industries that utilize big data and artificial intelligence can be found below:

Big data and artificial intelligence in healthcare

According to Accenture, integrating AI into the US healthcare system may save $150 billion annually by 2026 while also improving patient outcomes. Big data and artificial intelligence are predicted to transform a range of facets of healthcare, from robotic surgery, made possible by combining diagnostic imaging and pre-op medical data, to virtual nursing assistants that assist with initial diagnosis and patient logistics.

Big data and artificial intelligence in autonomous vehicle development

Autonomous vehicles (AVs), which are controlled by AI, are destined to cause a significant disruption in the transportation sector. In order to successfully observe the road and operate the vehicle, AI software included in an AV computes billions of data points every second using inputs from advanced sensors, GPS, cameras, and radar systems.

AI in agriculture: Computer vision and robots are being used for higher efficiency

While there are still challenges before complete automation, high-end vehicles can handle fundamental driving tasks with little to no human involvement, thanks to big data and artificial intelligence. Additionally, testing of automated vehicles (AVs) that, in some circumstances, may operate autonomously in all areas of driving has begun.

Big data and artificial intelligence smart assistant development 

Digital assistants are becoming more dynamic and practical due to advances in voice recognition, predictive analytics, and natural language processing. According to experts, as consumers move away from the keyboard, voice searches will account for 50% of all Internet queries by 2023 with the development of big data and artificial intelligence technologies.

Big data and artificial intelligence in industrial automation systems

Industrial automation is at the forefront of the application of big data and artificial intelligence in the physical world, spurred by soaring global investment in robots that may approach $180 billion by 2020. Advancements in both sectors are combining to produce machines that are smarter and more competent than before, with robotics serving as a machine’s body and AI serving as a machine’s mind. Robots may now function more freely in unstructured settings like factories or warehouses. They can work more closely with humans on assembly lines, meaning they are no longer limited to simple, repetitive jobs.

Conclusion

These days, two key areas of computer science are big data and artificial intelligence. Research in the areas of big data and artificial intelligence hasn’t halted recently. Artificial intelligence and big data are inseparable. First, because big data technology makes extensive use of artificial intelligence theories and techniques, it depends on AI’s progress. Second, big data technology is essential to the advancement of artificial intelligence because this field depends heavily on data. We still need to learn about new technologies because big data and artificial intelligence innovation has only just begun.

What is the impact of artificial intelligence in insurance? Well, there are a lot of use cases for artificial intelligence in everyday life, but what about AI in insurance? The effects of artificial intelligence in business heavily include insurance.

Are you scared of AI jargon? We have already created a detailed AI glossary for the most commonly used artificial intelligence terms and explained the basics of artificial intelligence as well as the risks and benefits of artificial intelligence for organizations and others. So, it’s time to explore the role of artificial intelligence in insurance sector.

Impact of artificial intelligence in insurance industry

One of the most revolutionary advances has been the use of AI in insurance, which has been hailed as having significant economic and societal advantages that eventually boost risk pooling and improve risk reduction, mitigation, and prevention.

Automation enables insurance businesses to quickly respond to requests and guarantee that the customers they pledge to serve will receive high-quality service.

“Machines have instructions; we have a purpose. We will need intelligent machines to help us turn our grandest dreams into reality.”

Garry Kasparov

Is Kasparov right? Absolutely. However, the insurance industry has not adapted to AI technologies despite their many benefits. The traditional insurance industry has generally been hesitant to adapt to new technologies. According to a Deloitte survey, while practically every business has found success with AI or has begun investing in it, the insurance sector appears to be far behind, with only 1.33% of insurance companies investing in AI compared to 32% in software and internet technologies.

The good news is that whoever adopts AI early in the insurance sector will be a pioneer and receive the largest piece of the pie.

The environment is currently evolving quickly with the emergence of InsureTech startups and technological incumbents. In addition to requiring less money and resources, they can provide on-demand plans, more transparent pricing, and quicker claim payments.

What is the InsurTech industry?

The term “InsurTech ” describes technical advancements developed and used to increase the effectiveness of the insurance sector. The invention, distribution, and management of the insurance industry are all supported by InsurTech.

The shifting dynamics create global prospects for the AI-enabled insurance sector. So, let’s see the benefits of artificial intelligence in insurance and explore the taste of “the pie.”

Benefits of artificial intelligence in insurance

These are some of the best benefits of artificial intelligence in insurance:

• Expanded data and insight access
• The right information at the right moment to the right people
• Consistent performance from employees
• Better, quicker decisions are driven by data

Let’s take a closer look at the advantages of artificial intelligence in insurance and find out how Artificial intelligence is helping the underwriting process in insurance.

Expanded data and insight access

Building a better, more precise data foundation is a prerequisite for integrating AI into a workflow, and doing so benefits people even before AI is used.

Consider a worker attempting to ascertain whether some clients are spending too much time in the service center, particularly if they have a low estimated lifetime value. The underwriter receives a forecasted lifetime value score and can use it to inform a better price decision thanks to access to customer journey information and insights.

After AI is implemented, any previous activities can be sent to the machine-learning model and the customer’s information. By targeting the most profitable customers and avoiding those who are most likely to be unprofitable, the sales and marketing teams can improve future results.

The right information at the right moment to the right people

A submission forwarded to underwriting is first evaluated in real-time using predictive models for criteria including “broker sincerity” and “projected loss ratio for this class.” To help with issues like “Which risk should I work on next that will be most advantageous for our company?” AI can then develop a scoring system for those inputs.

Given the insights provided, the underwriter can choose the optimal course of action by digitizing the underwriting process with AI. In this instance, AI aids in bridging the gap between the employee’s action based on the recommendation made by the AI engine and the information gained.

Consistent performance from employees

Decisions become more accurate, correct, and consistent thanks to AI’s elimination of a large portion of the guesswork involved in decision-making.

While training is still essential, applying AI enables less experienced employees to pick up new skills much faster because they receive recommendations based on decisions that have already been proven to be correct. This reduces a lot of the risk that comes with hiring a new employee.

Check out how is artificial intelligence changing the recruiting process

An insurance claims adjuster with less expertise might overcompensate a client for a claim. In contrast, an adjuster empowered by AI can be directed through suggested next actions based on prior experiences, all within the same analytics system.

Better, quicker decisions are driven by data

Think about an insurance provider attempting to prevent fraud. Unlike humans, AI can read and depend on vast amounts of historical data based on false claims.

As a result, future fraud is caught considerably more quickly and precisely. This also helps the AI swiftly enhance its grasp of typical fraud behaviors. Much more than a human counterpart could ever calculate or act upon.

Because of these benefits, there are a lot of use cases of artificial intelligence in insurance.

Check out how big data is changing the insurance industry

AI in insurance use cases

AI is increasingly important in the insurance industry, from claims processing to compliance to risk reduction and damage analysis. These are some of the best AI in insurance use cases:

• Claims processing
• Claims fraud detection
• Claims adjudication
• Automated underwriting
• Submission intake
• Pricing and risk management
• Policy servicing
• Insurance distribution
• Product recommendation
• Property damage analysis
• Automated inspections
• Customer lifetime value prediction
• Speech analytics
• Customer segmentation
• Workstream balancing for agents
• Self-servicing for policy management
• Claim volume forecasting

How is technology changing the insurance industry? How does AI & ml enable insurers to tackle current challenges? Let’s explore artificial intelligence in insurance use cases and find out!

Claims processing

In order to comply with policy and regulatory requirements, insurers must make sure that claims are valid throughout the whole process cycle.

Handling thousands of claims and client inquiries is a laborious task that takes time. The entire procedure is effective and efficient, thanks to machine learning. Moving claims through the first report, analysis, and contacting the consumers significantly increases the value chain of claims processes.

Check out the 15 real-life examples of machine learning

Employees could concentrate on more complicated claims and one-on-one customer interactions because of the time savings.

Claims fraud detection

According to research by the Federal Bureau of Investigation on US insurance firms, the total cost of insurance fraud (non-health insurance) is nearly $40 billion annually.

In terms of higher premiums, insurance fraud costs the typical US household $400 to $700 annually. These shocking figures highlight the critical need for precise automated theft detection solutions to enable insurance companies to improve their due diligence procedure.

Claims adjudication

According to the Council for Affordable Quality (CAQH) Index research, automating eligibility and claim verification can save the healthcare insurance industry alone $ 5.2 billion annually. With a chatbot that communicates with consumers and gathers the necessary data, the claim initiation automation process helps insurers save time.

A first-level validation can be done throughout the claim start process using chatbots to capture information in a structured way. According to a World Economic Forum (WEF) report, computers will be used to carry out 62% of an organization’s data processing and storage tasks by 2022. Due to the expanding automation industry, investing in auto-adjudication systems will help firms stay relevant shortly.

Automated underwriting

Do you know a better love story than AI in insurance underwriting? In the past, insurance underwriting relied mainly on employees to examine historical data and come to wise conclusions. Working with chaotic systems, procedures, and workflows was another challenge as they attempted to reduce risks and provide customer value. Intelligent process automation simplifies the underwriting process by offering Machine Learning algorithms that gather and make sense of enormous volumes of data. It is one of the most used artificial intelligence in insurance use cases.

Additionally, it enhances the performance of rules, controls straight-through acceptance (STA) rates, and guards against application mistakes. Underwriters can concentrate only on complex instances that may need manual attention as most of the procedure has been automated.

Submission intake

When combined with AI and NLP, automation can extract data from structured and unstructured sources, including brokers’ emails, spreadsheets, loss runs, and ACORD forms, facilitating effective teamwork and accelerating and improving risk assessment.

Additionally, automation makes managing various submission queues for new businesses, renewals, and endorsements easier. Machine learning models quickly sift through hundreds of submissions and rank the best entries following the underwriting triage criteria and risk appetite.

Pricing and risk management

Price optimization uses data analytic techniques to determine an organization’s ideal rates while considering its objectives. It is one of the best artificial intelligence in insurance use cases.

It analyzes how customers respond to various pricing strategies for goods and services. GLMs (Generalized Linear Models) are mostly used by insurance companies to optimize prices in industries like auto and life insurance. With this method, insurance businesses may better understand their clients, balance supply and demand, and increase conversion rates.

Automation of risk assessment also improves operational efficiency. Risk assessment automation increases efficiency by fusing RPA with machine learning and cognitive technologies to build intelligent operations. Insurance companies can provide a better client experience and lower turnover because the automated procedure takes much less time.

Check out cyber risk assessments examples

Policy servicing

The policy administration system can be integrated to get information about each policy thanks to the automated intake of policy data. This lessens the manual search and location effort needed to discover the pertinent fields for policy endorsements.

Additionally, it enables parallel processing to handle complex circumstances where many requests are made by different clients, which reduces the turnaround time for processing and servicing insurance policies. RPA in the insurance industry helps to efficiently complete various tasks without requiring extensive system navigation. It automates administrative and transactional tasks like accounting, settlements, risk capture, credit control, tax preparation, and regulatory compliance.

Insurance distribution

In the pre-digital era, insurance customers might visit a local carrier or contact a financial adviser to learn about coverage possibilities. In a specialized market, there would often be a leading carrier for a certain product. The carrier would carry out underwriting tasks and share a quote based on the customer’s submitted data. Digitalized insurance distribution methods flipped this scenario.

Today, almost all carriers have an online site where customers may browse their selection of products and services before making a choice. This change in consumer behavior brought on a significant disruption in the insurance industry. Beyond underwriting and claims clearance, AI has the ability to revolutionize the sales and distribution stage of the insurance value chain by utilizing cutting-edge AI algorithms that are now on the market.

Insurance companies can benefit from a customer’s digital behavior by using digital technologies like optical character recognition (OCR), machine learning (ML), and natural language processing (NLP).

Product recommendation

Each day, the insurance industry produces a large amount of transaction data. Automation can help businesses in this situation accurately and effectively propose insurance products to customers, increasing the insurance company’s ability to compete.

Price optimization uses data analytic techniques to determine an organization’s ideal rates while considering its objectives. It is one of the most common artificial intelligence in insurance use cases.

Property damage analysis

The first step in any damage insurance claim process, whether it involves a mobile phone, a car, or a piece of property, is inspection.

With physical intervention, estimating the damages to determine repair costs is difficult for insurance companies. Data analysis and AI-powered object detection compare the level of damage before and after the occurrence. Machine learning algorithms can identify broken auto parts and provide repair cost estimates.

Automated inspections

Motor insurance claim assessment has historically been handled manually by surveyors and claim adjusters. Manual inspection is expensive because it necessitates the adjuster or surveyor to contact the policyholder. Each examination costs between $50 to $200. The processing of claims would also take longer because report generation and estimation typically take one to seven days.

Insurance firms can examine car damage with AI-based image processing. The system then produces a thorough assessment report explaining the car parts that can be repaired and replaced and their approximate costs. Insurance companies can lower claim estimation expenses and improve the procedure’s effectiveness. Additionally, it populates reliable data to determine the final settlement sum.

Customer lifetime value prediction

One of the most important technologies that allow businesses to forecast client lifetime value using machine learning is the customer lifetime value (LTV).

According to research by Bain & Co., an improvement in retention of 5% can result in a profit increase of 25% to 95% for a business. A customer’s purchasing history is compared to a huge product inventory by machine learning algorithms to uncover hidden patterns and group products that are similar. It is one of the most important artificial intelligence in insurance use cases.

Customers are then given access to these products, eventually promoting product purchases. Insurance companies can strike the ideal balance between customer acquisition and retention by knowing the lifetime worth of each customer.

Speech analytics

Speech recognition is a potent tool for lead call analysis based on customer speech to enhance the personalization. It can detect fraud based on voice analysis of customer calls to increase security measures and identify customer pain points with products using speech analytics of comments to improve future products.

Do you know artificial intelligence customer services are on the rise?

Customer segmentation

The first step in developing customization is customer segmentation. It improves consumer happiness, product design, marketing, and budgeting. It is one of the most common artificial intelligence in insurance use cases.

Machine learning algorithms examine customer data to uncover trends and insights. Tools with AI assistance accurately identify client categories that are difficult to complete manually or use traditional analytical techniques.

Workstream balancing for agents

Utilizing AI-assisted models that give them access to consumers and enable them to grow their businesses is becoming increasingly popular among insurance agents.

AI will undoubtedly be the cornerstone for increasing consumer happiness and, in turn, expanding the reach of insurance brokers because simplicity is its defining characteristic.

Self-servicing for policy management

Self-service business intelligence (BI) is a data analytics platform that enables users to access, examine, and analyze data sets without prior knowledge of BI, data mining, or statistical analysis.

Self-service BI technologies allow users to filter, organize, analyze, and visualize data without the help of BI and IT teams in a company. These tools make it simpler for staff members to gain insightful business knowledge from the data gathered in BI systems. Ultimately, this strategy promotes more informed decision-making, which raises revenues, boosts productivity, and improves client happiness.

Check out the role of artificial intelligence in information systems

Claim volume forecasting

Setting the premium at the start of the insurance contract is fundamental to insurance practice. A precise and reliable assessment of the number of claims occurrences and the total claim amounts is crucial to arriving at an insurance company’s precise premium for the upcoming year. It is one of the most critical artificial intelligence in insurance use cases.

The forecasting for individual claims is faster and more accurate, thanks to machine learning. This enhances the effectiveness of an insurer’s pricing.

Check out how is artificial intelligence used in the military

Insurance companies using artificial intelligence (Top 5)

What insurance companies are using AI? Insurance companies are utilizing artificial intelligence to create customized plans, automate the underwriting process, and give customers worldwide more precise estimates. These are some of the best insurance companies using artificial intelligence:

• Liberty Mutual Insurance
• CCC Intelligent Solutions
• Insurify
• Clearcover
• Bold Penguin

Check out these Insurance companies using artificial intelligence to learn more about how AI affects the insurance sector.

Liberty Mutual Insurance

Through the Solaria Labs program, Liberty Mutual investigates AI in fields including computer vision and natural language processing. One outcome of their efforts is the Auto Damage Estimator. This AI solution uses comparative studies of anonymous claims images to swiftly evaluate vehicle damage and offer repair estimates after an accident. It is one of the firms that used artificial intelligence in insurance.

CCC Intelligent Solutions

Artificial intelligence is used by CCC Intelligent Solutions to digitize and automate the whole claims process. Photos taken at accident scenes are analyzed using AI and guidelines agreed by the insurance. Based on this information, CCC’s AI can determine the extent of the damage and promptly offer estimates that insurers can accept and forward to their clients for confirmation.

Insurify

Utilizing artificial intelligence, Insurify instantly connects clients with auto and home insurance providers that meet their individual requirements. The business uses RateRank algorithms to identify the insurance that would suit each client, taking into account details like location and desired discount level.

Clearcover

Clearcover uses artificial intelligence to process claims and insure users quickly. Users of Clearcover can receive AI-generated quotations and select the one that best suits their needs after completing a brief questionnaire. Users only need to take a few images and complete a brief form if they are ever in an accident before ClearAI jumpstarts the claims procedure.

Bold Penguin

With two AI-powered tools, SubmissionLink and ClauseLink, Bold Penguin enables insurance businesses to produce policies that stand out in the sector swiftly. SubmissionLink examines documents that carriers receive from authorities and identifies crucial information for underwriters. While this is going on, ClauseLink examines insurance provisions to assist providers in comparing their plans to those of rivals.

AI in insurance market size

With a predicted CAGR of 32.56% from 2022 to 2031, the global AI in the insurance market, valued at $2.74 billion in 2021, is expected to increase to $45.74 billion by 2031, according to AlliedMarketResearch.

The global AI in the insurance market is expanding due to an increase in investment by insurance companies in AI and machine learning, as well as a rise in demand for personalized insurance services.

Check out how data science helps insurance companies

Conclusion

AI will drive the future of insurance. Utilizing various AI techniques will quickly automate insurance processing, from claim submission to payment, without human involvement. Saving this money and effort will enable the insurance sector to develop better product categories and customized premium rates based on information gathered from multiple sources.

A wave of homogeneity across various market sectors, industrial verticals, and service providers is brought forth by AI. As a result, procedures for getting insurance and handling claims can be more consistently standardized.

Greater operational excellence, lower costs, and improved client experiences are other advantages that we can anticipate. It is clear that AI-driven insurance has a bright future, and the use of AI in the insurance sector will significantly increase in the years to come.

Is artificial intelligence better than human intelligence? Explore the cons of artificial intelligence before you decide whether artificial intelligence in insurance is good or bad.

The phrase “data in motion” refers to data traveling from one location to another. Many different kinds of networks can be utilized for data transportation in this way. Data in motion must be protected in order to increase the security of a network because a network often consists of several nodes with numerous clients interconnected to the same network. This procedure is known as encryption.

What is data in motion?

The process of moving digital information between locations, either within or between computer systems, is known as “data in motion,” also known as “data in transit” or “data in flight.” The phrase can also refer to data available for reading, accessing, updating, or processing and is kept in the RAM of a computer. One of the three data states—the other two being data at rest and data in use—is data in motion.

A book called “Managing Data in Motion” stresses the importance of data in motion:

“The average enterprise’s computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired.”

How do you handle data in motion?

Many distinct network types are compatible with data in motion:

• Data is sent from a web-facing service in a public or private cloud to an internet-connected device.
• Data travels across both reputable private networks and dubious public networks, like the internet.
• Data that is transferred between integrations and applications. Once the data arrives at its final destination, it becomes data at rest.
• Data is being transferred between virtual computers inside and outside of cloud services.

Data in motion is a crucial notion in data protection for companies and for adhering to legislative requirements like PCI DSS or GDPR. For individuals who work in big data analytics, data in motion is especially crucial since processing data enables an organization to evaluate and understand trends as they emerge.

Data in motion encryption

If data is not encrypted when being sent between devices, it could be intercepted, obtained, or leaked. Data in motion is frequently encrypted to prevent interception because it is susceptible to man-in-the-middle attacks, for instance. Whenever data travels across any internal or external networks, it should always be encrypted.

Data replication: One of the most powerful instruments to protect a company’s data

The following techniques can be used to encrypt data in motion:

• HTTPS. HTTPS is typically used to secure internet connections, but it has also established itself as a common encryption method for communications between web hosts and browsers as well as between hosts in the cloud- and non-cloud contexts.
• Cryptography. Users that utilize cloud-based services may additionally encrypt their own data while it is in the cloud using a variety of encryption techniques. For key exchange and content confidentiality, for instance, symmetric cryptography is sometimes utilized. The conventional encryption levels and strengths are strengthened and improved by this method.
• IPSec. Internet Protocol Security is used by the Internet Small Computer System Interface transport layer to protect data while it is in motion (IPSec). To prevent hackers from seeing the contents of the data being sent between two devices, IPSec can encrypt the data. Due to the fact that IPSec employs cryptographic techniques like Triple Data Encryption Standard (Triple DES) and Advanced Encryption Standard, it is widely utilized as a transit encryption protocol for virtual private network tunnels (AES). Encryption technologies can also be integrated with existing enterprise resource planning systems to keep data in motion secure.
• Asymmetric encryption. With this technique, a message is encrypted and decrypted using one public key and one private key. This is done to prevent the message from being read or used by unauthorized parties. When a sender encrypts a message with their private key, only their public key may decrypt the message, authenticating the sender. The encryption and decryption procedures are also automatic. Asymmetric cryptography is used by many protocols, such as Transport Layer Security (TLS) and Secure Sockets Layer (SSL), to enable HyperText Transfer Protocol Secure (HTTPS).
• TLS and SSL. TLS and SSL are two of the most well-known cryptography applications for data in motion. TLS offers a transport layer as an encrypted conduit between message transfer agents or email servers. On the other hand, SSL certificates use public and private keys to encrypt private conversations sent over the internet.

Why is data in motion important?

Big data analytics heavily relies on data in motion. An organization may benefit from processing this data in real-time to analyze current trends as they emerge. However, processing this form of data is more challenging, necessitating the adoption of novel techniques than for data that is at rest. An organization’s ability to get insightful information from data in motion is a crucial advantage.

How many types of data are there?

Structured and unstructured data are categorized into three states. Data can be in three different states: data at rest, data in motion, and data in use. Data can change states frequently and quickly, or it can stay in one state for the duration of a computer’s life. Organizations can handle sensitive information more safely by recognizing the traits and variations among data states.

Data mature businesses are more profitable than others

Data center administrators (DCAs) used to spend a lot of time maintaining data that was at rest, especially in market sectors with heavy compliance requirements. However, the extent to which businesses today rely on real-time analytics has increased the importance of managing data in use.

What are the 3 states of data?

There are 3 states of data: Data at rest, data in motion, and data in use. Let’s review each and one of them below.

Data at rest

Computer experts refer to all data in computer storage that is not currently being accessed or transferred as “data at rest.” Although some data may be kept in reference or archived files where it is infrequently or never read or moved, data at rest is not in a fixed state. The hard drive of a worker’s computer, files on an external hard drive, information left in a storage area network (SAN), or files on the servers of an off-site backup service provider are all examples of data at rest.

In contrast to data in the other states, data at rest is regarded as stable. It is not being processed by a CPU or transferred between systems or devices. Data is deemed to have arrived at its destination when it is at rest.

Business processes need data management for their continuous improvement

Data encryption, hierarchical password protection, secure server rooms, and outside data protection services are just a few of the safeguards used by companies, government organizations, and other institutions to stop threats posed by hackers to data that is at rest. Information that is stored at rest is further protected by multifactor authentication and stringent data security standards for employees. Specific security precautions are required by law for some data categories, such as medical records.

Data in motion

Data in motion is any information that is traveling or being transferred between points on one computer system or another. It can also refer to information that is stored in RAM and is available for updating, processing, accessing, and reading. Migrating from one network to another or between cloud storage and a local file storage location is also seen as moving data. Data in motion can go across a cable connection, wireless link, or even within a computer system. Additionally, emails and files moved between folders on an FTP server are regarded as data in motion.

Data in motion should be encrypted to prevent hackers from intercepting it, just like data in the other common states. Encrypting the data before it is transferred (when it is in a state of data at rest) or encrypting the path the data is sent along are two common types of encryption for data in motion.

Data in use

Data that is currently being updated, processed, accessed, and read by a system is known as data in use. This is the condition at which data is most susceptible to assaults and when encryption is most necessary since data in use is immediately accessible by one or more users. Along with encryption, solid identity management, up-to-date permissions for organizational profiles, and user authentication at all stages are some additional critical safeguards for data in use. Organizations frequently need their employees to sign non-disclosure agreements on safeguarding the data they have access to in addition to the digital forms of protection.

Data in motion vs data at rest

Data is always in motion in today’s digital workplaces. Employees send and receive data daily via email, online coworking spaces, and messaging programs. The solutions they utilize can be collaborative tools sanctioned by the organization, but they can also shadow IT—personal services that people use at work secretly from their employers.

Data is therefore regarded as being less secure while in motion. In addition to being exposed to transfer across potentially unsafe routes, it also leaves the protection of corporate networks, travels to perhaps unsafe locations, and is exposed to Man-in-the-Middle (MITM) cyberattacks that target data as it moves.

Data at rest is less vulnerable than data in motion since it is not exchanged over the internet and stays inside the boundaries of corporate networks and their security environment. Cybercriminals, however, are frequently more drawn to data at rest because it ensures a larger payoff than smaller data packets in transit. Malicious insiders who want to harm a company’s reputation or steal data before moving on to a new job frequently target data that is at rest.

How AI and data analytics impact the era of COVID-19

Despite not being exchanged over the internet, data at rest nevertheless travels. Data at rest was placed in a particularly vulnerable position during the COVID-19 pandemic as more and more work computers were removed from the security of office settings and placed in the low-security environs of homes.

Employee error can happen to data that is at rest or in motion. A moment of employee carelessness can expose data to a data breach or leak, regardless of whether the data is stored locally or sent over the internet.

Data in motion examples

Data in motion can be copied from one app to another or downloaded from a web browser to a local app and moved to other locations on the same machine. Additionally, it can be moved physically across short or long distances via portable storage devices like USB flash drives or through cloud services like email.

Data in motion security risks

Two major categories can be used to describe data in motion. The first is the virtual data transmission inside a private network’s confines. Firewalls and other internal data protection techniques secure this information to some extent. Information that is transported outside of the organization falls under the second category. Data in motion is most vulnerable when it is transported outside of an organization’s or private network since it is occasionally processed across shaky networks like the internet or through auxiliary devices that, if handled incorrectly, can be made accessible to unauthorized viewers.

Key takeaways

• Big data analytics heavily relies on data in motion.
• An organization may benefit from processing this data in real-time to analyze current trends as they emerge.
• This type of data processing is particularly challenging, hence different strategies must be applied than for data that is at rest.
• An organization’s ability to get insightful information from data in motion is a crucial advantage.

Conclusion

With the current state of big data, you need data in motion to respond swiftly. Data must be transferred from one place to another in order to complete a credit card transaction or send an email. In a database in your data center or the cloud, data is at rest when it is kept there. Data, on the other hand, is in motion when it is transferred between two resting locations.

Data management enables a business process to be more efficient. The majority of contemporary organizations are aware of the value of data. This frequently means depending on the reports produced by the third-party software platforms they use daily for small firms. It is important to combine this data into a single, standardized source at some point. Data management is a business process required to organize and secure this valuable information properly.

What is a business process, and why data management is vital?

A method for describing how data is gathered and processed within a company is what data management is all about. With the need for governance surrounding the citizen developer movement, it is a subject that is receiving more and more attention.

Data consistency, dependability, and security are all ensured by an efficient data management program. Typically, the program has a governance committee and a group of data stewards. In an organization, these teams collaborate to establish, create, apply, and enforce data procedures.

Gartner defines data management as:

“Data management (DM) consists of the practices, architectural techniques, and tools for achieving consistent access to and delivery of data across the spectrum of data subject areas and data structure types in the enterprise, to meet the data consumption requirements of all applications and business processes.”

How can data analytics improve business processes?

The integration of business processes with data is not a new idea. Concerning many of the current hot themes in data management, it appears to be seeing a rebirth, similar to many other basic architectural components.

Understanding that all of your clients will value connecting to business priorities and the business processes that support them is critical when dealing with various clients in different industries with quite diverse data management projects underway. Here are a few instances where business processes were used in various circumstances: 

• Big data analytics
• Master data management
• Data governance
• Data quality

Master data management

The discipline of master data management (MDM) aims to provide a “single version of the truth” for key business components such as customers, products, suppliers, etc. A “single version of the truth” is a compilation of multiple viewpoints on one reality, much like the well-known tale of the blind men and the elephant. If you are unfamiliar with the traditional fable of blind men and the elephant, it is about a group of blind men who each touch an elephant to get a feel for it.

Each man has a unique idea of what it means to be an elephant based on what he touches: the trunk, the tusk, the tail, and the hide. The “single version of the truth” is a superset of all of their experiences, but each is correct in its own way. A comparable situation is presented by master data management.

Consider a typical master data domain like Product. While multiple user groups within the business have access to a comprehensive view of a “Product” with a superset of attributes, each user group understands what “Product” information contains and how it should be used.

Each supply chain organization can view, add, modify, and/or delete certain data elements that make up the idea of “Product.” Identifying these stakeholder groups and working with them to comprehend their usage and requirements around the relevant data domain is crucial for the success of MDM.

Data governance

A structured process model can be useful when managing data in the data governance domain, particularly concerning people and processes. Figuring out how and by whom data is used throughout the business process can assist in establishing the correct data stewardship and ownership. It can assist in settling disputes if there are ownership issues.

Data quality

Similar to this, the business process is crucial in the domain of data quality. Data can be cleaned, verified, and enhanced in various ways, and numerous tools and techniques are available to support data quality in these ways. However, data quality strategies are destined to be ineffectual if used in a vacuum without considering how business processes are used.

The example of a lake that harmful substances have contaminated is a frequent analogy used to explain this situation. Biologists can try to purify a lake’s water, but their efforts will be in vain if they don’t consider the streams supplying the lake with toxins. The clean lake will once more contaminate the tainted water from the streams.

Big data analytics

Rich information from big data analytics can be provided for various sources, enhancing traditional data sources like a data warehouse. To generate a “360 picture of the consumer,” it is possible to use customer data such as social media sentiment analysis, buying trends, footfall analytics, and more. But if this analysis is carried out in a vacuum, it won’t be very useful. For instance, if we have data on customer sentiment, it’s crucial to comprehend where the client is in the product’s lifecycle when this emotion is conveyed.

Have they just bought the product, started a service complaint, returned the item, etc.? To fully comprehend their experience, it is essential to link their sentiment to where they are in the purchasing lifecycle.

Everything you should know about big data services

Customer journey maps are frequently developed to understand better the customer lifecycle and how data is changed at each stage. When big data analytics are connected to business processes, their value increases.

How can big data be used to understand or optimize business processes?

For a while, “Big Data” has generated buzz across industries. Every executive has been putting new plans into practice to benefit from big data. Truth be told, the idea of big data is always changing and continues to be a driving force behind a number of digital changes, including artificial intelligence (AI), data science, and the internet of things (IoT). But how exactly can the business world benefit from big data? Here are a few illustrations we wanted to provide to motivate your staff for upcoming tasks.

Customer management 

One industry that makes extensive use of big data is proper customer handling. Numerous data models have been created to evaluate customer data with great success. The analysis’s findings are efficiently streamlined to improve company choices. Applications for data analytics include:

• Creating effective pricing strategies
• Assessing the level of service and client satisfaction
• Evaluating the success of customer-related strategies
• Supply chain management improvements and Maximize customer value
• Acquiring new clients and maintaining current ones
• Carrying out precise prediction analysis 
• Certifying client data
• Providing and predicting accurate consumer classification and behavior 

Managing the waste

A significant share of business resources is being wasted. Businesses may effectively improve their waste management procedures with the right data management. The precision that big data analytics provides to business intelligence is its main advantage. This accuracy helps firms make informed decisions about trash management. The measurement is at the center, making it simpler to identify the business operations that generate the most waste. So, if you want to use big data to manage waste, the advice that follows will help you get the most out of it:

• Choose the data that your company wants to collect
• Take measurements at various times throughout the chosen process
• Utilize specialists and specialized software to examine the facts and consequences.
• Make the necessary changes to waste reduction.

Manufacturing methods

Big data analytics are used to improve industrial methods’ precision and effectiveness. Many modern manufacturing companies are embracing the Industrial Internet of Things (IIoT), which is already powered by data analytics and sensor technology. Manufacturers with processes requiring massive data sets are leading the adoption race, as was to be expected.

AI in the manufacturing market will rise by 14 billion dollars in 5 years

For instance, computer chips typically go through 15,000+ testing before being issued. Predictive data models are being used, which has decreased the number of tests needed and saved millions of dollars in production costs. Small manufacturing companies are likewise reorganizing their operations using data analytics. In the manufacturing industry, big data can be applied to:

• Product customization
• Assessing the quality of components and raw materials
• New product forecasting, testing, and simulation
• Increase in energy efficiency
• Evaluation of supplier performance
• Risks chain management in the supply
• Locating flaws and monitoring product attributes 

Developing a product

Any product’s development has historically involved extensive data collecting and analysis. It primarily explains why using big data to prepare a product has substantial commercial benefits. Before releasing any product to the market, developers must gather and analyze information about the competitors, customer experience, price, and product specs. Answering the above questions can be vital when developing a new product:

• Which trends are dominating the market?
• What deals and prices are being offered by rivals?
• What benefits and drawbacks do products from rivals have?
• What issues are we trying to solve with our products?
• What goods or services might astonish customers?

More process analysis of substantial data is required to fully address the abovementioned problems. Data management offers a more accurate and comprehensive approach to product development than traditional methods. This strategy guarantees that every product created is suitable to meet a market requirement.

Customer surveys, crowdfunding and manufacturer websites, marketing blogs, online product reviews, product associations, retailer catalogs, social media platforms, and other sources can all be leveraged to extract data using data analytics. When exploring the latest trends, you can also use market automation tools, for instance, we’ve listed 13 marketing automation tools that can help you boost your sales.

Finding new talent

One of the crucial parts of a firm is its human resources (HR) department. Big data analytics can be used in HR to manage and recruit talent with accuracy and thoroughness. Predictive data models, for instance, can help evaluate a worker’s performance. We have also discussed how important AI’s impact on recruitment is before. However, most companies still base these choices on insufficient information, costing them a sizable fortune over time. Big data can be used to generate more effective personnel management strategies for the following data types:

• Delays in production and delivery
• Absenteeism among workers
• Data on training, work production, employee error rates, and profiles
• The workload of employees and staffing levels
• Employee incentives and performance reviews
• Evaluation of revenue per employee
• The six-sigma data

Big Data’s application in talent management has many benefits, including assisting management in identifying productivity issues and locating individuals with the right needs and values. Additionally, it promotes creativity, aids in management predictions, and aids in understanding the skills and requirements of various people.

Who is responsible for data management?

The IT department often implements a data management system. Typically, a CDO or the project lead is in charge of this.

A business, however, has the option of outsourcing the execution of data management. This is advised for businesses that lack a full-time Chief Data Officer (CDO) with the necessary skills or whose IT team lacks the time or resources to execute the system.

Companies who want to swiftly deploy their data management system or have complicated data or requirements that will make the implementation difficult can consider outsourcing data management.

What are the 3 types of business process analysis?

According to Mark Von Rosing, the author of the book called “The Complete Corporate Process Handbook: Body of Knowledge from Process Modeling to BPM,” business processes should be organized into three subsections:

• Operational process
• Supporting process
• Management process

Operational business processes

Asking yourself, “how does, or will, your business create income” will help you identify your operational processes. Operational processes are the procedures and duties that directly contribute to the creation of outputs from inputs.

Items like labor, unprocessed equipment, and money are examples of inputs. The finished product or service and the degree of client pleasure results are examples of outputs.

Generally speaking, if your process fits into one of the categories listed below, you can classify it as operational.

• The process of developing or producing the finished good or service
• The promotion of the aforementioned good or service
• Even after the sale, the support and customer care you provide

Consider the scenario where you are a neighborhood greengrocer who serves your neighborhood with fresh veggies. All involved tasks—buying the fruit from the supplier, boxing it up, and distributing it to your customers—represent operational processes.

It’s important to remember that there may also be sub-processes, such as storage. Although it might not seem like it, this is an operational procedure because it is connected to your final product.

One of your top strategic priorities should be ensuring that your operational procedures integrate as effectively as possible.

Supporting business processes 

These make up the engine room’s cogs. The supporting processes are the items that operate quietly in the background to make sure the ship can continue to sail. This indicates that they are not self-sustaining but rather exist to support the internal employee population throughout the organization. They add value, but not in monetary terms.

The payroll department, for instance, may not always bring in revenue, but without them, your employees wouldn’t get paid. The same is true for someone who cleans houses or does the dishes; even if they may not make much money from their job, you would undoubtedly notice without them.

These are either or both, strategically significant and required processes that enable the effective execution of operational processes.

Business management processes

The coordination of the aforementioned procedures happens here. This calls for planning, oversight, and general supervision.

This entails, among other managerial responsibilities, ensuring that the team is fulfilling its goals, that the workplace is safe and compliant, and employee complaints are addressed. It also entails spotting possible risks or prospects for your company, such as talent in one of your employees who could benefit from training or a potential new client who could help your company get a good deal.

Management exists to maximize income potential and modify the firm as needed, even though, like supporting operations, it does not always result in direct money.

The resilience of a corporation is mostly a function of effective management procedures.

Business process management tools

A business process management (BPM) tool is a software program that supports you as a manager through all phases of business process management by assisting you in designing, modeling, executing, monitoring, and optimizing business processes.

Regardless of your present procedure’s effectiveness, there is always room for improvement. You can aim to save expenditures overall or the amount of time it takes to develop a certain asset. You can improve current business processes by using strategies like process standardization or automation.

AI and computer vision are becoming key tools for shop-and-go platforms

One of the best methods to boost productivity is through business process automation. Allow your automation platform to handle repetitive tasks rather than perform them manually.

The best business process management methods must be established as part of process standardization. Establishing defined stages will minimize failure rates while cutting expenses and time spent on repeat processes instead of doing them randomly.

Benefits of using a BPM tool

The question remains, why should YOU use a business process management (BPM) tool to optimize your business processes? In the following section, we summarize the most common benefits:

• It saves time: Time is a limited resource in all facets of a business, whether you need to build a new product or fulfill client requests that have already been made. You can automate processes and free up resources with the correct BPM technologies.
• It reduces costs: You will automatically save money if your staff is able to complete jobs more quickly. Use as many of these tools as you can to get the most out of automating business process management.
• Brings better and higher quality outputs: BPM and workflow management systems are the answers if you want to provide improved quality and consistency across all of your company’s outputs.
• Reduces failure rates: Your failure rate will drop through automated and standardized operations, improving your company’s bottom line. The advantages of the best BPM solutions can be used to the advantage of all enterprises. The top business process management tools are reviewed in the section that follows. These solutions can help you cut costs and time while improving the quality of your outputs.

Conclusion

Business processes offer a crucial context for how data is used inside a company, which is essential since data is only valuable when presented properly. Business process models provide insight into how and by whom information is used, which directly affects big data analytics, big data management, master data management, and other data management projects.

Importantly, it assists in determining company priorities. Prioritizing business-critical data is a crucial step in any data management discipline because it is difficult to manage all information in an organization closely. The business process creates the backdrop for setting priorities.

Does this information, for instance, support the revenue-generating sales cycle? Does the organization as a whole use this data across a variety of processes? Does this knowledge contribute to a more effective supply chain? If you can answer “yes” to questions like these, you can figure out the crucial information that underpins business success.

AI is the key to being a competitive business

Using process models to more completely comprehend how data is used in a company setting where cost-benefit analysis is constantly the driving factor helps grasp the benefits and drive efficiencies that cut costs. We urge you to consider incorporating business process models into your upcoming project.

The much-awaited comparison is finally here: machine learning vs data science. The terms “data science” and “machine learning” are among the most popular terms in the industry in the twenty-first century. These two methods are being used by everyone, from first-year computer science students to large organizations like Netflix and Amazon.

The fields of data science and machine learning are related to the use of data to improve the development of new products, services, infrastructure systems, and other things. Both correspond to highly sought-after and lucrative job options. But, they are not the same. So, what are the differences?

Machine learning vs data science: What is the difference?

Machine learning is the study of developing techniques for using data to enhance performance or inform predictions, while data science is the study of data and how to extract meaning from it.

Similar to how squares and rectangles are related to one another but not the other way around. Machine learning is the square that is its entity, whereas data science is the all-encompassing rectangle. Data scientists frequently employ both in their work, and practically every industry quickly embraces them.

The terms “machine learning” and “data science” are quite trendy. Even though these two words are frequently used interchangeably, they shouldn’t be considered synonymous. However, do not forget that machine learning is a part of data science, even though the topic is very broad and has many tools. So, what distinguishes them then? First, let’s briefly remember what they are.

What is data science?

As the name says, data science is all about the data. As a result, we can define it as “An area of a thorough study of data, including extracting relevant insights from the data, and processing that information using various tools, statistical models, and machine learning algorithms.” Data preparation, cleansing, analysis, and visualization are all included in this big data management paradigm.

Data scientists gather raw data from various sources, prepare and preprocess it, and then apply machine learning algorithms and predictive analysis to glean actionable insights from their gathered data. For instance, Netflix uses data science approaches to analyze user data and viewing habits to comprehend consumer interests.

Check out and learn about data science:

• Most popular data science techniques
• Best data science boot camps (and why are they so popular)
• How data science conquers the customer journey (with examples)
• What did Nvidia announce at GT 2022 about data science?

Data scientist skills

Skills needed to become a data scientist:

• Statistics
• Data mining and cleaning
• Data visualization
• Unstructured data management techniques
• Programming languages such as R and Python
• Understand SQL databases
• Use big data tools like Hadoop, Hive, and Pig

What is machine learning?

Artificial intelligence and the discipline of data science both include machine learning. Developing technology allows machines to complete a task and learn from previous data automatically.

Through machine learning, which uses statistical techniques to enhance performance and forecast outcomes without explicit programming, computers can learn from their prior experiences on their own. Email spam filtering, product suggestions, online fraud detection, etc., are some of the common uses of ML.

Check out and learn about machine learning:

• Real-life examples of machine learning
• What is linear regression in machine learning?
• Machine learning benefits for business
• How to manage the machine learning lifecycle?
• The history of machine learning
• What is quantum machine learning?
• What is machine learning engineering?
• How much are machine learning engineers’ salaries?

Machine learning engineer skills

Skills needed to become a machine learning engineer:

• Computer science fundamentals
• Statistical modeling
• Data evaluation and modeling
• Understanding and application of algorithms
• Natural language processing
• Data architecture design
• Text representation techniques

Comparison: Data science vs machine learning

Machine learning focuses on tools and strategies for creating models that can learn on their own by analyzing data, whereas data science investigates data and how to extract meaning from it.

A researcher who uses their expertise to develop a research methodology and who works with algorithm theory is often referred to as a data scientist. A machine learning engineer creates models. By conducting experiments on data, they strive to obtain specific reproducible outcomes while selecting the best algorithm for a certain problem.

The key distinctions between data science and machine learning are shown in the table below:

Data scientists vs machine learning engineers

Data scientists are frequently compared to “Masterchefs.” He learns how to cook a tasty meal, where his essential tasks are to clean the information, prepare the components, and carefully combine them. They must consistently make high-quality meals that can satiate the demands of both clients and businesses looking to provide the greatest service in the industry.

Machine learning engineers will package, utilize, deliver, maintain, and operationalize, guaranteeing that it reaches their clients in the manner they want it to.

Machine learning vs data science salary

According to Indeed, data scientists make an average yearly pay of $102,069, while machine learning engineers make an average annual compensation of $110,819. Across various industries, including healthcare, finance, marketing, eCommerce, and more, both jobs are in demand.

Similarities: Data science vs machine learning

The fact that data science and machine learning touch the model is arguably their most related idea. The key competencies shared by both fields are:

• SQL
• Python
• GitHub
• Concept of training and evaluating data

Check out what programming language for artificial intelligence is the best

Programming comparisons focus on each person’s language to carry out their separate tasks. Whether a data scientist using SQL to query a database or a machine learning engineer using SQL to insert model recommendations or predictions back into a newly labeled column or field, both professions include some engineering.

Both disciplines necessitate familiarity with Python (or R) and version control, code sharing, and pull requests via GitHub.

For performing research on memory and size restrictions, a machine learning engineer may occasionally wish to understand the workings of algorithms like XGBoost or Random Forest, for example, and will need to look at the model’s hyperparameters for tuning. Although data scientists can create extremely accurate models in academia and business, there may be greater limitations because of time, resource, and memory constraints.

What is machine learning in data science?

Machine learning automates data analysis and generates real-time predictions based on data without human interaction. A data model is automatically created and then trained to make predictions in the present. A data science lifecycle starts when machine learning algorithms are applied.

The standard machine learning process begins with you providing the data to be studied, followed by you defining the precise features of your Model and the creation of a Data Model by those features. The training dataset that was first provided to the data model is then used to train it. The next time you upload a fresh dataset, the machine learning algorithm is prepared to predict once the model has been trained.

Let’s use an instance to grasp this better. You must have heard of Google Lens, an app that lets you take a photo of someone who, let’s say, has good fashion sense, and then it helps you identify similar outfits.

Therefore, the App’s initial task is to identify the product it sees. Is it a dress, a jacket, or a pair of jeans? The characteristics of various products are described; for example, the App is informed that a dress has shoulder traps, no zippers, armholes on either side of the neck, etc. Thus, the characteristics of a dress’ appearance are established. Now that the features have been defined, the app can make a model of a dress.

When an image is uploaded, the app searches through all of the already available models to determine what it is actually looking at. The app then uses a machine learning algorithm to create a prediction and displays comparable models of the clothing it owns.

There are various use cases of machine learning in data science:

• Fraud detection,
• Speech recognition, and
• Online recommendation engines.

Should you learn data science or machine learning first?

Big data should be the starting point for any attempt to resolve the dilemma of learning data science or machine learning.

Both data science and machine learning appear to be utilized equally in all relevant fields. In the world of technology, they are both among the most commonly used expressions. Therefore, it should be no surprise that choosing between data science and machine learning to learn first is one of the issues plaguing those pursuing careers in technology.

Using data science is a good start if you want to make future predictions. On the other hand, machine learning is the best option if you want to simplify and automate the present.

Which is better, data science or machine learning?

Over the past few years, machine learning and data science have become increasingly important, and for a good reason. The desire among engineers to learn more about these two fields grows as the world becomes increasingly automated and computerized.

As of 2022, there will be more jobs in data science than machine learning combined. You can work as a data science professional as a data scientist, applied scientist, research scientist, statistician, etc. As a machine learning engineer, you concentrate on making the models into products.

Data science is ranked #2, while machine learning is #17 in Glassdoor’s list of the top careers in America for 2021. But the pay for machine learning engineers is a little higher, and their jobs and salaries are expanding quickly. So can we say machine learning is better than data science? Let’s sneak a peek into the future for better decisions first.

According to the Future of Occupations Report 2020, 12 million new AI-related jobs will be generated in 26 nations by 2025. On the other hand, the US Bureau of Labor Statistics reveals that there will be 11.5 million jobs in data science and analytics by 2026, a 28 percent increase in positions.

Of course, it depends on your skills to find the “best”. Data science may be your ideal next step if you only have a bachelor’s degree and little training or expertise in AI or machine learning because there’s still a shortage of skilled Data Scientists. However, if you have the needed skills and background for ML, it can be better to take the pay rise and work as an ML engineer.

Data science and machine learning are interrelated. Without data, machines cannot learn, and machine learning makes data science more effective. To model and interpret the big data produced daily, data scientists will need at least a fundamental understanding of machine learning in the future.

Can a data scientist become a machine learning engineer?

Data scientists can indeed specialize in machine learning. Since data scientists will have already worked closely on data science technologies widely utilized in machine learning, shifting to a machine learning job won’t be too tough for them.

Data science applications frequently use machine learning tools, including languages, libraries, etc. Therefore, making this change does not require a tremendous amount of effort on the part of data science professionals. So, yes, data scientists can become machine learning engineers with the correct kind of upskilling training.

Conclusion

Building statistical and machine learning models is where data scientists put more of their attention. On the other hand, machine learning engineers concentrate on making the model production-ready.

Without machine learning, data science is simply data analysis. Machine learning and data science work together seamlessly. By automating the activities, machine learning makes life easier for data scientists. Machine learning will soon play a significant role in analyzing big data. To increase their efficiency, data scientists must be well-versed in machine learning.

A machine learning engineer works in the still-emerging field of AI and is paid marginally more than a data scientist. Despite this, more data science positions are available than machine learning engineering. So, choose wisely.

To achieve business results, all businesses must establish a data governance framework that ensures that data is treated similarly across the organization. Without effective data governance, tracking when and from where erroneous data enters your systems and who is utilizing it is impossible.

Is it hard to follow your company’s strategic, tactical, and operational duties and responsibilities? We have some good news for you; they are all covered by a well-designed data governance framework. So let’s take a closer look at it.

What is a data governance framework?

A set of rules and procedures protects an organization’s corporate data management and role delegations called a data governance framework.

Every organization is led by business drivers, which are crucial elements or procedures for the company’s ongoing success. What data needs to be carefully controlled and to what extent in your data governance strategy depends on the specific business drivers of your firm.

The tasks and responsibilities of a well-designed data governance system include strategic, tactical, and operational aspects. It guarantees that data is reliable, well-documented, and simple to find within your business. It also guarantees that the data is secure, compliant, and private.

Data governance (DG), based on internal data standards and policies regulating data consumption, regulates the accessibility, usability, integrity, and security of the data in corporate systems. Effective data governance protects against misuse and maintains data consistency and reliability.

As businesses increasingly rely on data analytics to help them run more efficiently and inform business decisions, it is becoming increasingly important. If you want to understand what is data governance deeply, do not worry we have already explained it for you.

How important is data governance? Data inconsistencies in various systems might not be handled without proper data governance. For instance, customer names could be listed differently in the sales, logistics, and customer service systems. As a result, data integration projects may become more challenging, and problems with data integrity might arise that would impair the accuracy of business intelligence (BI), corporate reporting, and analytics systems. Additionally, data inaccuracies might not be found and corrected, reducing BI and analytics accuracy.

Regulations and compliance activities can be hampered by poor data governance. That could be problematic for businesses that must abide by the growing number of data privacy and protection laws, including the GDPR of the European Union and the California Consumer Privacy Act (CCPA).

Check out the best data governance practices for 2022

Data governance framework components

The policies, regulations, procedures, organizational structures, and technology implemented as part of a governance program make up a data governance framework. Additionally, it outlines the program’s mission, goals, and metrics for success, as well as decision-making roles and accountability for the several components that will make up the program.

The Data Governance Institute (DGI) states that the following are requirements for every organization for a data governance framework:

• A set of guidelines outlining how various parties collaborate to create and implement these guidelines (policies, requirements, standards, accountabilities, controls)
• Making and enforcing the regulations are individuals and institutional entities.
• Processes that will control data while generating value, controlling cost and complexity, and assuring compliance

The data governance framework of an organization should be established and distributed internally so that everyone engaged is aware of how the program will operate from the outset.

On the technical side, managing a governance program can be automated using data governance software. Data governance tools don’t have to be a part of the framework to enable program and workflow management, collaboration, the establishment of governance policies, process documentation, and other tasks. Additionally, they can be used in conjunction with tools for master data management (MDM), metadata management, and data quality.

The following criteria must be met for data to be useful in making trustworthy decisions:

• Relevant
• Of high quality and accuracy
• Trustworthy
• Easy-to-understand and use

Additionally, for data to meet legal requirements, it must:

• Enable source-to-lineage tracking
• Add metadata to a data dictionary or catalog together with its context
• Make sure you check and report on data quality
• Establish, uphold, and record access policies

Your data must satisfy the requirements listed above to be accurate, usable, and auditable. Good data governance makes sure this happens.

What are the 4 pillars of data governance?

The data governance framework has four pillars that help firms make the most of their data:

1. Identify distinct use cases
2. Quantify value
3. Improve data capabilities
4. Develop a scalable delivery model

Data governance framework examples

Top-down and bottom-up are the two traditional techniques for creating a data governance system. These two approaches come from different ideas. To improve data quality, one gives control of the data first priority. The other gives rapid access to data top priority to maximize end users’ data access across business units.

Traditional approaches: The top-down method focus on data control

The centralized strategy for data governance is the top-down approach. It is supported by a small group of data specialists who use established best practices and well-defined processes. This indicates that data modeling and governance are given top priority. The data is not first made more widely accessible to the rest of the firm for analytics.

The top-down method, meanwhile, produces a serious scaling problem. This approach distinguishes between data consumers and suppliers, often in IT (typically business units). The data providers are the only people with any control over the data. This was less of a problem in the past because there was less data that needed to be managed and fewer teams that required access to it.

Today the demand from data consumers is too great for these small teams of data providers to handle. The availability of clean, comprehensive, and unharmed data to anyone who wants it at any time has become a corporate need. Simply put, there are simply too many requests from business users for these teams to continue acting as gatekeepers.

Traditional approaches: The bottom-up method focus on data access

In contrast, the bottom-up approach to data management enables far greater agility. The bottom-up strategy begins with raw data, whereas the top-down approach begins with data modeling and governance.

Structures can be built on top of the raw data (a process known as “schema on read”), and data quality controls, security rules, and policies can be implemented after the raw data has been ingested.

This framework is more scalable than the centralized method and became popular with the introduction of big data. Nevertheless, it generates a fresh set of data problems. Because anyone can enter data, it is more difficult to establish control because data governance isn’t introduced until later in the process.

As we’ve already mentioned, a lack of data governance can also result in increased regulatory risk, a decline in stakeholder confidence in the organization’s data, and higher data management costs for a disorganized, expansive collection of data assets.

The modern approach: Collaborative data governance framework template

The main goal of a collaborative data governance system is to strike a balance between top-down and bottom-up issues. The success of this framework is based on teamwork with data; otherwise, the amount of labor required to confirm the reliability of the data will be prohibitive.

The collaborative architecture is scalable, enabling an increasing number of individuals from throughout the organization to introduce an expanding number of data sources.

Clear guidelines for collaborative content curation must be developed to keep this scalability. This may entail choosing data stewards who are subject matter experts in each business unit to assist preserve excellent data quality for the datasets they are most familiar with.

Anyone can collaborate as long as they adhere to the requirements after setting these guidelines for data curation. This guarantees to scale without lowering a predetermined degree of trust in the material.

The process of transforming unorganized raw data into a dependable, well-documented body of corporate data prepared to be shared and utilized can involve the entire organization, including IT, subject matter experts, and decision-makers.

For instance, data like consumer credit card information or risk data aggregation in financial services might not be the greatest fit for this technique. In these situations, a more controlled top-down strategy can supplement the collaborative framework rather than take its place. Which data governance approach is appropriate in these kinds of circumstances should be determined by the organization’s data governance team.

Best data governance framework practices

Data governance is not a job that can be completed in a week, and not simply because there is too much to do. These are some of the best data governance practices for those wanted to succeed:

Analyze your current situation

A combination of people, procedures, and technology is used for data governance. Start with the people, develop your processes, and ultimately incorporate your technology to develop the broader picture. Building the effective processes required for the technical implementation of data governance is challenging without the right people.

The correct people for your solution will help you construct your procedures and find the technology you need to accomplish the job well if you can find them or employ them.

Gather data

As with any goal, if you cannot measure it, you cannot reach it. When making any change, you should measure the baseline before to justify the results after. Collect those measurements early, and then consistently track each step along the way. You want your metrics to show overall changes over time and serve as checkpoints to ensure the processes are practical and effective.

Establish privacy regulations

Data governance is characterized by the safety of your customers’ and business’ personal information. Establishing your organization’s policy for handling personally identifiable information (PII) and personally identifiable health information is crucial (PHI).

Here is a list of privacy aspects to be aware of since any of these elements could be used to identify a person. To learn more about your company’s privacy strategy, speak with your chief privacy or security officer.

Outline your supply chain of data

Each organization has its own data supply chain. This supply chain is part of the actions required for information gathering and transmission to stakeholders. These activities enable those on the front lines to find the appropriate data, aid in creating policies and procedures to support data processing correctly and reliably and act as a framework to guarantee that the entire data supply chain is utilized to support the achievement of business objectives.

Assess the risk and security of your data

It’s more challenging than ever to protect data. Customers are increasingly attentive to potential threats, data breaches are rising, and brands’ reputations are at risk. When conducting data security and risk assessments, keep the following in mind:

Identify related roles and responsibilities

Data governance calls for collaboration between all of your departments with deliverables. Every data governance program needs clearly defined roles, and assigning different levels of responsibility within your organization is crucial.

Organizations differ somewhat in terms of the data governance roles, but some examples of the more common positions might be:

• Data governance council (steering committee/strategic level): The strategic direction of the data governance program, project and initiative prioritization, and organization-wide data policies and standards are all handled by a data governance council, which is a governing body.
• Data governance board (tactical level): An organization’s rules and procedures for treating data as a strategic asset are developed by a group of people known as a data governance board.
• Data managers: For the data that an organization intends to collect or has already collected, a data manager develops database systems that satisfy those needs.
• Data owners: An person responsible for a data asset is the data owner.
• Data stewards: Utilizing your data governance procedures, a data steward is in charge of guaranteeing the accuracy of all data pieces, including content and metadata.
• Data users: Team members that directly enter and use data as part of their everyday tasks are known as data users. They can immediately access and explore integrated unit record-level datasets for statistics and research reasons.

Automate anything you can

Any enterprise’s data governance is a difficult and complicated task.

Therefore, it is neither possible nor trustworthy to follow these efforts using documents or spreadsheets. Organizations should instead choose solutions that automate tasks like data discovery, quality assurance, profiling, categorization, lineage, and creating business glossaries.

Check out our blockchain glossary and AI glossary

Education and training

Do you offer a course that teaches employees and data owners the fundamentals of data governance? Do you train new Data Stewards? Create a continuing education program to keep data governance at the forefront.

Data governance ultimately revolves around people, processes, and technology. A good data governance framework clearly understands who owns what and where the data comes from.

What is the best data governance framework?

Here is a list of the data governance frameworks that are most frequently cited:

• DAMA DMBOK 
• The DGI data governance framework
• McKinsey 
• Eckerson
• The SAS data governance framework
• PwC enterprise data governance framework

Let’s focus on them more closely.

Data governance framework DAMA DMBOK

One of the most well-known data governance frameworks is DAMA DMBOK. It represents data management as a wheel with data governance and nine surrounding knowledge domains at the hub, which as:

• Management of data architecture
• Data development
• Database operations management
• Management of data security
• Reference & master data management
• Data warehousing & business intelligence management
• Document and content management
• Management of metadata
• Management of data quality

DGI

Ten common components that address the why-what-who-how of data governance are included in the DGI framework.

To make the concepts more understandable, DGI organizes each of its parts into three main categories: rules, people, and processes.

McKinsey

According to McKinsey, the best way to ensure success with data governance is to start by reevaluating the entire organizational structure. Their strategy focuses on three main areas:

• A data management office (DMO) establishes guidelines and standards, mentors and develops data leaders, and ensures that data governance is integrated with all other organizational functions.
• Domain-based roles manage the day-to-day operation of the data governance program.
• A data council oversees the overarching strategic direction of the data governance program. It brings domain leaders and the DMO together to assess progress, approve financing, and address problems and barriers to efficient governance.

Eckerson

39 components make up the six layers of the Eckerson Group’s framework. This framework emphasizes that people are at the center of data governance by establishing roles like data owners, stewards, curators, and stakeholders to clarify their roles and responsibilities while accessing, utilizing, and modifying data.

SAS

The framework’s main objective is to demonstrate how organizations may effectively manage and administer their data while valuing it.

PwC

To account for next-generation data environments, the PwC enterprise data framework goes above and beyond traditional models like DAMA, DMBOK, and DGI.

PwC’s structure consists of five parts, starting with a data governance plan and continuing with a management layer that covers every facet of a data ecosystem.

The lifecycle management layer includes all the regulations necessary to guarantee smooth data flow throughout its lifecycle. The governance enablers consider the people, procedures, and technologies required to provide successful governance, while the stewardship layer focuses on enforcing governance.

Check out the best data lifecycle management frameworks

Conclusion

A data governance framework’s organizational structure is intended to assist businesses in defining roles and duties connected to data, directing decision-making, and facilitating the use of data to uphold data quality and guarantee data protection.

A data governance framework is essential to maximize the benefits of data governance programs. It specifies data collection, use, and storage procedures and offers organizational norms guidelines. The framework for data governance identifies data owners, produces catalogs, enhances data accessibility, raises data literacy and access levels, and establishes protocols for enforcing data policies.

In today’s data-driven era, you should build your own data governance framework as a compass for the future.

Many businesses are not aware of the potential benefits of big data services. Despite the hype, they either aren’t aware they have a big data issue or don’t view it that way. Big data technologies are generally advantageous for an organization when data volume, variety, and velocity suddenly grow and the firm’s current databases and applications can no longer handle the load.

Big data concerns that are not properly addressed can increase expenses and have a negative impact on productivity and competitiveness. On the other hand, a strong big data strategy can assist organizations in lowering costs and improving operational efficiency by converting labor-intensive existing workloads to big data technology and introducing new applications to take advantage of untapped potential.

Big Data-as-a-Service (BDaaS): What are big data services?

Big data as a service provides data platforms and tools by a cloud provider to assist enterprises in handling, managing, and analyzing massive data sets to produce insights that may be used to enhance business operations and achieve a competitive advantage.

Big data as a service (BDaaS) is designed to free up organizational resources by utilizing an outside provider’s data management systems and IT expertise rather than deploying on-premises systems and employing in-house staff for those functions.

Many companies generate enormous amounts of structured, unstructured, and semistructured data. Big data as a service can be offered as a contract for a managed service hosted and administered by a cloud provider or as dedicated hardware and software running in the cloud.

Remembering what big data is can help us better understand the subject.

What is big data?

Big data refers to data management issues that, as a result of the growing amount, velocity, and variety of data, cannot be resolved by conventional databases.

There are several ways to define big data, but most of them contain the idea of the so-called “three V’s” of big data:

Volume: Data volume varies between terabytes and petabytes.

Variety: Variety includes information from many different sources and formats (e.g. web logs, social media interactions, ecommerce and online transactions, financial transactions, etc)

Velocity: Businesses are becoming more demanding from when data is collected to when users receive actionable insights. As a result, data must be gathered, saved, processed, and evaluated within relatively brief time frames, ranging from daily to real-time.

Evolution of big data processing

Big data ecosystem development is progressing quickly. Today, a variety of analytical approaches serve various organizational activities.

Users can respond to the question “What happened and why?” with the aid of descriptive analytics. Traditional query and reporting setups with scorecards and dashboards are some examples.

Users can evaluate the likelihood of a specific event in the feature with the aid of predictive analytics. Examples are early warning systems, fraud detection, preventive maintenance, and forecasting.

Prescriptive analytics offer the user particular (prescriptive) suggestions. They respond to the query: What should I do if “x” occurs?

Big data as a service examples

One of the most significant developments of the digital era is the technology known as “Big Data.” Powerful analytics reveal patterns and connections hidden in enormous data sets, informing planning and decision-making in almost every business, as we see in big data benefits for SMEs.

Are you wonder about data curation definition and benefits?

Big Data usage has increased so much in the past ten years that it affects almost every element of our lifestyles, purchasing patterns, and everyday consumer decisions.

Here are a few instances of Big Data applications that impact humans daily:

Transportation

The GPS smartphone applications that most of us rely on to go from place to place in the shortest amount of time are powered by big data. Government organizations and satellite photos are two suppliers of GPS data.

For transatlantic trips, an airplane can produce 1,000 terabytes or more worth of data. All of this data is ingested by aviation analytics systems, which then analyze fuel efficiency, passenger and cargo weights, and weather patterns to maximize safety and energy use.

Big Data makes transportation easier and more efficient by:

Congestion management and traffic control: Google Maps can now provide the least congested route to any location, thanks to big data analytics.

Route planning: To plan for maximum efficiency, different routes can be compared in terms of user needs, fuel consumption, and other elements.

Traffic safety: To identify accident-prone locations, real-time processing and predictive analytics are employed.

Advertising and marketing

Advertising has always been focused on particular customer groups. In the past, marketers have used focus groups, survey results, TV and radio preferences, and other methods to attempt and predict how consumers will react to advertisements. These techniques were, at best, informed guesses.

To find out what people actually click on, search for, and “like,” advertisers purchase or collect enormous amounts of data nowadays. Utilizing precise measures like views and click-through rates, marketing initiatives are also evaluated for efficacy.

As an illustration, Amazon gathers enormous amounts of information about its millions of customers’ purchases, shipping methods, and payment preferences. The business then offers highly targeted ad placements to narrow segments and subgroups.

We have already gathered the best real-life database marketing examples for you.

Banking and financial services

Big Data and analytics are used to great effect in the financial sector for:

Fraud detection: Banks track customers’ spending habits and other activities to spot unusual behavior and anomalies that could indicate fraudulent transactions.

Risk management: Banks can track and report on operational procedures, KPIs, and personnel activities thanks to big data analytics.

Customer relationship optimization: In order to better understand how to turn prospects into customers and encourage higher use of different financial products, financial institutions study data from website usage and transactional data.

Personalized marketing: Banks build detailed profiles of each customer’s lifestyle, tastes, and goals using big data, which are then applied to micro-targeted marketing campaigns.

Government

Government organizations gather enormous amounts of data, but many of them, particularly at the local level, don’t use cutting-edge data mining and analytics tools to get the most out of it.

The Social Security Administration and the IRS are examples of organizations that utilize data analysis to spot false disability claims and tax evasion. The FBI and SEC monitor markets using big data techniques to find illegal business practices. The Federal Housing Authority has been predicting mortgage default and repayment rates using big data analytics for years.

Media and entertainment

The entertainment sector uses Big Data to analyze consumer feedback, forecast audience interests and preferences, manage programming schedules, and target advertising efforts.

The two most notable examples are Spotify and Amazon Prime, which both use big data analytics to provide subscribers with customized programming recommendations.

Meteorology

Globally distributed weather sensors and satellites gather much data to monitor the environment.

Big Data is used by meteorologists to:

• Analyze the trends in disasters.
• Make weather predictions.
• Recognize the effects of global warming.
• Determine the locations of the world where drinking water will be available.
• Provide early notice of imminent emergencies like storms and tsunamis.

Healthcare

Big Data is steadily but significantly changing the enormous healthcare sector. Patients’ electronic health records are updated in real-time using wearable technology and sensors data.

Do you know using data brings down healthcare costs?

Big Data is currently being used by providers and practice organizations for a variety of purposes, such as the following:

• Predicting the onset of epidemics
• Early symptom recognition to avert diseases that can be prevented
• Digital health records
• Real-time notification
• Increasing patient involvement
• Prediction and averting the development of major medical disorders
• Plan strategically
• Research speed up
• Telemedicine
• Improved medical image analysis

Cybersecurity

Big Data may increase the danger of cyberattacks for enterprises, but machine learning and analytics can use the same datastores to deter and combat online crime. Analysis of historical data can produce intelligence to build more effective threat controls.

Additionally, machine learning can alert companies when patterns and sequences deviate from the norm, so effective countermeasures may be performed against risks like ransomware assaults, harmful insider programs, and attempts at unauthorized access.

After an intrusion or data theft has occurred at a corporation, post-attack analysis can reveal the techniques utilized. Machine learning can then be used to create defenses that will thwart such attempts in the future.

We have already gathered cybersecurity best practices in 2022.

Education

Big Data is being embraced by administrators, academics, and other stakeholders to assist them enhance their courses, entice top talent, and enhance the student experience.

Examples comprise:

Customizing curricula: Big Data makes it possible to customize academic programs to the needs of specific students, frequently combining online learning with conventional on-site classes and independent study.

Reducing dropout rates: Predictive analytics provides educational institutions with information on student performance, feedback on suggested courses of study, and advice on how graduates perform in the labor market.

Improving student outcomes: It is possible to better understand students’ learning preferences and habits by examining their individual “data trails,” which may then be applied to design an environment that fosters learning.

Targeted international recruiting: Institutions can more correctly anticipate applicants’ chances of success thanks to big data analysis. On the other hand, it helps overseas students identify the universities most likely to accept them and best meet their academic objectives.

5 best big data services company

In the modern world, gathering data allows you to identify the causes of failure, update risk profiles, and other issues. Faster decision-making and cost reduction are further benefits.

Cloud-based analytics and Hadoop technologies enable businesses to examine information or data, instantly accelerating decision-making. But which companies are the best?

IBM

American corporation International Business Machine (IBM) has its main office in New York. As of May 2017, IBM was ranked number 43 on the Forbes list with a market capitalization of $162.4 billion. With about 414,400 people, the firm is the largest employer and operates in 170 countries.

IBM made a profit of $11.9 billion on sales of about $79.9 billion. For 24 years running, IBM has the most patents produced by the industry as of 2017.

The largest supplier of goods and services for big data is IBM. IBM Big Data solutions offer functions like data management, data analysis, and data storage.

Oracle

Oracle provides fully integrated cloud applications and platform services with more than 420,000 clients and 136,000 employees working in 145 countries. According to Forbes’ ranking, it has a market valuation of $182.2 billion and annual sales of $37.4 B.

The largest player in the big data space is Oracle, which is also well-known for its leading database. Oracle makes use of big data’s advantages in the cloud. It aids firms in defining their big data and cloud technologies data strategy and approach.

It offers a business solution that uses big data applications, infrastructure, and analytics to offer insight into logistics, fraud, etc. Oracle also offers industry-specific solutions that guarantee your business can benefit from big data potential.

Amazon

In 1994, Amazon.com was established, with its headquarters in Washington.

Amazon’s cloud-based platform is well known. Elastic MapReduce, which is built on Hadoop, is its flagship product. It also provides Big Data products. Redshift, NoSQL, and DynamoDB Big Data databases are examples of data warehouses that utilize Amazon Web Services.

Microsoft

Microsoft is a US-based software and programming company with Washington as its corporate headquarters. According to Forbes, it has $85.27 billion in sales and a market capitalization of $507.5 billion. Around 114,000 people are currently employed by it worldwide.

Microsoft has a broad and expanding big data strategy. A collaboration with the Big Data firm Hortonworks is part of this plan. Through this cooperation, Hortonworks’ data platform will have access to the HDInsight tool for analyzing both structured and unstructured data (HDP)

Google

Google was founded in 1998, and California is headquartered. It has a $101.8 billion market capitalization and $80.5 billion of sales as of May 2017. Around 61,000 employees are currently working with Google across the globe.

Google provides integrated, end-to-end Big Data solutions based on innovation at Google and helps different organizations capture, process, analyze and transfer data in a single platform. Google is expanding its Big Data Analytics; BigQuery is a cloud-based analytics platform that analyzes a huge set of data quickly.

Best big data solutions

There are very successful big data solutions according to various needs.

Here are some of them:

Big data services in AWS

The most significant big data implementation support provided by AWS is in the form of analytics tools. You can utilize the provider’s wide range of services to automate data analysis, manipulate datasets, and gain insights.

Amazon Kinesis

With the help of the Kinesis service, you may gather and examine real-time data streams. Website clickstreams, application logs, and Internet of Things (IoT) telemetry data are a few examples of supported streams. Kinesis supports data export to Redshift, Lambda, Elastic MapReduce (Amazon EMR), and S3 storage, among other AWS services. Using the Kinesis Client Library, you may leverage Kinesis to create unique streaming data applications (KCL). Real-time dashboards, alert production, and dynamic content are all supported by this library.

Amazon EMR

You can analyze and store data using the EMR distributed computing framework. It is built using clustered EC2 instances and Apache Hadoop. A well-known platform for processing and analyzing massive data is Hadoop.

By managing and maintaining your Hadoop infrastructure when you deploy EMR, you are free to concentrate on analytics. The most popular Hadoop tools, such as Spark, Pig, and Hive, are supported by EMR.

Amazon Glue

You can process data and carry out extract, transform, and load (ETL) activities using the service Glue. It can be used to transport data between your data storage as well as clean, enrich, and catalog data. Being a serverless service, Glue frees you from the hassle of establishing infrastructure and only charges you for the resources you use.

Amazon Machine Learning (Amazon ML)

Without ML knowledge, Amazon ML is a service that supports creating machine learning models. It has wizards, visualization tools, and pre-built models to get you started.

The service can help you evaluate training data, optimize your trained model to suit business requirements, and more. Once finished, you can access your model’s output through batch exports or an API.

Amazon Redshift

You can use Redshift, a fully-managed data warehouse service for business intelligence analyses. It is designed for big SQL queries on structured and semi-structured data. SageMaker, Athena, and EMR are just a few analytics services that can access the S3 data lake storage where query results are stored after processing.

You can query data on S3 using Redshift’s Spectrum capability, which allows you to avoid using ETL procedures. This function analyses your data storage and query requirements, then optimizes the procedure to reduce the quantity of S3 data that needs to be read. This cuts down on expenses and expedites query processing.

Amazon QuickSight

You can create visualizations and analyze ad hoc data with the business analytics application QuickSight. It supports ingesting data from a wide range of sources, including on-premises databases, exported Excel or CSV files, and AWS services like S3, RDS, and Redshift.

A “super-fast, parallel, in-memory calculating engine” is used by QuickSight (SPICE). This engine employs machine code generation to create interactive searches based on columnar storage. To ensure that the following inquiries are as quick as possible, the engine maintains the data after a query has been executed until the user manually erases it.

Big data services in Oracle

The expanding need for many industries, including banking, healthcare, communications, public sector, retail, etc., is met by Oracle’s big data industry solutions. There are many different technological options, including system integration, cloud computing, and application development.

Oracle Big Data Preparation Cloud Services

With the help of the managed Platform as a Service (PaaS) cloud-based Oracle Big Data Preparation Cloud Service, you can quickly ingest, correct, enrich, and publish huge data sets in a collaborative setting. For downstream analysis, you can combine your data with other Oracle Cloud Services, such as Oracle Business Intelligence Cloud Service.

Oracle Big Data Appliance

Running a variety of workloads on Hadoop and NoSQL systems requires a high-performance, secure platform, such as the Oracle Big Data Appliance. You can use Oracle SQL to query data on these platforms once Oracle Big Data SQL is installed. Oracle Big Data Appliance is protected using Apache Sentry, Kerberos, network encryption, and data at rest encryption.

Oracle Big Data Discovery Cloud Service

Oracle Big Data Discovery Cloud Service is a collection of end-to-end visual analytics tools in the cloud that use Hadoop’s processing power to turn raw data into business insight in a matter of minutes without the need to master complicated software or rely solely on highly qualified personnel.

Data Visualization Cloud Service

Oracle Data Visualization Cloud Service (DVCS) enables seamless analysis across all environments with on-premises and cloud deployment options. It is a component of Oracle’s full analytics platform. The graphical display of abstract information is known as data visualization.

Big data services in IBM

Popular database solutions from IBM that allow big data analytics include DB2, Informix, and InfoSphere. Additionally, IBM offers well-known analytics programs like Cognos and SPSS.

Below are IBM’s Big Data Solutions:

Hadoop System

Data that is both structured and unstructured is stored on this platform. It is made to process a lot of data to find business insights.

Stream Computing

Thanks to stream computing, organizations can use in-motion analytics, such as the Internet of Things, real-time data processing, and analytics.

Federated discovery and Navigation

Software for federated discovery and navigation aids businesses in the analysis and access of data throughout the enterprise. The Big Data products from IBM described below can be used to gather, examine, and manage both structured and unstructured data.

IBM® BigInsights™ for Apache™ Hadoop®

It lets businesses easily and quickly evaluates massive amounts of data.

IBM BigInsights on Cloud

It offers Hadoop as a service via the IBM SoftLayer cloud computing platform.

IBM Streams

Organizations may gather and analyze data in motion for essential Internet of Things applications.

Best big data consulting services

Bigdata Analytics Consulting Companies offer expert consultants who share their extensive industry and domain knowledge with various organizations in big data technology, big data analytics, process, and methodologies, leveraging their real-world experience, industry best practices, and technology best practices, enabling the clients to succeed in big data projects.

These are some of the best big data consulting services:

IBM Analytics Consulting

IBM Bigdata Analytics provides access to IBM’s 9000 strategy, analytics, and technology experts and consultants from around the globe, who can assess the business and identify the specific areas in which analytics can bring the most value to the business.

HP Bigdata Services

In order to transform big data into useful information, HP Big Data Services assist in reshaping IT infrastructure. The Big Data solutions include compliance, protection, strategy, design, and implementation.

Dell Big Data Business Intelligence Consulting

Dell Big Data Business Intelligence Consulting helps businesses succeed and create new revenue streams with big data solutions. The big data business intelligence consulting services include assessments, proof of concept projects, and managed services.

Oracle Consulting

Enterprise performance management (EPM) and business intelligence (BI) solutions can be rapidly and successfully deployed with the help of architectural, upgrade, and implementation services from Oracle Consulting.

Conclusion

Big data is a term that is widely used in the business and technological worlds. In a nutshell, it is the process of obtaining extremely huge quantities of complicated data from various sources and analyzing it to uncover patterns, trends, issues, and presents chances to get useful insights.

Big data as a service (BDaaS) is designed to free up organizational resources by utilizing an outside provider’s data management systems and IT expertise rather than deploying on-premises systems and employing in-house staff. Many companies generate enormous amounts of structured, unstructured, and semistructured data.

Big Data projects that are currently most fascinating and gratifying offer insights based on what is occurring right now, not just what was happening last week, allowing for immediate action rather than merely learning from the past.

Despite the fact that certain consulting organizations may be able to assist you, only you can decide which big data solutions are appropriate for your business. So, what are you waiting for? Choose your solution and join the data-driven revolution!

For the greatest outcomes in data transformation, information analysis needs structured and easily accessible data. Organizations can change the format and structure of raw data through data transformation as needed. Your company has countless opportunities to improve decisions and actions because of the ever-growing amount of data. But how can you make what you already know about your company, clients, and rivals more available to everyone working there? Data transformation is the key.

What is data transformation?

The process of changing data from one format to another, usually from that of a source system into that needed by a destination system, is known as data transformation. Most data integration and management operations, including data wrangling and data warehousing, include some type of data transformation.

Data transformation serves as the middle phase in an ETL (extract, transform, load) process, which is commonly used by businesses with on-premises data warehouses. Today, the majority of businesses employ cloud-based data warehouses that expand compute and storage resources with latency measured in seconds or minutes. Organizations can load raw data into the data warehouse without preload transformations thanks to the cloud platform’s scalability; this is known as the ELT model ( extract, load, transform).

Depending on the required modifications to the data between the source (initial data) and the destination (final data), data transformation can be straightforward or difficult. The process of data transformation often involves both manual and automated procedures. Depending on the format, structure, complexity, and amount of the data being changed, a broad range of tools and technologies may be utilized.

For a number of uses, transformed data is useable, secure, and accessible. Data may be transformed by organizations so that it can be combined with other types of data, moved into the proper database, or made compatible with other critical pieces of knowledge. Data transformation gives organizations insights into crucial operational and informational internal and external operations. In order to keep information moving, businesses might use data transformation to move data from a storage database to the cloud.

Data transformation makes business and analytical processes more effective and improves the quality of data-driven decisions made by organizations. The structure of the data will be determined by an analyst throughout the data transformation process. Consequently, data transformation might be:

• Constructive: The process of data transformation adds, duplicates, or copies data.
• Destructive: The system deletes fields or records, which is destructive.
• Aesthetic: The data are standardized through the transformation to adhere to specifications or guidelines.
• Structural: Renaming, relocating, or merging columns allows for structural database reorganization.

Businesses have more resources than ever before for data collection. Businesses have more opportunities to make more informed decisions thanks to the never-ending supply of data.

Data transformation process

Data that is retrieved from a local source is frequently useless and raw. The data must be modified in order to solve this problem.

ETL, which stands for Extract, Load, and Transform, is the general term for the data transformation process. Analysts are able to transform data into the format they need through the ETL process. The steps in the data transformation process are as follows:

1. Data discovery: In the initial phase, analysts try to comprehend and locate data in its original format. They’ll employ data profiling techniques to accomplish this. This stage aids analysts in determining what needs to be done to transform data into the format they want.
2. Data mapping: To ascertain how certain fields are updated, mapped, filtered, combined, and aggregated, analysts do data mapping during this step. Many data operations depend on data mapping, and one mistake might cause inaccurate analysis that spreads throughout your entire organization.
3. Data extraction: Analysts extract the data from its original source during the data extraction process. These sources could be streaming sources like user log files from web apps or organized sources like databases.
4. Code generation and execution: Data extraction is followed by the creation of a code that analysts must then execute in order to complete the transformation. Frequently, platforms or tools for data transformation assist analysts in producing codes.
5. Review: Once the data has been transformed, analysts must examine it to make sure everything has been properly prepared.
6. Sending: Delivering the information to its intended recipient is the last step. The objective could be a database that manages both structured and unstructured data, such as a data warehouse.

Along with these essential processes, other tailored operations might be performed. Analysts might, for instance, filter the data by loading only particular columns. Alternately, they might improve the data by including names, places, etc. Additionally, analysts have the ability to combine data from several sources and delete duplicate data.

Data transformation rules

The structure and semantics of data are transformed from source systems to destination systems according to a set of computer instructions called “data transformation rules.” Although there are many other kinds of data transformation rules, taxonomy rules, reshape rules, and semantic rules are the most popular ones.

Taxonomy Rules

The columns and values of the source data are mapped to the target using these rules. As an illustration, a source might provide that each transaction has two columns: a settlement amount and a type, where the type can refer to one of three possibilities.

Reshape Rules

The distribution of the data items on the target side and how to gather them from the source side are both outlined in these guidelines. For instance, a store might offer all transaction data in one file, but the aggregator needs to separate it into three tables: one for transactions, one for merchant data, and one for consumer data.

Semantic Rules

These guidelines define the semantics of data items and explain how the business uses them to define its domain. For instance, what makes a transaction successful? And how should its ultimate settlement sum be determined after refunds are taken into account? Each data source has a unique semantics that makes sense in the context of its activities, but which the data aggregator must reconcile with all other providers’ data definitions.

Data transformation types

Data can be transformed in a variety of ways. These consist of:

Scripting

By employing scripting, data can be extracted and transformed by writing the necessary code in Python or SQL.

You can use scripting languages like Python and SQL to automate particular programmatic processes. You may also use them to extract data from sets. Scripting languages are less labor-intensive because they require less code than conventional programming languages.

On-premises ETL tools

ETL tools, as previously mentioned, let you extract, transform, and load data. By automating the process, ETL technologies eliminate the tedious work needed to script the data transformation. Company servers host on-premises ETL tools. Although using these tools can help you save time, doing so frequently necessitates significant infrastructure investment.

Cloud-based ETL tools

Cloud-based ETL tools are hosted in the cloud, as the name suggests. The use of these technologies is frequently made simpler for non-technical people. You can gather data from any cloud source and add it to your data warehouse using these tools.

You may choose how frequently to pull data from your source with cloud-based ETL solutions, and you can keep track of your consumption.

Data transformation techniques

Before analysis or storage in a data warehouse, there are a number of data transformation techniques that can help organize and clean up the data.

Here are a few of the more popular techniques:

• Data smoothing: The technique of removing skewed or nonsensical data from a dataset is known as data smoothing. To find particular patterns or trends, it also finds slight changes to the data.
• Data aggregation: For reliable analysis and reporting, data aggregation gathers unprocessed data from several sources and saves it in a single format. This method is essential if your company collects a lot of data.
• Discretization: In order to increase efficiency and facilitate analysis, this data transformation approach creates interval labels in continuous data. Decision tree techniques are used in the process to reduce a large dataset into a small set of categorical data.
• Generalization: Generalization converts low-level data qualities into high-level data attributes using idea hierarchies to produce an understandable data snapshot.
• Attribute construction: By constructing new attributes from an existing set of attributes, this technique enables the organization of a dataset.
• Normalization: For more effective extraction and deployment of data mining algorithms, normalization changes the data to ensure that the attributes remain within a given range.

Real-life data transformation examples

You probably do fundamental data transformations on a regular basis as a computer end user. For instance, data is transformed when a Microsoft Word document is converted to a PDF.

But in big data analytics, data transformation plays a more significant and complex function. This is due to the likelihood that you will run into scenarios where a significant amount of data needs to be converted from one format to another while working with big amounts of data, various types of data analytics tools, and various data storage systems.

Thus, that is a general explanation of data transformation. Let’s look at some examples of data transformation to better clarify the solution.

Character encoding and data transformation

Problems with character encoding are frequently the cause of data transformation.

There is a good probability that character encoding inconsistency is the cause if you have ever opened a file and discovered that some of the letters or numbers inside the text are displayed as gibberish or seemingly random symbols.

Most computers today use the UTF-8 encoding system or a more recent scheme that is backwards compatible with it in order to avoid encoding problems. However, it still occurs when an application encrypts data in a manner that other programs or systems do not anticipate. In these situations, it would be necessary to convert the data from one sort of character encoding format to another.

CSV to XML transformation

CSV, which stands for “comma-separated values,” and XML, often known as “extensible markup language,” are two common formats for storing data. However, they operate very differently.

You may automatically convert data from a CSV file into XML format using a data transformation tool so that you can open it with the appropriate software.

Transforming speech to text

The third instance of data transformation is when you need to convert human speech from an audio file into a text file.

Because it entails more than just handling discrepancies in data formatting, this example might not be one of the first that data transformation specialists think of. However, it serves as a good illustration of data transformation in general. It’s a situation that you would encounter if, for instance, you record customer phone calls and need a mechanism to make data from the discussions accessible for analysis by systems that can only decipher the text.

3 best data transformation tools

It’s vital to keep in mind that today’s hybrid data processing environments are considerably more sophisticated than those from the past while thinking about alternatives for data transformation. Big data analytics platforms are connected to conventional servers, and more data is stored locally and in the cloud. In order to handle a variety of data assets, there is also an increasing reliance on a growing number of “as-a-service” solutions. The connectors required to move data from these multiple sources are frequently included in ETL systems.

These are some of the best data transformation tools:

SQL Server Integration Services (SSIS) (Microsoft)

On-premises and in the cloud, Microsoft provides its data integration functionality (via Integration Platform as a Service). The SQL Server DBMS platform comes with the company’s standard integration tool, SQL Server Integration Services (SSIS). Azure Logic Apps and Microsoft Flow are two other cloud SaaS products that Microsoft promotes. The whole Azure Logic Apps solution includes Flow, which is integrator-centric and ad hoc.

Related products: Azure Data Factory cloud integration service

Oracle Data Integration Cloud Service

Oracle provides a wide range of data integration technologies for both classic and contemporary use cases, in on-premises and cloud deployments. The product line of the company includes technologies and services that enable businesses to transport and enrich data across its entire lifecycle. With the help of bulk data migration, transformation, bidirectional replication, metadata management, data services, and data quality for the customer and product domains, Oracle data integration enables constant and ubiquitous access to data across heterogeneous systems.

Related products: Oracle GoldenGate, Oracle Data Integrator, Oracle Big Data SQL, Oracle Service Bus, Oracle Integration Cloud Service (iPaaS)

SAS Data Management

One of the top independent vendors in the market for data integration technologies is SAS. Through SAS Data Management, which integrates data integration and quality solutions, the company makes available its fundamental capabilities. It offers push-down database processing, configurable query language support, metadata integration, and a range of performance and optimization features. Federation Server, the company’s data virtualization platform, enables sophisticated data masking and encryption that let customers choose who is authorized to view data.

Related products: SAS Data Integration Studio, SAS Federation Server, SAS/ACCESS, SAS Data Loader for Hadoop, SAS Data Preparation, SAS Event Stream Processing

Conclusion

It is necessary to alter the data set before analysis in order to improve its suitability for further analytical processing. In order to meet the needs of the algorithms used for predictive modeling, such as classification, regression, clustering, or association rule mining, the transformation modifies the values of a few chosen attributes.

You will always lag behind your competition if your business isn’t utilizing data transformation.

For many companies, organizing, transforming, and structuring data can be a daunting process. You need to have a plan in place before you look at your data so that you can see where you want your business to go as a result of your data.

When and how to alter your data are not subject to any strict guidelines. It relies on the data’s origin (and how much you know about it), the conclusions you want to draw from it, the significance of interpretability, and how much the actual distribution of the data differs from your ideal distribution, which is typically a normal distribution. If you want to learn more, you can check the article that explained data lifecycle management.

So, are you ready to join the data-driven revolution?

On Wednesday, IBM added the data observability company Databand to its data fabric platform. The deal’s financial details weren’t made public.

In order to develop its data observability technology, Tel Aviv, Israel-based Databand, founded in 2018, had raised $14.5 million in funding. This technology gives organizations visibility and monitoring for data pipelines that can be used for machine learning training, data analytics, and business intelligence.

Data observability is a highly competitive business

After acquiring application observability company Instana in November 2020, Databand, formerly known as Databand.ai, is the second observability vendor that IBM has purchased in as many years.

Data observability is a highly competitive business, with many providers vying for market share. According to Paige Bartley, an analyst at S&P Global Market Intelligence’s 451 Research, there is a rising need for data observability. Enterprises require more data observability solutions to preserve access to quality data when data is utilized by less technical personnel more often.

“While periodic, cyclical clean-up efforts for individual data sets will still remain necessary in certain cases, data observability efforts offer a more preventative and real-time approach to data pipeline maintenance, helping ensure a steady flow of high-integrity data through the organization,” explained Bartley.

Data dependability and data quality assurance applications are most frequently directly related to data observability technologies nowadays. According to Bartley, data observability technology still has capacity to develop and become more frequently utilized for other important business goals, such reducing the cost of data systems and better allocating cloud resources.

“Our clients are data-driven enterprises who rely on high-quality, trustworthy data to power their mission-critical processes. When they don’t have access to the data they need in any given moment, their business can grind to a halt. With the addition of Databand.ai, IBM offers the most comprehensive set of observability capabilities for IT across applications, data and machine learning, and is continuing to provide our clients and partners with the technology they need to deliver trustworthy data and AI at scale,” explained Daniel Hernandez, General Manager for Data and AI at IBM.

Why data observability is necessary for IBM’s data fabric?

Databand will be compatible with IBM’s data fabric platform, which enables businesses to manage and use data for analytics, business intelligence, and machine learning.

According to Michael Gilfix, vice president of product management for data at IBM, the data fabric enables businesses to link data consumers to the data’s locations, whether they are on-premises or in the cloud.

Making ensuring a BI dashboard is correct and up to date is one example of a common application that Databand will now make available to IBM consumers.

Typically, a data pipeline that pulls data from many sources powers a BI dashboard. The data might be erroneous or there may have been a problem in the pipeline, which the Databand technology can identify. Databand notifies users of errors and identifies their causes so they can be fixed.

The confluence of data observability and quality

The IBM Watson Knowledge Catalog is already a part of the IBM data fabric and offers data governance and data catalog features to help customers find and use data for data analytics or machine learning training.

Organizations may define guidelines for how data should be utilized using the Watson Knowledge Catalog, which also offers tools for enforcing those guidelines. Gilfix asserts that the data fabric’s technology and the data catalog’s combination will result in higher-quality data.

Data generation through the pipeline may be seen thanks to Databand technology. According to Gilfix, businesses’ ability to classify and use data of higher quality as a result of having visibility into the data creation process.

“Data observability is going to help people trust that the data that comes from different parts of the organization is reliable,” explained Gilfix.

Data is too valuable to backup traditionally, that is why firms are joining forces to both protect their data and manage it better. Also the regulations are changing as the technology advances, for instance, UK eases restrictions on data mining laws to facilitate AI industry growth.

In today’s article we will explain what are data points and their synonyms. We’ll also clarify how unit of observation is utilized in addition to types of data points. For digital marketing analytics, there are some important data point categories professionals need to be aware of. Finally we will learn differences between a data point, data set, data field and so on.

Data points are the same for big data, just as we define “family” as the smallest social unit. When we look at data processing technology history, it is all about how we use data points. Accordingly, professions such as data architect and data engineer are on the rise. They appear so basic at first glance that many experts simply ignore them. Data points, however, can be challenging because of restricted visibility at the level of data collection and ineffective exclusion through aggregations.

What are data points?

Any fact or piece of information is a data point, generally speaking.

A discrete unit of information is called a data point. Any single fact is a data point, broadly speaking. A data point can be quantitatively or graphically represented and is typically produced from a measurement or research in a statistical or analytical context. The singular form of data, or datum, is roughly equal to the term “data point.”

Be careful; data points should not be confused with informational tidbits obtained by data analysis, which frequently combines data to derive insights but is not the actual data point.

Another data point definition

A data point (also known as an observation) in statistics is a collection of one or more measurements made on a single person within a statistical population.

Data points synonym

Here’s a list of similar words for data points; data, facts, detail, points, particularity, particulars, niceties, circumstance, elements, specifics, statistics, components, traits, instances, counts, aspects, technicalities, units, specifications, facets, features, specialties, members, schedules, things, ingredients, singularity, portions, characteristics, accessories, nitty-gritty, respect, structure, factors, dope, and more.

What is the unit of observation?

The context of units of observation provides the best understanding of data points. The “objects” that your data depicts are observation units. Consider gathering information on butterflies. An observational unit is a butterfly.

You could compile data on the butterfly’s weight, speed, and wing color, as well as the continent on which it lives. Each of these pieces of information is referred to as a dimension, and a cell’s entry is referred to as a data point. Each observational unit is described by a data point (aka each butterfly).

Types of data points

Words, numbers, and other symbols are all examples of data points. These are the kinds of data points that we store in data tables and perform queries on. The standard five types of data points in software are:

• Integer: Any number without a decimal point is an integer.
• Date: The date is a particular year’s and month’s date.
• Time: The time of day is time.
• Text: Text, sometimes known as “string,” simply refers to any collection of letters rather than numerals or other symbols.
• Boolean: Boolean is a data type that can be TRUE, FALSE, YES, NO, 1, or 0 in numbers. Simply put, it is binary data.

The big-picture data points kinds mentioned above are straightforward, but they are not all-inclusive. Let’s look at some examples.

How are data points represented?

Point format is the most typical way to express a data point. When graphing points along a coordinate axis, point format is used. When using two coordinate axes, a point is written as (x, y), and when using three, it is written as (x, y, z). It is possible to number the values of x, y, and z, but this is not a guarantee. To see if there is a pattern in the data, data points are frequently graphed. Numbers, dates (12/10/2001), times (0730), words (green), and binary values are all examples of data points (1 or 0). An example of a data point would be (3, 4, 5), or (blue, 06252004), or (1, 1200).

Data points examples

An observation or data point is a collection of one or more measurements made on a single member of the observational unit. An example of a data point would be the values of income, wealth, age of the individual, and the number of dependents in a study of the factors that influence the desire for money with the individual as the unit of observation. A statistical sample made up of various such data points would be used to draw conclusions about the population using statistics.

Additionally, a “data point” in statistical graphics can refer to either a single person within a population or a summary statistic produced for a certain subpopulation; such points might be related to both.

For example, the data points that you should pay attention to during digital marketing analysis will help you explain your subject.

Important data points for digital marketing analytics

Statistics and analytics are what we mean by “social media data.” It is the data gathered from social media platforms that demonstrate how people view or interact with your profiles or content. This information offers insights into your social media strategy and expansion. Raw social data includes the following metrics:

• Shares
• Mentions
• Comments
• Likes
• New followers
• Impressions
• Keyword analysis
• Hashtag usage
• URL clicks

These significant data points demonstrate your growth on social media.

What is a good number of data points?

When an energy reading is taken, a data point is produced. A data point is a discrete string of data transmitted by a device, meter, or sensor inside a structure or other site. Not counting the meters and devices themselves, mind you! Consider data points as the variables in an algebraic equation.

For some key reasons, data points are a fundamental notion in energy management:

• They are essential for developing a clear budget for your energy platform.
• They are important to create a watertight energy savings strategy and assist in creating a strong energy monitoring structure.

The fact that the amount of data points always relies on the various variables that must be monitored in each unique energy-saving project is a given. Every energy project is special and different when it comes to the necessary quantity of data points, just like each snowflake is. As a result, until now it has been challenging to generalize when customers inquire about the normal number of data points needed for a project. But, you can try some tools such as Data Point Calculator and calculate.

Data point categories

There are several data point categories for customers such as:

• Aging: Information on open customer balances.
• Bank references: Information regarding consumer bank accounts is provided by bank references.
• Payments and billing: A history of customer transactions and payments.
• Business data and credit: Information on past credit histories of customers, both inside and outside your own company, with external credit agencies and monitoring services.
• Collateral: Information on client collateral as it relates to creating or obtaining credit is known as collateral.
• Financial information: Information on a customer’s company’s health, including profits, losses, and cash flow.
• Guarantors: Information on third parties who are prepared to guarantee customer credit.
• References: Details about the individuals who act like the customer’s references.
• Trade references: References from businesses in the same industry that offer statements about the customer’s creditworthiness.
• Venture financing: Details about customer investment financing.
• Additional: For user-defined categories and values, additional data points are accessible.

Comparison: Data point vs data set

Data sets are collections of one or more data objects (including tables) that are grouped together either because they are kept in the same location OR because they are connected to the same subject. Data sets are not just collections of data tables.

We’ve already discussed data points in data tables and demonstrated how one point equals one cell. All of the data objects that make up a data collection are subject to the same logic.

One point corresponds to one cell in an array, record, or set. Points also stand-in for 1 cell when an object with pointers is expressed as a dimension. A scalar object’s single scalar value is referred to as a data point.

There are no data points in files or schemas. This is because these things are of that sort. In certain ways, a file could be seen as a non-data object because it is code created to guarantee the correct structure of another data item.

Schemas are summaries of other things, and they completely ignore points in order to convey object information fast.

Comparison: Data point vs data attribute

A data dimension and a data attribute are the same things. It is the column header in a table. Wing color is an attribute in the butterfly data example.

Consequently, a data point is a single value entry for an attribute.

Comparison: Data point vs data field

The terms “data field” and “data attribute” are interchangeable, however, they are applied in slightly different contexts. In a table, “field” typically refers to the column itself, whereas “attribute” typically refers to the column when we’re discussing a particular row.
As opposed to saying “the Color of Wings attributes for Monarch butterflied is orange,” you may say “the Color of Wings is a data field.”

In the context of programming languages, “field” also has a technical meaning that “attribute” does not.

Comparison: Unit of observation vs a unit of analysis

The distinction between units of observation and units of analysis is the most frequent source of misunderstanding regarding data points.

After data has been analyzed and aggregated, the single rows that remain in a data table are the units of analysis. 

Each row that acts as a grouping of data points in the basic data set is a unit of observation.

Big data requires the removal of original data for analytical reasons, however, there is disagreement over when this should and shouldn’t be done.

Conclusion

The word “point” serves as a reminder that any dataset is essentially a type of “space.” A “data point” would actually be a spot within a conventional three-dimensional space that has specified coordinates so that you could “point” to it. You may indicate that location and the precise moment at which it was thereby including a time coordinate. We advise you to check out and learn how object storage helps address unstructured data’s increased security risks.

Regardless of how big or small the company is, data lifecycle management is a fundamental discipline. Data is now everywhere. Furthermore, everything is interrelated. The same body of information is distributed among many departments and employees at any organization. Corporate data has evolved into a unified, living entity that permeates all information systems.

What is the data life cycle?

The entire time that data is present in your system is referred to as the data life cycle, also known as the information life cycle. This life cycle includes every stage your data experiences, starting with the first capture and continuing on.

Each stage of life, according to life science, includes childhood, a time of growth and development, productive adulthood, and old age. These stages change as you move up the tree of life. While whales live to be grandmothers, salmon perish shortly after spawning. A mouse’s, a fox’s, and a butterfly’s life cycles will all be highly dissimilar even though they all inhabit the same field.

Data lifecycle framework: 7 data lifecycle stages

The DLM architecture has several variations because each organization has its own business model, software stack, and sorts of data.

Similar to how distinct data objects experience different life stages at their own cadences. Having stated that here is a sample data lifecycle framework:

Data creation, ingestion, or capture 

You obtain information in some way, whether you create data through data entry, obtain pre-existing data from other sources, or take in signals from equipment. This phase explains when data values enter your system’s firewalls.

Data processing

Cleaning and processing raw data for further analysis involve a number of steps. Data preparation often entails combining data from several sources, validating the data, and executing the transformation, though the exact order of processes may vary. The data processing pipeline frequently includes the reformatting, summarizing, subsetting, standardizing, and enhancing of data.

Data analysis

Regardless of how you examine and interpret your data, this is the crucial stage. A number of analyses might be necessary for exploring and analyzing your data. This could refer to visualization and statistical analysis. It might also refer to utilizing artificial intelligence or conventional data modeling (AI).

Data sharing or publication

Forecasts and insights are transformed into decisions and direction at this level. Your data offers its full commercial value when you share the knowledge you learned from data analysis.

Archiving

Normally, data is saved for later use once it has been gathered, handled, analyzed, and shared. It’s crucial to preserve metadata about each item in your records, especially regarding data provenance, if you want archives to retain any value in the future.

Visualization

The act of developing graphic representations of your data is known as data visualization, and it is often carried out with the aid of one or more visualization tools. By using data visualization, you may more easily explain your study to a larger audience both inside and outside of your company. Your visualization’s format will rely on the data you’re using and the narrative you wish to convey.

Data visualization has grown in importance throughout the data life cycle, despite the fact that it isn’t technically a step that must be taken for every data project.

Interpretation

Finally, you can make meaning of your analysis and visualization during the interpretation phase of the data life cycle. This is the time to do more with the data than just deliver it; instead, you should investigate it using your knowledge and insight. In addition to describing or explaining what the data indicates, your interpretation may also contain potential ramifications.

What is Data Lifecycle Management (DLM)?

By combining a business and technical approach, Data Lifecycle Management (DLM) enhances database development (or acquisition), delivery, and management.

As an abstract idea, the data life cycle serves no one. Its goal is to assist companies in providing end-users with the data health they require to support decisions. Data lifecycle management must be open and iterative in order to achieve this.

Document the movement of data across your company with a data lineage map to make its life cycle real. This entails graphically illustrating the starting point of your data, each stop it makes, and an explanation of why it might not have moved at that particular time. Documenting the life cycle of a process makes tracking for routine data operations easier. Additionally, it makes it simple to identify and fix failure locations and bottlenecks.

Processes that reduce the data’s usefulness are counterproductive and should be identified and changed in subsequent cycles. Utilize the knowledge gained throughout the process to guide the following cycle and improve data health.

For businesses, the data sharing stage is frequently difficult. A top-down strategy with tightly restricted data access does not scale up well. Gatekeeper-based data infrastructure leads to scenarios where IT is overloaded with requests and end-users struggle to receive the data they require in a timely manner. On the other side, it is challenging to ensure the security and privacy of sensitive data when using a bottom-up strategy with open access to all data. End-users receive only the data they require when they require it thanks to data governance.

Data lifecycle diagram

We can visualize the diagram such as:

Data Lifecycle Management’s three main goals

The basis of contemporary business is data. Consequently, a strong data lifecycle management strategy is necessary to guarantee its security, availability, and dependability. The necessity for proper data management is higher than ever due to the exponential growth of data.

DLM’s three key objectives are confidentiality, integrity, and availability to enable smooth information flow throughout its lifecycle.

Confidentiality

Huge amounts of data are used and shared daily by organizations. This raises the possibility of data loss and information misuse. In order to safeguard sensitive data from unauthorized access and cyberattacks, such as financial records, business plans, personally identifiable information (PII), etc., data security and confidentiality are essential.

Integrity

Data is accessed, used, and shared by several users after it is stored in an organization’s storage systems. Any time a piece of data is used, it will inevitably go through several adjustments. The accuracy, reliability, and currentness of the information made available to users must be guaranteed by an organization’s DLM strategy. Therefore, preserving data integrity by safeguarding the data while it is being used, transported, and stored is one of the objectives of a DLM strategy.

Availability

Although data security and integrity are crucial, they wouldn’t be much use if they weren’t accessible to users when they needed them. In today’s 24/7 global corporate world, data availability is particularly important. DLM seeks to guarantee that data is accessible and available to users when they need it, preventing interruptions to crucial business operations.

Why is the data life cycle important?

Understanding the data life cycle can help you communicate more successfully with individuals who do work directly with your organization’s data team or projects. Additionally, it might give you insights that help you come up with ideas for prospective projects or efforts. It has serious benefits.

DLM assists you in maximizing the use of your data up until the point at which it is removed by defining, organizing, and developing policies around how data should be managed at each stage of its existence.

Data Lifecycle Management benefits

Apart from streamlining the flow of information and optimizing data throughout its lifecycle, a DLM offers several other benefits.

Compliance

Organizations are required by some industry compliance standards to keep data for a specific amount of time. For instance, the Security Policy for the Criminal Justice Information Services (CJIS) says that the “agency shall maintain audit documents for at least one year. The agency must keep audit records after the minimum retention period has passed until it is decided they are no longer required for administrative, legal, audit, or other operational objectives. While also addressing additional requirements like audit, legal, and investigations, DLM assists organizations in adhering to rules (both local and regional).

Data governance

Data is used by organizations to enhance corporate operations and make wise decisions. In accordance with data privacy laws, a good data lifecycle management plan helps to ensure that data is consistently available, consistent, reliable, and safe.

If you wonder what is data governance and want to learn the best data governance practices, go to these articles.

Data protection

Data security is the top issue for both company leaders and IT workers given the threat landscape of today. DLM aids businesses in defending their data against theft, deletion, cyberattacks, and other threats. Businesses can specify how their data is handled, used, kept, and shared thanks to this. This reduces the possibility of data breaches and guards against the exploitation of sensitive information.

Value and efficiency

Today’s businesses are data-driven. An organization’s strategic initiatives are heavily reliant on data. As a result, it’s critical for businesses to make sure their data is accurate, current, and authentic. A sound DLM strategy makes sure that the data users have access to is accurate and trustworthy, allowing organizations to get the most value out of their data. DLM aids in preserving data quality throughout its lifecycle, allowing for process improvement and boosting productivity.

How can Data Lifecycle Management help small businesses?

The advantages of DLM may often be applied to smaller businesses as well. If you’re managing a very small organization, creating and executing all these policies and automated procedures could seem excessive. But it’s never too early to think about the DLM stages and develop a data management strategy that can scale with your business.

Small businesses often fail to properly file away “small” documents, allowing them to disappear through the cracks and potentially causing lost or destroyed files or data to get into the wrong hands.

Your organization will be able to handle the data safely and effectively from the point of creation to the point of deletion with the correct data lifecycle management methods and strategy.

For the various stages of DLM, you can think about doing the following things on a smaller scale:

• Collection
• Data storage
• For data maintenance
• For data usage
• Data cleaning

Best data management tools (2022)

Are you searching for the best data management tools for 2022? Let’s look at a few of the top tools from each category now that you’ve seen the different types of data management solutions. These solutions could be a fantastic addition to your pipeline for enterprise workflow.

ETL and data integration tools

In computers, the process of copying data from many sources into a system that represents the data is known as extracting, transforming, and loading. On the other side, data integration describes the procedure of merging data from various sources into a single destination.

These are some of the best ETL and data integration tools:

• Hevo Data
• Stitch Data
• Fivetran

Cloud data management tools

The availability of off-premise options for data warehousing and management has increased significantly since storage and bandwidth become more affordable. Businesses that must store, analyze, and sort through a lot of data have embraced cloud-based solutions to increase productivity. The development of powerful Cloud Data Management Tools over the past five to ten years has made this possible. However, with the development of technology, many smaller businesses are now providing tools for consumers with data demands of all kinds. Although these industries are still predominantly controlled by industry giants like Google and Amazon. Here are three of the most important tools in this area.

These are some of the best cloud data management tools:

• Amazon Web Services 
• Microsoft Azure
• Google Cloud Platform

Master data management tools

Utilizing master data management tools, you may combine all of the enterprise’s business applications from several departments into a single file. Here are a few master data management tools that might assist you in establishing a single point of contact for your company.

These are some of the best master data management tools:

• Dell Boomi’s Master Data Hub
• Profisee
• ONE

Data visualization and data analytics tools

With the help of data visualization tools, you may display your data in a visual format (such as graphs and charts), which makes it simpler to derive meaningful conclusions from it and streamline the analytical process. Here are some useful tools for data analytics and visualization that you may use into your business model.

These are some of the best data visualization and data analytics tools:

• Tableau
• Looker
• Microsoft Power BI

Data management challenges

Data management presents its own set of difficulties. The ever-growing amount of data is typically the cause of data management issues. The following is a list of challenges that organizations may have while attempting to integrate data management tools into their workflow:

Uncertain goals and objectives

One of the major problems with data management is that it is unclear what an organization expects from the processed data. The full potential of the Data Management Tools cannot be realized in the absence of a clear objective for gathering the appropriate data and analyzing it to support data-driven business choices.

Meeting regulatory standards

In order to comply with the continuously evolving compliance standards, organizations must frequently evaluate their data and procedures to make sure that everything is in line with the latest or newer requirements.

Multiple data storage options

Data is stored using a variety of platforms, making analysis challenging because there isn’t a single format or source for it. Therefore, in order to facilitate easier analysis, data must be translated into a consistent format.

Sparse usage of data management

Companies find it difficult to properly comprehend the location, volume, and use of the enterprise’s data due to the enormous amount of data that must be accounted for.

Extracting value that addresses

The biggest difficulty is making sense of data gathered from many sources. To get the most value out of data in the form of practical insights, it is important to comprehend how data management and analytics work together.

Conclusion

In order to make data manageable and accessible, databases were developed in the 1980s; nevertheless, these databases also brought new issues for gathering, storing, protecting, and erasing data. Data Lifecycle Management is a concept that has been developed through time by data and IT experts via theorizing and exchanging best practices (DLM).

The volume and complexity of your data will rise as your firm expands. You may view the whole route of your data across the enterprise by building a framework based on DLM, regardless of the size of your business or the IT infrastructure you manage.

Any organization, from large corporations to small and medium-sized enterprises, can create a structure for their data to flow through or update an existing one by understanding the DLM concepts.

Remember that there are goals and benefits, but there are also some challenges. You should carefully review the tools and select the one that best fits your needs.

When it comes to data – recognized by The Economist in 2017 as the most valuable commodity in the world – one phrase still rings true: garbage in, garbage out. That’s one of the reasons why data transformation solutions are getting so much attention right now. When it comes to getting real value from your data, ensuring it is clean and optimizing the processes and tools required is critical.

Clean and well-managed business information is also one of the reasons data engineers are in so much demand. More companies are expected to rely on data for strategic decision-making in 2022, and data engineering is the key to success.

Datorios, a data transformation framework, has announced the first version of its solution to this – and other – significant issues. Available now for data engineers looking for agile and scalable data infrastructure systems, Datorios is marking the launch of its inaugural framework with live demonstrations at the Las Vegas Snowflake Summit 2022, which takes place between June 13-16.

So what does Datorios do? 

Claiming to be the first infrastructure-as-a-platform (IaaS) of its kind, it is a real-time platform designed to diminish project complexities throughout an organization’s data pipelines. Essentially, it wraps data technologies together, which should shrink development cycles and abolish the need for a variety of tools typically used by data engineering teams. Importantly, it helps eradicate garbage, ensuring clean and inciteful data throughout the business.

At the Las Vegas summit, Datorios has been showcasing the interplay between its framework and the Snowflake data warehouse, demonstrating how companies can use Datorios to pre-process data before loading it into the repository at speed. The company claims the resulting architecture enables companies to slash the time-to-value for data projects, couple data warehouses with complex data types (like streaming data), and dramatically reduce costs.

“An all-around, unified data transformation solution is key to fulfilling the data promise,” Ronen Korman, co-founder and CEO at Datorios, said. “Datorios offers its users just that. Coupled with Snowflake data warehouses, it enables companies to create a cost-effective ecosystem that takes the business relevance of data to a whole new level.”

Formerly named Metrolink.ai, the Tel Aviv startup raised $23 million throughout its pre-seed and seed rounds, the latter led by Grove Ventures and Eclipse Ventures. 

The first version of its solution includes a wide range of ready-made data pipeline building blocks of transformers and stateful correlators for data engineers to use, an open SDK for user-made custom components, native streaming data processing for handling events-based data of any scale, autonomous scaling and dynamic optimization tools, and a debugging toolset to help facilitate smooth operations.

A spokesperson for Datorios claimed that the new framework enables companies to move through the entire data-to-value cycle 20 times faster than average and at a lower cost. 

“Time is of the essence in our fast-moving world, and this very much applies to the use of data,” Korman said. “Data engineers are the key team to make data flow. Without them, there will be no business intelligence and no data science. With our solution, data-driven businesses can move at the pace of the market, generating lightning-fast insights on issues that matter right here and now.”

This article originally appeared on Grit Daily and is republished with permission.

Device authentication, also known as multi-factor and two-factor authentication, is an increasingly popular way of verifying a person’s identity data online. Although this method mitigates the common security breaches caused by knowledge-based authentication, it also comes with a host of potential problems.

Service providers control our access to these digital identities linked through devices, apps, and services. Due to this, internet users have fallen victim to cybercriminals who misuse their online identities to access personal data and confidential information.

By allowing various third parties access to their digital identities through different applications, users give away their power to control their online identity data. This, in turn, makes it very difficult for people to control access to their data, whether that means shielding their private information from marketers or keeping confidential information hidden from fraudsters.

This article will discuss the concept of using a decentralized identity for authentication, how this relates to blockchain, and some of the benefits of adopting this new authentication method.

What exactly is a decentralized identity?

The concept of a decentralized identity depends on a framework that allows users to manage their identities directly. A decentralized identity, using a reliable software trusted in generating authentication of your identity, uses an “identity wallet” to verify a person’s identity data for a variety of different websites and applications.

Much like an ID card stored in a wallet in real life, the authentication can be presented for approval by the third party without ever leaving the hands of the user whose identity is being verified. By controlling your identity through one source – the digital identity wallet – you can avoid having copies of your identifying information stored in multiple places with multiple providers.

As companies and individuals migrate towards secure cloud-based computing, it has never been more important for people to take control of their digital identities. Although cloud-based data management relies on authentication methods that are preferable to easily guessed passwords or PINs, many risks are still present.

What is blockchain, and how does this relate to decentralized identities?

The invention of blockchain technology is one of the biggest technological developments that have occurred in the past several years. Blockchain’s extremely safe nature is why cryptocurrencies like Bitcoin have gained such a loyal following. It is poised to disrupt how our world transacts business, presenting an infinitely safer and more reliable way of recording instances when money or goods trade hands.

With the cost of living skyrocketing – for example, the average rent in Toronto for a 900 square foot apartment is between $2,300-$2,700 – more and more money is exchanging hands online than ever before. The internet has enabled globalization, which means your online rent payment may pass through various third-party providers into a bank account halfway around the world. Protecting your hard-earned cash – and your identity data – has never been more important.

Blockchain allows an identity wallet to be fully controlled by the identity owner yet still gives the issuer or verifier the means to sign off on a transaction with their private key. Service providers who accept this means of authentication would have to access the distributed ledger via the blockchain to look for the decentralized identifier (or DID) to authenticate the individual.

The DID is verified through respective cryptographic keys – a combination of a public and private key – which are generated at the request of the identity owner. Service providers can verify an identity owner by adding to the digital identity data in a process not unlike issuing a certificate. By using their own private keys, issuers represent an unbiased third party that can sign off on an identity owner’s credentials without providing private details regarding the individual.

The six steps of authentication using blockchain

1. The identity wallet is the sole source of details regarding a user, such as their name, social security number, phone number, shipping address, credit card numbers, etc.

2. The decentralized identity framework allows the public key to be linked to the private key associated with the identity wallet and records this data on a public distributed ledger powered by blockchain technology.

3. As the blockchain framework generates a public key to the distributed ledger, the identity wallet acknowledges a unique DID assigned to the user.

4. The identity owner uses this DID to verify him or herself to a service provider for authentication through the distributed ledger.

5. Once the service provider locates the shared DID, the identity owner signs off on the transaction by providing his private key to complete the process.

6. The service provider then confirms authentication success and permits the user to perform the transaction on the app or website.

Decentralized identity data is the way of the future

As our society increasingly moves towards digitalization, individuals must proactively learn to manage and protect their identities. Confidential medical software for patient communication can leave our most vulnerable citizens at risk of identity theft unless chosen through a trusted provider that is HIPAA compliant. Even a small, everyday online purchase can open a user up to potential cybercrime if financial data is not appropriately managed and protected.

Online shopping has greatly increased in popularity, but so has cybercrime. In 2021, a whopping $6.9 billion was stolen by cybercriminals using social engineering methods to gain the information needed for knowledge-based authentication procedures. If an online shopper uses a decentralized identity in a transaction, the necessary data will be generated from an identity wallet containing a verified identity, address, and financial data.

Users shopping online can control the data associated with their identity by submitting the requested information through their identity wallet. They can have their identities verified without sharing the actual data, transmitting the necessary information in an encrypted way that does not compromise the security of the information. The transaction is smooth, secure, and speedy and does not require that the user type in private data such as a credit card number, shipping address, or full legal name.

The benefits of embracing blockchain for identity management

The most obvious benefit of utilizing blockchain for decentralized identity management is the amount of security this method offers. Blockchain technology inherently relies on heavy encryption, a tried-and-true method of protecting data. Blockchain technology can reliably provide digital signatures, consensus algorithms, and cryptographic hash functions, which protect from cybercrime, identity theft, and security breaches.

Similar but distinct from security, the element of privacy offered by blockchain is not to be understated. Using pseudo-anonymity when engaging in transactions, users can avoid having their private information utilized in marketing campaigns or for political purposes. By using various nodes and acting as the source of trust to verify identity data, the blockchain also contains a feature that generates a hash if it detects that an outsider has tried to tamper with the data.

The elegant simplicity of blockchain allows users to engage in a seamless process of verification that makes digital transactions easy. The data integrity found through blockchain is unparalleled, with data storage remaining permanently and publicly on the distributed ledger. Modifying or deleting data on the blockchain is simply impossible, meaning no nefarious outsider can tamper with your data.

Conclusion

For all the hype about cryptocurrency, it seems clear now that blockchain technology is the real game-changer in the modern world. By allowing users to maintain control over their identity data while also providing a highly secure and seamless way of transacting on the internet, blockchain has solved a lot of problems inherent on the internet.

With no one organization governing access and control to a piece of confidential data, blockchain will democratize the online landscape and give users their rights back regarding their private data.

When one talks about immense computing powers, the question ‘what is a supercomputer’ pops up in some people’s heads. So let’s explain: A supercomputer is a computer with a high level of performance compared to a general-purpose computer. Floating-point operations per second (FLOPS) are used to measure the performance of supercomputers, instead of million instructions per second (MIPS). Supercomputing technology is made possible by supercomputers, the most powerful computers in the world, and they are made of interconnects, I/O systems, memory, and processor cores.

Traditionally, supercomputers have been utilized for scientific and engineering applications that require working with big data sets, a large amount of processing power, or both in some cases. Desktop supercomputers or GPU supercomputers have been made possible by multicore processors and general-purpose graphics processing units.

What is a supercomputer, and what makes it super?

Supercomputers, unlike traditional computers, have more than one central processing unit (CPU). Compute nodes are made up of a processor or a group of processors—symmetric multiprocessing (SMP)—as well as a memory block. These nodes can work together to solve a certain issue since they have interconnected communication abilities. Nodes also use interconnects to communicate with I/O systems, such as data storage and networking.

Supercomputers are frequently used to run artificial intelligence programs; thus, supercomputing has come to be associated with AI. This is because AI applications necessitate high-performance computing, which supercomputers provide. In other words, because supercomputers can manage the sorts of workloads typically seen in AI apps, they are ideal for powering AI systems.

The most advanced supercomputers contain many parallel computers that execute tasks in parallel. There are two types of parallel processing: symmetric multiprocessing and massively parallel processing. Supercomputers may be dispersed, which means they utilize the power of many different PCs at once instead of having all CPUs in one location.

“A petaflop is a supercomputer’s computational processing speed equal to one thousand trillion flops. A 1-petaflop computer system may carry out one quadrillion (1015) flops. On the other hand, supercomputers can have ten times more computing power than the world’s most powerful laptop”

Where are supercomputers used?

Supercomputers are used in data-intensive and computations-heavy scientific and engineering applications such as quantum mechanics, weather prediction, oil and gas prospecting, molecular modeling, physical simulations, aerodynamics, nuclear fusion research, and cryptoanalysis. For enhancing performance, early operating systems were tailored specifically for each supercomputer. In recent years, supercomputer architecture has moved away from proprietary, in-house operating systems, with Linux taking their place. Although most supercomputers run Linux, each manufacturer focuses on its version for optimum hardware performance.

Many academic and scientific research organizations, engineering firms, and big businesses use cloud computing rather than supercomputers to obtain massive computational power. The cloud offers higher performance computing (HPC) at a lower cost, with more scalability and speed to upgrade than on-premises supercomputers. Cloud-based HPC systems can be expanded, modified, and reduced as business demands change. High-performance computing (HPC) allows businesses to utilize their existing hardware for HPC calculations and data-intensive processes.

The world’s fastest supercomputers

The world’s fastest supercomputer is Fugaku, which has a speed of 442 petaflops as of June 2021. IBM supercomputers Summit and Sierra take second and third place, reaching 148.8 and 94.6 petaflops, respectively. Oak Ridge National Laboratory, a US Department of Energy facility in Tennessee, is home to Summit. Sierra is situated in California at Lawrence Livermore National Laboratory. China claims its Sunway Oceanlite is the most powerful supercomputer with an unofficial peak speed of 1.05 exaFLOPS.

Today’s speeds are measured in petaflops, with a thousand trillion (1015) flops per second. When the supercomputer Cray-1 was installed at Los Alamos National Laboratory in 1976, it reached speeds of approximately 160 megaflops. One megaflop is capable of performing a billion flops.

Various computing types compared with supercomputing

The term “supercomputer” is sometimes used interchangeably with other types of computing. However, the synonyms might be misleading at times. Here are some key distinctions and parallels to help you understand the similarities and variations between computer types. High-performance computing (HPC) uses many supercomputers to solve complicated and large problems. Both terms are sometimes confused with each other.

Supercomputers can be described as parallel computers since they may employ parallel processing. Parallel processing is when several CPUs work on a single problem simultaneously. On the other hand, HPC situations employ parallelism without necessitating a supercomputer. Another exception is that supercomputers can employ alternative processor technologies, such as vector processors, scalar processors, or multithreaded processors.

Quantum computing is a type of computing that uses the principles of quantum mechanics to solve problems. It seeks to crack difficult questions that even the world’s most powerful supercomputers can’t address and never will be able to.

Supercomputers and artificial intelligence

Supercomputers frequently utilize artificial intelligence (AI) programs since they need supercomputing-level performance and power. Supercomputers can process huge amounts of information that AI and machine learning applications demand.

Artificial intelligence is becoming an increasingly important part of modern technology. Some supercomputers are designed specifically for AI. For example, Microsoft created a custom supercomputer to train massive AI models that work with its Azure cloud platform. The objective is to provide developers, data scientists, and business users with supercomputing resources through Azure’s AI services. One such tool is Microsoft’s Turing Natural Language Generation, a natural language processing technique.

Nvidia’s Perlmutter is another example of a supercomputer created specifically for AI computations. It is ranked No. 5 in the most recent TOP500 list of the fastest supercomputers. It will be used to construct the world’s biggest 3D map of the visible universe, and it has 6,144 GPUs. It analyzes data from the Dark Energy Spectroscopic Instrument, a camera that takes hundreds of photographs every night and contains thousands of galaxies.

Data democratization is the practice of making digital data available to the average non-technical user of information systems without requiring IT’s assistance.

End of a reign

A few data analysts with the knowledge and skills to properly arrange, crunch, and interpret data for their company had wielded enormous power over organizations. This happened due to necessity – most employees were uneducated on employing the growing tide of data effectively. With the advent of technologies that enable data to be shared and interpreted by non-data experts, things have changed. Data democratization allows data to flow freely from the hands of a few experts into the hands of countless employees throughout a business, acting as a foundation for self-service analytics.

A recent Google Cloud and Harvard Business Review poll showed that 97% of the industry leaders believe free access to data and analytics throughout an organization is essential to success. However, only 60% of respondents think their companies presently distribute access equally. According to Exasol’s findings, 90% of CEOs and data specialists are focusing on data democratization for their businesses.

What is data democratization and why is it important?

Data democratization implies that everyone has access to data. The objective is for anybody to utilize data in any manner to make smart judgments with no limits on access or comprehension.

Data democratization entails that everyone has access to data, and there are no gatekeepers preventing people from accessing it. It necessitates that we provide easy access to the data and instructions on how to interpret it so that individuals may utilize it to hurry decision-making and uncover possibilities for an organization. The objective is for anybody at any time to use data.

Until recently, IT departments controlled the data. Marketers, business analysts, and executives used the data to make commercial judgments, but they had to go through with the IT department to obtain it. This was how it’s been for most of five decades, and there are still a few people who believe it should stay that way. However, data democratization aims otherwise.

The advocates of data democratization believe that allowing everyone accesses to the same data across all business teams gives your company a competitive edge. More individuals with diverse expertise who have easy and quick access to the data can help your business discover and act on key business insights. Many experts think that data democratization is a game-changer.

The capacity to access and comprehend data instantly will lead to faster decision-making, which will result in more agile teams. Those companies with an advantage over slower data-stingy organizations would have a leg up on the competition.

When a business gives data access to all levels of the organization, it allows individuals of all ownership and responsibility to utilize such data in their decision-making. Team members are more data-driven when data democratization encourages them to go around data to accomplish tasks on time. When bad or good events occur, the responsible professionals are promptly informed, and they can examine and comprehend those anomalies to help them be proactively aware.

Finally, data democratization is a must for marketers trying to deliver the best customer experience possible. The question they should be asking isn’t whether data democratization is a necessity; rather, it’s how they can get it implemented quickly and effectively for their company.

How to democratize data?

Data democratization implies a financial, software, and training commitment from management. But data democratization can’t be dissociated from data governance. Data democratization is an act of a data governance strategy.

Breaking down data silos is a necessary step to user empowerment. This can’t be done with generic analytics tools that can desegregate and link formerly segmented data, making it easier to access from a single location.

Ideally, according to their position, the tools will filter the information and visualizations supplied to each individual — whether they are a senior executive, a director, or a designer. Marketing managers, for example, will require data that lets them analyze customer groups leading up to a new campaign. On the other hand, CMOs will need data to evaluate marketing ROI as they create next year’s budgets.

For the most part, organizations place a high value on employee data visualization. These tools need to help people make sense of their data. Customers must understand how the information is represented graphically. These visualizations must be in line with corporate KPIs, such as metrics, goals, targets, and objectives aligned from the top that enable data-driven decisions.

Team training becomes the next crucial step with the appropriate tools in place. Because data democratization is based on self-service analytics, every team member must be trained to a certain level of competence with the technology, ideas, and procedures required to participate.

Finally, you can’t have a democracy without checks and balances, which is the final component of data governance. Data can be misused or mishandled in a variety of ways. As a result, setting up a data center of excellence is necessary to ensure that data usage is kept on track. Companies should encourage the adoption of data usage in line with their capacity to own data accuracy.

Steps for a successful data democratization

Three simple actions may be taken by businesses to begin the process of data democratization:

1. Build a robust data foundation that comprises an extensive range of internal and external data sources across the entire market, not just one brand or product. Data feeds that are constantly updated will guarantee that all information remains up to date, allowing leaders to make timely decisions based on changes in the market landscape.
2. Make data insights understandable by utilizing advanced analytics. Today, sophisticated machine learning (ML) and natural language processing (NLP)” algorithms can extract context from data by generating simplified representations of text and applying macros (or rules) to those representations to determine meanings. NLP can analyze a data point’s tone and connect it with taxonomies’ unique characteristics, allowing you to go deeper into the information.
3. Scale the insights within a user-friendly experience. The future of data accessible to everyone is accompanied by tools that enable individuals across a company to access simple-to-understand. These data-driven narratives address issues and solve problems. The key is for these tools to be attentive to user requirements; this is lacking in most of today’s data visualization and dashboard tools.

Benefits of data democratization

The advantages of data democracy become more apparent as organizations comprehend and effectively tackle the risks listed above:

• Improved decision-making: Businesses can benefit from a first-to-market position by taking advantage of current trends and consumer needs. The data is accessible to all employees, which allows the entire organization to make comparable and aligned judgments.
• Employee empowerment: Teams and individuals can have greater confidence in taking on a company problem with access to data. Data scientists devote about half of their time to making data usable. Reducing internal processes and diverting data teams toward more strategic activities may save time and effort.
• More data investment ROI: Empowering everyone in your company to utilize data to make informed judgments will guarantee you get the most out of every data point you invested.
• Better customer insights: There’s a plethora of external data on the market and customers. Understanding this data allows you to make better consumer-centric decisions that lead to a superior customer experience and greater market share.
• Unparalleled flexibility: When the market or consumer changes, the data will reflect it. Then you will be able to make proactive rather than reactive judgments.

Why do some organizations approach data democratization with caution?

Some organizations are still concerned that non-technical team members could misinterpret data, and these staff would make poor judgments due to their incorrect understanding of the data.

Another argument supports the notion that as the number of people who have access to data rises, the risk of data security breaches and difficulties in maintaining data integrity increases.

Although there has been significant progress in recent years, data silos still exist. This reality still makes it difficult for people from various departments to access information and view it.

Another worry about data decentralization is the potential for duplication of effort across several teams, which might be more expensive than a centralized analysis team.

Once a silo user, always a silo user?

Changing company cultures is easier said than done. A significant part of the difficulties that complicate data democratization stems from employee and team habits, which can be evaluated within the scope of company culture. Moreover, this situation often arises from the past decisions and approaches of the management. Teams are sometimes organized independently. They don’t share internal or external data to make decisions, and there isn’t a strong culture of sharing insights across functions.

These ongoing habits have increased the need for data scientists, analysts, and other technical experts to interpret data for many companies. Some of these companies have been so clogged with requests that decision-makers have come up with workarounds or stopped looking for data as part of their procedure. It may be tough to transform entrenched cultural habits, which will require a comprehensive overhaul of the company process.

Finally, as the technology gathers more and more data, the quantity of data sets has increased. Unless that data is gathered and contextualized, most people will not comprehend it. Data dashboards and visualizations have popped up as possible solutions to these challenges. 

The industrial revolution, which took place in the last several years, has been the most significant transformation ever faced by the industrial sector. It covers all of today’s cutting-edge technology trends, including autonomous cars, smart connected devices, sensors, computer chips, and other technologies. This metamorphosis was caused by advances in manufacturing technology that has always welcomed new ideas. One of them is AI in manufacturing.

Why do we need AI in manufacturing?

Manufacturing’s main cost is the ongoing maintenance of plant equipment and machinery, which has a major influence on any business. Also, billions of dollars in lost production line downtime occur each year due to unanticipated shutdowns. As a result, manufacturers are turning to advanced artificial intelligence-aided predictive maintenance to minimize these costs.

Manufacturers are finding it difficult to maintain high quality on agreed standards and regulations in today’s extremely short market deadlines with the heavy burden of products. Manufacturers may achieve the greatest degree of product quality through artificial intelligence in manufacturing.

Workers will be prepared for more advanced jobs in programming, design, and maintenance as millions of occupations are taken over by robots. Human-robot integration will have to be quick and secure during this periodic phase as robots entered the manufacturing floor alongside human employees, and artificial intelligence may meet this need.

Artificial intelligence methods are currently being used in the manufacturing sector for a variety of purposes:

• Neural Networks: In an input layer, a neural network receives information. The input is sent to a hidden layer, which assigns a value and sends it to the output layer. For example, there’s a new neural network that is able to read tree heights using satellite images.
• Machine Learning: An artificial intelligence procedure in which a computer program learns from training data and applies this learning to make decisions and detect patterns in actual-world data. You can learn more about the history of Machine Learning, by visiting our article.
• Deep Learning: A technique of employing machine learning that mimics the human brain, like a neural network, but the information is passed from one layer to the next for greater processing. You can find out how Deep Learning can solve the problem of global climate change here.

How is AI in manufacturing transforming the industry?

Artificial intelligence is unquestionably the key to future growth and success in the manufacturing industry. Because AI aids with problems such as decision making and information overload, almost 50% of manufacturers considered it highly important in their factories for the next five years. Using artificial intelligence in industrial firms allows them to completely revolutionize their operations.

Future of AI in manufacturing

In the near future, AI will have an enormous influence on the industrial sector in ways that we cannot yet predict, but which we can already observe. There are two intriguing developments on the horizon that include using AI with IoT to improve manufacturing and AI with computer vision.

Manufacturers in numerous sectors, such as pharmaceuticals, automobiles, food and beverages, and energy and power, have adopted artificial intelligence. The worldwide AI in the manufacturing market is attributed to growing venture capital investments, rising demand for automation, and rapidly changing industries.

According to the latest trends, increasing demand for hardware platforms and a growing need for high-performance computing processors to execute a variety of AI software are all expected to propel the worldwide artificial intelligence in the manufacturing market. AI in manufacturing is also beneficial in collecting and analyzing big data.

As a result, it is extensively utilized in numerous production applications such as machinery inspection, cybersecurity, quality control, and predictive analytics. All of these elements are anticipated to help drive the global AI in manufacturing sector forward.

Conclusion

AI is simply a more advanced version of automation, which is the inevitable consequence of the industry 4.0 transformation. It might be useful in generating new things and lowering manufacturing costs by improving quality. There’s no way that humans can be replaced, though.

The ability to adapt to changing conditions and generate higher margins is one of the most important advantages of AI in manufacturing. Companies that have embraced AI early, such as Google, have far outpaced their peers and grown rapidly, owing in large part to their superior capacity to anticipate and continually modify to ever-changing circumstances.

The UK’s digital watchdogs are seeking views on algorithmic processing and auditing, as well as areas of common interest between the organizations, in order to simplify and shape future cooperation.

Benefits and harms of algorithmic processing are being discussed

The Digital Regulation Cooperation Forum (DRCF) was formed in July 2020 to improve coordination among the UK’s regulators and develop a uniform regulatory approach for digital services and the economy. The DRCF features the Competition and Markets Authority (CMA), the Financial Conduct Authority (FCA), the Information Commissioner’s Office (ICO), and the Office of Communications (Ofcom).

Following the release of two discussion papers from the DRCF’s Algorithmic processing workstream, which examined the benefits and harms of algorithms and the landscape of algorithmic auditing, a call for comments has been made.

“As part of this workstream, we launched two separate research projects – one looking at the harms and benefits posed by algorithmic processing, including the use of artificial intelligence, and another looking at the merits of algorithmic auditing, as a way of documenting risks and assuring stakeholders that an algorithmic system behaves and is governed as intended,” explained the DRCF.

“We are now launching a call for input alongside the publication of these two papers and we welcome and encourage all interested parties to engage with us in helping shape our agenda.”

DRCF has underlined “6 common areas of focus among the DRCF members” in order to explain the pros and cons of algorithmic processing: transparency of algorithmic processing; fairness for individuals affected by algorithmic processing; access to information, products, services, and rights; resilience of infrastructure and algorithmic systems; individual autonomy for informed decision-making and participating in the economy; healthy competition to foster innovation and better outcomes for consumers.

The DRCF stated regarding algorithmic auditing that the stakeholders identified several problems in the present environment: “First, they said there was a lack of effective governance in the auditing ecosystem, including a lack of clarity around the standards that auditors should be auditing against and what good auditing and outcomes look like.”

“Second, they told us that it was difficult for some auditors, such as academics or civil society bodies, to access algorithmic systems to scrutinize them effectively. Third, they highlighted that there were insufficient avenues for those impacted by algorithmic processing to seek redress and that it was important for regulators to ensure action is taken to remedy harms that have been surfaced by audits.”

How watchdogs should regulate algorithms?

The DRCF is now asking for comments on the papers, particularly in terms of how watchdogs, both individually and together, should regulate algorithms. The comment period will continue until Wednesday 8 June 2022, with a summary of the responses coming shortly after.

“The task ahead is significant – but by working together as regulators and in close co-operation with others, we intend for the DRCF to make an important contribution to the UK’s digital landscape to the benefit of people and businesses online,” explained Gill Whitehead, CEO of the DRCF.

“Just one of those areas is algorithms. Whether you’re scrolling on social media, flicking through films, or deciding on dinner, algorithms are busy but hidden in the background of our digital lives.”

“That’s good news for a lot of us a lot of the time, but there’s also a problematic side to algorithms. They can be manipulated to cause harm or misused because firms plugging them into websites and apps simply don’t understand them well enough. As regulators, we need to make sure the benefits win out.”

The DRCF has published its first annual report as well as a call for comments on algorithms, and it has outlined its work plan for the next year.

“In 2021 to 2022, we focused on laying the groundwork for effective and joined-up collaboration. Through the DRCF, we created single cross-regulatory teams to share knowledge and develop collective views on complex digital issues. We prioritized the following four digital trends and technologies: algorithmic processing, design frameworks, digital advertising technologies, and end-to-end encryption.”

The document stated that horizon scanning is crucial to understanding the consequences of emerging technologies, as well as assisting the coalition in anticipating and preparing for future regulatory issues.

“Doing this collectively helps us to share expertise and quickly accelerate our knowledge-building in new or rapidly developing subject areas.”

The DRCF’s work plan focuses on continuing to strengthen the regulatory framework. The three main areas for future focus outlined are coherence between regimes, collaboration on projects, and capability building across regulators.

The strategy also outlined several concrete steps that DRCF plans to take in the following two years, believing they will assist with a variety of major regulatory concerns. These include initiatives to safeguard children online, promote competition and privacy in online advertising, encourage technological transparency improvements, and enable innovation in the sectors that the DRCF regulates.

The Communications and Digital Committee inquiry of the House of Lords found in December 2021 that better processes and cooperation between regulators, industry and specialists would be required to address rapid technological evolution while limiting both potential harms as well as unnecessary regulatory restrictions that stifle the benefits of any breakthroughs.

Regarding the DRCF’s establishment, the committee noted that this was a modest step and that additional measures such as the expansion and formalization of coordination were needed in the long run.

Despite the DRCF’s current cooperative efforts, the new forum “lacks robust systems to coordinate objectives and to sort out potential conflicts between different regulators as the workload expands,” according to the Lords’ committee.

In September 2021, the UK’s incoming information commissioner, Elizabeth Denham explained that to offer clear focus and allow for increased collaboration between their various but interconnected authorities, digital economy regulators need distinct mandates backed up by powerful information-sharing technologies.

“We need to be able to share information, because from a competition aspect, a content regulation aspect or a data protection aspect, we are talking to the same companies, and I think it is important for us to be able to share that information,” Denham said.

The healthcare industry, like many sectors, is undergoing a substantial data-driven transformation. New technologies like telehealth platforms and the internet of things (IoT) generate more granular medical data and make it more accessible. While this has many benefits, it also raises considerable healthcare data security concerns.

There were 714 healthcare data breaches of 500 or more records in 2021, almost doubling 2018’s figure. Personal health information (PHI) is highly sensitive, making it a tempting target for cybercriminals. As the industry becomes increasingly data-centric and embraces new data-sharing technologies, security must evolve alongside it.

Here’s a closer look at the future of healthcare data security.

Changing regulatory landscape

One of the most substantial changes taking place is an evolving regulatory landscape. Laws like HIPAA provide little specific guidance for today’s data transfer and security needs, so new legislation will likely replace or amend them. Data professionals in the sector must prepare to adapt to these changing regulations.

The Trusted Exchange Framework and the Common Agreement (TEFCA) is one such new regulation. While TEFCA is a non-binding agreement, many healthcare organizations will likely join it to enable easier cross-country medical data sharing. Participants’ data workers must then ensure their processes don’t fall under new definitions for information blocking and meet TEFCA’s security standards.

Even regulations that aren’t necessarily about security will impact data privacy considerations. The No Surprises Act, which applies to virtually all health plans in 2022, prohibits billing for emergency services by out-of-network providers. This will likely require more remote data sharing, which data professionals must ensure is secure.

Increased patient access and control

Another trend that’s reshaping healthcare data security is increasing patient access. Consumers demand more transparency and control over their medical information, and technologies like telehealth provide it. Balancing this accessibility with privacy may prove challenging.

Limiting access privileges is crucial in data security, so expanding access to patients who may lack thorough cybersecurity awareness raises concerns. Basic human error accounted for 31% of all healthcare data breaches in 2019, and medical organizations can’t train consumers as they can employees. Therefore, data professionals must design a data access platform that accounts for users who will likely make mistakes.

By default, medical apps and consumer IoT devices should enable security measures like two-factor authentication and encryption. Teams can also lean into increasing user control by informing users of relevant security concerns and letting them choose how these apps use their data.

The rise of synthetic data

Machine learning is also gaining rising prominence in healthcare applications. Intelligent algorithms can help make faster and more accurate diagnoses and enable hyper-individualized healthcare, but training them poses a problem. Data scientists must ensure they don’t accidentally expose sensitive medical information while building these models.

The answer lies in synthetic data. Using this artificially generated information instead of real-world PII eliminates the risk of accidental exposure during training. The Office of the National Coordinator for Health Information Technology (ONC) has recognized this need, leading to the creation of Synthea this year.

Synthea is a healthcare data engine that generates synthetic medical records based on publicly available health information. Similar resources could arise in the near future, too. As machine learning in healthcare rises, data scientists must embrace these tools to train models on synthetic data instead of the riskier but potentially more relevant real-world PII.

Healthcare data security is evolving

The rise of data-centric technologies and processes presents both a boon and a challenge for data professionals. This evolution in industries like healthcare offers new, promising business opportunities, but it comes with rising security concerns. As data scientists help the sector capitalize on digital data, they must ensure they don’t increase cyber vulnerabilities.

These three trends represent some of the most significant changes in the future of healthcare data security. Data professionals must monitor these developments to adapt as necessary, providing optimal value while improving safety and compliance.

Artificial Intelligence in sports makes its presence felt in every corner of the world, from post-game analysis to in-game action to fan experience. If you watched the movie Moneyball, you must be in your element about how data-driven performance optimization in sports works and changes the games we dearly love for good.

Data-driven tactical clashes

Coaches have employed data science in sports to enhance their players’ performance for the previous two decades. They’ve been using big data to make split-second on-the-field judgments, and they’ve been relying on sports analytics to help them discover the next big thing for their game’s and team’s sake or a particular player’s growth.

Referees have also embraced Video Assistant Technology (VAR) in football to aid them in making more accurate judgments on the biggest calls, such as penalties, free kicks, and red cards. On the other hand, the line technology investigates images to decide if the ball passed the line or not to decide a goal or offside. As you see, AI keeps calling more and more shots at football pitches now.

Those above are just a couple of examples of how AI changes the games we love. The sports experience will change even more now that Deep Learning has gotten involved.

Artificial intelligence in sports

Data analytics and artificial intelligence can predict anything quantifiable with accuracy. The world of sports is rife with measurable components, making it an excellent testing ground for artificial intelligence. In recent years, artificial intelligence applications in sports have become more common. Given the good influence they’ve had due to their improving capabilities, they’ll continue to expand into the world of sports. On the other hand, artificial intelligence already plays a significant role in the following areas of sports:

Performance analysis

Analysts and coaches must examine a wide range of data points to assess performances. This allows them to see where players excel and fall short. The metrics used to evaluate their contribution varies depending on the position of the team’s individual players. For instance, in soccer, the key performance indicators of goal-oriented offensive players are different from those of creative midfielders or defenders. Although not all performance elements can be quantified yet, a growing portion of a player’s game is being quantifiable and measurable.

Players’ personality characteristics can be determined by applying artificial intelligence to compare qualitative factors and numerical variables and then measuring the results to predict the players’ corresponding qualitative worth. Artificial Intelligence in sports is also used to find patterns in an opponent’s plan, strengths, and flaws before games. This helps coaches create highly focused gamelans based on their study of the opposition and raising the chance of victory.

Health, fitness, and safety

AI has become the newest tool in teams’ medical kits. Players are frequently subjected to physical examinations that employ AI to analyze a variety of health variables and player movements to assess their fitness and even detect early indicators of tiredness or stress-induced injuries. Taking immediate action may assist the medical staff of sports teams in keeping their athletes healthy and safe from harm by promptly taking care of these problems.

Wearable technology is increasingly popular among top sports organizations to track athletes’ movements and physical characteristics during practice and monitor the overall health of the squad. Artificial Intelligence in sports continuously monitors the data stream gathered by these wearables to spot the warning signals that might indicate players are developing musculoskeletal or cardiovascular diseases. Sports clubs preserve their most important assets in peak condition over lengthy seasons.

Talent scouting

There is chemistry in all teams, not only in sports but in all kinds of squads, and the factors that will determine whether the new talent to join the team is the right person is of a kind that greatly exceeds the big data capabilities of a human mind. Fortunately, many details that make the games we love unique and amazing have come to light thanks to big data, and it’s not ours to make sense of them; it’s artificial intelligence.

Artificial Intelligence in sports utilizes historical data to predict a player’s future potential before investing. It is also used to calculate market values for players to make the right offers while acquiring new talent. In this way, clubs find the right talents for their teams more easily and eliminate the potential losses from unfitting transfers, blind assumptions, and faulty valuations.

Refereeing and journalism

Refereeing is one of the first instances of artificial Intelligence in sports. Hawk-eye technology has been employed in cricket to determine whether a batter is out or not based on LBW circumstances. Technology has improved the fairness and law-abiding nature of racing. NASCAR has used video surveillance to identify rule violations, as it has done in other sports.

NLP and AI are set to revolutionize news reporting. Automated journalism is about to arrive, and it has long been influenced by sports journalism. NLP and AI in news reporting will have a huge impact on how news coverage is delivered. Artificial Intelligence in sports uses data to produce legible text about sporting events.

3 main types of sports data

Artificial intelligence works with data from various sources to make predictions on various sports, including but not limited to football, basketball, baseball, volleyball, tennis, swimming, martial arts, and many more. Box scores, event data, and tracking data are the three most common sports data. The more detailed the temporal and spatial information for a game is, the better an analyst can dig it deeper.

Box-score statistics

High-level box-score statistics (half-time match score, full-time match score, goal scorers, time of goals, yellow cards, etc.) can summarize a full game in only a few seconds to show how it was played. On the other hand, basic box-score statistics can tell you who won the match, which team took the lead first, when the goals were scored, and how far apart they were. Box-score statistics provide a good picture of a game and some degree of match recuperation.

Box scores also provide a more detailed level of information. They can, for example, demonstrate which team took more shots and the quality of those shots by displaying the number of attempts and goals scored. They can also break down the possession distribution between teams, which team had more corners, committed more fouls, made more saves, etc. They may record the match’s narrative, which team dominated, or how close that game was in a few seconds.

Play-by-play data

Play-by-play data (also known as event data) is more detailed than box score statistics because it includes additional surrounding context about important occurrences in a game. Play-by-play comments on a match can, for example, provide textual descriptions of each minute of the encounter. Similarly, game spatial data (i.e., players’ spatial locations) may be used to generate visual representations of some of the most significant events in a match, such as how a particular goal was scored. It’s not the same as watching the video, but it’s a brief digitized look at the actual-world play that can be reconstructed in seconds.

Tracking data

The most detailed level of data that is presently being collected in sports is tracking data. It allows the projection of all players and the ball onto a pitch diagram that best re-creates a match from raw video footage—having a digital representation of all players on the entire field allows analysts to do more precise searching than simply viewing a video feed that only shows a part of the pitch.

Where does sports data come from?

The most popular approach to collecting sports data is through video analysis. Raw match video serves as the basis for video analysis, which involves manually viewing or automatically recording (i.e., computer vision) relevant events from the bout to create statistics. Today, all three sorts of sports data (box scores, event data, and player tracking data) are essentially video-based. However, new technologies have been implemented in various sports to collect outstanding data in recent years.

The NFL is now employing Radio Frequency Identification (RFID) trackers built into players’ shoulder pads to keep track of each player’s x and y positions on the field. In golf, radar and other sensor technology have tracked the ball’s path and rendered spectacular visualizations.

GPS devices are used in football, as well as other team sports, to track the player’s movements and other data such as heart rate and intensity of effort. These wearable gadgets benefit that they may be used in both training and competitive games. In the world of sports analytics, market data usually refers to betting data. It’s an indirect approach to recreating the match’s narrative that takes advantage of individuals providing their forecasts.

How to collect deeper sports data?

The main goal of capturing sports data is to recreate the tale of a match as precisely as possible, using human or camera vision based on the raw footage. The video is then processed to generate a digitized format that may be read and understood, allowing us to develop actionable insights.

Reconstructing performance with data usually begins by breaking down a game into digestible parts, such as objectives. We attempt to figure out what occurred in each segment of this game, how it occurred, and how well it was done for each part.

The work of video analysts is presently used for digitizing play-by-play sports data from the video footage. Humans record (or live in the sports facility) the events in a game using their notes. The play-by-play system of data collection provides a narrative of end-of-possession activities, which describes what occurred in a specific play or possession.

However, human notational systems do not give the best information for recreating the tale when it comes to comprehending how that play evolved or how well it was performed. Humans face cognitive and subjective constraints when recording very fine levels of data by hand, such as determining the precise date for each occurrence or offering an objective assessment of how well a play was performed.

How was Marcelo Bielsa’s Leeds United preparing for the upcoming games?

The better analysis we can do based on data, the more granular the data. The need for greater detail in tracking data presents us with the needed level of granularity to perform sophisticated analytics. Strategy, search, and simulation are complex activities that superior data and improved metrics might accomplish considerably better than humans.

Marcelo Bielsa once explained his approach to analysis at Leeds United. His analysis team watches all 51 matches from their upcoming opponent’s current and prior seasons, each taking 4 hours to study. They look for certain information about the team’s starting XI, tactical system and formations, and strategic decisions that they take on set pieces in this research. However, it may be argued that this method is time-consuming, subjective, and frequently incorrect. This is where technology can come in and save the day by automating the analysis process more than having a group of Performance Analysts spend 200 hours analyzing the next opponent.

The intent is to shift sports strategy analysis from a qualitative approach to a more quantitative one. There are hidden patterns in the data. All of the data points tracked by monitoring data are useful for analyzing teams’ tactics and formations in a football game. Analytics can’t tell you anything about these topics without more work on the data. This is because tracking data is noisy, owing to players changing positions on the field. However, tracking data may help you recognize a team’s or player’s hidden mentality and structure, allowing it to emerge.

AI predicts game results

Artificial Intelligence in sports can assist with match predictions in various ways. One is through crowd-sourced information, which is sometimes implicitly used. Customers may bet on the outcome of various events using prediction markets, such as betting exchanges. It’s a crowd-sourced approach, and if the market has enough participants to represent the entire market’s collective knowledge, with a good variety of information and freedom of decision in a distributed way, it’s the most reliable predictor available. It is not interpretable because we don’t know why people have made their betting decisions. If enough individuals participate in these markets, all imaginable information for a prediction is available. It’s impossible to outperform the market’s accuracy if that’s the case.

Another approach is to employ an explicit data-driven method that relies solely on historical matches and machine learning methods to predict match outcomes. This technique requires accurate and deep data to work, and it may only record the performance represented by the data points gathered. The advantages of employing a data-driven strategy are that it can be dynamic and interpretable. It also only needs a data feed of events, making it scalable. However, because not all data may be included in the dataset utilized (e.g., injury data), the analysis may have holes that impact the predictions made.

Some of the most prominent betting organizations use a mixed technique of crowd-sourced data and data-driven methods to balance the action on both sides of the wager while ensuring that their level of risk is manageable. They start with a data-driven approach and human instinct, then iterate based on volume and other sportsbooks lines.

The technology behind AI-based solutions and tracking data might be used to support these prediction markets, especially in markets where there isn’t enough coverage for crowd knowledge. The calculation of win probability is one approach to do so. Win probability is a frequently utilized analytics technique across almost every sport for media purposes. The present restriction of win probability is determined by the likelihood that an average team would succeed in a given match scenario. However, relying on an average may overlook crucial contextual information about specific teams’ or players’ strengths. The most efficient technique to deal with this is to utilize specialized models that include the players, teams, and lineups of the game in question.

A brief history of AI-driven sports analysis

The majority of sports analysis has been based on box score and event data up until now. One example is Bill James’ Project Scoresheet, which sought to create a network of baseball fans to collect and distribute information. In 2007, the Houston Rockets added Daryl Morey’s use of sophisticated statistical analysis to improve their game.

However, now tracking data began to set a new course for sports analytics. Over the last decade, a new era of sports analysis has emerged that takes advantage of traditional box-score and event information by adding more comprehensive tracking data. Artificial Intelligence in sports now can analyze tracking data to collect deeper data, perform deeper analysis, and forecast.

The amount of data we create is increasing by the hour, which has resulted in organizations struggling to deal with data accumulation and analysis. Things can get chaotic pretty quickly with IoT devices, applications, manual entry, and many other sources constantly generating data with different or no structures.

Anyone who has had to deal with data knows that good data architecture is crucial for the correct functioning of any system. No matter how much data is being dealt with, implementing the right models, policies, and standards will directly impact how successfully information is used from the moment it is captured to the decision-making process.

Databases: The Heart and Soul of Data Architecture

When it comes to dealing with data, file systems have long been the preferred tool to deal with data storage, where databases are the preference for querying and using that data operationally. Unfortunately, legacy database models have struggled to keep up with the increasing need for real-time data ingestion, immediate and low-latency queries on that real-time data along with historical data, and handle increased demands from a growing user base for quick access through interactive cloud-native applications, SaaS and mobile apps, and APIs. The industry’s response has been the creation of highly specialized database engines, which break this workload challenge into parts to that each of these can have the speed and scale required. The unintended consequence has been an increase in the complexity of these applications due to the multiple underlying database technologies which are stitched together to serve as a data system for an application. 

With applications becoming increasingly reliant on connecting to multiple databases, the existing overspecialization had become a significant problem, eroding its initially offered value. Unfortunately, the shift to cloud-native architectures and the growing demand for more efficient data management is not going anywhere, meaning that if the paradigm doesn’t change, problems will ensue.

SingleStore’s Approach

The developers of database management systems are aware of the problems plaguing the industry, so most of them are looking to find new ways to move away from nuanced, specialty databases. Take SingleStore as an example. The company aims to harvest the benefits of elastic cloud infrastructure to create an integrated and scalable database that supports multiple types of applications.

With this goal in mind, SingleStore has designed multiple features to change how businesses access and use their data. These range from using Pipelines to ingest data from any source to the distribution of the storage and the execution of queries.

By developing a distributed framework for the creation, upkeep, and use of databases, SingleStore has built a new paradigm for database management.

Using Distributed Architecture to Improve Performance

SingleStore distributes its databases across many machines. By distributing the load in this way, SingleStore seeks to put performance first while facilitating online database operations and providing powerful cluster scaling. As single points of failure are removed by adding redundancy, any data in the database is accessible at all times.

Continuous Data Loading with Pipelines

SingleStore also uses its native Pipelines feature to allow users to perform real-time analytical queries and real-time data ingestion. By providing easy continuous loading, scalability, easy debugging, and high performance, Pipelines effectively acts as a more than valid alternative to ETL middleware. 

The fact that popular data sources and formats are also supported makes the feature easy to integrate. These include:

• Data sources: Apache Kafka, Amazon S3, Azure Blob, file system, Google Cloud Storage, and HDFS data sources.
• Data formats: JSON, Avro, Parquet, and CSV data formats.

PIpelines can be easily backed up and used to restore a state at any given point, which further adds to the stability of any database.

Bottomless Storage for Additional Durability

In addition to dividing the load between nodes and facilitating real-time data ingestion through Pipelines, SingleStore has also developed a great way to separate storage and computing: Unlimited Storage. With Bottomless, long-term storage is moved to blob storage while the most recent data is kept in the SingleStore cluster, resulting in higher availability and flexibility.

Some of the benefits of this approach are flexibility when scaling up and down, allowing for the addition of reading replicas, low recovery time objectives, and read-only point-in-time recovery. 

Distributed Infrastructure is the Future

Distributed technology has become increasingly relevant over the past years. Blockchain, distributed ledgers, distributed computing, P2P apps, and many more use cases have caught the attention of investors worldwide.

In the case of SingleStore, its approach has been attractive enough to raise over $318 million in funding from names like Khosla Ventures, Accel, Google Ventures, Dell Capital, and HPE. What started with a $5 series A round back in 2013 grew to raise $80 million in Series F funding in 2021. 

This success has also seen the platform’s user base grow and the industry is recognizing its contributions. Such recognitions include being listed in Deloitte’s “2021 Technology Fast 500™ Rankings”, San Francisco Business Times’ “Fast 100”, and INC’s “5000”  2020 awards.

A few months ago, Gartner also added SingleStore to its Magic Quadrant for Cloud Database Management Systems, one of the most trusted reports in the industry.

Data as a service (DaaS) is a data management approach that uses the cloud to offer storage, integration, processing, and analytics capabilities through a network connection. The DaaS architecture is based on a cloud-based system that supports Web services and service-oriented architecture (SOA). DaaS data is stored in the cloud, which all authorized business users can view from numerous devices.

What is Data as a Service (DaaS)?

Data as a Service (DaaS) is a data management approach that attempts to capitalize on data as a company asset for enhanced business agility. Since the 1990s, “as a service” models have become increasingly popular. Like other “as a service” approaches, DaaS gives a method to handle the vast amounts of data generated by businesses every day while also delivering that critical information to all sections of the organization for data-driven decision-making.

Why do we need Data as a Service?

DaaS is a popular choice for organizations with a large volume of data. Maintaining such data may be difficult and expensive, making data as a service an appealing option. The transition to SOA has made the platform’s significance in storing data irrelevant.

Data as a service enables but does not demand data cost and separation from software or platform cost and usage. There are hundreds of DaaS providers worldwide, each with its pricing model. The price may be volume-based (for example, a fixed cost per megabyte of data in the entire repository) or format-based.

While the SaaS business model has been around for more than a decade, DaaS is a concept that is just now gaining traction

While the SaaS business model has been around for more than a decade, DaaS is a concept that is just now gaining traction. That is partly because generic cloud computing services were not initially built with large data workloads; instead, they focused on application hosting and simple data storage. Before cloud computing, transferring big data sets across the internet was also challenging. When bandwidth was limited in the past, it wasn’t easy to process massive data sets over the network.

For a long time, businesses and private users have used software as a Service. It has become standard in computing over the last decade. However, as the amounts of data generated and utilized by enterprises continue to rise accelerated, data as a service becomes essential. This also implies that data ages more quickly, making it more challenging to gather and keep relevant data, making access to the most up-to-date information even more crucial than ever before.

All company’s two primary objectives in any sector are to grow profits and decrease expenditures. Data as a service model aids with both of these goals. On the one hand, organizing work around data increases efficiency and speeds up business procedures, resulting in lower costs while also improving the top line without requiring any new invention.

The DaaS approach allows organizations to identify bottlenecks and potential growth areas in the manufacturing cycle, such as implementing predictive analytics and optimizing logistics, resulting in actual, game-changing increases in revenue. DaaS is utilized for both company purposes and customer fulfillment. Furthermore, in both situations, DaaS organizes the process and speeds up the delivery of the outcome.

DaaS can also benefit the entire organization and its customers when utilized correctly

Managing numerous data sources across multiple systems is difficult. And the time it takes to tidy, enhance, and unify data manually detracts from more beneficial activities and prevents other teams from working with that data.

Bad data can cause segmentation and routing issues for marketing and sales teams. Operations teams will have to resolve numerous conflicts due to incorrect data. The world of Big Data is rife with opportunities. However, data governance and analytics professionals must confront substantial third-party data quality and coverage concerns, resulting in incorrect modeling and a broken master database. Bad, siloed, or missing data also harms the customer experience.

How does DaaS work?

The data-as-a-service platforms are end-to-end solutions that enable the integration of various data sources and tools such as self-service reporting, BI, microservices, and apps. Users can access data using standard SQL over ODBC, JDBC, or REST. External DaaS services can also be used to obtain information. Many businesses provide simple APIs for accessing data as a service.

Benefits of Data as a Service

The potential advantages of data as a service are enormous. DaaS can also benefit the entire organization and its customers when utilized correctly.

Accelerate innovation

Data as a service may be regarded as a gateway to expansion. Development is expedited when data is at the core of a firm. That’s because data-informed methods allow for more innovation without the danger. Ideas based on reliable data have a greater chance of getting accepted by other parts of the organization and eventually succeeding once implemented if accessible to all departments and teams that need them. With access to information that promotes new ideas and encourages growth, innovative ideas may take off more quickly.

Agility boost

Many organizations may find that data as a service provides an excellent platform for treating data as a critical business asset for more strategic decision-making and effective data management. A complete corporate view may integrate internal and external data sources, such as customers, partners, and the public. DaaS can also be used to provide fast access to data for purpose-built analytics with end-to-end APIs that serve exceptional business use cases. DaaS can assist with self-service data access, making it easier for businesses to give their users easy, self-service access to their data. This can cut down on the amount of time spent looking for information and spend more time analyzing and acting upon it.

Risk reduction

DaaS can assist decrease some of the personal views that influence decision-making, putting firms at risk. Businesses founded on conjecture frequently fail. Data empower businesses reliant on data as a service provider to make the appropriate decisions and succeed. With data as a service, organizations may use data virtualization and other technologies to access, combine, transform, and deliver data through reusable data services, optimizing query performance while maintaining data security and governance. Data as a service trend benefits from these changes. In this manner, it aids in the reduction of risks due to inconsistent or incorrect data views or poor data quality.

Data monetization

For most businesses, having enough data is no longer an issue. Managing and operationalizing the data presents the most significant challenge in today’s market. While many CEOs have invested heavily in data monetization efforts, few have effectively exploited the total value of their information. DaaS is an appealing technique to achieve it.

Data-centric culture

Today’s business leaders struggle to break down data silos and provide teams with the information they require. Data as a service model provides businesses access to a growing range of data sources, promoting a data-driven culture and making data use accessible across all departments. DaaS also aids businesses in managing today’s data tide and complexity via reusable datasets that a wide range of users may use. These configurable, reusable data assets can help companies build a business-wide picture. Data as a service can assist businesses in applying data to their operations by opening up access to critical data sources.

Cost reduction

Capitalizing on a company’s wide range of data sources, extracting insights, and delivering those insights to various areas of the firm to make better decisions can significantly cut down on time and money spent on incorrect judgments. Data as a service reduces the influence of your gut and encourages data-driven decisions. It also wastes less of your resources on pointless, ill-informed efforts. DaaS can also help businesses develop customized customer experiences by leveraging predictive analytics to understand consumer behaviors and patterns, better serve customers, and build loyalty.

Challenges of Data as a Service

Security, privacy, governance issues, and possible limitations are the most common concerns associated with DaaS. Because data must be moved into the cloud for DaaS to work, further issues arise over sensitive personal information and the security of critical corporate data.

When sensitive data is transmitted over a network, it is more vulnerable than if it were held on the company’s internal servers. This problem may be overcome by sharing encrypted data and using a reliable data source.

Common concerns associated with DaaS mostly revolve around
security, privacy and governance

There are, however, some security risks that businesses must consider when adopting DaaS solutions. Wider accessibility provided by having data in the cloud also implies additional security threats that may result in breaches. As a result, data as a service providers must employ stringent security measures to keep Data as a Service going strong in the business world.

Another problem emerges if a DaaS platform restricts the number of tools that can be used to analyze data. Providers may only provide the tools they host for data management, which may be insufficient compared to the required tools. As a result, it’s critical to pick the most adaptable service provider possible, removing this issue entirely.

Pillars of advanced DaaS solutions

A data as a service solution is a bundle of solutions and services that delivers an end-to-end data management platform. Some of the critical features of a DaaS service include data processing, management, storage, integration, and analytics.

Businesses may use the first-party and third-party data they purchase to develop predictive go-to-market processes and outcomes when working with a tried-and-true DaaS provider.

A DaaS platform comprises two interconnected layers: A data access layer that supplies data points woven together and a data management layer that provides maintenance and development services for those data.

Data access layer

The data access layer uses business-related intelligence, such as firmographics, parent-child hierarchy, technographic, intent, scoops, location, contacts, and advanced insights.

Data management layer

The data management layer makes sure that the correct data reaches the right person, platform, or system. It necessitates complex operations such as cleansing, multi-vendor enrichment, routing, data brick, APIs, webhooks, modeling, and scoring. A DaaS solution also includes data services for teams with specific demands, complex analysis, or larger-scale data delivery requirements.

How to use DaaS?

DaaS is popular among businesses for achieving go-to-market success. Having precise location data is critical for enterprises that rely on physical address information, such as shipping and freight carriers. Teams can use third-party data alongside their internal consumer records to cover even the most difficult addresses, such as warehouses, small company storefronts, branch offices, and satellite structures with DaaS.

It might be challenging to prioritize new consumer categories if a product caters to a niche market. Traditional firmographics, like employee size or annual revenue, may not always define the company’s most significant accounts. Teams may also use DaaS to link detailed company and contact information with internal customer data to find new industrial segments with potential customers.

Every revenue team wants to know more about its target audience to segment and prioritize accounts. Industry segmentation of target account lists is typical, but a default industry classification such as “technology” or “manufacturing” might be too broad on occasion. DaaS enables businesses to choose a few ideal accounts and plot their relevant terms or keywords onto a company semantics graph. This displays related corporations in new or adjacent industry categories that may well suit the offered goods.

DaaS’s advanced capabilities help convert unstructured business data
into structured intelligence

Real-time enrichment may automatically include any lead with necessary business data like area of operation or annual revenue, improving the analytics and optimizing lead conversion on your website traffic. Inbound lead processing is optimized when your website traffic data is automatically cleaned, enhanced, and linked to specific CRM fields. Every department and sales representative has access to the information they require, while marketing qualified leads (MQLs) become highly trusted by sales.

Many companies use industry classification codes to determine the level of risk presented by a new client during the underwriting process. But industry codes only tell you about a company, especially if they’re broad and lumped together.

DaaS’s advanced capabilities help convert unstructured business data into structured, useable signals and intelligence, such as a company’s industry sector or how far it has progressed in its technological stack. Advanced indicators like a company’s technology competence rating or previous finance history may provide compelling evidence of creditworthiness.

Why did COVID-19 drive an increase in DaaS?

In the first quarter of 2020, a prominent global software firm boasted triple growth in desktop-as-a-service (DaaS) projects. Gartner predicts that DaaS users will increase by over 150 percent between 2020 and 2023. Setting up cost-effective, secure remote working spaces for organizations that embrace the advantages of dispersed work will be one of the major drivers behind this growth.

The number of DaaS projects grew throughout the pandemic, but those created simply to save money are more likely to fail

DaaS has always been considered an IT cost-saving solution for businesses – a business case that failed 80% of the time. However, the epidemic created a compelling and straightforward need: Organizations had to keep working with employees at home and using various devices. DaaS provided a secure and scalable option.

The future of Data as a Service model

DaaS extends a broader shift by businesses to cloud-first ways of doing things. Given the prevalence of a cloud focus in many sectors and among large and small organizations, there’s cause to think that DaaS use will continue to increase alongside other cloud services.

Even among organizations that have not previously used the cloud significantly, DaaS may help increase interest in cloud-first architecture. Typically, only enterprises capable of profitably utilizing SaaS delivery models adopted the cloud on a large scale in earlier years of cloud computing’s existence. Now, the cloud is capable enough for data workloads and intensive applications.

Gartner suggests that DaaS is still almost a decade away from reaching its actual productivity peak

DaaS is one way for businesses to use the speed and dependability of the cloud, whether they are new to it or have extensive expertise. Compared to on-premises data solutions, data as a service offers several advantages, ranging from more straightforward setup and usage to cost savings and increased dependability. While DaaS has its own set of problems, they can be addressed and managed.

Organizations already use DaaS to speed and simplify extracting insights from data and enhance data integration and governance. As a result, these businesses may maintain a competitive advantage over their rivals by implementing more effective data governance and integrity.

All these tempting advantages aside, Gartner’s hype cycle suggests that DaaS is still almost a decade away from reaching its true productivity peak. Because DaaS can become the analytics/big data center of gravity, it is expected to be more revolutionary than most other data-related advancements.

Combining data from various sources into a single, coherent picture is known as data integration. The ingestion procedure starts the integration process, including cleaning, ETL mapping, and transformation. Analytics tools can’t function without data integration since it allows them to generate valuable business intelligence.

There is no one-size-fits-all solution when it comes to data integration. On the other hand, data integration technologies generally include a few standard features, such as a network of data sources, a master server, and clients accessing data from the master server.

What is data integration?

Data integration, in general, is the process of bringing data from diverse sources together to provide a consistent overview to consumers. Data integration makes data more readily available and easier to consume and analyze by systems and users. Without changing current applications or data structures, data integration may save money, free up resources, improve data quality, and foster innovation. And while IT organizations have always had to integrate, the potential benefit has never been as significant as it is now.

Mature data integration capabilities benefit competitors who do not have them. The following are some of the advantages enjoyed by businesses with significant data integration skills:

• Reducing the time it takes to inter-convert and integrates data sets will improve operational efficiency.
• Analytics is a powerful tool for improving data quality. Through automated data transformations that apply business rules to data, you can improve the accuracy of your data and enhance your decision-making capabilities.
• Using a whole picture of data that businesses can more readily analyze, you may obtain more critical insights.

A digital firm is based on data and algorithms that analyze it to extract the most value from its information assets—from across the business ecosystem, at any moment. Data and associated services flow freely, securely, and unobstructed across the IT landscape in a digital firm. Data integration provides a complete overview of all the data moving through an organization, ensuring that it is ready for examination.

Data integration types

There are a variety of data integration techniques:

Data warehousing

Data warehousing is a data integration approach that uses a data warehouse to cleanse, format, and store data. Data warehousing is one of many integration systems that allows analysts to view statistics from several heterogeneous sources to provide insights into an organization.

Middleware data integratıon

Middleware data integration aims to use middleware software as a gateway, moving data between source systems and the central data repository. Before sending information to the repository, the middleware may help format and check it for errors.

Data consolidation

Data integration is bringing data from many systems into a single data source. Data consolidation is often aided by ETL software.

Application-based integration

An Application-based integration is one in which data is extracted and integrated using the software. The application validates the data during integration to ensure that it is compatible with other source systems and with the target system.

Data virtualization

Users may get a near real-time, consolidated view of data via a single interface even though the data is kept in separate source systems when using a virtualization approach.

Comparison: Data integration vs application integration vs ETL

The terms data integration, application integration, and ETL/ELT are often used interchangeably. While they are linked, there are several differences between the three phrases.

Data integration merges data from many sources into a centralized location, which is frequently a data warehouse. The ultimate destination must be adaptable to handle various data types at potentially huge quantities. Data integration is ideal for performing analytical activities.

The term “application integration” refers to moving information between applications for them to remain up to date. Each application has its method of emitting and receiving data delivered in significantly smaller quantities. Integration is perfect for operational use cases that need to be maintained. For example, making sure that a customer support system contains the same customer data as an accounting system is one way it can be done.

The term ETL is the acronym for extract, transform, and load. This refers to extracting data from a source system, changing it into a different form or structure, and loading it into a destination. There are two types of ETL: data integration and application integration.

Importance of data integration

With the ever-increasing volume of data, data integrity has become more vital. Data integrity is all about assuring that your data is recorded and kept as intended. And that when you look for information, you receive what you want and anticipate.

Businesses must be able to trust the data that goes into analytics tools to trust the outcomes. You get reliable results if you feed good data.

Maintaining a single location to view all of your data, such as a cloud data warehouse, can aid in data integrity. Data integration projects help to improve the quality and validity of your data over time. Data transformation methods can spot problems with data quality while it is being moved into the primary repository and correct it.

Data integration use cases

Many types of areas can benefit from data integration.

Multicloud data integration

Connecting the correct data to the appropriate people is a simple way to enhance security and speed innovation. Connect diverse data sources promptly so that businesses may combine them into beneficial data sets.

Customer data integration

To improve customer relationship management (CRM), you need data from distributed databases and networks.

Healthcare data integration

To make rapid data available for patient treatment, cohort treatment, and population health analytics, combine clinical, genomic, radiology, and image data.

Big data integration

Businesses use sophisticated data warehouses to deliver a unified picture of big data from various sources to make things easier.

How does data integration work?

One of the most challenging tasks organizations face is getting and understanding data about their environment. Every day, businesses collect more data from a broader range of sources. Employees, users, and clients need a mechanism for capturing value from the data. This entails organizations being able to assemble relevant data from wherever it is found to help with reporting and business processes.

However, essential data is frequently split across applications, databases, and other data sources hosted on-premises, in the cloud, on Internet of Things devices, or delivered via third parties. Traditional master and transactional data and new sorts of structured and unstructured data are no longer kept in a single database; instead, they’re maintained across multiple sources. An organization may have data in a flat file or request information from a web service.

The physical data integration approach is the conventional method of data integration. And it entails moving data from its source system to a staging area, where cleansing, mapping, and transformation take place before the information is transferred to a target system, such as a data warehouse or a data mart. The second choice is data virtualization, a software-based data integration type. This approach uses a virtualization layer to connect to real-world data stores. Unlike physical data integration, data virtualization does not require the movement of any actual data.

Extract, Transform, and Load (ETL) is a widely used data integration method. Information is physically taken from several source systems, transformed into a new form, and stored in a single data repository.

Data integration example

Let’s assume that a firm called See Food, Inc. (SFI) makes a mobile app in which users can photograph different items and determine whether or not they are hot dogs. SFI uses numerous tools to conduct its operations:

• To acquire new consumers, you’ll want to use Facebook Ads and Google Ads in tandem.
• Using Google Analytics to keep track of events on its website and mobile app.
• To store user data and image metadata (e.g., hot dog or not hot dog), we’ll use a MySQ l database.
• Send marketing emails and nurture leads via Marketo.
• Zendesk to handle customer service.
• Netsuite for accounting and financial management

Each of those applications contains a silo of information about SFI’s operations. That data must be combined in one location for SFI to acquire a 360-degree view of the business. Data integration is how it’s done.

How to choose data integration tools?

Compared to custom coding, an integration platform may save time to value integration logic by up to 75%. For organizations that wish to use an integration platform within their approach, the first step is to consider three essential factors: 

Company size 

SMBs have different needs than large businesses. According to industry experts, small and medium-sized businesses typically prefer cloud-based integration solutions for applications. Most recent application server architectures have moved away from on-premises servers and toward enterprise integration or hybrid integrations.

Source data and target systems 

Do you have access to the data, or are you currently using any specialized software? What data do you currently possess, and how is it structured? Is it primarily structured or a mix of structured and unstructured information?

Consider which sources you want to incorporate. Integrating your transaction and purchasing data with your CRM data is a more straightforward endeavor. Alternatively, integrating your entire multi-channel marketing stack may be more difficult, as it might include connecting all of your customer touchpoints into a single view of them.

Required tasks 

A strategy to achieve your goals is critical in any integration project.

Businesses can use integration projects for various activities, including data integration, application integrations, cloud computing, real-time operation, virtualization, cleaning, profiling, and so on. Some jobs are more specialized than others; understanding what you need and what you don’t will assist you in keeping your costs low.

Types of data integration tools

Here are the various types of data integration solutions:

On-premise data integration tools

These are the tools you’ll need to combine data from various local or on-premises sources. They’re coupled with unique native connectors for batch loading from diverse data sources housed in a private cloud or local network.

Cloud-based data integration tools

iPaaS, or integration platforms as a service, is the term given to services that aid in integrating data from diverse sources and then placing it into a cloud-based Data Warehouse.

Open-source data integration tools

These are the most pleasing alternatives if you’re attempting to avoid proprietary and possibly costly enterprise software development solutions. It’s also ideal if you want complete control of your data within your organization.

Proprietary data integration tools

The majority of these software systems are intended to be more expensive than open-source alternatives. They’re also frequently built to cater to particular business use cases.

3 best data integration tools

Now that you’ve learned about the criteria and types to consider when selecting data integration solutions. Let’s take a closer look at the top data integration tools.

Dataddo

Dataddo‘s goal is to make it easier for businesses of all sizes to get valuable insights from their data. Data integration, ETL, and data governance are just a few processes simplified by our solution. A no-code, cloud-based ETL platform that prioritizes flexibility – with a wide range of connections and fully customizable metrics, Dataddo makes building automated data pipelines simple. 

The platform seamlessly links with your existing data stack, saving you money on needless software. With a user-friendly interface and straightforward setup, Dataddo allows you to focus on putting your data together rather than wasting time learning new activities. It’s 100% managed by API updates so that you can create and forget your pipelines. If Dataddo does not already have a connection available, it may be included in the platform within ten days of submitting an inquiry.

Key features: 

• Easy, quick deployment.
• Flexible and scalable.
• Connectors that have been installed in less than ten days.
• Security: GDPR, SOC2, and ISO 27001 compliant
• Connects to existing data infrastructure

Informatica PowerCenter

The Informatica Powercenter software is a cloud-native integration service that incorporates artificial intelligence. Its simple user interface lets users take decisive transformative action, allowing them to pick between the ETL and ELT approaches. PowerCenter’s multi-cloud capabilities are focused on giving customers complete control over their data, with several pathways depending on client needs, such as Data Warehouse modernization, high-level data security, and advanced business data analytics.

Key features: 

• A metadata-driven AI engine, CLAIRE, is at the heart of this content creation system.
• High-level data security for any business.
• Interoperable with a wide range of third-party platforms and apps and other software.
• Designed to assist businesses in gaining new insights from their data.

Pandoply

Through a combination of pre-built SQL schema and rapid compatibility with any and all business intelligence platforms, Pandoply fulfills its promise of “analysis-ready data” by providing a series of pre-built SQL schema. It gives complete control over how a source is built, allowing the user to participate in the table creation process when creating a data source. Built-in performance monitoring and simple scaling for growing enterprises are additional advantages.

Key features: 

• Users and data queries are unrestricted.
• The number of data sources accessible is over 100.
• Artificial Intelligence-driven automation in a Smart Data Warehouse.
• Data schema modeling is easier.

What do data governance practices help for? Or we should ask first, do you know where to seek out particular data in your company, or who to contact for it?

Businesses that are still in their early phases understand the importance of data-driven choices in boosting their financial performance. A strong data governance plan may help you save time and money by raising the quality and ease with which teams access data. Following recommended data governance standards can guarantee that you benefit from a policy strategy, but first, what is data governance?

A data governance strategy focuses on establishing who has control and power over data assets within an organization. It includes people, procedures, and technology to handle and protect data assets. We explained data governance definition in detail in a previous article.

Organizations of different types and industries require varying degrees of data governance. It’s especially crucial for firms that adhere to regulatory standards, such as finance and insurance. Organizations must have formal data management procedures to control their data throughout its lifecycle to comply with regulations.

Another aspect of data governance is protecting the company and sensitive consumer data, which should be a top priority for businesses nowadays. Data breaches are becoming increasingly common, with governments passing legislation – as evidenced by HIPAA, GDPR, CCPA, and other privacy laws. A data governance strategy creates management to safeguard data and help organizations comply with regulatory requirements.

Despite the fact that data governance is a major area of concern for many businesses, not all methods deliver the intended benefits. Because of it, you need the best data governance practices for your businesses.

What does it mean to govern data?

Data Governance is the term used to describe a company’s data management, usage, and protection activities. Governing data refers to either all or a part of a firm’s digital and hard copy assets in this context. Indeed, defining what data means to an organization is one of the best practices for data governance.

Consider data governance to be the who, what, when, where, and why of your company’s data.

Why is data governance important?

The value of data is becoming increasingly crucial for businesses. Everywhere you look, digital transformation is a hot topic. You must be able to control your data to profit from your data assets and achieve a successful digital transformation. This implies choosing a data governance framework customized to your organization and future business goals and models. The framework must establish the required data standards for this journey and delegate roles and responsibilities inside your company and within the business ecosystem where it is based.

A well-designed data governance framework will support the business transformation toward operating on a digital platform at many levels within an organization. You should add these components to your data governance practices.

• Management: This will guarantee top management’s commitment to corporate data assets, their value, and their potential impact on the company’s evolving business operations and market opportunities.
• Finance: This will protect accurate and consistent reporting for finance.
• Sales: This will allow accurate knowledge of consumer preferences and behavior for sales and marketing.
• Procurement: Because of the use of data and business ecosystem collaboration, this will help to increase cost reduction and operational efficiency initiatives based on tapping into data and integrating with the business ecosystem.
• Production: This will be necessary for production use in putting automation into action.
• Legal: This will be the only option for legal and compliance as new regulation standards emerge.

Data inconsistencies in different systems across an organization may go unresolved because of ineffective data governance. For instance, customer names, for example, might be presented differently in sales, logistics, and customer service systems. Integrating data from various sources and formats into single reports and dashboards may be complex. These changes could create data integrity issues that harm the effectiveness of business intelligence (BI), enterprise reporting, and analytics tools. Furthermore, incorrect data might go unnoticed and unaddressed, which will impact BI and analytics accuracy.

Data governance framework

Data management is the process of organizing, understanding, and leveraging data to meet organizational goals. A data governance framework can help ensure that your organization follows best practices for collecting, managing, securing, and storing data.

To assist you to figure out what a framework should include, DAMA imagines data management as a wheel with data governance as the center from which ten specific data management skills radiate:

• Data architecture: Overall, the data structure and data-related resources are essential components of the company architecture.
• Data modeling and design: Data governance is for analysis, design, building, testing, and maintenance.
• Data storage and operations: Storing structured physical data assets, including deployment and maintenance.
• Data security: Data governance ensures privacy, confidentiality, and appropriate access.
• Data integration and interoperability: Data governance is for acquisition, extraction, transformation, movement, delivery, replication, federation, virtualization, and operational support.
• Documents and content: Data governance is the practice of managing, archiving, indexing, and providing access to data from non-structured sources.
• Reference and master data: Standardization of data definition and usage and shared data reduction to improve data quality and reduce redundancy.
• Data warehousing and business intelligence (BI): Data management analyzes data and gives access to decision support data for reporting and analysis.
• Metadata: Metadata is a term that refers to any information associated with a digital item, such as title and author. It collects, classifies, keeps, integrates, controls, manages, and delivers metadata.
• Data quality: Defining, tracking, and ensuring data integrity and quality are essential aspects of data quality.

When developing data governance practices, businesses should consider each preceding aspect: collecting, managing, archiving, and utilizing data.

The Business Application Research Center (BARC) cautions that it is not a “grand slam.” Data governance can erode participants’ trust and interest over time as a very complicated, continuous effort. BARC advises starting with a minor or application-specific prototype project and gradually expanding throughout the firm based on learnings.

BARC developed the following procedure to aid in the implementation of a successful program:

• Define objectives and analyze the advantages.
• Examine the existing condition and delta changes.
• Create a route map by combining the product plan and feature roadmaps.
• Convince stakeholders and obtain funding for the project.
• Develop and implement a data governance program.
• Implement the data governance program.
• Monitor and control.

What are data governance best practices?

We gathered the best data governance practices for your organization. A data governance strategy is only as effective as the company that uses it. You should follow rigorous data governance procedures to get the most out of a data governance plan. We all know the most effective methods in creating a data governance policy.

Check out our top six data governance practices to get you started collecting, storing, and utilizing your data more successfully.

Begin small and work your way up to the big picture

People, procedures, and technology are all critical aspects of data management. Keep all three elements in mind when developing and executing your data plan. However, you don’t have to improve all three areas simultaneously.

Start with the essential components and work your way up to the final image. Begin with people, progress to the procedure, and conclude with technology. Before any component may proceed, it must build on top of the preceding ones for the whole data governance plan to be well-rounded.

The process won’t work without the correct individuals. If the people and procedures in your company aren’t managing your data as you intended, no cutting-edge technology can suddenly repair it.

Before developing a process, search for and hire the proper people. Use these data specialists to help you establish a data governance strategy. After that, you may use whatever technology best automates your processes and gets the work done correctly and swiftly.

Get business stakeholders on board

You need top-level executive buy-in to develop a data governance strategy, but getting the go-ahead is only the beginning. You also want to Engage your audience and encourage them to take action so that your data governance plan is implemented throughout your business.

The ideal approach to get executives interested in your data governance strategy is to make a business case for it. You demonstrate leadership by creating a business case, demonstrating the specific advantages they might anticipate from a data governance approach.

Define data governance team roles

When roles, responsibilities, and ownership structures are well-defined, data governance methods are more likely to be effective. The foundation for any data governance strategy is the creation of team members’ data governance functions across your company.

Data governance practices aim to improve data quality and collaboration across departments. It necessitates input and data ownership from all levels of the company. While each organization’s data governance framework will appear unique, there are undoubtedly vital players that should be included in your structure:

• Data governance council or board: The data governance team is responsible for the overall governance plan. They provide strategic input as part of the data governance strategy. This team also frequently prioritizes elements of the plan and approves new policies.
• Tactical team members: The tactical data governance team members create data governance policies and approaches based on the council’s recommendations. They develop the data processes and rules, which are later approved by the data governance council.
• Owners: The people in charge of particular data are known as data owners. This is the person to reach out to when someone requests information. For example, if you need sales data from last month, you would contact the sales data owner.
• Data users: The team members frequently input and utilize data as part of their regular job duties.

To measure progress, use metrics

It is critical to track progress and display the effectiveness of your data governance strategy, just as it would be with any other shift. Once you’ve acquired executive buy-in for your business case, you’ll need evidence to support each stage of your transition. Prepare ahead of time to establish metrics before implementing data policies so that you can build a baseline based on your current data management strategies.

Using the original metrics regularly allows you to track your development. This demonstrates how far you’ve come, but it also serves as a checkpoint to make sure your data governance best practices are working in practice rather than just on paper. A plan that works perfectly, in theory, may fail to work in reality. It’s critical to keep an eye on your data governance strategy and remain open to changes and improvements.

Encourage open and frequent communication

Whether you’re just getting started with a data governance initiative or have been using one for some time, staying in touch early and often is critical, communicating regularly and effectively allows you to illustrate the strategy’s impact—from highlighting triumphs to re-organizing after a failure.

The Chief Data Officer (CDO) or equivalent role should be given to an executive team member, such as the CIO or CDO, to take on the leadership of the data governance program. These executives are in charge of keeping track of the organization’s governance standards across teams and departments. Team leaders and data owners may provide regular progress updates to the senior management. The executive team member then delivers essential information to the rest of the leadership team and the entire organization.

Data governance is not a project; see it as a method

Creating a data governance plan can feel like starting a new initiative. You might be inclined to form a group to work on the project while the rest of the organization waits for you to finish it. This is when many organizations’ data governance plans come to a halt.

It is not enough to implement a data governance strategy once and then declare it finished. There is no defined ending date or conclusion. Instead, it’s a continuing practice added as part of your organization’s standard policy. Data governance becomes an aspect of everyday life at your company in the same way dress codes or leaves policies are.

Real-time data is more critical than ever. We need it for quick decisions and pivot timely. Yet, most businesses can’t do this because they must upgrade their software and hardware to cope with real-time data processing’s demanding performance and scale standards. And when they can’t, we are left with stale data.

DataStax recently announced its Change Data Capture (CDC) feature for Astra DB, which brings data streaming capabilities built on Apache Pulsar to its multi-cloud database built on Apache Cassandra.

The new functionality offers real-time data for use across data lakes, warehouses, search, artificial intelligence, and machine learning by processing database changes in real-time via event streams. It will enable more reactive applications that can benefit from connected real-time data.

Solving today’s problem: How to use real-time data?

To get more details on the matter, we had a chance to talk with Chris Latimer, Vice President of Product Management at DataStax, about their new offering and the current landscape in data.

Can you inform our readers about the current market in real time data streaming?

The demand for real-time data streaming is growing rapidly. Chief data officers and technology leaders have recognized that they need to get serious about their real time data strategy to support the needs of their business. As a result, business leaders are putting more and more pressure on IT organizations to give them faster access to data that’s reliable and complete. As enterprises get better at data science, being able to apply AI to augment data in real time is becoming a critical capability that offers competitive advantages to companies that master these techniques and pose a major threat to companies that can’t.

How does real time data streaming affect user experience?

We’ve grown accustomed to data streaming in the consumer apps that we all use. We can watch the driver’s location when we’re ordering food or when we’re waiting for an online purchase we made to be delivered. While those features have clearly improved our experience, they also provide valuable data that can be used later to create second and third order effects which have less obvious impacts on user experiences.

These effects range from new optimizations that can be made by recording data streams. For example, a food delivery service might analyze driver location, drive times, selected routes and start incentivizing customers to order from restaurants which have a lower overall delivery time, letting drivers complete more deliveries and reducing wait times for consumers.

Likewise, in applications such as retail, capturing clickstream data from the collective audience of shoppers in an e-commerce app can enable retailers to select the best offers to put in front of customers to improve conversions and order size. While consumers are now accustomed to and demanding these types of interactions, many of these improvements are invisible and the end user sees a relevant discount on a product on food or clothing that can get delivered to them quickly

How did Pulsar tech help you build the new CDC feature?

Pulsar is the foundation for these new CDC capabilities. With Pulsar we’re able to offer customers more than just a CDC solution; we’re able to offer a complete streaming solution. This means that customers can send data change streams to a wide range of different destinations such as data warehouses, SaaS platforms or other data stores. They can also build smarter data pipelines by leveraging the serverless function capabilities built into our CDC streaming solution. Better still, changes are recorded so users can replay those change streams to do things like train ML models to create smarter applications.

How will your users benefit from this new feature?

This feature makes it a lot easier for users to build real time applications by listening to change events and providing more responsive experiences. At the same time, it provides the best of both worlds to users that need a massively scalable, high performance, best of breed NoSQL solution while delivering that data throughout the rest of their data ecosystem.

If you compare your CDC solution with others, what are the advantages?

The biggest difference is that DataStax is providing a comprehensive event streaming platform as part of CDC. Other solutions out there provide a raw API that sends changes as they happen. With CDC for Astra DB, we offer customers all the tools needed to quickly connect their change data streams to other platforms with a library of connectors and a full serverless function platform to facilitate smarter real time data pipelines.

We also provide developer friendly libraries so that change streams can power real time applications in Java, Golang, Python, Node.js and other languages.

With the ability to replay change streams, DataStax also offers differentiated capabilities for organizations as they build machine learning algorithms and other data science use cases.

Did you know that common data quality difficulties affect 91% of businesses? Incorrect data, out-of-date contacts, incomplete records, and duplicates are the most prevalent. It’s impossible to identify new clients, better understand existing client needs, or increase the lifetime value of each customer today and in the future if there isn’t clean and accurate data.

As data has become a critical component of every company’s activity, the quality of the data collected, stored, and consumed during business operations will significantly impact the company’s current and future success.

What is data quality?

Data quality is an essential component of data governance, ensuring that your organization’s data is suitable for its intended purpose. It refers to the entire usefulness of a dataset and ease of processing and analysis for other purposes. Its dimensions, such as completeness, conformity, consistency, accuracy, and integrity, ensure that your data governance, analytics, and AI/ML projects deliver consistently reliable results.

To evaluate it, one must consider data as the cornerstone of a hierarchy built on it. Information is placed in context over the foundation of data, and information comes next. Inferior quality data will produce inferior information quality, which will raise the hierarchy, leading to poor business judgments.

According to a study, the most common reason for incorrect quality is human error. Working on improving low-quality data is time-consuming and requires a lot of effort. Other factors contributing to bad quality include a lack of communication between departments and faulty data management techniques. Proactive leadership is required to address these issues.

Poor quality has a significant impact on your company at all levels:

• Higher processing cost: It takes ten times as long to complete a unit of work when the data is wrong than accurate.
• Unreliable analysis: Lower confidence levels in reporting and analysis make bottom-line management a difficult task.
• Poor governance and compliance risk: Compliance is no longer optional, and business survival becomes more difficult without them.
• Loss of brand value: When businesses make frequent mistakes and judgments, their brand value rapidly decreases.

How is data quality measured?

It’s easy to spot data quality, and it may be evident. It’s difficult to assess precisely because the data quality is ambiguous. To obtain the appropriate context and measurement technique for data quality, you may utilize numerous variables.

Customer information must be complete, precise, and accessible during a marketing campaign. Customer data must be unique, accurate, and consistent across all engagement channels for a marketing campaign. Data quality dimensions are concerned with characteristics that are particular to your situation.

Data quality dimensions

Dimensions of quality are elements of measurement that you may each evaluate, interpret, and improve. The aggregate scores of many dimensions represent data quality in your specific situation and indicate whether the data is fit for use.

There are six fundamental dimensions of data quality. These are the standards that analysts use to assess a data’s viability and usefulness to those who will use it.

Accuracy

Businesses should reflect real-world situations and occurrences in the data. Analysts should rely on verifiable sources to validate the measure of accuracy, which is influenced by how close the values match with verified accurate information sources.

Completeness

The data’s completeness assesses whether it can successfully deliver all required values.

Consistency

The uniformity of data as it travels across applications and networks and comes from many sources is data consistency. Consistency implies that identical datasets should be present in distinct locations and not clash. Keep in mind that consistent data may be incorrect.

Timeliness

Data that is timely is information that is readily available when needed. This aspect also entails keeping data up to date, which entails real-time updates to ensure that it is always accessible and current.

Uniqueness

Each entity, event, or piece of information in a dataset must be unique from all others. No duplicate records exist in the data set. Businesses may use data cleansing and deduplication to assist with a low uniqueness rating.

Validity

Businesses should gather data following the organization’s established business rules and parameters. All data values should also be within the correct range, and all dataset values should correspond to acceptable formats.

Data quality issues

Poor quality has a wide range of obligations and potential consequences, both minor and severe. Data quality problems waste time, lower productivity, and raises expenses. They may also harm consumer satisfaction, damage corporate reputation, necessitate costly fines for regulatory non-compliance, or even put customers or the public in danger.

How to improve the data quality?

Improving data quality is about finding the right balance of qualified people, analytical processes, and accurate technology for your company. Along with proactive top-level management, all of this can significantly enhance data quality.

Let’s start basic and follow the four-step program:

Discover

To be able to plan your data quality journey, you must first determine where you are today. To do so, you’ll need to look at the status of your data right now: what you have, where it’s kept, its sensitivity level, data connections, and any quality concerns it has.

Define rules

The data quality measures you choose and the rules you’ll establish to get there are determined by what you learn throughout the discovery phase. For example, you may need to cleanse and deduplicate data, standardize its form, or delete data before a specific date. This is a collaborative effort between IT and business.

Apply rules

After you’ve established regulations, you’ll connect them to your data pipelines. Don’t get trapped in a silo; Businesses must integrate their data quality tools across all data sources and targets to remediate data quality throughout the company.

Monitor and manage

Data quality is a long-term commitment. To keep it, you must be able to track and report on all data quality processes both in-house and in the cloud using dashboards, scorecards, and visualizations.

Following are the disciplines that can help you prevent data quality concerns and eventual data cleansing:

• Data governance
• Data profiling
• Data matching
• Data quality reporting
• Master Data Management (MDM)
• Customer Data Integration (CDI)
• Product Information Management (PIM)
• Digital Asset Management (DAM)

You should use some tools for the best results.

What are data quality tools?

Data quality tools clean data by correcting formatting mistakes, typos, and redundancies while also following processes. These data quality solutions may eliminate anomalies that increase company costs and irritate consumers and business partners when used effectively. They also contribute to revenue growth and employee productivity.

Business intelligence software addresses four crucial aspects of data management: data cleaning, data integration, master data management, and metadata management. These tools go beyond basic human analysis by identifying faults and anomalies using algorithms and lookup tables.

How to choose a data quality tool?

Consider these three aspects while selecting a data quality management software to fulfill your company’s requirements:

• You should be able to identify the information issues that exist.
• Recognize what data quality solutions can and cannot accomplish.
• Understand the advantages and drawbacks of different data cleaning solutions.

3 best data quality tool you might need

Data quality management software is essential for data managers who want to assess and improve the overall usability of their databases. Finding a suitable data quality solution necessitates consideration of various criteria, including how and where an organization saves and utilizes information, how data moves across networks, and what sort of data a team wants to tackle.

Basic data quality tools are freely available through open source technologies, but many of today’s solutions include sophisticated features across multiple platforms and database formats. It’s crucial to figure out precisely what a specific data quality solution can accomplish for your company – and whether you’ll need several tools to handle more complex situations.

IBM InfoSphere QualityStage

The Data Quality Appliance from IBM, available on-premises or in the cloud, is a versatile and comprehensive data cleaning and management tool. The objective is to achieve a uniform and correct view of clients, suppliers, regions, and goods. InfoSphere QualityStage was created with big data, business intelligence, data warehousing, application migration, and master data management.

Key values/differentiators:

• IBM provides a variety of key features that help to ensure high-quality data. Deep data profiling software delivers analysis to aid in the comprehension of content, quality, and structure of tables, files, and other formats. Machine learning may auto-tag information and spot possible problems.
• The platform’s data quality rules (approximately 200 of them) manage the intake of bad data. The tool can route difficulties to the correct person to resolve the underlying data issue.
• Personal data that includes taxpayer IDs, credit cards, phone numbers, and other information is identified as personally identifiable information (PII). This feature aids in the removal of duplicate records or orphan data that might otherwise wind up in the wrong hands.
• The platform offers excellent governance and rule-based data handling. It provides strong security measures.

SAS Data Management

The Data Integration and Cleaning Management workstation is a role-based graphical environment for managing data integration and cleaning. It includes sophisticated tools for data governance and metadata management, ETL/ELT, migration and synchronization capabilities, a big data loader, and a metadata bridge to handle big data. SAS was ranked as a “Leader” in Gartner’s 2020 Magic Quadrant for Data Integration Tools.

Key values/differentiators:

• The Data Quality Management (DQM) wizards provided by SAS Data Management are handy in data quality management. These include tools for data integration, process design, metadata management, data quality controls, ETL and ELT, data governance, migration and synchronization, and more.
• Metadata is more challenging to manage in a large organization with numerous users, and it has the potential to lose impact over time as information is exchanged. Metadata management capabilities provided by this tool include accurate data preservation. Mapping, data lineage tools that validate facts, wizard-driven metadata import and export, and column standardization features help maintain data integrity.
• Thirty-eight countries worldwide use native languages for data cleansing, with language and location awareness. The program includes reusable data quality business rules implemented into batch, near-time, and real-time procedures.

Informatica Quality Data And Master Data Management

Informatica has developed a framework to handle various operations connected with data quality and Master Data Management (MDM) to manage and track data quality. This includes role-based abilities, exception management, artificial intelligence insights into issues, pre-built rules and accelerators, and a comprehensive range of data quality transformation solutions.

Key values/differentiators:

• The vendor’s Data Quality solution is excellent at standardizing, validating, enriching, deduplicating, and compressing data. Versions are available for cloud data stored in Microsoft Azure and Amazon Web Services.
• The firm’s Master Data Management (MDM) solution guarantees data integrity via matching and modeling, metadata and governance, and cleaning and enriching. Informatica MDM automates data profiling, discovery, cleansing, standardizing, enriching, matching, and merging within a single central repository.
• Applications, legacy systems, product data, third-party data, online data, interaction data, and IoT data are examples of structured and unstructured information that the MDM platform can capture.

Data cleaning is the backbone of healthy data analysis. When it comes to data, most people believe that the quality of your insights and analysis is only as good as the quality of your data. Garbage data equals garbage analysis out in this case.

If you want to establish a culture around good data decision-making, one of the most crucial phases is data cleaning, also known as data scrubbing.

What is data cleaning, cleansing, and scrubbing?

Clean data is crucial for practical analysis. The first stage in data preparation is data cleansing, cleaning, or scrubbing. It’s the process of analyzing, recognizing, and correcting disorganized, raw data.
Data cleaning entails replacing missing values, detecting and correcting mistakes, and determining whether all data is in the correct rows and columns. A thorough data cleansing procedure is required when looking at organizational data to make strategic decisions.

Clean data is vital for data analysis. Data cleaning sets the foundation for successful, accurate, and efficient data analysis. Because the information in the dataset will be disorganized and scattered without first cleaning it, the analysis process won’t be clear or as precise. Clean data is required for effective analysis; it’s as simple as that.

Data cleaning aims to produce standard and uniform data sets that allow business intelligence and data analytics tools to access and find the relevant data for each query.

What are the benefits of data cleaning? 

Data cleaning is beneficial to your career as a data specialist. Data cleaning helps other businesses, making your position as a data professional easier.

The longer you store insufficient data, the more it will cost your firm in both money and time. This also applies to quantitative (structured) and qualitative (unstructured) data.

It’s the 1-10-100 principle:

It is better to invest $1 in prevention than spend $10 on correction or $100 on fixing a problem after failure.

These are just a few of how it will assist you in your job:

Efficiency

Clean data allows you to conduct your study faster. Because having clean data avoids the creation of numerous mistakes, and your findings will be more accurate, you won’t have to repeat the entire operation because of incorrect results.

Error Margin

Even if you are highly eager for outcomes, the results will not be accurate if the data isn’t clean. As a result, the result may or may not be accurate when you present your work. As a consequence of adopting this practice, you must become accustomed to slowing down and correcting data before presenting it. There’s less room for errors as a result of this.

Accuracy

You’ll soon learn to be more exact with the data you put in at first since data cleaning takes up so much time. Data cleaning will still be required for various reasons, but doing it helps you get used to be more precise from the start.

Data cleaning challenges

Analysts may have difficulties with the data cleaning process since good analysis requires ample data cleaning. Organizations frequently lack the attention and resources to affect the study’s conclusion due to a lack of data scrubbing efficiency. Inadequate data cleansing and preparation are often a cause for inaccuracies slipping through the gaps.

The lack of data scrubbing, which allows for inaccuracies, is not the fault of the data analyst. It’s a symptom of a more significant problem: manual and siloed data cleaning and preparation. Traditional data cleansing and preparation also take too much time beyond the shoddy and faulty analysis.

Forrester Research claims that up to 80% of an analyst’s time is spent on data cleansing and preparation. So much time is spent cleaning data that it’s easy to overlook data cleaning processes. Most businesses require a data cleansing tool to help them analyze the data more efficiently while saving time and money on preparation.

The least enjoyable activity for data scientists is the cleaning and organizing their data, according to 57% of respondents.

Comparison: Data cleaning vs data transformation

Removing data that does not belong in your dataset is known as data cleaning. Data conversion from one form or structure to another is called data transformation.

Cleaning data is one of the most critical tasks for every business intelligence (BI) team. Data cleaning processes are sometimes known as data wrangling, data mongering, transforming, and mapping raw data from one form to another before storing it. This post focuses on the techniques of cleaning up your information.

How to clean data in 6 steps?

The first step in any data cleaning project is to take a step back and assess the overall picture. Consider, what are your objectives and expectations?

You’ll need to develop a data cleanup strategy next to reach those objectives. Focus on your top metrics is a fantastic starting point, but what questions should you ask?

• What is the most important measurement you want to achieve?
• What is your firm’s objective, and what do each of your employees hope to get out of it?

The first step is to gather the key stakeholders and get them to brainstorm.

Here are some best practices for developing a data cleaning procedure:

Monitor errors

Keep track of trends where most of your mistakes originate from. This will make it easier to spot and correct incorrect or faulty data. Records are particularly significant if you’re incorporating multiple solutions into your fleet management system so that other teams don’t get bogged down.

Standardize your process

Make sure that the point of entry is standardized to help minimize duplication.

Validate data accuracy

When you’ve finished cleaning your current database, double-check the consistency of your data. Invest in real-time data management technologies so that you may clean your data regularly. Some tools even employ artificial intelligence (AI) or machine learning to improve testing for accuracy.

Scrub for duplicate data

To help save time when examining data, look for duplicates. Repeated data can be avoided by researching and purchasing various data cleaning tools that may process raw data in bulk and automate the procedure.

Analyze your data

Use third-party sources to integrate it after cleaning, validating, and scrubbing your data for duplicates. Third-party suppliers can obtain information directly from first-party sites and then clean and combine the data to provide more thorough business intelligence and analytics insights.

Communicate with your team

Share the new procedure for cleaning your data with your team to help promote its use. It’s critical to keep your data clean now that you’ve cleaned it. Keeping your teammates informed will assist you in generating and strengthening customer segmentation while also sending more relevant information to consumers and prospects. 

Finally, check and review data regularly to discover any anomalies.

When you’re done with your data, make sure it’s clean. Whether you’re using simple numerical analysis or sophisticated machine learning on huge documents, open-ended survey responses, or consumer comments worldwide, cleaning up your data is crucial in any well-executed study.

7 best data cleaning tools

There is no debate about the value of big data these days. However, if you want the best data possible, it must be as accurate as possible. This implies that your data must be current, accurate, and clean. Using one of these top data cleaning tools might help guarantee this for you.

Several variables determine the specifics of the program you pick. This includes your data source, administration procedures, programs you use, and more. Remember that low-quality data can cause a slew of problems in your company. You could waste money on duplicate records while also missing out on sales. Incorrect addresses may lead to dissatisfied customers or lost income.

Data cleansing tools help you maintain high data quality. These are the some of the best ones:

IBM Infosphere Information Server

The IBM Infosphere Information Server is a data integration platform. It has many of the best data cleaning tools available. IBM’s deal may use end-to-end solutions for a variety of services. This package deal includes standardizing information, classifying and validating data, removing duplicate records, and researching source data. Ongoing monitoring ensures that your data stays clean by catching insufficient information before reaching your applications and services. You can use USAC and AVI to clean your mailing addresses.

This platform offers several additional features, including data monitoring, data transformation, data governance, near-real-time integration, digital transformation, and scalable data quality operations.

Key benefits of IBM Infosphere Information Server

• The project’s goal is to build a comprehensive end-to-end data integration platform.
• It protects against poor-quality data from being exported to other systems.

Oracle Enterprise Data Quality

Oracle Enterprise Data Quality is an excellent data quality management solution. It’s made to supply reliable master data for integrating with your company applications. Address verification, standardization, real-time and batch comparison, and profiling are available data cleaning tools.

The following software is designed for more experienced technical users. It does, however, provide several capabilities that even non-technical persons may utilize right out of the box. Governance, integration, migration, master data management, and business intelligence are all supported by Oracle Enterprise Data Quality.

Key benefits of Oracle Enterprise Data Quality

• Data quality management software with a complete feature set.
• For commercial applications, it provides reliable master data.

SAS Data Quality

Data cleaning software from SAS, known as the SAS Data Quality Tool, is a data quality solution that works to clean data rather than moving it from its origin. Businesses may use this platform for on-premises and hybrid solutions. SAS Data Quality Tool can also utilize it with cloud-based data, relational databases, and data lakes. Deduping, correction, entity identification, and data cleanup are just a few data cleansing tools available.

With this broad range of features, SAS Data Quality is one of the most effective data cleanup solutions. That isn’t all, though. Data quality monitoring, master data management, data visualization, business glossary, and integration are all included in SAS Data Quality.

Key benefits of SAS Data Quality

• This tool works with a lot of different data sources.
• Cleans data at the source

Integrate.io 

Integrate.io is a data pipeline platform that includes ETL, ELT, and replication functionality. With a no-code graphic user interface, you can set up these features in minutes. Before moving it to a data lake, data warehouse, or Salesforce, the transformation layer may clean your data and change it into something different. Integrate.io is one of the best data cleaning solutions because of its wide range of services.

You also have access to several other helpful data integration features in addition to those offered by ETL. The easy-to-use design allows anyone in your company to establish a data pipeline. You may thus free up IT and data team time for other activities. The cloud-based platform also relieves you of routine maintenance and management duties, allowing you to integrate as much or as little as you need. This ensures that you don’t add new technology on top of what you already have. With this adaptable ETL software, you can quickly increase or decrease your usage.

Key benefits of Integrate.io

• User-friendly interface with no programming necessary.
• Data sent to data warehouses are cleaned and masked before it reaches them.
• Cloud-based

Informatica Cloud Data Quality

In Informatica Cloud Data Quality, data quality and data governance are addressed. It does so through a self-service approach that makes it one of the top data cleaning solutions. As a result, it gives everyone in your company the tools they need to access high-quality information for their apps.

Prebuilt data quality rules may be used to quickly deploy numerous services, including deduplication, data enrichment, and standardization procedures. This software package includes data discovery, transformation, address verification, reusable rules, accelerators, and AI. Artificial intelligence is essential since it will allow you to automate many aspects of the data cleaning process.

Key benefits of Informatica Cloud Data Quality

• Data cleansing, transformation, discovery, and governance platform for self-service
• Built-in data quality rules

Tibco Clarity

Tibco Clarity is a one-stop-shop for data cleaning that utilizes a visual interface to simplify data quality improvements, discovery, and conversion. Businesses may use this tool to transform any raw data into usable information for their apps.

You may use deduplication techniques and check addresses before shipping data to the target. While data is being processed, Tibco Clarity provides several graphical representations that you can utilize. This allows you to have a deeper understanding of the data set. For another layer of data quality control, define rules-based validation. After its setup, you may reuse the cleaning procedure configuration for future raw data. Thanks to this unique configuration, Tibco has earned a place on our top data cleansing tools list.

Key benefits of Tibco Clarity

• Visual data cleansing interface
• Data visualizations
• Rules-based validation

Melissa Clean Suite

Melissa Clean Suite is a data cleaning software that improves data quality in many major CRM and ERP systems. It works with Salesforce, Oracle CRM, Oracle ERP, and Microsoft Dynamics CRM. Indeed, one of the most prominent data cleaning programs because of its extensive integration with other applications.

The Melissa Clean Suite has a lot of functions. These include data reduction, contact autocompletion, data verification, data enrichment, up-to-date contact information, real-time and batch processing, and data appendage are just a few examples. Using the supplied plugins, you may integrate this solution with your CRM in minutes.

Key benefits of Melissa Clean Suite

• It works with a wide range of CRM and ERP solutions.
• Cleaning application dedicated to data

Regardless of what type of company you run, you undoubtedly deal with a lot of data. That is why you must do all possible to improve the quality of your data. This implies using one of the top data cleansing tools on the market. The services offered here provide unique advantages and have different pricing plans based on your needs.

You may also tailor your program to suit the needs of particular businesses. Depending on the software you require, you may select from various permission settings, integration choices, and administrative capabilities.

Your objective in business is to produce money, not time. This implies you’ll need to spend less time and resources dealing with duplicated records, managing an unmanageable number of records, and correcting false information.

Data curation is the active management of data throughout its lifecycle of interest and usefulness. The lifespan of data is determined by how long analysts and researchers are interested in it, which means as long as it can be reused to create more value.

What is data curation?

The process of data curation involves the creation, organization, and maintenance of data sets so that they can be accessed and utilized by organizations. Curation entails collecting, structuring, indexing, and cataloging data. Curated data is used by businesses to make decisions, while academics use it for scientific research purposes.

The overall objective of data curation is to reduce the time it takes to get insights from raw data by organizing and bringing together relevant information into structured, searchable data assets. An organization’s data strategy must include data curation, an essential element of a corporate data plan since it supports companies’ ability to utilize their data and adhere to data-related legal and security obligations.

Data curation allows data to be gathered and controlled so that everyone can utilize it. It would be hard to acquire, process, and validate big data in organizations without data curation. Data curation can be aware of the quality of the data. This way, organizations keep the valuable data and let go inapplicable.

In some cases, data curation refers to various tasks, including data management, data generation, modification, verification, extraction, integration, standardization, conversion, maintenance, quality assurance, and validation. It also includes integrity as well as provenance checks.

How is data curated?

Data curation primarily focuses on comprehending and organizing data metadata, the set of information about the data itself. Therefore, data curation involves comprehending where and how data is generated and what is stored. The process includes building searchable indexes on the data sets being curated; a data catalog also is frequently developed.

The data curation process involves identifying, cleaning, and transforming data. The first step is data identification. It ensures that the correct dataset is provided to the right team. The next step is to clean the data by looking for anomalies such as missing values. Lastly, data transformation formats the data for specific consumption scenarios.

Self-service analytical tools and contemporary data catalogs are becoming more popular as data curation becomes necessary. These assist in curating both data and metadata, which means that data management efforts are more successful.

Data curation organizes the data that is accumulating every second. Even if the datasets are huge, the curation process can assist organizations in managing them methodically so that researchers and scientists can work with them most helpfully. The data then becomes accessible to data scientists, and they may utilize it to produce insights that the company can trust.

Benefits of data curation

Data curation organizes data and makes it findable and accessible. It also enables business users to trace data lineage. The process categorizes data by various characteristics, such as whether it’s public, private, or protected.

Data curation helps organizations see what data they can utilize. This is an essential need as the generated, and collected data grows. This visibility also aids in the optimal use of data since BI and data science teams, corporate executives, and other teams can discover and access the information they require for analytics applications and operational decision-making.

Users will have more confidence in the data if they know it’s accurate, trustworthy, and up to date. Trust towards data builds faith in data-driven decisions and initiatives and speeds business activities based on data analytics.

Data curation helps organizations avoid being overwhelmed by the growth in data volumes and the diversification of data sources

Data is collected by many source systems in many organizations, ranging from conventional business applications to new edge computing devices linked to the internet of things. For analysis, big data systems frequently keep a mix of structured, unstructured, and semi-structured data. More business-related data is collected through various external sources.

Data curation helps organizations avoid being overwhelmed by the growth in data volumes and the diversification of data sources by organizing what might otherwise be a disorganized procedure of data ingestion and utilization. The ability to track data sets and users who cannot access the data they need would be impossible without it.

In recent years, machine learning algorithms have made significant progress in comprehending the consumer market. AI is made up of neural networks that communicate and can apply Deep Learning to recognize patterns. However, humans must at least initially intervene to have algorithmic behavior directed towards practical learning. The aim of data curation is for people to add their expertise to what the machine has automated. This leads to preparing for intelligent self-service procedures and establishing organizations for insights.

What is the difference between data curation vs data governance?

Data governance is a company approach, and data curation is an iterative process. While data governance establishes the responsibilities, procedures, and rules that regulate data management activities, data curation focuses on optimizing metadata to make data available, attainable, and permanent. Data governance and data curation are inextricably linked. Data curation is an integral part of successful data governance.

Who is responsible for the data curation process?

Data curators are in charge of the curation throughout the data lifecycle from ingestion to consumption. Data curators are experts in business data who understand the company’s circumstances and can generate valuable data assets for company users. Multiple data curators may be employed by an organization to manage data from various domains, each with their own domain.

Domain curators maintain and share data domain knowledge, which aids data analysts in comprehending the characteristics of the data they deal with. Researchers, data curators, and developers may all contribute to enriching a database with information.

Data curators may add metadata and necessary context. Their work is often confused with the database administrator, who creates datasets and metadata from several databases. It’s also critical for data curators to observe data governance regulations while organizing data for a company. Lead curators are the individuals who moderate data catalog content for companies. Lead curators have a significant level of responsibility for metadata and catalog quality.

What is the difference between data curators vs data stewards?

The difference between data curators and data stewards lies in what data curators eventually aim to do.

It is worth repeating that data curators are not database designers or database administrators. They are the people who maintain and manage a data set’s metadata to provide greater context for data users. Their responsibilities extend beyond databases to include the company’s data process and data roadmap. Data stewards are in charge of an organization’s databases and overall data strategy.

Data curators are data scientists who specialize in the domain and industry-specific data sets, data groupings, analysis variables, and data pipelines. The goal is to ensure that the correct person receives data when needed and that data users know how to utilize it when they find it. Data curators also verify security and privacy standards and quality when dealing with specific data sets.

Data stewards maintain databases, data processes, and overall data vision. They’re concerned with laying the groundwork for data governance and access controls, mapping data to business needs, and developing strategic data plans.

Challenges of data curation

Curation can be time-consuming and costly, especially for big data curation. Different data curation methods are required to sort and manage many diverse data sets correctly. Furthermore, for decades, businesses have stockpiled data without giving it much thought about what they plan to do with it or how to keep it safe from deterioration. Many organizations would like to utilize this data. Still, they have no idea where to begin or lack a solid business data strategy for the journey ahead. Organizations must first clearly understand what data provides the most significant value, why and how it can be utilized, and ensure success before data curation.

Future of data curation

Organizations and enterprises continue to apply big data concepts. Data has demonstrated how crucial it is in expanding previously unknown opportunities in business operation and success. As data grows, organizations will increasingly invest in data curation to speed up processing and analysis to enhance operations and produce better outcomes.

The ability to quickly monitor and analyze data on their own becomes the difference between successful organizations and others. Those who master data curation will be the most successful and surpass their industry competition.

Data curation allows organizations to crystallize their data stores and value them. Using a smart data curation platform ensures that a company is fed with clean, helpful data to gain a competitive edge and take the lead in the market.

Organizations can effectively manage the quality of their information by doing data profiling. Businesses must first profile data metrics to extract valuable and practical insights from data.

Data profiling is becoming increasingly essential as more firms generate huge quantities of data every day. Businesses currently manage an average of 162.9 terabytes of data, while enterprises handle 347.56 terabytes of data on average.

What is data profiling?

Data profiling is the technique of collecting data and analyzing it to determine its structure, components, and relationships. It is the process of examining source data, understanding structure, content, and interaction, and identifying opportunities for data projects.

Best ways to utilize data profiling

Data warehouse and business intelligence (DW/BI) projects

Data profiling can reveal data quality flaws in data sources and what has to be improved in ETL.

Data conversion and migration projects

You can use data profiling to find data quality problems, which you may resolve with scripts and data integration technologies. It can also identify new requirements for the intended system.

Source system data quality projects

Data quality analysis may discover data with severe or numerous defects, as well as the source of the problems (e.g., user inputs, interface errors, data corruption).

Importance of data profiling

Analyzing your data and conducting some sort of analysis is the first step in understanding it. It should be a necessary component of how your organization handles its data.

Data profiling is increasingly popular among organizations because it may help improve a variety of procedures across the company by offering several advantages, which we’ll look at in further detail below.

Aiding project management

• Data analysis may be used as a preliminary step to see whether there is enough information to proceed with a project. As a result, this minimizes time and money loss while lowering the overall project lifecycle and improving the chances of success.

Improving data quality

• Profiling may assist firms in keeping their data clean, accurate, and ready for distribution across the organization. This is especially crucial for extracting information from paper and spreadsheet systems and databases where data was typed in manually.
• Project managers can assess data quality to see whether the information will fulfill its intended business purpose. They may also determine whether additional data is required before starting.

Enabling searchability

• Employees in the agile organization’s era must be able to find specific sorts of data quickly and simply during projects. It may be tough to discover data within a larger string when data is unsearchable.
• To make data more discoverable, corporations label and categorize it so that users may search for specific keywords to access the relevant items and groups.
• Inside the source database, it’s also critical to identify and evaluate all metadata. As a result, metadata should be thoroughly reviewed and updated before beginning any large data project to ensure accuracy and optimum discoverability.

Data profiling types

There are three primary types of data profile tools that companies frequently use. These approaches may help individuals better understand their information sources and enhance data quality. The three most important methods to profile data are as follows:

Structure discovery

Data structure discovery aims to validate data to ensure it is adequately formatted and comparable with other data sets. This procedure, also known as structure analysis, may be utilized for various methods.

Identifying patterns in data is pattern matching, which organizations may accomplish using structure discovery. A business may have a database of addresses and conduct pattern matching to discover its subsets.

Organizations may use structure discovery to examine simple data. Using this approach, they can discover minimum and maximum values, averages, modes, and standard deviations in their data.

Content discovery

The process of data quality discovery entails studying every component in a database to ensure that the data is accurate. This approach aids business owners in locating missing or incorrect values, allowing them to correct them right away.

The data must also be organized in a consistent way across the organization. For example, to ensure correct analysis and extraction, a database with customers’ phone numbers must be in the proper format of 1-123-456-7890. If data is not presented suitably, the company will be unable to interact with its consumers appropriately.

Relationship discovery

Relationship discovery aims to determine which data the firm uses and how different sources are connected. Marketers must undertake metadata analysis to discover connections and overlapping data to identify links and overlap between datasets.

Data profiling process

You utilize the data profiling method to assess the quality of your data. The data profiling process includes several analyses that investigate the structure and content of your data and make inferences about it. After an analysis is finished, you may accept or reject the conclusions.

The data profiling process is made up of several tests that collaborate to assess your data:

Column analysis

All other analyses, except cross-domain analysis, require a column analysis. The column or field data is evaluated in a table or file, generating a frequency distribution during a column study. A frequency distribution summarizes the findings for each column, including statistics and inferences regarding your data’s features. You look for irregularities in your data by examining the frequency distribution.

The frequency distribution is also the input for further analyses such as primary fundamental analysis and baseline evaluation.

The process for creating a column analysis consists of four analyses:

Domain analysis

Purges invalid and incomplete data values. Irrelevant and deficient information may harm the quality of your data since it makes accessing and utilizing it difficult. When using data cleaning software to remove anomalies, use the findings from domain analysis.

Data classification analysis

For each column in your data, it generates a data class. Data classes are used to distinguish different types of information. It isn’t easy to compare your data with other data domains if it isn’t categorized correctly. When you want to discover information with similar values, you compare data domains.

Format analysis

Format analysis creates a format expression for the values in your data. A format expression is a pattern that includes a character symbol for each distinct character in a column. For example, alphabetic characters might have a character symbol of A, and numeric digits may have a character symbol of 9. Correct formats guarantee that your data conforms to specified criteria.

Data properties analysis

An additional analysis, called data properties analysis, compare the quality of defined properties about your data before analysis to the system-inferred properties produced during analysis. Data properties define data features, such as field length or data type. Data properties analysis helps guarantee that data is utilized effectively.

Key and cross-domain analysis

When performing a key and cross-domain analysis, your data is examined for connections between tables. The values in your data are evaluated for potential foreign keys, and they’re classified as such. When the values in a column match the corresponding values of a primary or natural key in another table, it may be inferred that the column is a candidate for a foreign key. If an erroneous foreign key is deleted, its association with a primary or natural key in another table is broken.

After your data has been thoroughly evaluated, you can execute a referential integrity analysis job. Referential integrity checking is a type of analysis that allows you to identify any problems between foreign keys and primary or natural keys. During a referential integrity analysis, foreign key candidates are thoroughly examined to ensure that they correspond with a primary or natural key’s values.

A common domain is defined as a set of columns with identical data. A frequent domain is one in which several columns include overlapping data. Columns with a similar domain might indicate the connection between a foreign key and a primary key, which you can investigate further during a foreign critical analysis job. On the other hand, most types of domains are duplicates between columns. If your data contains redundancies, you might want to utilize a data cleaning program to eliminate them because redundant data consumes memory and slows down processes.

Baseline analysis

You run a baseline analysis job to examine how things have changed between the previous version of analysis results and the current analysis results for the same data source. You can determine if there’s been an improvement in quality if differences are discovered between both versions.

The Data Profiling Process in ETL

Data profiling necessitated using a programming language such as SQL to query data in the past. This was a time-consuming and often-complex procedure that many businesses couldn’t afford. Data profiling in an ETL scenario entails obtaining data from several sources for analysis.

A single repository for data results and metadata is required during the ETL data profiling procedure. Organizations discover data consistency and quality concerns and repair them in real-time, resulting in fewer errors and higher-quality data analysis.

Data profiling techniques

According to a recent study, 31% of businesses are data-driven. This involves using metrics and analytics and data management tools like data profiling. To properly assess their database of information, companies have been employing the following analytical approaches.

Column Profiling

The technique of analyzing tables and quantifying the items in each column is known as table profiling. This can assist reveal column frequency distributions and data developments.

Cross-Column Profiling

Cross-column profiling determines a data set’s cross-cumulative relationships, including key and dependency analysis. A primary key is used to assess data values for a fundamental key in organizations. On the other hand, dependency analysis is a sophisticated method of seeing connections and structures in a data set. Business teams may use both of these analysis approaches to examine how one table’s attributes rely on others.

Cross-Table Profiling

This approach focuses on crucial analysis to find stray data and semantic and syntactic inconsistencies. As a result, duplicates and extra information are eliminated, making the data mapping process more efficient. Cross-table profiling may also be used by businesses to assess the link between columns from different tables.

Data Rule Validation

Validation of data rules and measurement standards determines whether datasets are in accordance with established norms and measuring standards. This method is used by businesses to improve the quality and usefulness of their data.

Even the most massive data sets may be analyzed and debugged with data profiling. Examining metadata is a good place to start since it allows you to troubleshoot issues in even the biggest data sets.

Cisco coined fog computing to describe extending cloud computing to the enterprise’s edge. It’s a decentralized computing platform in which data, computation, storage, and applications are stored somewhere between the data source and the cloud.

What is fog computing?

The cloud is connected to the physical host via a network connection in fog computing. The storage capacity, computational power, data, and applications are located in this middle space. These functionalities focus on the host, place close to it, and make processing faster as it is done close to where data is created.

Fog, like edge computing, brings the benefits and power of the cloud closer to where data is generated and utilized. Many people confuse fog and edge computing since both imply bringing smarts and processing closer to the data’s source. This is frequently done to enhance productivity, but it can also be used for security and regulatory motivations.

The origins of fog computing

The term fog computing was coined by one of Cisco’s product line managers, Ginny Nichols, in 2014. As we know from meteorology, fog describes clouds close to the ground. This computing method is called “fog” because it focuses on the network’s edge. After fog computing gained traction, IBM coined a similar term for a similar computing method, edge computing.

The OpenFog Consortium was formed as a joint venture between Cisco, Microsoft, Dell, Intel, Arm, and Princeton University. Other organizations that contributed to the consortium include General Electric (GE), Foxconn Technology Group, and Hitachi. The primary objectives of the consortium were to both promote and standardize fog computing. The OpenFog Consortium merged with the Industrial Internet Consortium (IIC) in 2019.

How does fog computing work?

Fog computing is not a substitute for cloud computing; it works in tandem with cloud technology. Although fog networking complements cloud processing, it does not entirely replace it. Edge analytics is possible using fogging, but the cloud performs resource-intensive, longer-term analyses.

Edge devices and sensors collect data, but they sometimes lack the compute and storage capabilities to execute sophisticated analytics and machine learning algorithms. However, cloud servers are generally too far away to handle the data and respond promptly.

Furthermore, having all endpoints connected to and delivering raw data to the cloud over the internet may have privacy, security, and legal implications, especially when dealing with sensitive data subject to various country regulations. Smart grids, smart cities, smart buildings, vehicle networks, and software-defined networking are just a few popular fog computing systems.

What is the difference between fog computing vs edge computing?

Surprisingly, fog computing doesn’t aim to replace edge computing either. According to the OpenFog Consortium of Cisco, fundamental differences exist between these two methods. The intelligence and processing power location distinguish edge computing technology from fog computing. Intelligence is at the local network (LAN) in foggy environments with little visibility. Data is sent from endpoints to a fog gateway, which transmits it to sources for processing and returns transmission.

In edge computing, intelligence and power can exist in either the endpoints or gateways. Edge computing is said to have several advantages, one of which is that it eliminates points of failure because each device independently executes and determines which data to store locally and which data to send to a gateway or the cloud for further analysis.

Both methods identify the other as a subtype of themselves

Some defend that fog computing over edge computing is more scalable and provides a better big-picture perspective of the network as data from numerous sources is integrated. However, many network engineers argue that fog computing is simply a Cisco brand for one form of edge computing.

The OpenFog Consortium, on the other hand, defines edge computing as a component or a subset of fog computing. Consider fog computing to be how data is handled from its inception to its final storage location. Edge computing entails processing data as close to its creation as possible. Fog computing refers to everything from the network connections that bring data from the edge to its endpoint to the edge processing itself.

Benefits of fog computing

Fog computing platforms provide organizations with more options for data processing wherever it is most efficient to do so. For specific purposes, such as in a manufacturing scenario, when connected devices must be able to react to an emergency as soon as possible, data needs to be processed as quickly as feasible.

Fog computing enables low-latency networking connections between devices and analytics endpoints. The architecture minimizes bandwidth requirements compared to if that data had to be transferred back to a data center or cloud for analysis. Organizations can also utilize it when there is no bandwidth connection to send data, so it must be handled promptly. Users can utilize fog networks to create security functions such as segmented network traffic, virtual firewalls, and more.

Use cases of fog computing

The field of fog computing is still in its nascent phases. Still, there are a variety of possibilities for utilizing it. It has been shown that fog computing can be used for various tasks.

The rise of semi-autonomous and self-driving cars will only exacerbate the massive amount of data created by automobiles today. To operate autonomous cars effectively, you need the ability to evaluate certain data in real-time, such as weather, driving conditions, and instructions. Other data may be needed to help improve vehicle maintenance or monitor vehicle use. A fog computing environment would allow these data sources’ communications to occur both at the edge (in the vehicle) and the endpoint (the manufacturer).

Utility systems are also increasingly using real-time data to run processes efficiently. Because this data is frequently located in remote areas, it must be processed near where it was generated. Other times, the data must be aggregated from many sensors. Both of these difficulties can be addressed by fog and edge computing architectures.

From manufacturing systems that must be able to react to events as they occur to financial institutions that use real-time data to guide trading decisions or detect fraud. Fog computing solutions may help make data transfers easier by connecting places generated with destinations where it needs to go.

How does fog computing affect the Internet of Things?

Fog computing is frequently employed in IoT applications since cloud computing isn’t suitable for many. The distributed approach addresses the demands of IoT and industrial IoT (IIoT) as well as the massive amounts of data generated by smart sensors and IoT devices, which would be costly and time-consuming to send to the cloud for processing and analysis. IoT systems require a lot of data to function correctly, so there’s a significant amount of traffic on the network. The fog computing approach reduces bandwidth consumption and back-and-forth communication between devices and the cloud, lowering IoT performance.

What does 5G connectivity mean for fog computing?

Fog computing is a type of architecture in which data from IoT devices is transmitted via a network of nodes in real-time. The information gathered by distributed sensors is usually processed at the sensor node, with a millisecond response time. The nodes send analytical summary data to the cloud regularly. That isn’t all. The data from the various nodes is then processed in cloud-based software, aiming to offer practical information.

Fog computing needs more than just computing functions. It necessitates the fast transfer of data between IoT devices and nodes. The aim is to be able to process data in milliseconds. Of course, different connectivity choices are available depending on the scenario. A connected factory floor sensor, for example, may require a wired connection. On the other hand, a mobile resource, such as an autonomous car or a wind turbine in the middle of a field, will necessitate another kind of connection. The 5G is a compelling wireless technology that offers gigabit connectivity, crucial for data analysis in near-real-time.

Hybrid cloud computing unifies private, public, and on-premises IT infrastructures to form a single flexible, cost-effective IT infrastructure. The hybrid cloud provides orchestration, management, and application portability across these environments.

What is hybrid cloud computing?

A hybrid cloud is an IT architecture that incorporates workload portability, orchestration, and management across multiple cloud environments. Hybrid cloud computing delivers a unified operating model that manages application workloads across both environments. Allowing for seamless movement of applications from private to public cloud and vice versa as business demands change.

Hybrid cloud computing enables organizations to fulfill their technical and business objectives more effectively and efficiently than public or private cloud. According to IBM, enterprises get up to 2.5 times more value from a hybrid cloud than a single-cloud, single-vendor strategy.

How does hybrid cloud work?

Hybrid cloud architecture is a variation of the traditional on-premises data center/cloud hybrid approach. Initially, the focus was on converting part of a company’s on-premises data center into private cloud infrastructure and then connecting it to public cloud environments hosted off-premises by a public cloud provider. This was done using a prepackaged hybrid cloud solution or sophisticated enterprise middleware to link cloud assets across environments, and unified management tools for monitoring, allocating and managing those resources from a central console.

Hybrid cloud computing has evolved, moving beyond merely connecting physical infrastructures and being more concerned with the portability of workloads across all cloud platforms. It’s also focused on automating the deployment of those applications to the most suitable cloud environment for a given business objective.

Organizations modernize legacy apps and create new applications with cloud-native technologies as part of their digital transformations. They’re developing or upgrading applications to employ the microservices architecture, breaking apps into smaller, more loosely coupled, reusable components concerned with specific business activities. They’re also putting these apps in containers, which are lightweight executable units that include only the application code and virtual-machine operating system dependencies required.

Public and private clouds no longer exist as physical “locations” to connect. Many cloud providers now provide public cloud services that run in their customers’ on-premises data centers and private clouds hosted off-premises, which have long been hosted exclusively on-premises.

Furthermore, infrastructure virtualization, often known as infrastructure as code, allows developers to create these enclaves on demand using any computing resources or cloud resources located behind or beyond the firewall. Since cloud computing is becoming increasingly popular, getting good performance out of your data in real-time has never been more important. This becomes even more crucial in the era of edge computing, allowing applications to run faster by moving workloads and data closer to where the actual processing occurs.

As a result, modern hybrid cloud infrastructure coalesces around a unified hybrid multicloud platform that supports cloud-native application development and deployment across all cloud types. It provides a single operating system across all environments and a container orchestration platform to automate app deployment across cloud environments.

Cloud-native development allows developers to take monolithic apps and convert them into units of business-focused functionality that may be deployed anywhere and used in various applications. A typical operating system allows developers to incorporate any hardware dependency into any container. Developers have greater, set-it-and-forget-it control over container configuration and deployment, including security, load balancing, scalability, and more, across numerous cloud platforms thanks to Kubernetes orchestration and automation.

What are the hybrid cloud’s benefits?

Hybrid cloud architecture is one of the most prevalent infrastructure designs today. It allows for simultaneously utilizing on-premises servers and cloud services. Hybrid cloud computing offers many advantages for organizations:

Application governance

Organizations can use the hybrid cloud computing approach to decide where applications reside and where hybrid computing takes place. This option is vital for improving privacy and ensuring compliance for regulated apps.

Management

Both on-premises and cloud solutions are used in hybrid cloud computing. Applications, databases, and components, on the other hand, are managed under a single data management framework, allowing for interoperability.

Developer productivity

Agile and DevOps methodologies may be more successful when employed on a unified hybrid cloud platform, which can help organizations expand Agile and DevOps adoption and enable development teams to develop once and deploy to all clouds.

Automation

Cloud-based applications frequently include more powerful connectivity and automation capabilities than on-premises software. Hybrid cloud systems allow users to utilize these capabilities. This makes it easier to plan for future full-cloud implementation by providing IT managers a sense of what may be possible with automation once everything is moved to the cloud.

Compliance and security

A unified architecture allows enterprises to utilize cloud security and regulatory compliance technologies while delivering security and compliance across all environments. A well-designed, integrated and managed hybrid cloud can provide the same level of security as on-premise IT infrastructure.

Infrastructure efficiency

Developers, IT operations staff, and project managers can use microservices to optimize spending across public cloud providers, private clouds, and cloud vendors. The hybrid cloud also aids organizations in implementing new value by allowing them to connect cloud services to data on the cloud or on-premises infrastructure more quickly.

What is the difference between hybrid cloud and multi-cloud?

Hybrid cloud and multi-cloud are cloud deployments that include more than one cloud environment. They differ in the types of cloud infrastructure they use. The term “hybrid cloud” refers to an infrastructure that mixes two or more types of clouds. On the other hand, a multi-cloud environment combines various clouds of the same type. Multi-cloud deployment is a hybrid cloud that incorporates more than one public cloud.

How to effectively utilize hybrid cloud?

A hybrid cloud is an ideal option for a variety of circumstances. The following are examples of how hybrid cloud computing may be effectively utilized.

A hybrid cloud enables a public cloud that is rapidly scalable and widely available for dynamic workloads while leaving more volatile or sensitive tasks in a private cloud or on-premises data center.

It’s always a good idea to minimize the amount of data available on public clouds. Organizations may store sensitive financial or customer information on their private cloud while using a public cloud to execute the rest of their business apps.

It’s beyond unlikely for any corporation to continuously handle big data at a high rate. Instead, organizations utilize highly scalable public cloud services while also utilizing a private cloud to safeguard sensitive big data and keep it behind the firewall.

A hybrid cloud is an excellent approach to migrating to the cloud gradually. Organizations may put some of their workloads on a public cloud or a small-scale private cloud to learn what works best for their business. They can expand their cloud presence by utilizing public clouds, private clouds, and hybrid clouds.

The hybrid cloud computing allows companies to use public cloud resources for short-term projects at a lower cost than their data center’s IT infrastructure. This way, businesses don’t waste money on equipment they won’t need for long.

Hybrid cloud solutions let organizations match their real data management needs with the public cloud, private cloud, or on-premises resources that can best fulfill them.

Unless an organization doesn’t simply require a public cloud solution or just a private cloud solution, it’s best to go with the hybrid cloud approach and combine the benefits of both.

Hybrid cloud use cases

Hybrid clouds are utilized in many different practical ways nowadays by enterprises:

Legacy app support

Although many tools, applications, and resources may be moved to the cloud, some require on-premises infrastructure. Hybrid cloud computing enables businesses to develop their hybrid cloud solutions while still benefiting from the flexibility of migrating to the cloud at their own pace.

Workload migration

A hybrid cloud solution might be a transitory setup that allows you to move to a more permanent cloud. In some situations, an organization’s cloud migration might take months. A hybrid cloud approach to transitioning allows for phased movement and rollback while still conserving time and minimizing or even eliminating downtime.

Development lifecycle

Resource needs will vary throughout the development lifecycle. Certain assets may be required during the test phase that will not be needed until after beta or even launch. These resources can scale up appropriately depending on the demands of each stage in a hybrid cloud environment. It’s possible to adapt quickly as new requirements emerge without replacing hardware or changing settings.

Disaster recovery

Hybrid cloud solutions allow private and public disaster recovery tailored to an organization’s specific requirements. This leads to a simplified method that reduces local storage space and bandwidth consumption while improving the backup process—ensuring an efficient and quick recovery of locally stored proprietary data.

Data marts are one critical tool in successfully turning data into insights in a market dominated by big data and analytics. A data mart is a type of access layer in a data warehouse that is used to give users data. Data marts are often viewed as tiny pieces of the entire data warehouse. Enterprise-wide information is typically kept in a data warehouse, and the material stored in a data mart usually pertains to a specific department or group.

The main aim of data marts is to give the business user the most relevant information in the quickest time feasible. Users can create and follow a train of thought without waiting long periods for queries to finish. A data mart is an information management system tailored to a specific group’s demands and has a restricted subject area. Narrow in scope does not necessarily imply little in size, however. Data marts might have hundreds of thousands of records and take up terabytes of storage space.

What is a data mart?

A data mart is a type of data warehouse that focuses on a specific line of business, department, or subject area. Data marts allow users to access critical insights quickly by providing restricted information from a subset of the entire data warehouse. For instance, many organizations may have it tailored to a certain department in the business, such as finance, sales, or marketing.

Data marts speed up business processes by allowing access to critical information in a data warehouse or operational data store within days rather than weeks or months. Because it only contains the data pertinent to a particular industry sector, it is an economically beneficial technique to get rapid actionable insights.

Comparison: Data lake vs data warehouse vs data mart

The terms data lake, data warehouse, and data mart are not synonymous. They each fulfill different requirements in your company, so we’ll go through the most significant distinctions between them.

Comparison: Data mart vs data warehouse

Warehouses and mart data are organized, read-only data storage facilities for transactional information. However, they differ in the scope of data kept. Data warehouses combine multiple sources’ large quantities of data into a single repository with highly structured and integrated historical information.

Data marts are a subset of this warehouse data relevant to your business’s specific topic or department. As shown above, they’re inserted between the warehouse and the analytics solutions.

Comparison: Data mart vs data lake

The most significant distinction between the two is the stored sort and quantity of information. On the other hand, data lakes contain huge quantities of raw, unstructured data.

Another distinction to consider is that data in marts have been selected to meet a well-defined need, while the aim of data in data lakes has not necessarily been determined. Many businesses employ both technologies to meet their various storage demands.

Key advantages of data mart

The amount of data you must manage is mind-boggling. You receive hundreds of documents from historical data and real-time streaming information from multiple sources. The majority of this big data resides in a data warehouse, where users must write complex queries to obtain the facts they need.

A data warehouse is an extensive database that houses all of your company’s information in one place. You’ll find it challenging to keep track of everything you need to be done if you have many items on your plate. A data mart is an efficient approach for analytics and business users to explore and analyze more manageable subsets of data directly relevant to them.

A mart has several advantages for the end-user, including the following:

• Cost-efficiency: There are several variables to consider when establishing a data mart, such as the scope, integrations, and ETL extraction, transformation, and loading (ETL) process. On the other hand, a mart is somewhat less expensive than a data warehouse.
• Simplified data access: Data marts contain a limited amount of data, so users may quickly access the information they require with less effort than when working with a data warehouse’s larger pool.
• Quicker access to insights: Intuition gained from a data warehouse aids strategic decision-making at the enterprise level, impacting the entire company. A data mart improves business intelligence and analytics. Teams may use focused data insights to help them reach their specific objectives. The business benefits from increased business processes and greater productivity as teams discover and extract valuable data in a shorter time.
• More straightforward data maintenance: A data warehouse is a collection of data that contains important information about the company. A mart on the other hand, focuses on a single line and has a capacity limit of fewer than 100GB, resulting in fewer distractions and easier maintenance.
• Easier and faster implementation: A data warehouse requires a significant amount of setup time in any company because it gathers data from various internal and external sources. When creating a one, you only require a tiny portion of information, so implementation is more efficient and requires less setup time.
• More reliable data: The data analytics tool creates a “single source of truth” for a particular area or department. This gives your teams a shared perspective on the data and enables them to focus on discovering insights, making judgments, and taking action rather than sharing spreadsheets and figuring out which information is correct.
• Better support short-term projects: They’re ideal for analyzing short-term data, such as assessing the success of an advertising campaign. You may quickly and cheaply create a data mart, making them ideal for quick data analysis projects like measuring the effectiveness of a marketing campaign.

Disadvantages of data mart

The following are a few drawbacks of using data marts:

• The business may not have access to data cross-data-mart reporting if it uses an independent data mart model.
• It can be not easy to set up Data Marts. Because data mart must align fields, this task may be time-consuming. There could be problems generating reports to compare data across Data Marts if it isn’t handled correctly.
• The first step is to figure out what the organization’s needs are. Data Marts aren’t always the ideal answer for every team.

Data mart types

There are three types of data marts: Dependent, Independent, and Hybrid. This categorization is based on how the data has been acquired from a data warehouse or any other information source.

Collecting its data from any source system is called Extraction, Transformation, and Transportation (ETT).

Dependent data mart

The primary data in an organization’s data warehouse is divided into dependent data marts. Storing all corporate information in a single central location starts this top-down approach. When it’s time for analysis, the new data marts isolate a particular section of the original data.

Independent data mart

It is a self-contained system that does not rely on a data warehouse. Analysts may extract information from either internal or external data sources, process it, and then store it in a repository until the team requires it.

Hybrid data mart

Hybrid data marts integrate data from various operational sources and existing data warehouses. This unified technique utilizes the top-down approach’s speed and user-friendly interface while incorporating enterprise-level integration features.

Data mart structure 

It is a relational database that contains transactional information in rows and columns, making it simple to get at, arrange, and interpret. Because it’s based on historical information, this design makes it easier for an analyst to identify trends in the data. Numerical position, time value, and references to one or more items are common data fields.

Data marts are designed in a multidimensional structure as templates for people using the databases for analytical work. The three most common schema are star, snowflake, and vault.

Star

A STAR schema is a star-shaped layout of tables in a multidimensional database that makes logical sense. One fact table—a metric set that pertains to a specific business event or process—is located at the star’s center, surrounded by several dependent dimension tables in this plan.

There’s no relationship between dimension tables, so star schemas need fewer joins when generating queries. This design makes data access and navigation easier. Therefore, the star schema is exceptionally efficient for analysts who want to analyze vast amounts of information.

Snowflake

A snowflake schema is a logical extension of a star schema that adds dimension tables to the blueprint. The dimensions are normalized to maintain data integrity and minimize data redundancy.

Dimension tables require less space to store than traditional relational tables, but they are more complicated to manage. The significant advantage of the snowflake schema is that it uses fewer disk resources, but there is a drawback in terms of performance due to the additional tables.

Vault

The data vault is a contemporary database modeling technique that allows IT professionals to build agile corporate data warehouses. This method, designed specifically to tackle difficulties with agility, flexibility, and scalability that may arise when utilizing other schema models, requires a layered structure.

Star schema’s need for cleaning and the ease of adding new data sources are eliminated by the data vault, which streamlines the introduction of new sources without disturbing existing schemas.

Why do we use a data mart?

Determine the data needs of your department and develop it to meet these requirements based on the need and after consulting with stakeholders since operational costs of data marts might be high at times.

Consider the following reasons for establishing a data mart:

• If you’d like to partition the data using a specific access control approach.
• If a department needs to access the query results more quickly than they may do with whole DW data.
• If a department needs data to be processed on different hardware (or) software platforms.
• If a department needs data to be prepared in a manner that is compatible with its tools.

What is data modeling is a question of the day. Databases help run applications and provide almost any information a company might require. But what makes a database valuable and practical? How can you be sure you’re building a database that’ll fulfill all of your requirements?

Consider data modeling as the bridge between acquiring data and turning it into an actionable database, and learn how data modeling uses abstraction to represent and comprehend the nature of data flow within an organization’s information system.

What is data modeling?

The process of building a data model for the data to be stored in a database is known as data modeling (data modelling). This data model is a logical representation of Data objects, their connections, and the laws that govern them.

Models visualize data, enforce business rules, and comply with legal requirements and government regulations. Models guarantee data quality by consistent data in its naming structures, default values, meanings, and security controls.

The data model is essentially a blueprint for an architect’s building project. It’s a technique for documenting intricate software system designs in the form of a diagram that anybody can comprehend. The text and symbols show how data will be used to produce the diagram. It is also referred to as a software or application blueprint because it serves as the foundation for developing new software or re-engineering any existing system.

Metadata and data modeling tools aid in the development and documentation of models that describe the structures, flows, mappings, and transformations among data as well as the quality of the information.

To obtain an accurate picture, a data modeler creates a model of data relationships between the data elements and attributes. Data architects are also concerned with developing physical blueprints for databases.

The use of standardized schemas and formal data modeling approaches permits a uniform, consistent, and predictable approach to defining and managing data resources across an organization or even beyond.

Ideally, data models should be living documents that adapt to changing business demands. They’re crucial for ensuring that company processes run smoothly and that IT architecture and strategy are planned correctly. Data models may be shared with vendors, consultants, and/or industry colleagues.

What is data model?

The Data Model is a framework that organizes data description, data semantics, and consistency constraints. Instead of focusing on what operations will be done on the data, the data model emphasizes what data is required and how it should be organized. The Data Model serves as a building plan for architects, helping them build concepts and establish connections between pieces of information.

Advantages of data model

• A significant aim of a data model is to ensure that the functional team’s data objects are accurately represented.
• The data model should be detailed enough to serve as the basis for the physical database.
• The data model defines the relationship between tables, primary and foreign keys, and stored procedures.
• The data model supports businesses in communicating both inside and across organizations.
• The data model aids the ETL process by providing records mappings between documents.
• Assist the model is populated with correct data by recognizing valid data sources.

Disadvantages of data model

• To create a Data model, you must first understand the physical data stored properties.
• Data modeling is a method of generating complicated application development and management. As a result, it necessitates an understanding of biographical reality.
• Minor changes in the structure must be reflected throughout the entire application.
• There is no standard for data manipulation in a DBMS.

Types of data models

Data modeling aims to develop a visual representation of your raw data and its relationships. Data modeling necessitates dealing with three points of view on a data model.

Conceptual Model

The data model is a representation of the data that is required to enable business operations. It also tracks company activities and measures performance in conjunction with them.

Conceptual modeling is a method of representing information and its structure to explain the system as a whole. This form of Data Modelling is concerned with finding the data utilized by a company rather than processing flow. This data model aims to arrange and define business policies and concepts. It aids executives in seeing any data, such as market statistics, consumer insights, and purchase behavior.

Logical Model

The map of rules and data structures in the logical data model includes the relevant data, such as tables, columns, etc. Data Architects and Business Analysts create the Logical Model. We may use the logical model to convert it into a database.

This sort of Data Modelling is always included in the root package object. This data model serves as the foundation for the physical model. There are no secondary or primary keys in this example.

Physical Data Model

Implementing a physical data model is described using a particular database system. It lists all of the components and services necessary to construct a database. It’s generated using the database language and queries. The physical data model represents each table, column, and restrictions such as primary key, foreign key, NOT NULL, etc.

The essential function of the physical data model is to create a database. This model is created by the Database Administrator (DBA) and developers. The Data Modelling technique allows us to abstract away from databases and constructs the schema. This model explains how the data model is implemented in this situation. The physical data model aids in database column keys, constraints, and RDBMS capabilities.

Importance of data modeling

• A data model aids in the creation of a database at all three physical, logical, and conceptual levels.
• Stored procedures, relational tables, and foreign and primary keys are part of the data model.
• It’s a simple yet powerful tool that allows database users to see the data in its entirety. Database designers may use it to design physical databases.
• The data model depicts the most significant level of comprehension of business needs.
• The data model aids in the detection of duplicate and missing information.

Advantages of data modeling

• The data model assists us in identifying proper data sources to inhabit the model.
• The Data Model ensures that information is shared throughout the company.
• The data model aids in the documentation of the ETL process’s data mapping.
• Data modeling enables us to query the database’s data and get various reports based on it. Data modeling aids in data analysis by providing reports.

Data modeling techniques

Model types have evolved alongside database management systems, getting more complex as organizations’ data storage requirements have increased. Here are five types of techniques for arranging data:

Hierarchical Technique

In the hierarchical model, each node is connected to the next via a hierarchy of subordinate nodes. There is one root node or parent node, and the other child nodes are organized in a particular order. However, this model isn’t widely used these days. This method may be used to represent real-world model relationships.

Object-oriented Model

The object-oriented approach aims to build objects that contain stored values. The object-oriented approach enables data abstraction, inheritance, and encapsulation while also communicating.

Network Technique

The network model makes it possible to represent objects and their relationships more flexibly. It has a schema, which is a graph that depicts the data. An object is represented as a node with an edge connecting it to other nodes, allowing them to keep track of many parent and child records.

Entity-relationship Model

A business system is a collection of interrelated data and information. The ER model (Entity-relationship model) is a high-level relational model for describing data elements and associations between entities in a system. This abstract design provides a clearer view of the data, making it easier to understand. In this framework, the whole database is represented by an entity-relationship diagram, which comprises Entities, Attributes, and Relationships.

Relational Technique

Relationships are defined between the items, as well. There are several connections between the things, such as one-to-one, one-to-many, many-to-one, and many-to-many.

Data modeling process

Data modeling as a field invites stakeholders to scrutinize data processing and storage in great detail. Various conventions govern the use of tokens, how models are arranged, and how business demands are expressed in data modeling. All methods provide formalized processes with a series of activities that must be completed iteratively. These workflows resemble this:

1. Identify the people, places, and things that you want to monitor.
2. Define the critical characteristics of each entity.
3. Determine how entities are connected.
4. Map attributes to entities entirely.
5. Determine how many keys you’ll need and what degree of normalization is appropriate.
6. Validate the data model and conclude it.

Data model examples in real-life

To better comprehend the critical activities involved in generating accurate data models, study one or more real-world data modeling instances.

It would be best to start by defining your entities, the critical things of interest. Entities are the subjects about which you wish to keep records. For example, you might want to define an entity called EMPLOYEE for your employees since you must store information about everyone who works for your firm. You could also create a departmental entity called DEPARTMENT.

Next, you define entity primary keys. A primary key is a distinct identifier for an entity. You probably need to store a lot of data concerning the EMPLOYEE entity. However, most of this information (such as gender and birth date) would be a poor choice for the primary key. You could use a unique employee ID or number (EMPLOYEE_NUMBER) as the primary key in the DEPARTMENT entity scenario.

You may now define the relationships that exist between your entities. The primary keys are used to establish the connections. According to the relationship between EMPLOYEE and DEPARTMENT, employees are assigned to departments. 

You may add new attributes to the entities, their primary keys, and their relationships at this point. For example, you might define the following additional attributes for the EMPLOYEE entity:

• Birthdate
• Hire date
• Home address
• Office phone number
• Gender
• Resume

Finally, you standardize the data.

3 best data modeling tools

A large number of Data Modeling Tools are available for various database platforms. It’s difficult to choose one that meets the user’s demands. To make this time-consuming task more manageable, look at the three most popular Data Modeling Tools listed below.

ER/Studio

Idera’s ER/Studio is a data modeling software that allows you to identify and manage your company’s data assets and sources across several databases. It can build and share data models and track them from beginning to end. ER/Studio runs on Windows, Linux, and Mac computers. A comprehensive corporate lexicon may assist you in defining your business terminology, ideas, and relationships. With ER/Studio, businesses may rapidly model and comprehend the interaction between procedures, data, and people.

Key Features:

• It may be used for both logical and physical layouts.
• The tool does an impact analysis for new database changes.
• It’s an integrated development environment that supports both scripting and automation.
• The following display types are supported: HTML, PNG, JPEG, RTF, XML, Schema, and DTD.
• It ensures that models and databases are in agreement.

To learn more, go to the ER/Studio page here.

IBM InfoSphere Data Architect

InfoSphere Data Architect is a data integration design tool from IBM that simplifies and speeds up the process of connecting services, applications, data structures, and processes. It’s one of the most efficient Data Modeling Tools for aligning services, apps, data structures, and procedures.

Key Features:

• The software is simple to use and quick to create.
• Analytics capabilities allow you to understand data assets to improve productivity and reduce time to market.
• The primary goal of a document management system is to help teams work together and integrate.
• You may both import and export custom mapping.
• The program automatically detects the structure of diverse data sources by scanning metadata.
• Data models for both physical and logical data can be constructed.
• It’s also possible to connect with other solutions like data studio and query workload tuner.

To learn more, go to the IBM InfoSphere Data Architect website here.

Oracle SQL Developer Data Modeler

Oracle SQL Developer Data Modeler is a tool that aids in the creation of physical database architecture for the Oracle platform. Data analysis, study, management, and insights are all addressed. It’s a program that increases productivity and makes various data modeling tasks more efficient.

Key Features:

• The ability to build and update relational, multi-dimensional, and data type models will be available.
• It can do both forward and reverse engineering.
• The program is designed to support collaborative development via source code management.
• It’s one of the most powerful free data modeling software, and it works in both cloud and traditional environments.

To learn more about Oracle SQL Developer Data Modeler, visit the official website.

Metadata, in its most basic sense, is simply data about data. It’s a method for determining what your data means or represents. It generally includes a description of the data and key background information.

The definition of metadata is “a set of data that describes and gives information about other data.”

What is metadata?

It is information about a document or other digital content that helps describe it compared to other documents, similar materials, and similar objects. The document’s author might be specified, as the file size and the material’s data were first published. In a song it might include the artist’s name, title, and year of release.

It may be stored inside a file or in another location, like some EPUB book files that store it in an associated ANNOT file.

It is a term that refers to information about an item’s existence, such as who created it and when. It’s used in every industry and by people in many different ways, from data systems to social media to websites to software music services commerce. It can be generated manually or automatically based on the data, intentionally or automatically.

What is not metadata?

Metadata is data that describes other data, but it isn’t the actual data. The author and creation date metadata in a Microsoft Word document, for example, aren’t the whole file; rather, they’re just a few details about the file.

Unlike the data it describes, it is typically assumed to be public due to its lack of privacy. Because it does not provide access to the raw data, metadata may usually be freely disseminated since it provides no one with access. Understanding summary information about a web page or video file, for example, is enough to comprehend what the file is but not enough to view the entire page or watch the whole film.

For example, think of it as a card file in your childhood library that lists the details of a book; it isn’t the book itself. Examining a book’s card file may tell you a lot about it, but you must first open the book to read it.

Types of metadata

It is available in various forms and has a wide range of applications roughly divided into business, technical, social, and operational.

Today, metadata is all around us. Every component of the current data architecture and each user action generates it. Apart from the conventional sorts of like technical and business types (e.g., schemas), our data systems now generate entirely new metadata.

Metadata’s four main types

1. Technical (Definitional): Schemas, data types, models, etc.
2. Operational (Descriptive): Process outputs, lineage metadata, ETL, etc.
3. Business (Descriptive): Data tags, classifications, mappings to business relationships, etc.
4. Social (Descriptive): Data about user-generated content, user knowledge, etc.

Every piece of content includes relevant information. It is everywhere. There are several different types of it, and here are some examples of their use.

• Title, subject, genre, author, and creation date are a few examples of descriptive type.
• Copyright status, rights holders, and licensing terms are examples of usage rights metadata.
• Metadata includes file types, file sizes, creation time and date, and compression type. Technical metadata is frequently utilized for digital object management and interoperability.
• The preservation metadata is utilized in navigation. The location of an item in a hierarchy or sequence is an example of preservation metadata properties.
•  For navigation and interoperability, the data is included in Markup languages. Heading, name, date, list, and paragraph are examples of properties.

Metadata usage in different areas: Examples of metadata

Beyond its four primary types, it may be utilized in a wide range of applications, as we’ve previously said. Let’s look at how it’s used in some critical areas.

Social media

It is always at work in the background whenever you friend someone on Facebook, download music Spotify suggests for you, publish a status, share someone’s tweet, etc. Because of the metadata preserved with those items, Pinterest users may build collections of related articles.

It is useful in various social media scenarios, such as when you’re seeking someone on Facebook. Look at a user’s profile picture and a brief description to learn just the basics about them, and thanks to what metadata provides, you will learn everything you need about the person.

Computer files

Every file you save on your computer includes basic information about the file so that the operating system can handle it. You or someone else may obtain details from the metadata promptly.

When you view the properties of a file in Windows, for example, you can see the file’s name, type, where it’s stored, when it was created and last modified, how much space it’s taking up on the hard drive, who owns the file, and more.

Other applications can also utilize the data in the journal. For example, you might utilize a file search program to quickly discover all of your computer’s files created today and have a size bigger than 3 megabytes.

Website searches

Metadata is a vital aspect of any website’s success. It comprises a description of the site, keywords, metatags, and more, which influence search results.

Examples of it such as meta titles and meta descriptions are used to construct a web page. The meta title summarizes the subject of the website for those who browse it, allowing them to understand what they’ll get from it if they click through. The meta description is additional information that is nonetheless brief.

The title and description of your page are also two distinct types of meta-information used by search engines to group related elements. The results are relevant to your request when you search for a particular term or phrase.

The language of the page, for example, is also included in its metadata.

Why is metadata important?

The sum of all data’s metadata is known as data. It allows us to build a comprehensive picture of our data and fully comprehend it.

Let’s take a scenario. You’ve just introduced a new ice cream flavor, and you want to know whether it sells more in cities or rural areas. You would usually look at an Excel spreadsheet with current sales data.

It would be utterly perplexing if a meta-less version of this data were presented because you wouldn’t know what each column meant. That’s where the metadata catalog comes in handy.

Since businesses are spending more on and betting on data to make better decisions, we will only grow the amount of data we use. To extend data’s shelf life and longevity, organizations must also invest in metadata management.

What is metadata management?

Metadata management is a cooperative effort to establish how to describe data assets for conversion into an enterprise asset across organizational borders. As data quantities and variety increase, metadata management becomes more essential to extract economic benefits from the massive stockpiles of information.

Why is metadata management important? 

Metadata is essential for managing information because it may be utilized to understand, aggregate, group, and sort data. Metadata also plays a big part in identifying many data quality issues.

The demand for MDM is increasing due to the growth of data culture in business. They create a large amount of data and ingest it in huge quantities. Metadata management, which provides a clear and rich context for both scenarios, ensures that data becomes a vital company asset by defining what information should be produced and consumed.

It is essential in data management because it ensures organizations can answer questions about their data, maintain an audit trail for each record and document, and classify records with relative ease based on their information. Organizational metadata management is required as a result of these factors:

• Increased demand for data governance, regulatory, and compliance requirements, as well as data enablement
• Business value from data is gaining prominence as data quality and trusted analytics improve in importance.
• The complexity of data is increasing with new sources adding to the current ones.
• More company users are actively using data to conduct business activities.
• Increased pressure to speed up transformation efforts. Such as digitization, omnichannel deployment, and data modernization.

Current challenges

One of the most common issues facing companies is that despite understanding the value of metadata and having invested in its management, they have yet to receive a sufficient return on their investment.

Unfortunately, businesses have historically spent more time and money on manual, ad-hoc methods to handle their problems. The information would be shared verbally or by keeping Excel/doc files to document data in separate departments. The most common challenges are:

• It’s not known where the papers are—there’s a lot of information missing.
• No one updates the papers, significantly when people change jobs or retire—bad data is all over the place.
• No one knows how various data sets are connected or how to correct varying values across all of them. There is no way to determine where changes originated.
• There’s no way to keep track of all changes or versions of data.
• There’s no way to keep records of the data, resulting in even more silos and versions of reality.

To overcome these challenges, you should build your data retention policy. Do not know what is it? You can find everything you need to know about the data retention policy in our article.

It is possible that simply connecting an isolated metadata management solution or a metadata catalog to your data lake will not solve your data issues. Today’s corporate requirements demand that data be accessible to whoever needs it, whenever and however they need it—with all of the contexts they require.

Data is the currency of our future, and metadata is a guide on this road. Without data, companies will cease to exist. By embracing and utilizing data in your company, you will succeed in business life. It’s simply a question of if you’re willing to put in the time and effort required to overcome these stumbling blocks and discover the value of data.

Let’s start by answering the first thing that comes to mind: What is a data dictionary? A data dictionary, also known as a data definition matrix, contains comprehensive data about the company’s data, such as the definition of data elements, their meanings, and allowable values.

The dictionary, in essence, is a tool that allows you to convey business stakeholder needs in a way that allows your technical team to build a relational database or data structure faster. It aids in the prevention of project disasters, such as requiring information in a field for which a business stakeholder can’t reasonably be asked or expecting the wrong type of information in a field.

Data dictionary definition

It is a compendium of terms, definitions, and attributes that apply to data elements in a database, information system, or study portion. It explains the denotation and connotation of data elements in the context of a project and offers recommendations on how they should be interpreted.

A data dictionary also includes data element metadata. The information included in a data dictionary may help you establish the scope and characteristics of data elements and the management that governs their usage and application.

Why is a data dictionary important?

Data dictionaries are helpful for a variety of reasons. To summarize, they have the following characteristics:

• Assist in eliminating project data inconsistencies.
• Define conventions that will be utilized throughout the project to avoid confusion.
• Provide consistency in data collection and usage across the team.
• Make it easier to analyze data.
• Enforce the use of data standards

What are data standards?

Standardized data follow standards. Data are gathered, recorded, and represented in accordance with standards. Standards provide a common framework for interpreting and utilizing data sets.

Researchers in different fields must use comparable standards to know that the manner their data are collected and described will be consistent across different projects. Using Data Standards as part of a well-designed dictionary might help make your research data more accessible. It will guarantee that data will be identifiable and usable by others.

The key elements of a data dictionary

It is a document that explains the meaning of each attribute in a data model. An attribute is a database position that contains information. For example, if we wanted to represent the articles on this website, we could have attributes for article title, author, category, and content.

It is generally organized in a spreadsheet. The spreadsheet contains rows for each attribute, with columns labeled for each piece of information relevant to the attribute.

A data dictionary has two essential elements:

1. List of tables (or entities)
2. List of columns (or fields, or attributes)

Let’s look at the most frequent components included in the dictionary.

• Attribute Name – A distinguishing name that is used to identify each feature.
• Optional/Required – Whether information is necessary for an attribute before a record may be stored is indicated by the presence of this checkbox.
• Attribute Type – How you determine what data will be included in a field is defined by this setting. Text, numeric, date/time, enumerated list, look-ups, booleans, and unique identifiers are just a few possible data types.

A data dictionary may include the origin of the data, the table or concept in which the attribute is found, and additional information about each component.

Types of data dictionary

It’s possible to split the dictionary into two categories:

1. Active data dictionary
2. Passive data dictionary

Active data dictionary

When the data definition language (DDL) changes the database object structure, it must be reflected in the data dictionary. The job of updating the data dictionary tables for any modifications is solely that of the database in which the data dictionary is located.The data dictionary is automatically updated if created in the same database. As a result, there will be no mismatch between the actual structure and the data dictionary details. An active data dictionary is current at the time of writing this book.

System Catalog

It’s a term used to describe several things: the System Catalog, system tables, data dictionary views, etc. The System Catalog is a collection of system tables or views incorporated into a database engine (DBMS) to allow users to access data in the database. It also contains information about security, logs, and health.

Moreover, System Catalog has some standards, such as Information Schema.

Information Schema

The Information Schema is a popular System Catalog standard defined by SQL-92. It’s a unique schema named information_schema with preconfigured system views and tables. Despite being a norm, each vendor implemented this standard to some degree, adding its tables and columns.

Some of the tables in information_schema:

• tables
• columns
• views
• referential_constraints
• table_constraints

Passive data dictionary

Some databases include a dictionary in a separate, independent database only used to store dictionary components. It’s often saved as XML, Excel files, or other file formats.

In this scenario, a concerted effort is required to keep the data dictionary in sync with the database objects. A passive dictionary is what you’re dealing with here. There’s a chance that the database objects and the data dictionary won’t match in this instance. This kind of DD has to be handled with great sensitivity.

The passive data dictionary is distinct from the database and must be updated manually or with specialized software whenever the database structure changes.

A passive dictionary might be implemented in a variety of ways:

• A document or spreadsheet
• Tools
  • Data Catalogs
  • Data integration/ETL metadata repositories
  • Data modeling tools
• Custom implementations

Data dictionary example

You’re undoubtedly asking how everything fits together.

Here’s a look at an inventory list, a basic example of a data dictionary.

As you can see, a dictionary organizes critical information about each attribute in a business-oriented way. It also groups information that may be found in multiple documents and specifications, making it more straightforward for your database developer to create or change a database that fulfills company demands.

Functions of data dictionary

A dictionary may be used for a variety of things. The following are some important uses:

Data dictionary in database systems (DBMS)

The information about data structures is kept in special formats in most relational database management systems – predefined tables or views that contain metadata for each component of a database, such as tables, columns, indexes, foreign keys, and constraints.

A data-driven tool generates reports based on the database schema, including all parts of the data model and programs.

Data modeling

Data models can be constructed with the data dictionary as a tool. This may be accomplished using specialized data modeling software or simply a spreadsheet or document. In this instance, the dictionary acts as a specification of entities and their fields, assisting business analysts, subject matter experts, and architects in collecting requirements and modeling the domain. You’ll develop and deploy a physical database and application following this document.

Documentation

It is also possible to use a data dictionary as a reference and cataloging tool for existing data assets – databases, spreadsheets, files, etc.

With a few formats and programs, you can achieve this:

• You may export read-only HTML or PDFs from a DBMS with database tools.
• Excel spreadsheets that have been manually created and maintained.
• The data modeling tools utilize reverse engineering.
• Database documentation tools.
• Metadata management/data catalogs

All of these efforts are for healthy database management.

What is database management?

A database’s data can be organized, stored, and retrieved using Database Management. A Database Administrator (DBA) may use various tools to manage data throughout its lifecycle.

Designing, implementing, and supporting stored data to increase its value is the goal of database management. There are different types of Database Management Systems.

• Centralized: All data resides in one system handled by a single person or team. Users go to that one system to access the information.
• Distributed: The organization wanted a highly scalable system that allowed data to be accessed quickly.
• Federated: Data is extracted from your existing source data without the requirement for extra storage or replication of original material. It combines numerous independent databases into a single colossal item. This style of Database Architecture is ideal for integration projects involving many different types of data. The following are examples of federated databases:
  • Loosely Coupled: The relational structure of a component database is defined by its federated schema, which must be accessed via a multi-database language to access other component database systems.
  • Tightly Coupled: Components use separate processes to generate and publish into a connected federal schema.
• Blockchain: A decentralized database architecture that allows you to keep track of your finances and other transactions securely.

Do you think your data dictionary will lead the road to successful database management?

Times are changing. We are breaking new thresholds in managing data. But getting rid of old habits is easier said than done. Data silos, an institutional phenomenon, are still mushrooming in today’s increasingly connected and shared world focused on accessibility. Top companies are now busy breaking down data silos to converge operations and experiences. Various factors help emerge data silos at enterprises, including technical, organizational, and cultural. In any case, they endanger data security severely. We will probe what data silos are, how they arise, and their risks for enterprises.

What are data silos?

Silos are a challenge for modern data policies. A data silo is a collection of data kept by one department that is not readily or fully accessible by other departments in the same organization. They occur because departments store the data they need in separate locations. These silos are often isolated from the rest of the organization and only accessible to a particular department and group.

The number of data silos grows as the amount and diversity of an organization’s data assets increases. However, even though data silos sound like a practical approach adopted by departments with different goals, priorities, and budgets, they are not as innocent as they seem.

Where do data silos come from?

Data silos often occur in organizations without a well-planned data management strategy. But a department or user may establish its data silo even in a company with solid data management processes. However, data silos are most often the result of how an organization is structured and managed.

Many businesses allow departments and business units to make their own IT purchases. This decision frequently results in databases and applications that aren’t compatible with or linked to other systems, resulting in data silos. Another ideal scenario for data silos is where business units are wholly decentralized and managed as separate entities. While this is often common in big enterprises with many divisions and operating companies, it can also occur in smaller organizations with a comparable structure and management technique.

Company culture and principles can also cause the emergence of data silos. Company cultures where data sharing is not a norm and the organization lacks common goals and principles in data management, create data silos. Worse, departments may see their data as a valuable asset that they own and control in this culture, encouraging the formation of data silos.

Ironically, success can also lead to silos if not managed well. That’s why data silos are typical in growing enterprises. Expanding organizations must rapidly meet new business needs and form additional business divisions. Both of those situations are common causes for data silo development. Mergers and acquisitions also bring silos into an organization, and some may stay very well hidden for a long time.

What is the problem with data silos?

Data silos jeopardize the overall management of how data is gathered and analyzed, putting organizations at greater risk of data breaches. There’s a higher danger that the information will be lost or otherwise damaged since employees would be keeping data on non-approved applications and devices.

Siloed data frequently signals an isolated workplace and a corporate culture where divisions operate independently and no information is shared outside the department. Integrating corporate data can help bring down overly strict team structures where data isn’t shared and utilized to the company’s full potential.

When there’s limited visibility across an organization, members of different teams can do the same work in parallel. A shared, transparent data culture can avoid wasting time and resources.

When there are data silos, you may confuse permissions and information access hierarchy. The level and type of security provided might vary, depending on the silo. This can create a significant lag factor when benchmarking data or constructing a longitudinal study that revisits past material or incorporates data from various company sections. It jeopardizes productivity and lowers the return on investment for projects.

Silos can cause difficulties for data analysis since the data might be kept in non-compliant formats. Before any valuable insights may be obtained from it, standardizing the data and converting it into new interoperable formats is a time-consuming manual process.

The financial cost of silos is determined by the organization’s size, the effectiveness of its efforts to eliminate them, and whether they continue to develop. The most apparent cost is increased IT and data management expenditures.

How to dismantle data silos?

While data silos are easy to spot in small companies, it can be challenging to understand the number and full impact of data silos in large organizations. A brief survey sent to important data stakeholders throughout the organization might help identify siloes at their source.

Although cultural habits or hierarchical HR structures sometimes cause data silos, the technology an IT department employs might also contribute. Many existing systems may not be set up for data sharing or compatible with modern formats, and technological solutions might differ depending on departments. The key is to bring your data on a contemporary platform for sharing and collaboration via a simple interface. This may be a long-term initiative rather than a short-term fix, but it could pay off as an organization expands.

A great example of handling data from various data silos

For a long term fix, polypoly MultiBrand comes to your help. Let’s take customer data management as an example.

Today, companies have multiple touchpoints with customers. From all these points, channels and various sources, lots and lots of data flow. Data-privacy regulations such as GDPR prevents the group-wide customer journey from being recorded. This leaves companies in a one-way street, where they create maintenance-intensive and costly data silos, which is also a common problem for companies that own multiple brands.

What would you think if I told you that the companies can dismantle these data silos with the help of their customers?

By using the polyPod, a Super App infrastructure, this is possible. Let me explain to you step by step.

• Companies provide their customers the polyPod app.
• Users download their data from various data silos to their device. Thus, a detailed data set for this user is created across departmental and corporate boundaries. On the other hand, integrated consent management helps the user have more control over personal data.
• The company creates an incentive for the customers, encouraging them to add data from platforms such as social media, and correct their own data.
• This way, a sloppy data silo can turn into well-structred data, creating savings which then the company can pass on in parts as incentives or extra benefits.

By using polyPod app, companies can have additional benefits, like big data analyzing power from the same end devices its customers use. The app’s resource sharing function allows the consented party to use these resources for computing, which in turn lowers data intermediary and data center costs. The final benefit is the increased customer satisfaction due to transparency and data privacy.

Large-scale data gathering has numerous benefits for many sectors, including business intelligence and research. Large-scale data collection can provide essential insights for businesses, academics, and governments.

Analysts employ various techniques to aggregate data to develop predictions, assess processes, and influence decisions. This post will go over what aggregate data is and why it’s significant, provide several examples of its prevalent applications with exact quotes, and distinguish between disaggregate and aggregated data. After these, we will explain how to analyze aggregate data and find the importance of data aggregation in data mining.

Aggregate data definition

What is aggregate data? It referred to data gathered and reported at the group, cohort, or institutional level and is aggregated using techniques that preserve each individual’s anonymity.

An aggregate analysis produces a summary of data from several sources. Collecting relevant data from various locations or data aggregation may provide valuable insights. When assembling aggregate data, it’s critical to verify that the information is correct and complete since missing or misinterpreted details can affect the validity of your findings. It’s also essential to be sure you have enough accessible data and sources to back your claims and give intelligence for your analysis to succeed.

Comparison: Aggregate data vs disaggregate data

The distinction between aggregate and disaggregate data is subtle but essential. Aggregate data combines and summarizes information, whereas disaggregate data separate aggregated data into separate points or pieces of information. Disaggregating data might help gain a deeper understanding of various subsets within a larger dataset.

For example, a school district wanting to analyze standardized test results might separate data by concentrating on specific subsamples’ performance. Understanding how students perform against specific, targeted groups may assist them in optimizing their resource allocation and developing valuable initiatives. It can use as an example of aggregate data in education.

Importance of aggregate data

In our ever-changing, expanding, and maddeningly complicated technological world, data is constantly changing, growing, and becoming more complicated with each action taken. Data is one of the most critical currencies in today’s economy, but it’s essentially useless without organization, segmentation, and comprehension.

The extraction of insights that point to key trends and results and a greater understanding of the data make it valuable. Data aggregation allows businesses to achieve particular business goals or perform process/human analysis at almost any scale by searching, gathering, and presenting data in a summarized, report-based form.

The process of data gathering and condensing it into a summary form for statistical evaluation is known as data aggregation.

In addition, data can be aggregated over a specific time to provide statistics such as mean, minimum, maximum, total, and count. You may analyze the aggregated data to get insights about specific resources or resource groups after combining and recording it to a view or report.

Data aggregation types

Aggregation of data can be divided into two categories:

Types of aggregation by period

Time aggregation

Data on a single resource in a given period.

Spatial aggregation

A time period for which all data points for a group of resources are collected.

Types of aggregation with mathematical functions

• Sum: The sum of all the specified data is computed.
• Average: The sum of the data points is divided by the number of data points.
• Max: The highest value for each category is shown.
• Min: Displays the lowest value for each category.
• Count: The sum total of data entries for each category is counted.

Although there are many ways to aggregate data sources into a strategy, they all follow the same basic data acquisition and processing pattern.

Another critical point to consider is that data integration is a process that can be referred to as data ingestion.

What is data ingestion?

The process of moving data from one or more sources to a target location for processing and analysis is known as data ingestion. This data may come from various places, including data lakes, IoT devices, on-premises databases, and SaaS applications, before ending up in various target environments like cloud data warehouses or data marts.

Data ingestion is a fundamental technology that allows companies to make sense of data’s ever-increasing amount and complexity. We’ll go deeper into this subject to help organizations get more value from data ingestion. Types of data ingestion, how data is ingested, the distinction between ETL and data ingestion, tools for data ingestion, and more will all be discussed.

Data ingestion types

There are three types of data ingestion, each with its pros and drawbacks. Real-time is the most common method, followed by batch ingestion. In a lambda architecture, you can utilize either real-time or batches for data intake in combination. Business goals, IT infrastructure, and financial restrictions determine which one to use.

Real-time data ingestion

Data ingestion in real-time is collecting and transferring data from source systems in real-time using technologies like change data capture (CDC).

Batch-based data ingestion

The method of collecting and transferring data in batches at defined intervals is called batch-based data ingestion.

Lambda architecture-based data ingestion

Lambda architecture is a data ingestion solution that uses both real-time and batch techniques.

Data ingestion tools

Data ingestion tools are software solutions that collect and transfer structured, semi-structured, and unstructured data from source to target systems. These technologies automate the ingestion of previously time-consuming and manual processes. Data is transferred along a data ingestion pipeline, a chain of processing steps that takes data from one place to another.

There are many different types of data ingestion solutions available.

How to choose data ingestion tools?

To choose the solution that best suits your needs, you must weigh several factors and make an informed decision:

• Format: Is it coming in as structured, semi-structured, or unstructured data?
• Frequency: In real-time or in batches?
• Size: What’s the amount of data an ingestion tool is required to handle?
• Privacy: Is there any sensitive data you need to conceal or safeguard?

Ingestion tools aren’t limited to one way of performing data ingestion. Every day, for example, they may move millions of records into Salesforce. They can also ensure that several apps exchange data regularly. Ingestion tools can also supply information from marketing sources to a business intelligence platform for additional analysis.

Once you know which tool is suitable for you, you can use the data aggregation process in education, health, and research.

What is the importance of data aggregation in data mining?

In data analysis, aggregation is finding, collecting, and presenting data in a summarized form to perform statistical analysis on business methods or human patterns. When data from several sources are collected, it’s important to get accurate information to get valuable results.

You may use data aggregation to help you do innovative marketing, financial, pricing decisions, etc. Statistical summaries are used to replace aggregated data groups. Aggregated data stored in the data warehouse can aid one in solving logical issues, which can assist in reducing query time strain. In that data mining process, the backbone is data aggregation, and it requires data aggregators.

Data aggregators and analysis of aggregate data

A data aggregator is a software program used in data mining to gather data from several sources and then process it and extract useful information into a preliminary form. They play an important part in improving customer information by serving as a middleman. It also aids in the search for data instances relating to a specific product when the consumer requests them.

The data team gathers the information, which is then used by the marketing team to personalize messaging, offers, and other elements in the customer’s digital interactions with the company. It also aids any business’s product management staff to determine which products generate more money. Financial and company executive teams also utilize the aggregated data to help them decide how to allocate their budgets between marketing and product development initiatives.

Aggregate data examples

Companies can use aggregate data in a variety of ways across many industries. Here are some instances of how a firm, government, or researcher might utilize aggregate data:

Pharmaceutical trials

Another situation where aggregate data is crucial is in pharmaceutical trials. It is an example of aggregate data in healthcare. When pharmaceutical firms develop new drugs, they frequently devote significant resources to assessing their effectiveness, safety, and adverse effects. Researchers conduct clinical studies to observe the drug’s impact on various population segments. They may use data from many patients to understand better how a drug works by merging or grouping it.

Buyer metrics

Another advantage of aggregate data is businesses life may use it to analyze essential metrics like client engagement, website visits, and user demographics. Knowing the characteristics of one or even a few consumers isn’t very beneficial to firms searching for better insight into their target audience.

Companies may use big data to gain critical insights into their consumers and purchasing patterns by combining numerous data points from many sources. Marketing teams can utilize this information to tailor messaging, create bespoke discounts, and improve targeting techniques. Product organizations might also use aggregated consumer data to determine the most popular products or services.

Financial analysis

The usage of aggregate data in financial analysis is critical. Many money and investment firms utilize data to generate suggestions, forecast market movements, and detect events or shifts in public sentiment that might influence a company or an economy.

Their data is frequently obtained from news headlines, article content, and market data. Financial experts may use their accumulated sources to develop well-informed expectations about a company’s or product’s financial performance.

Government policy

Governments frequently rely on demographic data to guide their policy decisions. They may look at vital indicators, including employment rates, income levels, and public health statistics, to assess the health and well-being of their populations.

A government might use data from various sources after a natural calamity to figure out how many people were forced from their homes or were harmed in some manner. They may then utilize the information to provide additional aid to areas that require it.

Academic research

Researchers should start with data and build their thesis when writing a thesis. For example, researchers use statistics from individual people over time when analyzing divorce rates in a nation. The researchers then extract other components of their thesis from the dataset.

We can give a lot of aggregate data examples in various sectors. The vital point is that the data aggregate is essential for every individual, firm, or government that wants to succeed using data.

Is data science becoming more important every day? We believe you already know the answer.

Enterprise Risk Management (ERM) refers to businesses’ techniques and procedures to manage hazards and seize opportunities to achieve their goals.

ERM is an architecture for risk management that comprises five main elements: Continuity of operations, prevention and detection, response, mitigation, and recovery. It follows a structure based on identifying key events or circumstances connected to the organization’s goals (threats and opportunities), assessing their probability and consequence, determining a reaction strategy, and monitoring the process. Business organizations safeguard and create value for their stakeholders, including owners, employees, consumers, authorities, and society as a whole, by detecting and addressing risks and opportunities.

What is Enterprise Risk Management (ERM)?

Enterprise Risk Management (ERM) is a company-wide plan to identify and prepare for risks, especially those involving the company’s finances, operations, and goals. Managers can use ERM to define the overall risk posture of the firm by requiring certain business segments to engage or disengage with specific activities.

Types of enterprise risks

There are many different categories that companies must consider when managing their risk. The major enterprise risks are as follows:

Financial risks

All enterprise risks may have various costs or lost income, depending on the type. On the other hand, financial risk concerns money flowing in and out of your company and the chance for financial loss. For example, suppose a company grows overseas. In that case, fluctuating currency rates might expose it to a financial risk that should be considered, as they will influence the amount of money it receives. Businesses can’t achieve their goals without sound financial management. It is critical to anticipate economic risks, evaluate the consequences of those risks, and be prepared to react or prevent harmful scenarios.

Strategic risks

While day-to-day operations are crucial, managing long-term objectives is just as important. External risks, often known as strategic risks, are events or circumstances that, if they occurred, would be significant enough to alter the strategic course of a company, its future success, or failure. Every company is vulnerable to both positive and negative strategic developments. 

Compliance risks

Industry laws, rules, policies, and best practices are in place by various government agencies to guarantee ethical business operations. Compliance with these standards is critical to ensure that organizations are not held liable for any damages or injuries caused by their products. Failure to do so can have significant financial and legal consequences, posing security threats to achieving business goals and running as a whole. While the legal systems in different nations might differ somewhat, they must generally balance one another and their conflicting interests. Today’s globally connected and fast-paced world, on the other hand, may generate new rules and regulations at any time.

Operational risks

Incidents or unexpected events may happen at any time, regardless of how well routine tasks are tested. Operational risk is defined as the potential for loss due to faulty internal processes, people, systems, and external events. Examples are catastrophic events such as global crises, IT systems failure, data breaches, fraud, personnel loss, and litigation. From a business perspective, determining what needs to be done on any day is complicated enough. When conflicts emerge, organizations must know the daily functions, processes, and systems vital to their operations to resolve them and maintain company stability.

Reputational risks

With their stakeholders, including investors, employees, and customers, every business has a reputation to preserve. Organizations’ decisions and instances where they are accountable might result in negative media coverage and negatively impact brand reputation. Reputational risk has grown increasingly severe in recent years, in part due to the growth of social media, which allows for almost instantaneous worldwide communications that make it more difficult for firms to manage how they are perceived. It is critical to understand the risks to reputation and deal with them.

Health and safety risk

Regardless of the sort of workplace, health and safety concerns may be presented in various ways. The first step is to identify hazards, such as physical, ergonomic, chemical, and biological dangers. Assessing the risks and putting appropriate protection measures in place to ensure that employees are safe and cared for physically and mentally are critical. The workplace’s health and safety policies are the most effective means of protection and dependability.

Difference between Traditional and Enterprise Risk Management

Traditional and Enterprise Risk Management are two methods for dealing with the risks. While they are based on similar approaches, there are several important yet subtle differences between them.

The distinction between insurable and non-insurable risks is one of the most important differences between Traditional Risk Management (TRM) and Enterprise Risk Management (ERM). TRM focuses only on insured risks. ERM focuses on non-insurable risks such as war or data breaches. These are problems with the potential to be very costly, and no amount of money can compensate for them. ERM frameworks are designed to identify these possible hazards and select the best response strategy to prevent these types of scenarios from recurring.

TRM is generally done after an event has occurred and is intended to prevent it from happening again. Enterprise Risk Management, on the other hand, focuses on the future and attempts to forecast potential events and circumstances that may or will happen. After this, a strategic approach is produced to reduce the risk of that occurrence in the first place, as well as how to deal with it if it does happen.

Enterprise Risk Management frameworks

There are many critical Enterprise Risk Management frameworks, each of which offers guidance on how to identify, assess, respond to, and monitor risks and opportunities both inside and outside the company’s internal and external environments. Risk responses for specific hazards determined and evaluated by risk management may include:

• Avoidance: Ceasing risk creating business activities
• Reduction: Taking steps to reduce the chance or impact of a risk
• Alternative Actions: Considering alternative measures to reduce risks
• Share or Insure: Sharing or transferring a portion of the risk in order to finance it
• Acceptance: No action against the risk because of a cost/benefit analysis

There are many different Enterprise Risk Management frameworks and standards employing approaches in use today. The most popular of these are Casualty Actual Society (CAS), COSO ERM, ISO 31000, and RIMS Risk Maturity Model (RMM).

How technology changes Enterprise Risk Management?

The influence of information technology on various areas of our life, such as learning, marketing, business, entertainment, and politics, has been tremendous. Risk management is one of the domains that has been greatly impacted by this transformation since it is largely based on data. The IT allows companies to automate all of the steps from risk identification to monitoring. The new technologies that are being utilized, such as Big Data, analytics, mobile apps, cloud computing, enterprise resource planning (ERP), and governance risk management systems, are quite essential for risk management. These technical advancements provide opportunities for companies to further reduce their risks.

The foundation of less sophisticated and less costly applications like office automation tools such as Microsoft Excel, PowerPoint, and SharePoint, which are used extensively in large, medium, and small companies for risk tracking and reporting reasons, is the first fundamental change brought about by information technology. There are several fundamental threat modeling techniques developed by well-known service providers such as Microsoft and additional programs like CORAS threat modeling.

Many organizations actively scan social media postings for timely insights on customer service, product quality, and service delivery. Social media content that is widely and immediately accessible gives important insights into customers’ opinions on the firm’s goods and services, allowing organizations to avoid reputation harm by providing management solutions that may be used to address service and product quality concerns quickly before they can do significant reputational damage.

Many organizations already have massive databases, and many IT departments are actively engaged in connecting these with existing applications to gain even more value from their IT investments. Many databases include risk data points that can be mined, or absorbed by more powerful computing platforms to offer even greater organizational value over time. To help execute such efforts, CIOs now employ electronic data warehouses (EDWs), Big Data, business intelligence (BI) applications, and information analytical technologies.

Organizations may also benefit from data mining techniques to forecast component or equipment failure, identify fraud, and even estimate company profits through the use of data analytics. Prediction is the process of analyzing trends, classifying objects, pattern matching, and relating events. You may make a prediction about an event by looking at past occurrences or situations.

Although Big Data is primarily thought of as a technology used by large companies, many benefits can be derived from big data technologies by small and medium-sized companies (SMEs).

Big Data benefits for SMEs at a glance

One of the primary benefits of Big Data is the ability to gain insights into customer behavior that would not be possible with traditional data analysis methods. Big Data technologies make it possible to quickly analyze large volumes of data, which can be used to identify trends and patterns that would not be detectable with smaller data sets. SMEs can then use this insight to improve their products and services and better meet the needs of their customers. Additionally, SMEs can use Big Data to enhance marketing efforts, target customers more effectively, and create a personalized customer experience.

SMEs can also use Big Data to improve their operations. For example, companies can use Big Data to optimize business processes, identify areas of waste or inefficiency, and improve decision-making. Additionally, Big Data can help SMEs better understand their customers and the markets in which they operate. This understanding can then be used to make more informed business decisions and improve the competitiveness of SMEs.

Overall, big data provides small and medium enterprises with many opportunities to improve their businesses and compete more effectively in today’s economy. While Big Data technologies may seem daunting at first, they can be quickly learned and used to significant effect by SMEs. With the right tools and resources, SMEs can harness the power of Big Data to improve their bottom line and stay ahead of the competition.

You may be wondering whether these Big Data approaches are that complicated and elaborate and if they’re only appropriate for big businesses. The answer is no. Let’s take away the idea of volume (or amount of data) from the definition of Big Data, and it becomes transferable and applicable in a small or medium-sized business setting. Thanks to the decline in technology costs and innovative tools that provide new methods to interact with databases, SMEs can obtain much more valuable insights data from their data.

Deeply understand customers

With the help of a range of communication channels and in-house data we have today, it is possible to capture consumer behavior and interpret it. For example, to anticipate their future purchases, all you have to do is analyze their buying habits. Information shared on social media must also be considered.

It’s also critical to understand how to ask the right questions when conducting Big Data analyses. Answers to these questions might enhance your existing services or develop your following best-selling product.

Optimize operations

Data analysis allows for better management of the distribution chain, allowing you to redirect your efforts to sectors that need it. In a nutshell, using Big Data means modifying your company’s operations plan.

Some companies will alter their services to consumers, while others will sell their data to third parties. And here we are talking about 80 percent of unstructured data that companies will have in 2025 according to an IDC report.

Just remember, it’s critical to figure out what the issue is and how you’ll address it before beginning a data analysis project. I should also mention that you may need to enhance your company strategy.

Discover upcoming trends

One of the Big Data benefits for SMEs is to discover the trends. Big Data is replacing the gut instinct for good. It takes out the guesswork and helps identify and track behaviors and patterns to forecast where things are heading, how demand will change over time, and what will influence it.

Social networks create trending topics by mining their data. And as such, Big Data can form trends by looking at retail, online, and offline customer behaviors, comparing them to external conditions, and capturing patterns between them.

Know your competitor

Understanding your competition is another area where Big Data is better than your gut instinct. Today you have a better chance to predict some of the things your competitors are doing, as financial data, product trends, or social media analysis results can be easily accessed through the internet. But, Big Data offers more by examining more than a team of people can do faster and more precisely.

It would be best to keep in mind that your competitors can do all these about your business. So being first and prioritizing your Big Data investment might give you a headstart.

Remember that it’s also easy for your competitors to glean more information on your business than ever before. There’s no way around this, but you can stay one step ahead by keeping up-to-date on the latest big data technologies and uses.

There is more to Big Data benefits for SMEs from an industry perspective, and I plan to cover these topics soon. Until then, I would recommend reading these related articles:

• Top 6 trends in data analytics for 2022
• Unstructured Data Will Be Key to Analytics in 2022

Data sovereignty is becoming a hot topic, but almost all articles on the subject and the bulk of the proposed solutions focus on consumers and their ability to own their data. Enterprise data sovereignty is a different beast, but it operates on the same principle – making data “free.”

By free, we don’t mean “without charge or compensation.” Free in this sense means available and actionable data to all business units, departments, and territories. That may seem like a utopian organizational vision, but true enterprise data sovereignty is achievable. However, reaching the point where managing, monetizing, and unlocking the value of arguably every organization’s most valuable enterprise asset comes with significant challenges and changes to the status quo.

Enterprise data sovereignty requires implementing modern approaches to management and architecture, global regulatory compliance, and new policies for data ownership.

What is enterprise data sovereignty?

Enterprise data sovereignty (EDS) means different things to different organizations. But the commonality is that it’s about data ownership, access, and monetization. At its most basic level, EDS boils down to these three points:

• Data availability and sharing within the organization
• Metadata management
• Data monetization

For example, if the finance department needs to aggregate data from all over the world to perform its job function, it should have ready access to that data. If you consider all of the different business units in an organization, they need their metadata repositories to control who has access to what data within their department. If you consider the enterprise as a whole, it should be able to monetize its data for re-use or sell it through an open marketplace.

Underpinning all of these points is compliance with local legislation governing trade and the use of personally identifiable information (PII), not just within the organization’s control but also globally. The primary driver for enterprise data sovereignty isn’t to share data with all employees, departments, and business units at will. It’s about enabling the company to function in different jurisdictions worldwide while respecting local laws.

The bottom line is that having large amounts of your company’s data locked up in data islands isn’t good for business. The only way to unlock it is all at once, which requires a new approach.

Why do we need enterprise data sovereignty?

The primary motivation behind EDS is the demand for global access to information. It becomes exponentially more challenging to manage data when adding more data sources and people who need access. Just as scale creates technical challenges, it also creates governance and security issues.

Although each group of users might not need all of the data in their silos, they do need the ability to access the relevant information they require when it’s needed. Allowing groups to both control what information they’re granted access to without having IT interfere while also allowing that information to be centrally managed for policy compliance is the only way to go.

Then there are regulatory considerations, which come in different flavors globally. GDPR rules apply to Europe (no matter how flawed they may be), but other countries such as Vietnam and Indonesia have complex and challenging data privacy laws. The Equifax data breach in 2017 is a clear-cut example of the need for enterprise data sovereignty. Because of its cross-border nature, the data was easily accessible to anyone who cared to try and had access credentials.

What are the benefits of enterprise data sovereignty?

EDS enables your company to operate in different regions more seamlessly. For some businesses, having the option to store data locally means circumventing oppressive currency exchange rates. Others might have compliance rules that require them to keep certain types of information within the host country, so EDS is their only way to access it.

Likewise, companies can use EDS as a differentiator for business growth by using geographic data sets. This is especially true for ecommerce companies who can sell a product in a particular region if they know where their customers live.

What are the biggest challenges of enterprise data sovereignty?

EDS requires greater visibility into what information is being accessed and by whom. Each department or business unit should specify exactly who can access its metadata and when, but this has to be done within the context of a globally compliant system. It’s not enough to safelist IP addresses or trusted business units because it becomes too easy for someone to get around this by using a VPN or other workarounds.

Another challenge is that EDS requires an entirely different architecture from traditional data storage and management systems. Instead of a network of isolated database servers, EDS has to function globally while still allowing individual groups the autonomy they need. Centralized management is excellent for tracking access and compliance, but it’s not conducive to enabling rapid data sharing.

How do you implement an enterprise data sovereignty strategy?

Enterprise data sovereignty isn’t just about talking the talk; it’s about walking the walk. And that means having a global system in place that will enable your company to operate as needed wherever you’re required to do business.

To start with, you need an encryption solution that allows your data to be encrypted at rest and in motion. This is foundational for enterprise data sovereignty because, without it, you don’t have anything to define what’s being shared, with whom, or how you should handle it.

Next up is having a system that will facilitate enterprise data integrity. This means ensuring the data can only be accessed by authorized users and applications while not allowing sensitive information to fall into the wrong hands.

Finally, you need to monitor access and enforce policies through an authentication solution. This is crucial because it allows your data sovereignty strategy to be audited, which helps ensure compliance and protects against breaches.

Enterprise Data Sovereignty: the time is now

Enterprise data sovereignty will continue to trend upwards at a considerable rate. It’s early enough to start planning, building, and implementing the new systems to deliver the evident and apparent benefits, but don’t wait too long. Your competitors will be moving quickly too, and with data being the most valuable commodity on earth, those who act now will reap the substantial rewards soonest.

Cooperatives are best understood as groups of people with identical or highly similar needs. These needs have traditionally included insurance, money lending and saving, achieving economies of scale, or marketing goods. With data being one of the most valuable commodities on earth (if not the most valuable), it is only natural to apply cooperative principles to the use of information.

The first recognized cooperative business is the “Philadelphia Contributionship for the Insurance of Houses from Loss by Fire.” It was established in 1752 with none other than Benjamin Franklin as one of its founders, and it is still in operation today.

The dictionary defines a cooperative as “a jointly owned enterprise engaging in the production or distribution of goods or supplying services, operated by its members for their mutual benefit, typically organized by consumers.” Of course, the dictionary definition does not tell us the guiding principles that make cooperatives successful. It also doesn’t explain whether they stand the test of time and can be applied to data sovereignty as successfully as we used them for insurance and savings. 

The seven cooperative principles

Cooperatives worldwide generally operate according to the same core principles and values, adopted by the International Co-operative Alliance in 1995, which in turn were based on those laid out by the Rochdale Society of Equitable Pioneers in 1844, otherwise known as “Rochdale Principles.”

Voluntary and open membership

Anyone can join a co-op – they don’t discriminate based on gender, social, racial, political, or religious factors.

Democratic member control

Members control their business by deciding how it’s run and who leads it.

Members’ economic participation

All co-op members invest in their cooperative so that people, not shareholders, benefit from a co-op’s profits.

Autonomy and independence

When making business deals or raising money, co-ops never compromise their autonomy or democratic member control.

Education, training, and information

Co-ops provide education, training, and information so their members can contribute effectively to the success of their co-op.

Cooperation among cooperatives

Co-ops believe working together is the best strategy to empower their members and build a robust co-op economy.

Concern for the community

Co-ops are community-minded and contribute to the sustainable development of their communities by sourcing and investing locally.

Applying 19th-century cooperative principles to 21st-century data

Everyone should have a right to privacy, especially on the internet. It has become impossible to move around on the internet without leaving traces of personal data. And in what seems an unfair twist, we give that information away for free while giant corporations make billions from our data at the expense of our privacy.

If we want a fairer solution, we all have to be part of it. It’s easy to see how most cooperative principles, including voluntary and open membership, democratic member control, economic participation, and concern for the community, are the cornerstones of such an endeavor.

And let’s be clear – nobody suggests that data collection is inherently evil or wrong. Think of all the services that you enjoy using – ordering food, calling a ride, streaming movies, or paying with one click. For all of these things, data is required. It’s not just about the pleasant but also about the necessary, such as fighting a global pandemic or coordinating aid in the event of a disaster.

The problem is we don’t control our data. Monopolies like Facebook, Google, and Amazon do. They have built empires based on the information we give away for free in return for personalized services. This not only harms us, but it also harms all citizens, and our economy, which is why a cooperative fits the solution so ideally – an opportunity for us to come together and take back control of our data.

Of course, any cooperative concerned with the fair use of personal information needs to work with those organizations that require data to provide their services, and that’s where the fourth and sixth principles become essential.

Another way to think about principle four – autonomy and independence – is that if the cooperative enters into agreements with other organizations, including governments, or raises capital from external sources, it should do so on terms that uphold democratic control by their members and maintain their cooperative autonomy.

Similarly, principle six – cooperation among cooperatives – will become crucial to building a worldwide data sovereignty movement. Cooperatives serve their members most effectively and strengthen the cooperative movement by working together through local, national, regional, and international structures and partnering with other co-ops.

Finally, education, training, and information – the fifth principle – are more critical now than ever. While data privacy, security, and sovereignty are undoubtedly more mainstream than ever before – thanks to whistleblowers like Brittany Kaiser and Frances Haugen and documentaries such as The Great Hack and The Social Dilemma – there’s a long way to go in educating the world’s five billion internet users.

Thanks to their structure, cooperatives offer the ability to give away knowledge at scale in ways that a single organization simply can’t.

Standing the test of time

There are few examples of anything created in the 18th and 19th centuries that still apply to the modern world, but clearly, the seven cooperative principles are an outlier.

Not only do they form a foundation upon which to build a strong cooperative, but they also offer a blueprint for every data stakeholder – consumers, businesses, corporations, organizations, governments, regulatory bodies, and more – to create a fair solution for all.

Cooperatives offer a unique opportunity to bring privacy back in an era where we have almost given up on the idea of returning control of information to the consumers that provide it.

Now, all that remains is for enough people to want to see – and be – the change and for the platforms and solutions to enable a data sovereignty revolution. 

Towards the end of 2021, I spoke with Julio Santos, the technical cofounder of Fractal – creators of the Fractal Protocol – on some of the essential topics in data sovereignty, privacy, and security. 

As with my organization, polypoly, we have concerns over the free access significant companies have over your data and how to keep the web open, practical, and accessible for everyone while regaining control of our information. 

You can read part one of this deep dive in full. We discussed, in detail, Facebook, data sovereignty, and the flaws in regulations such as GDPR. And now, we’ll plunge in again for the second part of this conversation, where topics like data income plans and why it is vitally important to redress the balance and know as much about governments and organizations as they know about us.

Here’s a recap of the final statement from part one of the conversation for context.

Dittmar: 

“When it comes to health data, we are not an expert in it. We are an expert in decentralized data systems. But there are experts out there, who maybe would like to use a decentralized solution, but have no clue how to build these kinds of technology. And so our role is to create the underlying infrastructure, and everybody else can sit on top of that and interact with the user. The idea of the polyPod is that it is extendable. Everybody can build features for the polyPod. If the user wants to have it, they can download that feature and use it or not, depending on whether the user likes that feature or trusts the supplier.”

Santos:

Does this mean data never leaves the polypod?

Dittmar:

For example, if you were managing a fleet of shared cars, you would want to know the schedule of citizens tomorrow, when they will leave their homes for work, and so on. 

Therefore, one way to achieve this is to expose them to sensible data, which they are not likely to do. Another way is to send an untrained model to a federated AI platform. And then, during the night, millions of these networks, or millions of pods, will train that model. So that early in the morning, we’ll train the model. For instance, if you can predict the fleet’s timings, costs, and best routes, you will have a better commute to work.

Data sovereignty and trust

Santos:

Agree. And that also means you’re saving a lot of it off the cloud. And you don’t have substantial IP risks, such as hacking, because everything is done locally.

But how can the user trust these events, algorithms, models, and computation tools sent to the edge and sent to their devices? Is there a vetting process for who was involved in that?

Dittmar:

We will bring this to life at the beginning of 2022. It’s like an app store. And basically, everybody can open such a feature repository. An NGO like the Chaos Computer Club can access such a thing, and they can certify their stored features, so if you trust these kinds of NGOs more than us or more than the government, you can go to this depot then download the elements from there. Also, huge companies like Adidas or Nike can build something like that and have all the features of their products stored here. 

We talked about trust and education earlier. Besides educating people, there’s another ingredient needed to make our data economy understandable for non-tech people. One important aspect is that confidence in the virtual world should work like trust in the analog world.

The trust mechanisms we have in the digital world look completely different. First of all, trust usually is zero or one – if you have a certificate for your HTTPS connection, you trust it or not. And the certificate is typically made by somebody you have never heard of. 

It is always global, and trust for normal human beings is always subjective. For example, I’m using our insurance company for a straightforward reason, because my mom said 30 years ago, go there. And I trust my mom when it comes to money. That’s the way we are building trust – it is emotional. So that means my trust, my personal trust in a company, in a future developer, in somebody who wants to use my data, or in another person, is always subjective. 

If I install our feature, whether you build one now or in the future depends highly on my trust, but also when other organizations or friends, who are trusting you, who have had a fantastic experience with your product.

The ranking of features is not dependent on Google Ads anymore; it is based on your trust and your influence sphere. 

That also means that if a government likes our position on physician informatics, they can be sure they’re acting responsibly for securing the IT systems that store that highly sensitive information. They can publish, with full transparency, explaining that they looked at these features and certify them. 

For example, for features that allow citizens to request data from governments (GDPR is relevant here) when it comes to saying “please send me all the data you store about me,” they can show and state clearly that they trust this feature or this company, and show why. That means that if somebody acts incorrectly, such as selling your data to another company without permission, they can say with absolute clarity and evidence they don’t trust them anymore. 

And these will have an immediate impact on the whole ecosystem because it’s something that happens in real-time. It is always good to understand how we think they’re transposing working mechanisms from the real world to the digital world.

My privacy is your privacy

Santos:

I have a question about how your privacy is connected to other people’s privacy. We’ve started to realize that the concept of personal data is sometimes a little bit blurry. Often data that’s about you is also about someone else. So, for example, if you and I are known to spend time together, and I’m sharing my location, but you’re not, then I am violating your privacy. At Fractal, we’re working on the concept of privacy, preserving data sharing, and one of the ways that we can make that work is by grouping users in different cohorts or different unions based on these privacy preferences to make sure that these externalities aren’t randomly placed on people who aren’t ready to accept them.

If you have any thoughts on this idea, I wanted to know that personal data is sometimes a bit blurry, and it applies to more than you, and if polypoly has taken this into account in any way.

Dittmar:

That’s an old-fashioned problem; we had images in the analog world. So when somebody took a picture with the two of us, it’s precisely the same problem. There are no rules for that in place. Implementing the laws exactly as written is a different story, but you can use them as a guide. It is a good idea to find out how that works in the real world here, too. 

We, as tech people, should not try to implement something better than the real world. First of all, we should try to implement something like the real world because it’s easy to understand. Nevertheless, you’re right. It needs to be as simple as “is it my data, your data, or our data?” And then, there’s a fantastic protocol called the Open Digital Rights Language (ODRL). 

That’s about how to model rights for digital assets. So, it was initially made for digital rights management (DRM). You have an acquisition, and this comes with a policy that includes what you are allowed to do and what is forbidden, and what kind of duties are coming with these purchases.

What you just said about these duties is interesting. If you’re sharing your location and this is close to my location, you are only allowed to do so if you fulfill those duties. 

But at the end of the day, something like this scenario (if it is your location and my location simultaneously), we should find a way to control that. Because the way we want to do it is maybe different than others would like to do it. There cannot be a static solution for something like that. It makes people aware that if they share their location, that means as long as we are in a meeting together, they will share my location. 

So your system, taking care of your private sphere, should be aware that I’m close to you and then send a notification before you can share your location. Is it okay? If I’m saying yes, it’s fine. And if I’m saying no, then both of us will get notified on our phones. 

Santos:

I like your point of view – looking at what has already been deployed in the real world. I think there’s a big difference here, which is scale. Like the fact that I have a picture, an analog picture of you and me, my ability to distribute is quite limited compared to having a digital device with the internet in front of me. 

So I think the additional friction that the analog world brings us is possibly even beneficial for many use cases, and perhaps some stuff will need to be tweaked, reinvented for the digital sphere. But yeah, I agree with your point in general. And again, it takes us back to education and making people aware of what is going on. 

I’ve got a question about user compensation, which I believe polypoly isn’t thinking about right now. Our approach with Fractal Protocol is to compensate the users for their data. So first, we offer blockchain token incentives, just for them to provide data, there’s no sharing in that moment, and then we layer revenue on top of that from an actual buy-side. 

I wanted to understand from your perspective, what are the tradeoffs involved in paying users for data?

Rewards and incentives: it’s not all about the money

Dittmar:

There is a Digital Income Plan. But it will take a while before we bring that to life. We spent a lot of time thinking about this mechanism. And if you pay people for access to the data, you’re creating an incentive for, you know, getting naked in some way. 

People who are as privileged as we are can say – I don’t need these few cents. I will keep my privacy. But what is in it for people who are more in a less privileged position? Now, if we are creating our new system for data economy, we should build it from scratch with suitable incentive mechanisms for all. 

What we would like to do instead of paying people for giving access to their data is to pay people for renting out computing power in the context of the data. At least here in Europe, people often have a lot of computational power because they’re spending some money on Playstations, smartphones, and laptops – around €1 trillion is invested in hardware every three years. However, some reports suggest that these devices only use a fraction (daily) of the possible computing power.

Usually, these many different devices are waiting for us to use them for a few minutes or hours. If you’re combining this computing power when those devices would otherwise be dormant, this is an unbelievable asset that can help make all our vision happen. 

If you want to change the economy, that will cost a lot of money. If you can activate 1% of these unused assets, that’s already a billion. Yeah. From our perspective, incentivizing people to share their computing power, generally in the context of their data, but later on also for other things, is a different incentive than getting paid for giving access to data. And it is more socially balanced.

For decades, managing data essentially meant collecting, storing, and occasionally accessing it. That has all changed in recent years, as businesses look for the critical information that can be pulled from the massive amounts of data being generated, accessed, and stored in myriad locations, from corporate data centers to the cloud and the edge. Given that, data analytics – helped by such modern technologies as artificial intelligence (AI) and machine learning – has become a must-have capability, and in 2022, the importance will be amplified.

Enterprises need to rapidly parse through data – much of it unstructured – to find the information that will drive business decisions. They also need to create a modern data environment in which to make that happen.

Below are a few trends in data management that will come to the fore in 2022.

Data lakes get more organized, but the unstructured data gap still exists

There are two approaches to enterprise data analytics. The first is taking data from business applications such as CRM and ERP and importing it into a data warehouse to feed BI tools. Now those data warehouses are moving to the cloud, with technologies like Snowflake. This approach is well understood, as the data has a consistent schema.

The second approach is to take any raw data and import it directly into a data lake without requiring any pre-processing. This is appealing because any type of data can be funneled into a data lake, and this is why Amazon S3 has become a massive data lake. The trouble is, some data is easier to process than others. For instance, log files, genomics data, audio, video, image files, and the like don’t fit neatly into data warehouses because they lack a consistent structure, which means it’s hard to search across the data. Because of this, data lakes end up becoming data swamps: it is too hard to search, extract and analyze what you need.

The big trend now and a continuing data trend for 2022 is the emergence of data lake houses, made popular by DataBricks, to create data lakes with semi-structured data that does have some semantic consistency. For example, an Excel file is like a database even though it isn’t one, so data lake houses leverage the consistent schema of semi-structured data. While this works for .csv files, Parquet files, and other semi-structured data, it still does not address the problem of unstructured data since this data has no obvious common structure. You need some way of indexing and inferring a common structure for unstructured data, so it can be optimized for data analytics. This optimization of unstructured data for analytics is a big area for innovation, especially since at least 80% of the world’s data today is unstructured.

Citizen science will be an influential, related 2022 trend

In an effort to democratize data science, cloud providers will be developing and releasing more machine learning applications and other building block tools such as domain-specific machine learning workflows. This is a seminal trend, because, over time, the level of what individuals will need to code is going to decrease. This will open up machine learning to many more job roles: some of these citizen scientists will be within central IT, and some will live within lines of business. Amazon Sagemaker Canvas is just one example of the low-code/no-code tools that we’re going to see more of in 2022. Citizen science is quite nascent, but it’s definitely where the market is heading and an upcoming data trend for 2022. Data platforms and data management solutions that provide consumer-like simplicity for users to search, extract and use data will gain prominence.

‘Right data’ analytics will surpass Big Data analytics as a key 2022 trend

Big Data is almost too big and is creating data swamps that are hard to leverage. Precisely finding the right data in place no matter where it was created and ingesting it for data analytics is a game-changer because it will save ample time and manual effort while delivering more relevant analysis. So, instead of Big Data, a new trend will be the development of so-called “right data analytics”.

Data analytics ‘in place’ will dominate

Some prognosticators say that the cloud data lake will be the ultimate place where data will be collected and processed for different research activities. While cloud data lakes will assuredly gain traction, data is piling up everywhere: on the edge, in the cloud, and in on-premises storage. This calls for the need to, in some cases, process and analyzes data where it is, versus moving it into a central location because it’s faster and cheaper to do so. How can you not only search for data at the edge, but also process a lot of it locally, before even sending it to the cloud? You might use cloud-based data analytics tools for larger, more complex projects. We will see more “edge clouds,” where the compute comes to the edge of the data center instead of the data going to the cloud.

Storage-agnostic data management will become a critical component of the modern data fabric

A data fabric is an architecture that provides visibility of data and the ability to move, replicate and access data across hybrid storage and cloud resources. Through near real-time analytics, it puts data owners in control of where their data lives across clouds and storage so that data can reside in the right place at the right time. IT and storage managers will choose data fabric architectures to unlock data from storage and enable data-centric vs. storage-centric management. For example, instead of storing all medical images on the same NAS, storage pros can use analytics and user feedback to segment these files, such as by copying medical images for access by machine learning in a clinical study or moving critical data to immutable cloud storage to defend against ransomware.

Multicloud will evolve with different data strategies

Many organizations today have a hybrid cloud environment in which the bulk of data is stored and backed up in private data centers across multiple vendor systems. As unstructured (file) data has grown exponentially, the cloud is being used as a secondary or tertiary storage tier. It can be difficult to see across the silos to manage costs, ensure performance and manage risk. As a result, IT leaders realize that extracting value from data across clouds and on-premises environments is a formidable challenge. Multicloud strategies work best when organizations use different clouds for different use cases and data sets. However, this brings about another issue: moving data is very expensive when and if you need to later move data from one cloud to another. A newer concept is to pull compute toward data that lives in one place. That central place could be a colocation center with direct links to cloud providers. Multicloud will evolve with different strategies: sometimes compute comes to your data, sometimes the data resides in multiple clouds.

Enterprises continue to come under increasing pressure to adopt data management strategies that will enable them to derive useful information from the data tsunami to drive critical business decisions. Data analytics will be central to this effort, as well as creating open and standards-based data fabrics that enable organizations to bring all this data under control for analysis and action.

This article on data analytics was originally published in VentureBeat and is reproduced with permission.

According to IDC, IoT data is forecasted to reach 73 zettabytes by 2025, while a recent study by Deloitte estimates that 40% of IoT devices will be capable of sharing location in 2025, up from 10% in 2020. This means time and space data is the fastest-growing big data category this decade. 

The next few years will see the geospatial technology industry experience rapid growth and change. More location-aware devices and services will expose the world to how technology can utilize data across time and space. Early adopters that take advantage of this will have a vast market opportunity within their respective industries, while slower organizations will risk getting left behind. The key to being an early adopter will be to understand the following: the trends behind this market opportunity, the need for new analytics technology, and the crucial role of the cloud in leveling the playing field.

Time and Space Data: The Rise of Geospatial Insights and Analytics 

The global geographic information systems (GIS) market will be more than double to $13.6 billion by 2027. Three particular industry trends create this.

1. The cost of sensors and devices that collect geospatial data is falling rapidly.
2. The expansion of 5G networks will accelerate IoT deployments. 
3. The cost of launching satellites is falling on a per-kilogram basis, meaning more satellites will be gathering data with a spatial dimension.

A new breed of analytic geospatial capabilities is becoming widely available in the market, allowing more organizations to begin experimenting with geospatial data and analytics. Opportunities abound across industries such as proximity marketing in retail, smart grid operations management in energy, real-time patient tracking in healthcare, fleet optimization in logistics, and autonomous driving in automotive.   

Out With The Old (Traditional Databases) and In With The New (Vectorization) 

As more organizations begin experimenting with geospatial data and analytics, they must understand the need for new analytics technology to successfully process and analyze massive amounts of data in a fast and reasonable amount of time. The current generation of massive parallel processing (MPP) databases for big data analytics simply weren’t designed to handle the speed, unique data integration requirements, and advanced spatial and temporal analytics on data across time and space. The result is slow decision-making, a lack of critical context, and sub-optimized insight. On top of that, using prior generation databases for spatial and temporal data analytics is expensive due to inherent compute inefficiencies, forcing organizations to explore new approaches and technologies. 

Vectorization, which accelerates analytics exponentially by performing the same operation on different data sets at once for maximum performance and efficiency, is one such approach. This method is particularly adept at functions required to perform advanced calculations on time-series and geospatial data, giving organizations full context and results in seconds where traditional analytics took hours. Early adopters that recognize the ability to analyze and track real-time data through many fused sensors enabled by vectorization will have a vast market opportunity within their respective industries. At the same time, slower organizations will risk getting left behind. The idea of using advanced technology such as vectorization and focusing on data with a spatial component may seem daunting and only relevant for big tech companies. However, like other once-flashy technologies such as containers and blockchain, vectorization could soon be the next “must-have” for every organization in the next few years. 

Yet Another Reason to Move to the Cloud

However, organizations should be wary that properly utilizing the onslaught of geospatial data isn’t something that teams can handle in-house. Traditionally, only the most significant organizations (think Fortune 100’s or government agencies) have had the resources to leverage the advanced computing needs (like vectorization) such as high-end computing processors and primitives from NVIDIA and Intel. Furthermore, companies used those initiatives almost exclusively for deep learning and virtual reality simulation projects, using cases that focused on far-sighted research vs. business objectives.

Organizations that invest in new sensor hardware will rightfully be wary of spending even more funds on advanced chips of their own. Instead, they should turn to major cloud service providers like Microsoft Azure. As-a-service databases are readily available and easily capable of leveraging vectorized computing processors for common big data analytics workloads such as time series analysis, location intelligence, visual scenario planning, and other forms of complex mathematics at a scale that incoming geospatial data will fuel.

The Future of Time and Space Data 

As data across time and space continues to rise, organizations must also ensure they are set up with a database that is designed to process and analyze massive amounts of data in a fast and reasonable amount of time. These two elements will be vital to unlocking opportunities, innovations, and instrumental in organization-wide transformation. 

The power of geospatial data lies in answering “where” questions: Where do organizations have exposure to supply chain or regulatory risk? Where should organizations improve product selections to increase sales? Beyond telling us where things are, analyzing data through the lens of location provides organizations new information to make better-informed decisions and enhance performance. The future for organizations across all industries entails taking advantage of geospatial data capabilities.

Big data has already transformed how many industries operate. Now that the pandemic has accelerated digital transformation around the globe, the field has grown faster than most could have predicted. This unprecedented growth will undoubtedly bring considerable disruption in 2022.

Big data will disrupt industries further as new challenges and opportunities arise in the upcoming year. Here are five of the most significant changes professionals can expect in 2022.

1. Big Data Becomes a Matter of Foreign Policy

Governments will regulate big data more closely as it becomes a larger industry. This trend has already begun to take shape with laws like the GDPR and China’s Data Security Law, but government interest will expand in 2022. China’s recently announced plan to triple its big data industry by 2025 is a sign of things to come.

Big data will become a foreign policy issue as more governments take steps to regulate the industry and support their local sectors. Nations may start to draw lines and issue digital trade restrictions relating to the industry. Operations will have to navigate increasingly complex regulatory issues as a result.

2. Big Data Optimizes Recruiting and Training

Businesses in 2022 will apply big data more heavily to recruitment amid widespread worker shortages. The Marine Corps has announced it will use big data to match recruits to roles where they’re best suited. Other organizations will likely employ similar tactics as capitalizing on available workers becomes more important.

Passive candidates make up 70% of the workforce, and big data analytics can help companies recruit workers they wouldn’t find otherwise. Similarly, organizations will use information to personalize training programs and maximize their staff’s potential. These operations will help mitigate worker shortages and boost productivity.

3. Real-Time Analytics Sustains E-Commerce

Another big data application that will grow in 2022 is real-time analytics, specifically in e-commerce. It has skyrocketed throughout the pandemic, and brands will have to capitalize on big data to make the most of it. Real-time analytics can help online stores market more effectively and optimize shipping routes for greater customer satisfaction.

Map data can already define deliverability polygons, which inform what steps are necessary for deliveries, and real-time analytics can take these further. In 2022, e-commerce delivery routes will update in real-time according to traffic patterns, weather developments, and other factors. Companies and their logistics partners will then reduce expenses and vastly improve efficiency.

4. Data Poisoning Grows More Severe

One of big data’s most significant applications is machine learning. Already, 50% of surveyed companies have implemented ML in at least one function, and that number will only grow. As additional businesses rely heavily on these models, data poisoning will become a more relevant and severe problem.

Data professionals preparing for 2022 must anticipate a wave of data poisoning attacks. Companies must understand these threats to improve security around their machine learning models and data pools. If cybersecurity standards in 2022 don’t adapt to meet these threats, machine learning may cause more harm than good.

5. The Rise of Green Data Centers

As big data demands rise, so will their impact on the environment. With climate change growing increasingly severe, more companies will look for ways to use big data sustainably in 2022. Namely, green data centers and renewable energy facilities will become more popular.

Businesses that transition to green data centers early could gain more loyalty from eco-conscious consumers. Companies may force government pressure to use these facilities in some areas as sustainability becomes a larger focus for world politics.  This transition may cause initial disruption for the industry, but it will ensure success and protect the planet in the long term.

Big Data Is Reaching New Heights in 2022

Big data has already made impressive strides in its relatively short history, and it’s only going to keep growing in 2022. This means there are still many disruptions ahead before the field reaches maturity. Changing technologies, social trends, and legal developments will reshape big data and how companies use it.

These five shifts represent the most significant disruptions that will likely come to big data in 2022. If companies and data professionals can prepare for them now, they can ensure success in the future.

Big data, specifically with the help of artificial intelligence (AI), empowers insurance companies to make better financial decisions. Data science can help mitigate fraudulent claims, enhance risk management, optimize customer support, and predict future events, among many other benefits. The result is higher profits for insurance companies and lower premiums for their customers. 

In this article, we’ll look at three ways big data can help insurance companies manage their losses and protect their customers and why this is so beneficial for both parties. 

Detecting insurance fraud

Insurance fraud causes an estimated $34 billion worth of lost revenue for insurance companies. Detecting insurance fraud is difficult, as a thorough investigation can be very time-consuming and yield vague results. Typically, insurance fraud involves deliberate damage to an insured item or a staged event to trigger an insurance payout.

Insurance companies must consider this lost revenue when pricing out premiums for customers, which results in a higher overall price for insurance coverage. Unfortunately, like in many aspects of life, law-abiding citizens end up paying the price for the actions of a few dishonest individuals. 

In some cases, the cost of insurance prohibits some individuals from having it at all. In Canada, for instance, only 33% of adults with children report having a life insurance policy. Life insurance ownership is higher in the US at 52%, but this is still barely half of the country. 

But now, with technology giving insurance companies the tools to avoid losing money on fraudulent claims, life insurance can be more affordable for everyone. For example, big data combined with AI can create a virtual catalog of legitimate insurance claims and those discovered to be fraudulent. 

By using algorithms, you can detect similarities between fraudulent claims to “red flag” potentially fraudulent claims for further investigation. Image analysis can also pinpoint whether photos have been altered or time stamps have been changed in any way. 

Furthermore, AI can detect anomalies in a customer’s claim by providing an in-depth look at a variety of factors. For example, for an automobile insurer, AI can quickly and accurately analyze the reported location of an accident, the position of the vehicles, the speed of the crash, and the time of the incident. They can also detect inconsistencies by factoring in additional data such as reports from involved parties, injury details, vehicle damages, weather data, doctor’s notes, and prescriptions, and notes from law enforcement or auto body shop workers. 

Predictive analytics for risk management

In the past, insurance companies relied on broad-scale data for risk assessments. One commonly known fact is that young men pay higher insurance rates than young women or older men. This is based on statistics that show that teenagers, specifically those that are male, are more likely to drive above the speed limit or engage in risky behavior when behind the wheel.

Basing premiums on factors such as gender has met with some pushback for being discriminatory. However, developments in predictive analytics can help eliminate this issue by creating insurance rates that are customized for the individual. 

For example, the Snapshot device by automobile insurer Progressive can be hooked up to a customer’s car to provide personal data about the driver. Data like the rate of speed, amount of short stops, and the average amount of driving time and distance covered can be used to create a more accurate risk assessment for the individual driver. 

However, using big data to assess the lifestyle and habits of individuals comes with legitimate data privacy concerns for consumers. Insurance companies who want to use telematics devices such as Snapshot must take care to protect customer data privacy as they gather, store, and utilize user data. Depending on the country or even state the insurance company operates in, data breaches or compromised customer data can result in legal action or hefty fines.

Big data in health insurance

Big data is perhaps the most useful in health insurance scenarios when a variety of different factors can influence a patient’s risk of health concerns. For example, in the Affordable Care Act, federal legislation regarding health insurance premiums in the United States, health insurance companies can charge smokers a premium up to 50% higher than other patients. This is based on statistics that show that smokers are more likely to need extensive medical treatment due to the damage tobacco smoke causes to the lungs.

Health insurance companies can now gather sensitive health data through many other methods, such as smartwatches (such as FitBit) or health apps on mobile phones. They can also factor in a customer’s online behavior when paying out claims or detecting potential fraud. If, for example, a client reported having an expensive medical procedure on a particular day during which he was also very active on social media, this may raise red flags for further questioning. 

A group of former NBA players recently revealed how easy it is to commit health insurance fraud, racking up $3.9 million in fake claims, $2.5 million of which were paid out. The group’s scheme was discovered when one filed a claim for a pricy dental procedure in Beverly Hills during the same week he was playing televised basketball in Taiwan. Digital travel itineraries, email correspondence, and publicly available box scores helped prosecutors prove the fraud in court. 

Conclusion

The amount of data gathered by governments and corporations about individuals is a cause of concern for many. However, when placed in good hands and used for beneficial purposes, big data and AI can increase insurance companies’ profits and lower premiums for customers.  

By leveraging the power of AI to interpret large swathes of data, insurance companies can more accurately pinpoint fraud. They can also use this information to engage in predictive analytics that can help accurately assess risk levels. This all results in an insurance plan that is genuinely custom-fit for your lifestyle, providing rewards for your good behavior and ensuring you are covered for whatever life may throw at you in the future – as predicted by AI.

Many company representatives commit to moving massive amounts of data to a new location and anticipate numerous advantages. For example, a small business could become more competitive after transferring some content to the cloud. Managing data on-site is a labor-intensive exercise that often becomes prohibitively challenging.

Off-site service providers typically have the infrastructure needed to help customers scale up as their needs change. However, moving data elsewhere is not always the reasonably priced option people expect. That’s one reason why executives who are initially eager to proceed with migration are later shocked by the bill. 

Cost-control measures exist, and they’re easier to implement once people understand the factors that drive up migration prices. Here are four reasons why moving data is often such an expensive venture. 

People Lose Track of How the Data Travels

Many people who need to move their data don’t realize there are associated costs with certain types of travel. Additionally, customers typically don’t realize their content doesn’t progress in the straightforward way they might imagine.

For example, people pay to move their data to Amazon Web Services (AWS), as well as between Amazon’s various storage offerings. The same is true if clients need to move their information across multiple AWS availability zones or regions. 

A related issue is that people don’t always look for less-expensive alternatives that may be appropriate, particularly if they don’t need many additional services. They embrace the name recognition AWS enjoys and quickly assume it must be the best option, if not necessarily the most budget-friendly one in all cases. 

Increasing Data Accumulation

Individuals interested in moving their data often want to take advantage of related technologies, such as edge computing. It distributes the cloud’s functions and can be particularly advantageous for companies that often use Internet of Things (IoT) devices. 

However, as the number of internet-connected devices rises, so does the data they gather. Besides charging customers migration fees, companies have specific rates based on how much storage space a client requires. As a service collects and keeps more information, both those rates will increase. 

One study showed that 80% of enterprise customers that moved to the public cloud began transferring some of their data elsewhere within two years of the initial migration. In such cases, people often realize that splitting data between the cloud and on-premise facilities is more cost-effective. 

Moreover, people don’t always take the time to compress files and check for duplicates before moving their data. They likely won’t when dealing with massive amounts of digital material due to the labor involved. However, such oversight tends to bring higher costs. 

Migration Roadblocks Can Raise Costs

A successful data migration effort takes time, effort, and coordination. Encountering challenges along the way can increase the overall expenses associated with moving the information to its new location. A company may try to cut costs by going with the least expensive migration partner they can find. However, that decision could prove costly if the service provider makes numerous mistakes.

Gartner analysts recently described how dependency bottlenecks could pose expensive problems, particularly if companies don’t perform complete application assessments. It’s crucial to understand how apps relate to each other, especially if moving some while keeping others in their current locations. 

Roadblocks can also crop up if people don’t calculate how their workforce sizes could affect data movement and storage budgets. For example, some technology providers offer storage solutions that charge monthly per user. That could result in millions of dollars of unexpected costs in a large organization with tens of thousands of employees. However, doing business with providers that have fixed rates based on the number of data centers or servers required can keep expenses manageable. 

Migration Costs Vary Significantly Depending on Client Needs 

Another matter that often gets overlooked when moving data is the substantial amount of variation depending on what a customer wants and needs. For example, recent rates for migrating data to a virtual server in Microsoft Azure ranged from 0.018-60 cents per hour, depending on factors like the amount of memory and cloud storage space desired. 

Azure also offers virtual hard drives for clients and charges them for every 100,000 transactions. However, people mention that it’s not clear what constitutes a transaction. 

Many cloud providers offer migration assistance for a fee, too. It could be worth the money for a smaller company that does not have a readily available on-site team or has not secured a service provider already. However, anyone considering using it should weigh the costs to determine the most reasonable option. 

Moving Data Could Still Prove Worthwhile

This overview highlights why data migrations often cost more than people expect. However, it doesn’t mean decision-makers should automatically hesitate due to the expenses. Moving information is often necessary to meet other company objectives. Fortunately, practical ways exist to make the costs as low as possible. 

Doing extensive research to determine all the associated expected costs and potential unplanned expenses is an excellent way to prepare. That puts people in better positions to choose the best options for their situations.

This year a significant event will take place: somewhere in the world, the billionth CCTV camera will be installed. This means that a camera already monitors every seventh person on the planet. And in some cities, more than a million cameras are already in use, making the ratio even more impressive.

That’s a great deal of surveillance. But cameras are used for more than just security. They also help businesses ensure quality control of processes, improve logistics, get better product placement, recognize privileged customers the moment they enter the sales area, and so on.

RAIDIX sees the usage of video analytics tools for enterprise tasks as an appealing challenge, so they have developed a line of solutions based on:

• scalable video archive with zero point of failure architecture and the most reliable RAID in the industry;
• high-performance storage system, which will significantly increase the speed of training models;
• high-performance solutions for edge infrastructures;
• mini-hyperconverged solution.

RAIDIX offers three types of solutions that can be used in high-performance infrastructures:

• centralized solution based on high-performance RAIDIX ERA engine, NVMe drives and high-performance network from NVIDIA:

• a centralized solution for creating video archives that provide the highest access speed and availability of large amounts of data:

• RAIDIX ERA-based solution for edge infrastructures:

• mini-hyperconverged platform for smaller projects:

Below there is a closer look at implementing a video archive in modern installations.

Industry Challenges and Storage Requirements

Video surveillance projects face new challenges at the data storage level. These are not only large requirements for bandwidth and storage capacity but there are also changes in the type of load on the storage system.

Now, most of the workload falls on these tasks and processes:

• continuous random write operations from multiple cameras and video servers;
• unpredictable random read operations of the video archive on demand;
• high transactional load on databases;
• high-speed work with memory for analytics.

In addition to managing the variety and intensity of these storage workloads, scalability is critical to accommodating new cameras and continually increasing resolutions. Also, to meet the growing needs of video surveillance, companies need high-performance, reliable, and efficient storage systems.

Solution: NAS and…?

Large video surveillance projects go well beyond network video recorders and storage on video surveillance servers.

Modern VSS requires an enterprise-grade infrastructure with separate servers and storage units. The layered approach allows for increased processing power, faster I / O processing, and increased throughput and capacity.

With these requirements in mind, enterprise storage systems are dominated by two architectures:

• NAS: stores data as files and presents these files to the application as a network folder;
• SAN: looks like local storage, allowing the operating system to manage the disk.

In the context of video surveillance applications, these two approaches are polar.

Recently, SAN has become the preferred option for enterprise VSS. Sure, NAS technology does a good job for many tasks, but multi-camera, database, and analytics recording workloads require performance that requires a direct connection or SAN approach. IHS forecasts show that the SAN market will grow by more than 15% in 2020-2022, while the NAS segment’s annual growth will drop from 5% to about 2%.

For this reason, video surveillance software vendors recommend local or SAN-attached storage.

Also, many video surveillance projects operate in virtual environments. In these cases, each virtual video surveillance server requires high-performance storage not only for its video content, but also for the operating system, applications, and databases.

Make it VSS (Viable Simple Storage) 

Clearly, both SAN and NAS are easy to use, and the deployment steps applying to them are almost the same since both architectures may require Ethernet-based connectivity (although SANs can use other media such as FC) so that files and directories can be accessed from multiple systems. These solutions should use file locking to prevent multiple systems from modifying files at the same time.

Since many video surveillance systems do not require common video sharing, all this file locking and the complexity of the shared file system is unnecessary overhead that limits performance and adds complexity to maintenance and protection.

Deduplication and compression, also offered by many NAS and SAN systems, are unnecessary for video surveillance solutions. Choosing a solution with these features incurs additional costs for unused technologies. These useless features built into the software negatively impact overall performance and require maintenance to ensure safety and reliability.

Storing data at different levels can be useful when deploying video surveillance. However, video surveillance software already knows how to manage this, as it can create separate storage for databases, real-time recording, and archives. As long as the data is managed by video surveillance software, there is no need for storage in the storage system to move data between tiers dynamically. Consequently, data tiering or automated management is not required as a storage function and also increases risks and complexity.

Why SAN is effective

Most scalable file systems require multiple servers for their functioning. Solutions with multiple servers, in their turn, require an internal network, which can create the following problems:

• Each write operation creates a series of data transfers over the internal network, which limits performance;
• peer-to-peer connections create more potential points of failure, which can make it harder to increase storage or replace equipment;
• while achieving the same redundancy levels as the SAN, scalable file systems provide less bandwidth.

SAN solutions for VSS are also offered by RAIDIX. These solutions are based on software RAID, capable of performing checksum calculations faster than any similar solution in the industry. Also, RAIDIX supports various SAN protocols (iSCSI, FC, iSER, SRP), which help to achieve a number of goals:

• providing high bandwidth (up to 22GB / s) to work with thousands of high-resolution cameras that can be connected through dozens of video servers;
• cost-effective maintenance with an increase in the number of cameras and in archive depth: due to the use of proprietary RAID-array technologies, fewer disks are required to obtain the required storage volume and performance;
• vertical scalability up to 11PB per storage system due to the ability to work with large RAID groups of up to 64 disks and provide failover for two or more disks (when using RAID 7.3 / N + M), as well as combining these groups into a single volume;
• high reliability of data storage when using RAID 7.3 or RAID N + M, the most fault-tolerant RAID-arrays on the market, which makes possible the use of large disks (up to 18-20TB) without compromising data safety. With an increase in the volume of disks and their number in a RAID array, the likelihood of data loss increases sharply, as the reliability of the disks decreases as well. So, the probability of data loss for RAID6 of 24 18TB disks after one year in operation is 1%, while for RAID 7.3 it is only 0.001%;
• stability of operation during sudden increases in workload due to sufficient performance headroom, even in situations where drive failure coincides with peaks of intensive work of the video surveillance system. This is achieved thanks to unique technologies of proactive and partial reconstruction;
• the high performance of RAIDIX storage system does not limit the capabilities of analytical software for video surveillance. Face recognition, motion capture, and other video analytics functions will work without downtime and with minimal latency;
• the possibility of using the obtained video surveillance data simultaneously not only in security tasks, but also in business tasks for carrying out various analytics. It does not require additional copying operations to analytical systems, while the use of smart prioritization due to QoSmic technology allows users to avoid the influence of additional storage tasks on the main recording function;
• building an enterprise-level architecture without a single point of failure: RAIDIX 5.X supports dual-controller operation with possible replication to remote systems.

Where to start choosing an archive storage system?

When calculating and selecting an archived data storage system, the following parameters should be considered:

• type of cameras and their number;
• archive depth in days;
• additional retention requirements (if any);
• the intensity of movement in the frame, its distribution over the time of day or depending on events;
• type of network infrastructure, its need for updates;
• how the video analytics software is deployed;
• whether it is required to use the resources of cloud infrastructures;
• when and what kind of upgrade is expected (type and number of cameras, list of services, depth of the archive, etc.)

For a basic calculation, one can use the calculators available at specialized software vendors’ websites. For a more accurate calculation in complex projects, the participation of professionals will be required.

In addition, there are two important points to consider when calculating.

Firstly, calculating desirable characteristics of data storage systems should be carried out with the worst-case scenario in mind: the maximum load in case of failure of storage system components, controllers, and drives. Unfortunately, this is what usually happens in real life: with an increase in the load, physical components begin to fail as their capabilities reach the limit.

Secondly, the volume of drives is gradually increasing, but their performance is still the same, and classic RAIDs simply cannot make it. We need technologies that will ensure the availability of large data volumes over the long term. However, with the mass adaptation of the two actuarial accumulators, this will soon change.

Thus, the elements of a modern video archive are:

• large volume drives (16-18TB);

• two or more controllers;

• high-performance access interfaces (FC> 16GBps, Eth> 10GBps);
• controller software that allows easy scaling of the volume without service downtime, makes it possible to survive the failure of multiple drives without losing performance and at least one storage controller, and is also adapted to continuous recording.

Conclusion

The demand for video surveillance projects is steadily growing and entails demand for solutions that create fault-tolerant storage systems. The two main approaches to media storage that the enterprise segment is targeting are NAS and SAN. The second type of configuration seems to be more optimal for video surveillance projects because of its higher performance, the ability to function in different environments, and the use of a large number of servers. For customers looking for high performance and fault tolerance, RAIDIX provides advanced SAN storage solutions based on fast software RAID.

In general, modern data storage systems provide a great number of options, and the user’s task is to determine what is important and to avoid overpaying and bringing unnecessary loads on the system. For example, video surveillance does not actually require storage tiering or automatic management as a storage function. At the same time, this does not mean that the choice of data storage systems should be a piece of cake: there are about a dozen software and hardware-related factors you should pay attention to. Also, when calculating performance indicators and fault tolerance of future storage systems, one should always focus on the worst possible scenario, which is the maximum load in case of storage system components’ failure.

If there’s one topic that has captured the public’s attention consistently over the decades, it is this: have aliens visited Earth, and have we caught them in the act on camera? Unidentified Flying Objects (UFOs) and Unidentified Aerial Phenomena (UAPs) tick all the boxes regarding our love of conspiracy theories, explaining the unexplainable, and after-hours conversation starters.

As with many things in life, data may have the answer. From Peter Sturrock’s survey of professional astronomers that found nearly half of the respondents thought UFOs were worthy of scientific study, to the SETI@Home initiative, which used millions of home computers to process radio signal data in an attempt to find alien communications, UFOs and UAPs continue to fascinate the world.

However, the scientific community seems to have a dim view of studying these phenomena. A search of over 90,000 grants awarded by the National Science Foundation finds none addressing UFOs, UAPs, or related topics.

But the tide may be turning.

A US Intelligence report released in June 2021 (on UAPs specifically – the US military is keen to rebrand UFOs to avoid the “alien” stigma associated with the UFO acronym) has rekindled interest within a broad audience.

Among other findings, the report noted that 80 of the 144 reported sightings were caught by multiple sensors. However, it also stated that of those 144 sightings, the task force was “able to identify one reported UAP with high confidence. In that case, we identified the object as a large, deflating balloon. The others remain unexplained.”

UAP data requires new ways of working. The ability to fuse, analyze, and act on inherently spatial and temporal data in real-time requires new computing architectures beyond the first generation of big data. 

Vectorization and the quest to identify UFOs/UAPs

Enter “vectorization.” A next-generation technique, it allows for the analysis of data that tracks objects across space and time. Vectorization can be 100 times faster than prior generation computing frameworks. And it has the attention of significant players, such as Intel and NVIDIA, which are both pointing towards vectorization as the next big thing in accelerating computing.

NORAD and USNORTHCOM’s Pathfinder initiative aims to better track and assess objects through the air, sea, and land through a multitude of fused sensor readings. As part of the program, it will be ‘vectorizing’ targets. One company helping to make sense of this is Kinetica, a vectorization technology startup, which provides real-time analysis and visualization of the massive amounts of data the Pathfinder initiative monitors.

“After a year-long prototyping effort with the Defense Innovation Unit, Kinetica was selected to support the North American Aerospace Defense Command and Northern Command Pathfinder program to deliver a real-time, scalable database to analyze entities across space and time,” Amit Vij, president and cofounder at Kinetica, told me. “The ability to fuse, analyze, and act across many different massive data streams in real-time has helped NORAD and USNORTHCOM enhance situational awareness and model possible outcomes while accessing risks.”

The platform allows data scientists and other stakeholders to reduce the technology footprint and consolidate information to increase operational efficiency.

“Military operators can deepen their data analysis capabilities and increase their situational awareness across North America by combining functions currently performed by multiple isolated systems into a unified cloud database producing intelligence for leadership to act on in real-time,” Vij said. “Kinetica quickly ingests and correlates sensor data from airborne objects, builds feature-rich entities, and deepens the analysis capabilities of military operators. Teams of data scientists can then bring in their machine learning models for entity classification and anomaly detection.”

Parallel (data) universe

Vectorization technology is relatively new in data science and analysis and shows promise for specific applications. Vectorization is different from other data processing methodologies.

“Vectorization, or data-level parallelism, accelerates analytics exponentially by performing the same operation on different sets of data at once, for maximum performance and efficiency,” Nima Negahban, CEO and cofounder at Kinetica, told me. “Previous generation task-level parallelism can’t keep pace with the intense speed requirements to process IoT and machine data because it is limited to performing multiple tasks at one time.” 

The way we have dealt with these problems is unsustainable from a cost standpoint and other factors such as energy use.

“Prior generation big data analytics platforms seek to overcome these inefficiencies by throwing more cloud hardware at the problem, which still comes up short on performance and at a much higher cost,” Negahban said. “In an almost industry-agnostic revelation, companies can implement this style anywhere their data requires the same simple operation to be performed on multiple elements in a data set.”

How does that apply to the Pathfinder program and its objectives?

“For the Pathfinder program, vectorization enables better analysis and tracking of objects throughout the air, sea, and land through a multitude of fused sensor readings much faster and with less processor power,” Negahban said. “The technology’s speed and ability to identify the rate of change/direction attributes algorithms that can disguise planes, missiles and potentially help the government better understand what these UAPs or UFOs really are. This means that NORAD can understand what they see in the sky much faster than before, and with much less cost to the taxpayer!”

Vectorization technology is known for its high-speed results, and recent investments in the supporting infrastructure from some of the world’s most significant hardware manufacturers have helped advance the field.

“Every five to 10 years, an engineering breakthrough emerges that disrupts database software for the better,” Negahban said. “The last few years have seen the rise of new technologies like CUDA from Nvidia and advanced vector extensions from Intel that have dramatically shifted our ability to apply vectorization to data operations.”

Negahban likens the process, and the resulting speed vectorization achieves, to a symphony. 

“You can think of vector processing like an orchestra,” Negahban said. “The control unit is the conductor, and the instructions are a musical score. The processors are the violins and cellos. Each vector has only one control unit plus dozens of small processors. Each small processor receives the same instruction from the control unit. Each processor operates on a different section of memory. Hence, every processor has its own vector pointer. Vector instructions include mathematics, comparisons, data conversions, and bit functions. In this way, vector processing exploits the relational database model of rows and columns. This also means columnar tables fit well into vector processing.”

Data has the answer

We can’t have an article about UFOs and UAPs without talking about the sizeable grey lifeform in the room. I’ve been fascinated by the subject of flying objects and aliens since I was a child, but if I were an X-Files character, I’d be the ever-cynical Scully. So here’s one of my many hypotheses.

Throughout the 1980s and into the 90s, newspapers regularly featured “martian invaders” and other alien visitors, with front-page blurry photos and tabloid headlines. Caught mainly on 35mm cameras and basic video cameras, the images of cigar and saucer-shaped objects in the sky would always be blurry and debunked a few weeks later.

There are 3.6 billion smartphone users today. The majority of these devices have incredibly high-quality cameras. Not only that, but taking photos, capturing Instagram Stories, and recording TikTok videos is now so ubiquitous, the smartphone has become an extension of our arms.

Yet, we do not see countless videos or photos of UFOs and UAPs anymore. Sightings are rare compared to when there were significantly fewer cameras in use at any given time and when we used them with specific intention instead of part of our daily lives. So just how likely is it that any of these sightings are alien in origin versus human-made objects and natural phenomena? I couldn’t resist posing this to Kinetica.

“What we know from government-issued statements is that no conclusions have been drawn at this time,” Vij said. “The June 25th preliminary assessment of UAPs by Director of National Intelligence calls for an effort to ‘standardize the reporting, consolidate the data, and deepen the analysis that will allow for a more sophisticated analysis of UAP that is likely to deepen our understanding.'” 

If we are going to find an answer, it will be data-driven and not opinion-based, that’s for sure. 

“What’s interesting is that much of the data from radar, satellites, and military footage has been around for decades, but it was previously an intractable problem to fuse and analyze that volume and type of data until recently,” Vij said. “The answer to this question now feels within reach.”  

Vectorization technology certainly offers the performance and flexibility needed to help find the answers we all seek. How can the data science community take advantage?

“What has recently changed is that the vectorized hardware is now available in the cloud, making it more of a commodity,” Negahban said. This has allowed us to offer Kinetica as-a-service, reducing the traditional friction associated with what was traditionally viewed as exotic hardware, requiring specialized and scarce resources to utilize. Our goal is to take vectorization from extreme to mainstream, so we’ll continue to make it easier for developers to take advantage of this new paradigm.”

The truth is out there, and it’s being processed in parallel.

“Infobesity” is a term we’re hearing more frequently these days as consumers are creating exponentially more data in their daily lives, and brands are similarly consuming larger portions. From eCommerce sites to a multitude of social media channels, there are terabytes of data available in today’s increasingly digital world.

But, companies can’t possibly collect, filter, manage and use all the data that’s out there. Consider user-generated content alone, like videos, images, and audio. Just on Instagram, 95 million photos and videos are shared each day – there’s simply too much data to process. As any teen who’s tried will attest, you simply can’t consume it all.

There’s too much noise and not enough control over the data collection process. Big datasets may be a good thing when it comes to finding aggregate level trends, but how much of it is actually needed to deliver great customer service support and brand experiences?

Instead of succumbing to the siren song of more data, companies should be focusing on the caliber of the information. In other words, prioritizing data quality over quantity. You don’t always need massive amounts of data to come up with valid insights. In certain scenarios, smaller sets of actionable information can be just as insightful.

Small data’s big value

Many companies have fallen into the habit of collecting data simply because they can. What they’re finding, though, is that big data isn’t a cure-all for what ails them. Companies typically think of big data in terms of the 5Vs: volume, the variety of data types, the velocity at which it’s processed, value and veracity – its accuracy. All of these combine to make big data very useful, but at the same time, can be difficult to manage and extract meaning from without the right tech and tools, or a third-party provider.

In contrast, small data consists of usable chunks that are easily consumed. According to Martin Lindstrom, a business and culture transformation expert and author of Small Data: The Tiny Clues that Uncover Huge Trends, big data is about correlations, but small data “is all about finding the causation, the reason why.”

In the customer experience (CX) industry, small data can take the form of research sourced from consumer surveys, focus groups, qualitative interviews or comments captured via a CRM (customer relationship management) system by a customer service representative. It can also be customized to your specific industry, needs, customers and the channels your business prefers to use, thus making it all the more functional.

Anticipating and personalizing your CX

Another perk of small data is its ability to impact your brand’s bottom line. Small data’s manageability makes it more actionable.

Consider the importance of delivering a personalized customer experience to consumers. When you access small data through CRMs and tap into its knowledge of customers, it can help you refine the customer journey. Based on customer preferences, brands can better tailor and target email campaigns or send a web visitor content relating specifically to their searches. This customization makes people feel ‘known’ and makes the interactions effortless, ultimately leading to increased revenue.

There’s also tremendous value in the interactions that customer support personnel have with consumers on a daily basis. Even the most detailed data analysis may not give you the same clarity as an actual 1:1 conversation — and the small data contained in those moments of authentic connection can be amplified to transform the overall quality of your customer experience.

Making faster, more informed decisions

Because small data is agile, it can be collected and transformed into useful insights within a short time frame. While AI-powered algorithms can make decisions and recommendations based on terabytes of data by analyzing and identifying correlations and trends, these valuable insights take time to derive. The data needs to be accurately labeled, training datasets need to be created, and machine learning platforms need time to ‘learn.’ Small data, on the other hand, can be processed, analyzed, and used immediately.

With real-time insights at your fingertips, you can optimize your customer experience on the spot. As insideBIGDATA reports, “The customer sentiments that small data findings uncover give marketers the unique ability to observe whether changes to their products or services have a positive or negative effect on customer experience.”

Providing a better employee experience

It isn’t just customers and companies who stand to gain from small data. Small data can also empower employees, leading to more engaging, rewarding, and impactful work.

For example, TELUS International’s Agent Assist chatbot was created by a group of our team members that were delivering customer support services for a specific client. By comparing their experiences and challenges amongst themselves and looking at the data available to them, such as the time it was taking to resolve inquiries and customers’ satisfaction levels, they found immediate opportunities to enhance the client’s existing platform to make it more efficient. The accessibility of small data is an invitation to employee innovation, ultimately leading to a better employee experience.

Small but mighty – the answer to infobesity

Combating infobesity isn’t just about reducing the amount of data you have but understanding and fully leveraging the smaller data that you may currently be overlooking. These small bits of information, extracted from daily customer touchpoints, can provide real-time insights and benefits and ultimately make a big impact on both customer and employee experiences. Simply ask yourself what kind of data your team actually needs to do their job effectively and work to mine that data on their behalf.

Good things really do come in small packages!

Big data is one of the most rapidly growing industries in the world and was valued at $169 billion in 2018, with expectations to approach the $300 billion mark by the end of next year. Even with such monetary influence in the world already, the industry is still figuring itself out, and new uses for data (and new jobs for data analysts) are being discovered all the time, including predictive analytics. 

From videogames to healthcare to sports, individuals with analytic backgrounds and beliefs are moving to the forefront of their respective industries, and the insurance industry is no different. Insurance rates are based on trends in given demographics, and young men tend to pay more for the exact vehicle than middle-aged women because data shows that young men are more likely to crash. That is a very simple example of data use in insurance, but as the ability to share data evolves and becomes more secure, so do the abilities to utilize it in different ways, including when making predictions for the future, otherwise known as predictive analytics. 

What is Predictive Analytics?

When analytics and data science methods combine to focus on the future, the result is predictive analysis. Predictive analytics utilizes past and present trend data and extremely advanced computing methods to paint a proverbial picture for analysts regarding what the past and present data means for the future of a given industry. 

One step further is machine learning, where analytic programs no longer need to be programmed with data. They simply take it in and automatically change their predictive analyses, hence the name “machine learning.”

How is Predictive Analytics Affecting the Insurance Industry Today?

One of the primary uses of predictive analytics in the insurance industry is in risk assessment. Whether life, auto, home, or otherwise, insurance companies must weigh everything about a given client to determine their insurance rates. 

To use auto insurance as an example again, companies look at driving records, age, location, and more to determine a rate. When this information is put into a system, it can be compared to other individuals who had similar demographics and then can take into account how well those similar individuals did relative to the insurance (for cars, this may mean they crashed a lot, had a bunch of speeding tickets, or had squeaky clean records). 

In life insurance, health records are often the main subject of predictive analytics, as evolutions in EHR sharing allow companies to utilize similar methods as auto insurance to determine what the future may hold for a given client with a shared medical past. Ultimately, insurance companies have been known to err on the side of caution, so this uptick in the availability of relevant data saves consumers money more often than it costs them. 

How will Predictive Analytics Affect the Insurance Industry Tomorrow?

The mere youth of predictive analytics makes it appealing because more and more capabilities are being discovered, and the insurance industry assumes the same. Significant investments are being made into the industry, and Forbes recently released an article encouraging investment into predictive analytics. 

With this trend in mind, consumers are already asking, “how do you utilize predictive analytics?” meaning a front-runner (in the sense of insurance use of data) can be more appealing to said consumers. They are more likely to switch to forward-looking insurance companies, especially when their commitment to predictive analytics means money saved for those consumers. 

Also, corporations that utilize predictive analytics grow at a rate 7% faster than their counterparts that do not. The future of predictive analytics in insurance is more likely to be a refined version of what is already happening, but as the industry is, indeed, very young, keeping an eye on new evolutions in the use of data can mean staying ahead of severe business curves that may arise from this rapidly growing data industry. 

Like oil or money, data is a valuable resource that holds great potential. But too many organizations set their sights on amassing data, or worse, using it only in isolated processes or departments when they should be focused on applying data throughout their everyday operations.  

Technology is, of course, part of the puzzle, but in an authentic data culture, execs have the ability to spark innovation and identify new business opportunities via more efficient processes. Lasting success relies on smarter analytics-infused processes and the merging of culture and people. It’s a bold move that requires commitment from the top – fueling strategic decision-making by all stakeholders, no matter their title or department. 

A new perspective 

Organizations that take full advantage of data and analytics hold this strategic mindset. They see, use, and perfect data to gain a competitive edge. In a December 2020 Harvard Business Review (HBR) survey, close to 90 percent of respondents felt analyzed data was critical to their company’s business innovation strategy. Yet, while data analytics was cited as enhancing the customer experience (CX) and operational efficiency, participants said it was not routinely applied to fuel innovation or new business opportunities. So, what’s the holdup? It is far easier to acquire data than it is to convert it into usable insights. In addition, moving to a data-led business culture is challenging. It can seem risky, for example, if those in charge lack confidence that the right policies, ethics, and governance are in place for ideal enterprise-wide sharing of data. But leaders in a healthy data culture know better and accept the risk, doing what is necessary to make the data useful. 

Start at the top 

The infusion of analytics throughout an organization requires strategic planning, thoughtful execution, and unwavering commitment from executive leadership for real and lasting impact – a big task for any company. 

Leading a data-centric organization doesn’t necessarily require data science degrees across the C-suite, although it is imperative that those in charge have a working knowledge of standard data principles. Armed with an understanding of the insights desired, an appreciation for clean data, and the ability to identify data gaps, leaders are better equipped to revamp the decision-making process. Leading by example, executives have an opportunity to educate their workforce on the importance of data literacy ahead of infusing analytics into existing workflows. 

Within the comfort zone  

Respondents in the HBR survey voiced concerns around poor training and lack of employee skills as impediments to broad use of data. They highlighted a lack of quality data as well. But in a data-driven culture, routine applications, workflows, and processes are infused with analytics, making training unnecessary or minimal at best. Multiple steps can more easily be automated for a frictionless user experience. Analytics capabilities are woven into actively-used tools, putting insight and actionable intelligence within reach – it’s there when and where it’s needed, enabling informed, real-time decisions in context.  

Consider that devices such as smartphones, ‘fitness’ watches, and immersive applications and websites have us craving instant gratification and personalization. It is no different in the workplace. Employees want relevant, up-to-the-minute insights delivered when and where they need them, in context, and without having to learn how to use new software. Alerts to changing conditions or issues are a welcome bonus that extend the utility of infused analytics.  

Weaving analytics into current technology 

It’s important to note that an organization’s technology choices can reduce data visibility. In contrast, analytics infusion presents data and actionable intelligence to the people who need it, when they need it, in the workflows, they are accustomed to.  

How data will be used simply must influence technology choices. This presents an ongoing opportunity to reinforce the adoption of data strategies throughout the organization – and even outside the organization, with partners, suppliers, and customers. With consensus at the C-level, goals are defined, assessed, and modified as needed. While democratizing data is not without risk, it defines leadership that recognizes the intrinsic value of a data-centric organization. 

The analytics-infused enterprise 

Data can be a blessing and a curse, especially without the infrastructure and processes in place to tap into that data’s inherent value – compounded by the constant stream of data being gathered and added to your existing arsenal. Data that is dirty, inaccessible across systems, or shared with only a handful of stakeholders only exacerbates the matter. Fortunately for most organizations, leveraging data to its full extent can be accomplished with an adjustment in company mindset. Enabling data access – throughout systems, processes, and people – provides the foundation to an analytics-infused environment. It’s a new landscape where smart decision-making happens at the point of need. For company leaders, it is a bold step toward fostering a data-driven culture, empowered and motivated for success across the organization. 

Digital transformation is more than a mythical buzzword these days. Technological improvements have disrupted many industries, and innovation has transformed the way businesses execute their processes. The number of advances healthcare has made in recent years is mind-boggling, but they’re just getting started.

Healthcare innovation has always centered around improving patient outcomes, increasing preventive healthcare, and reducing physician workloads. A study by Grand View Research projects the American digital healthcare market, currently valued at $110.2 billion, to earn $295.4 billion in 2028. 

Here are four key trends that will drive the way forward as digital transformation changes the way we look at healthcare.

AI and Predictive Healthcare

Data collection has been a pivotal element in healthcare for a long time. Patient medical histories and treatment information are now being used to transform the way hospitals and clinics prescribe treatment. 

Identifying and prescribing preventive plans has helped hospitals reduce loads on emergency rooms and clinics. Big data analysis has also helped hospitals predict the number of admissions they can expect during different seasons of the year and staff them appropriately. 

As big data collection has grown, companies have begun investing in AI-enhanced solutions that have been trained on historical datasets. The public has already been exposed to robots such as Moxi, which is designed to assist nurses with routine tasks.

AI-powered chatbots are increasingly finding their way into customer service and even therapeutic roles. However, AI’s power can be fully unleashed in the field of medical research. Precision medicine, genomics, medical imaging, and drug discovery will benefit from AI algorithms’ ability to quickly process large data sets and discover hidden patterns in them.

Big pharmaceutical companies already use AI to shorten the drug development cycle and have found that discovery timelines have been reduced by four years on average. To fully embrace AI’s potential, healthcare companies need to invest in making AI more friendly to humans.

As industry thought leader Koen Kas says, “The future of healthcare is not so much about adoption of technology, it is about changing behavior. And doing that in an invisible, delightful fashion, by surprise and reward, in the background.”

On-Demand Healthcare

More than half of all internet traffic is from mobile phones today, as they’re used to communicate, research, transact and carry out daily tasks. Add to this fact that more than 4 billion people worldwide have access to the internet, and it’s easy to see how healthcare can be provided at a patient’s convenience.

People use online information hubs primarily to research doctors and medical facilities, but they don’t use them to schedule appointments. The healthcare booking process is an anomaly compared to the progress achieved in the rest of the sector.

Patients still dial into clinics and have operators book them into slots manually. Research conducted by scheduling solutions provider Deputy reveals that young adults are more likely to book appointments by calling instead of via apps or online channels. The lack of usability inherent to online channels is the major reason for this. 

Aside from making online channels more usable, healthcare has also witnessed the rise of the freelance medical professional. Companies such as Nomad Health link doctors and professionals with medical centers that need their skills.

As a result, hospitals can now accommodate a wider range of treatments, even if they don’t have staff on-site with the necessary skills. This prevents the need for patients to travel to specialty hospitals and instead receive treatment at their preferred venues.

Wearable Health Devices

Wearable medical devices are a fast-growing market. Some estimates expect the market to reach $195.57 billion in size by 2027. The appeal of wearables lies in their ability to inform preventive healthcare procedures.

Fitbit, perhaps the most popular wearable biometric collection device on the market, revealed how wearables could play a role in combating the COVID-19 pandemic. The company found that its devices can detect about half of all COVID-19 cases one day before participants report the onset of symptoms. 

“If we can let people know they should get tested a day before symptoms begin,” wrote Fitbit Director of Research Conor Heneghan about the implications of these findings, “they can isolate and seek care sooner, helping to reduce the spread of COVID-19.”

As the adoption of wearables grows, companies are discovering new ways of personalizing the healthcare experience. From empowering individuals to take better care of themselves, to providing insurance incentives, the ceiling is very high when it comes to healthcare wearables.

The US healthcare system will receive the greatest benefit. Approximately 90% of the $3.5 trillion spent annually goes towards treating chronic and mental conditions that can be better managed via preventive healthcare programs. Wearables are the key to deploying more effective preventive healthcare programs, and they’re just getting started.

Decentralized Databases for Record Storage

As the amount of data gathered by companies grows, security is increasingly becoming a necessity. Cybercrime is increasing across the globe, and this trend is particularly alarming for healthcare due to the sensitive nature of medical records and data.

A persistent problem healthcare professionals have faced is the existence of fragmented medical records. People receive treatment from different doctors for different diseases at various points in their lives, and any of their prior treatments can cause adverse reactions in the present.

The lack of a centralized database that records every person’s medical history is both a risk and an impediment. It creates a single point of failure, but it also increases the chances of an inappropriate treatment being prescribed. 

The blockchain is an elegant solution to this problem. Thanks to its nature, a blockchain network is close to impossible to hack. The network can also detect conflicting information and alert administrators automatically. 

Australia and the UK have begun experimenting with migrating patient records to the blockchain and handling data transfers between providers.

In the United States, patient privacy is a hurdle, but there is an increasing number of startups that are bringing app-based security to these records. It’s no wonder that the blockchain for the healthcare market is expected to reach $5.5 million by 2027.

Digital Transformation = Instant Healthcare Access

All of these trends ensure that shortly, people will have the power to address all aspects of their health within the palms of their hands. The rise of preventive healthcare also promises to relieve the burden hospitals and healthcare providers currently experience. 

With data increasingly being analyzed and transformed to actionable advice, the world is set to become a healthier place. 

The Western Digital Ultrastar® DC SN640 NVMe SSD is a mainstream NVMe^TM SSD targeting broad deployment as boot, caching, or primary storage in data center IT and cloud environments. It is a popular and efficient storage solution for enterprise-scale tasks that require consistent quality of service and low latency for mixed random read/write workloads commonly generated by applications such as virtualization, OLTP, NoSQL, web servers, file servers, and mail servers.

However, when a fault-tolerant solution is needed, traditional RAIDs on NVMe are unable to deliver full drive performance levels, which can be a problem for data-driven businesses.

The solution is to combine the Ultrastar DC SN640 SSDs with RAIDIX ERA software. Purpose-built for NVMe, with lockless datapath and I/O parallelization, RAIDIX ERA creates a highly performant software RAID from NVMe drives, putting out up to 97% of their raw performance. 

Testing performance

This impressive boost was seen during benchmarking tests performed by Western Digital engineers. When using 8 NVMe drives, 3,890,000 IOps for random read performance (RAID 5, 4k block), 454,000 IOps for random write performance (RAID 5, 4k block), and 1,260,000 IOps for random mixed performance (RAID 5, 4k block, 70/20 r/w) were reached.

These numbers are a big step up from the performance of traditional RAIDs. The tests also proved that RAIDIX ERA increases sequential read performance (RAID 5, 128k block), allowing it to reach 18.5 GB/s and boosts sequential write performance up to 11.5 GB/s.

Additional testing was also carried out to push the limits and see how RAIDIX ERA would perform in combination with RAID 5 volume consisting of twenty Ultrastar DC SN640 drives. During this testing, figures of 1,250,000 IOPS on random write performance were obtained, while random read performance reached 10,200,000 steady-state IOPS.

The Ultrastar DC SN640 NVMe drives also demonstrated their reliability as they were subjected to heavy workloads, sometimes exceeding ten full drive writes per day, over the course of several weeks, demonstrating stable performance with good thermal results. 

Testing parameters

It should be noted that while there is no single industry-accepted methodology for measuring the performance of multiple SSDs in a RAID configuration, Western Digital engineers chose to follow SNIA PTS, which is a standard for single-drive performance evaluation. 

In addition to the tests outlined above, RAIDIX’s own tests showed that ERA reduces latency to the limit of 0.5 ms. Even in degraded mode, performance remains high: for RAID 50, the penalty does not exceed 25%. Other RAIDIX ERA features are a wide range of supported RAID levels (RAID 0, 1, 5, 6, 7.3, 10, 50, 60, 70), POSIX API support, and surely being lightweight, as it only uses less than 20% CPU under the maximum array load, and no more than 4GB RAM is needed for full-featured work.

Such performance boosts overall efficiency and slashes business costs: for example, RAID 5 performance with 8 NVMe under RAIDIX ERA is comparable with RAID 10 performance with 16 NVMe drives.

To get a FREE RAIDIX ERA license for Western Digital Ultrastar SN640 drives, visit the RAIDIX website. The EU holders of the ERA license will also get access to an offer from Exertis Hammer with special prices for Western Digital Ultrastar SN640 SSDs and server platforms powered by Intel and AMD.

Data professionals have long debated the merits of the data lake versus the data warehouse. But this debate has become increasingly intense in recent times with the prevalence of data and analytics workloads in the cloud, the growing frustration with the brittleness of Hadoop, and hype around a new architectural pattern – the “data lakehouse.”

The data lakehouse is a relatively new paradigm that refers to a hybrid data architecture that aims to mix the best of a data warehouse and data lake. If the term is new to you, you’re not alone.

The terms explained

To fully understand how these terms fit into the overall data landscape, it’s worth unpicking their similarities and differences. 

To begin with, all are used for the management of operational and transactional data, which support business intelligence (BI) and analytical workloads across both business departments and developer functions. Digging into their specific definitions also reveals the different goals they serve.  

Data warehouses, for example, are optimized for predefined and repeatable analytics queries where structured data can be scaled across an organization. Because they are often used for business performance and regulatory reporting, data warehouses are highly governed data environments and are suited towards high-performance, sometimes complex queries and high levels of concurrent access.

Data lakes collate unrefined structured and semi-structured data from multiple different sources and are subject to less rigorous data governance regimes. They often use cheaper and scalable storage where different processing styles and methods, including machine learning (ML) and batch-orientated workloads, are supported. However, data lakes are rarely optimized for the demands of production delivery – such as concurrency, latency, and workload management.

Despite some apparent differences, overlaps between the two architectural patterns do exist. For example, a data lake can use approaches that employ star schemas for batch-orientated queries, and a data warehouse could be leveraged to operationalize data science with ML models running against governed data. 

Cutting through the data lakehouse hype

Conceptually, a data lakehouse is designed to combine the core elements of data warehousing with the core concepts of a data lake, for example, by providing the lower costs of cloud storage for raw data with support for high-performance processing of ML, BI, analytics workloads, and data governance.

This might sound like a good idea, but the lakehouse is an emerging concept that is still misunderstood by many and subject to a lot of hype and speculation. 

Despite this, there are strong advocates on both sides of the data architecture divide. Those with a background in data warehousing position the lakehouse around relational technology concepts. Those on the data lake side have roots in ML and Spark processing, where support for Java, Python, and R workloads is a higher priority. Both, however, promote the use of the cloud for storage and analytical processing. 

It’s rarely an either/or decision

While the debate continues, the lakehouse is unlikely to remove the need for either the data lake or data warehouse, at least in the short term, not least for those organizations who have made significant investments in either or both. Likewise, as an emerging concept, it still has a lot of catching up to do in terms of the decades of innovation we have seen in areas such as in-database analytics, query and performance optimization, and columnar storage and compression.

There is also still a sound argument for the co-existence of data warehouses and data lakes where it provides a basis for businesses to scale and democratize data as well as rationalizing data ecosystems. A co-existence approach, in whatever combination, draws on the strengths of each architectural design to serve a wider number of use cases than any of these architectures can support independently.

Prioritize flexibility 

With the backdrop of an ever-changing and complex data landscape, data professionals need to ensure their existing environment that utilizes data warehouses and/or data lakes work together rather than against each other. For example, the data warehouse can provide well-defined and repeatable data analytics while the data lake supports more experimental or developer-led ML use cases utilizing a wider pool of data. Combining both gives organizations the ability to support different use cases and different audiences – such as business users and data scientists, and apply different data governance treatments, data curation, and data quality.

Exactly where and how a data lakehouse fits in this environment remains to be seen. The concept is still untested by the market at large, with the promise of the one-size-fits-all approach likely to be a step too far for those organizations who have invested significantly in data lakes and warehouses. It is, however, an important debate to have in such an innovative and fast-moving data infrastructure market that continues to evolve.  

Data science is one of the hottest jobs in IT and one of the best paid too. And while it is essential to have the right academic background, it can also be crucial to back those up with the proper certifications.

Certifications are a great way to give you an edge as a data scientist; they provide you with validation, helping you get hired above others with similar qualifications and experience.

Data science certifications come in many forms. From universities to specific vendors, any of the following are recognized by the industry and will help you hone your skills while demonstrating that you fully understand this area of expertise and have a great work ethic.

Certified Analytics Professional

The Certified Analytics Professional (CAP) is a vendor-neutral certification. You need to meet specific criteria before you can take the CAP or the associate level aCAP exams. To qualify for the CAP certification, you’ll need three years of related experience if you have a master’s in a related field, five years of related experience if you hold a bachelor’s in a related field, and seven years of experience if you have any degree unrelated to analytics. To qualify for the aCAP exam, you will need a master’s degree and less than three years of related data or analytics experience.

The CAP certification program is sponsored by INFORMS and was created by teams of subject matter experts from practice, academia, and government.

The base price is $495 for an INFORMS member and $695 for non-members. You need to renew it every three years through professional development units.

Cloudera Certified Associate Data Analyst

The Cloudera Certified Associate (CCA) Data Analyst certification shows your ability as a SQL developer to pull and generate reports in Cloudera’s CDH environment using Impala and Hive. In a two-hour exam, you have to solve several customer problems and show your ability to analyze each scenario and “implement a technical solution with a high degree of precision.”

It costs $295 and is valid for two years.

Cloudera Certified Professional Data Engineer

Cloudera also provides a Certified Professional (CCP) Data Engineer certification. According to Cloudera, those looking to earn their CCP Data Engineer certification should have in-depth experience in data engineering and a “high-level of mastery” of common data science skills. The exam lasts four hours, and like its other certification, you’ll need to earn 70 percent or higher to pass.

The cost is $400 per attempt, and it is valid for three years.

DAMA International CDMP

The DAMA International CDMP certification is a program that allows data management professionals to enhance their personal and career goals.

The exam covers 14 topics and 11 knowledge areas, including big data, data management processes, and data ethics. DAMA also offers specialist exams, such as data modeling and design, and data governance.

Data Science Council of America Senior Data Scientist

The Data Science Council of America Senior Data Scientist certification program is for those with five or more years of research and analytics experience. There are five tracks, each with different focuses and requirements, and you’ll need a bachelor’s degree as a minimum. Some tracks require a master’s degree.

The cost is $650, and it expires after five years.

Data Science Council of America Principal Data Scientist

The Data Science Council of America also offers the Principal Data Scientist certification for data scientists with ten or more years of big data experience. The exam is designed for “seasoned and high-achiever Data Science thought and practice leaders.”

Costs range from $300 to $950, depending on which track you choose. Unlike the other certifications so far, this does not expire.

Google Professional Data Engineer Certification

The Google Professional Data Engineer certification is for those with basic knowledge of the Google Cloud Platform (GCP) and at least one year of experience designing and managing solutions using GCP. You are recommended to have at least three years of industry experience.

It costs $200, and the credentials don’t expire.

IBM Data Science Professional Certificate

The IBM Data Science Professional certificate comprises nine courses, covering everything from data science to open-source tools, Python to SQL, and more. In an online course, you’ll create a portfolio of projects as part of the certification, which is useful for employers who need to see practical examples of your work.

There is no charge for this course and no expiry.

Microsoft Azure AI Fundamentals

Microsoft’s Azure AI Fundamentals certification focuses on machine learning and AI but specific to Microsoft Azure services. A foundational course, it is suitable for those new to the field.

It costs $99 with no credentials expiry.

Microsoft Azure Data Scientist Associate

Microsoft also provides the Azure Data Scientist Associate certification focused on machine learning workloads on Azure. You’ll be tested on ML, AI, NLP, computer vision, and predictive analytics, and it requires more advanced knowledge of the field than its other certification program.

The cost is $165, and again, credentials don’t expire.

Open Group Certified Data Scientist

The Open Group Certified Data Scientist (Open CDS) certification is markedly different from the other programs listed here. There are no traditional training courses or exams. Instead, you gain levels of certification based on your experience and a board approval process.

The cost depends on which level you are applying for, but the minimum fee is $1,100 to reach level one. Credentials don’t expire.

TensorFlow Developer Certificate

The TensorFlow Developer Certificate is for those who want to show their machine learning skills using TensorFlow. You will need experience with ML and deep learning’s basic principles, building ML models, image recognition, NLP, and deep neural networks.

This certification costs $100 per exam, and credentials don’t expire.

The saying “knowledge is power” doesn’t just apply to individuals but also businesses. While individuals can learn from books and life lessons, businesses need a tool to gather market insights. This is where online data collection comes in. It is one of the best tools because it provides up-to-date and reliable information for businesses to stay informed – and make critical business decisions. 

Fortunately, there is no shortage of online data being generated. In fact, a recent survey from Frost & Sullivan found that 49% of IT decision-makers use data collection for business-critical operations, such as market research. Another 44% said they use data collection to gather competitive public data. 

As online data continues to be generated at record rates, it also continues to be a driving force behind decision-making for businesses that embrace data collection. We have created a lot of online data this year, in large part because of the tremendous digital shift we experienced starting in March. Take online shopping, for example. Those who wanted to avoid crowded stores because of the pandemic turned to online retailers, like Amazon, for quick and easy delivery. This resulted in Amazon shipping 6,659 packages per minute in 2020.

Given the increased need for data-driven insights in these unprecedented times, we expect to see the following trends emerge in 2021.  

Fraudulent online activity will keep online data shielded 

Sometimes, a few bad apples spoil the bunch. This saying is especially true for fraudsters who, even during these challenging times, continue to improperly use online data to conduct illegal activities, including fake reviews, illegal purchases, and phishing. 

For example, a scam called brushing emerged this summer, where fraudsters sent free merchandise to addresses that are publicly available online. In reality, the scammers were only using the addresses to make people look like verified buyers of those products so they could write glowing (and fake) reviews. This is obviously a clear breach of ethics and not the way online data collection is intended to be used. Unfortunately, the amount of fraudulent activity like this is on the rise due to the pandemic and recent US presidential election season.

As would be expected, these fraudulent activities lead companies to take security precautions to protect themselves. Such precautions include limiting the amount of data they make freely and publicly available online. Those responsible for abusing open data are causing these limitations, which affect everyone else, especially the business community that only seeks to use online data for legitimate purposes. This is a trend that I, unfortunately, believe will continue well into 2021.

Online data will continue to drive decision-making

It’s expected that companies will continue to legitimately protect confidential and proprietary information with data privacy and copyright restrictions. However, plenty of online data that is publicly available and free for businesses can be leveraged to make informed decisions. This includes using data to drive innovation and product development forward, pricing products competitively, improving customer service, and enhancing the quality of products.

For example, in the world of e-commerce, shipping data can help companies estimate how much they should ramp up or slow down their e-commerce efforts. Online data can also give insight into purchasing trends. Another example is a study from earlier this year that found that sales of electronics, cooking appliances, and grocery items significantly increased year-over-year from April 2019 to April 2020.  

Data can also be used to predict and react to market shifts as they unfold by following financial or consumer behavior trends in real time. For instance, 2020 has certainly been an unpredictable year, and in 2021, I expect businesses across industries will continue to turn towards online data to guide their decisions.

Evolved data markets will promote open and on-demand data

The Frost & Sullivan survey also found that 54% of IT decision-makers expressed a need for large-scale data collection to keep pace with their businesses’ growing demand for data. However, in order for businesses to be able to utilize online data, it needs to be accessible – not blocked. Today, businesses often block public data collection attempts while collecting it themselves. This situation is caused by two major factors: the continuous need to block malicious online activity, and the notion that somehow this public data is part of what gives a company its competitive edge. 

I believe that during 2021 and onwards companies will realize that public data collection is part of the general ongoing business conduct and is necessary for everyone. They will also realize that when it comes to a business’s competitive edge, areas such as inventory, prices, product quality, service quality, etc. play big role as well.  Once that realization settles in, blocking data will serve only to protect against abusive online activities. 

So, how do we ensure that the right people get access to ethical public data while blocking out the abusers? While this is not a simple task it is certainly a doable one. One solution that could serve all is promoting the open exchange of information in central data hubs. Sites will continue to block abusers; this will not change. However, maybe they will permit ethical data collectors. Why? The answer does not rely on ideals but on numbers. 

It is estimated that 40% of internet traffic is based on bots, for better and for worse. Therefore, reducing your website traffic by 15-20% will improve the quality of your user tracking statistics. This, in turn, will boost the quality of your service and reel in more revenue while driving down costs. Talk about a huge motivator! So, actually, by allowing your openly available data to be collected by ethical data hubs, you can significantly increase your bottom line.  

The good news is that central data hubs are already in use, and I predict that they will only grow in popularity as data markets expand over the next few years. Research shows that IT managers already desire on-demand, quality, and verified public data. It will be interesting to watch these data markets take shape.

Ultimately, the future of online data collection is up to those who control it. At the rapid rate data is being produced, future data collection efforts will need to evolve and grow. Companies will need automated data collection to keep up with their competitors and be able to gather data at a rate that would otherwise be impossible to complete manually. After all, the speed at which companies can collect fresh data will determine their relevancy and success. 

Data comes in many shapes and forms, but two of its core structures are stacks and queues. TechTarget’s definition states the following; “In programming, a stack is a data area or buffer used for storing requests that need to be handled.”

And what’s inside that data stack? It’s not just a data warehouse. Data stacks are composed of tools that perform four essential functions; collect, store, model and report. But the stack itself and the data warehouse are the two we’ll focus on in this article since they have a high importance level.

To get the lowdown on why it is essential to focus on your data stack and warehouse, we talked with Archit Goyal, Solutions Architecture Lead at Snowplow Analytics to understand more.

What are the opportunities and challenges that arise when choosing and developing a data stack?

“Choosing a data stack will depend on multiple factors: the company’s core use cases, the size and capabilities of their data team, their budget, their data maturity, and so on,” Goyal said. “One of the key choices is choosing between packaged analytics solutions (think GA or Adobe) versus more modular components that combine to make up a data stack. The main advantage of packaged products is that they have a lot of analytics tooling ready to go right out of the box. However, the main drawback is that you sacrifice control and flexibility over your data management in favor of simplicity and ease-of-use. Picking and setting up multiple best-in-breed tools to make up the analytics stack is harder work, but will give you greater control over your data asset in the long term.”

So what is a data warehouse, and why do companies need it? For example, what’s the difference between a data warehouse and a MySQL database?

“A Data Warehouse is a centralized data repository which can be queried for business benefit,” Goyal said. “They can contain data from heterogeneous sources, such as SQL, CSV files, text files, and more. Comparatively, data warehouses are columnar databases, and MySQL is a relational database. This means that warehouses are optimized for historical analysis of data as it is easy to aggregate values across rows (e.g., count sessions over time), whereas MySQL databases are good for storing and retrieving individual entries as a transactional store in an app.”

What are some excellent examples of data warehouses? 

“The big three (currently on the scene) are Google’s BigQuery, Amazon’s Redshift, and Snowflake,” Goyal said. “These are typically used to store a company’s data in a columnar format to allow for easy analysis and reporting. When used as the source of truth for a company to answer business questions, particularly about its users, it can be extremely powerful.”

So that covers warehouses, but what is our definition of a data stack, and what should be inside a good data stack?

“At Snowplow, we think about the data stack in four different stages,” Goyal said. “First, we collect. Data quality matters. With high-quality and complete data, attribution models are accurate, it’s easy to track and understand user behavior, and customer experiences can be optimized. That’s why our customers choose Snowplow, as we provide flexibility to collect data from multiple platforms and channels, as well as delivering clean and structured data.”

“Then, we store. Snowflake, BigQuery, Redshift, and S3 are all examples of tools for storing data that is collected.”

“The third stage is to model. Data modeling can help teams democratize their data – At Snowplow, our customers use tools like Snowplow SQL Runner, dbt, and Dataform to model their data.”

“Finally, we report. At this stage, data teams want to enable self-service of analytics within their organization. This includes the use of tools such as Looker, Redash, PowerBI, and Amplitude.”

“There is no one size fits all approach,” Goyal said. “Many teams opt for the out of the box solutions mentioned earlier while, increasingly, sophisticated data teams are combining the modular components outlined above to build a robust data stack which they can control from the get-go.”

What is an excellent data stack use case?

“Snowplow customers and recruitment marketing specialists VONQ wanted to use data to attract talent and advertise jobs on behalf of their customers,” Goyal said. “To make better recommendations and provide actionable insights for recruiters, VONQ invested in a data warehouse and data model that fit their business needs. For their use case, VONQ chose to implement a Snowflake data warehouse, citing the pricing model, user management, and performance as some of the key drivers behind their decision.”

“In addition to implementing Snowflake, VONQ needed a way to serve their data as well as near real-time responses for their customers. They decided to take a small amount of their data from their data warehouse and put it in the database Postgres where they could configure indexes, for example. For this data movement, they implemented Airflow because of its functionality with batch ETLs. Once their data was in Postgres, it allowed the data team to build an Analytics Service to serve actionable data to the wider team.”

“Natalia, Data Engineer at VONQ, shared this data journey with us in a recent webinar – you can watch it on-demand here.”

Which data models are out there, and how should you navigate them to make the best choice for better business insights?

“Data modeling is an essential step in socializing event-level data around your organization and performing data analysis,” Goyal said. “In its most basic form, data modeling is a way of giving structure to raw, event-level data. This structure is essentially your business logic applied to the data you bring into your data warehouse – making it easier to query and use for your specific use cases.”

“There are many ways to model your data to make it easier to query and use, and at the end of the day, the way you’ll model it will depend on your business logic and analysis use cases. If you’re modeling your data for visualization in a BI tool, you’d want to follow the logic required by the BI tool, or do the modeling within the BI tool itself (e.g., using Looker’s LookML product).”

“For most retailers and ecommerce companies, Google and Adobe’s data model will suit their use case. These giants have built their platforms and logic for retailers — conversion and goal tracking, funnel analysis, etc. is optimized for a traditional ecommerce customer journey. That said, many businesses struggle to make Google and Adobe work for them, e.g., if you’re a two-sided marketplace with two distinct groups of buyers and sellers or a (mobile) subscription business that wants to understand retention.”

“Say you’re a recruitment marketplace, and you have job seekers and recruiters interacting with your platform (two distinct user groups with different behavior). When a job seeker is looking for a job, one search on the site might result in five job applications. This means that the traditional funnel or conversion rate would make no sense.”

“Here are some examples of data models that we see with our customers: Modeling macro events from micro-events (e.g., video views); Modeling workflows (e.g., sign-up funnels); Modeling sessions; and Modeling users.”

“Check out our guide to data modeling to learn more about each example and tips on how to turn your raw data into easy-to-consume data sets.”

What should data professionals pay attention to when developing a data stack and data warehouse?

“This question has a long answer full of ‘it depends,'” Goyal said. “However, it’s important to consider two things: data quality and transparency. Having high quality – complete and accurate – data in a granular format is often key to setting data science teams up for success. Transparency into how data is processed upstream of a data science model is important to be able to justify the output.”

Archit Goyal will be speaking at DN Unlimited Conference on November 18-20, 2020 – meet him at the Data Science track during his talk “Building a strategic data capability.”

AI is now prevalent in major companies and services that we use every day. From product recommendations to personalized ads, from voice assistants to image recognition, AI helps us in hundreds of ways.

That’s fine if you’re a digital-first company, born on the internet and versed in data science, machine learning, and AI from the outset. However, if AI is genuinely going to change the world, it needs to be accessible to every business, regardless of digital heritage.

We spoke to Ann-Elise Delbecq, Data Science & AI Elite Team EMEA Program Director at IBM, about how to put AI to work for every organization.

So what types of customers do you and IBM encounter in your work?

“AI has proven beneficial to companies across all industries and has solved a wide range of use-cases,” Delbecq said. “We have worked with Telco, manufacturing companies, financial institutions, retail, airlines, etc. In terms of maturity, we are dealing with customers all over the spectrum, from customers starting to embrace AI to customers having advanced use-cases that optimize business processes.”

What are the critical implications of machine learning (ML), data science (DS), and artificial intelligence (AI) for enterprises?

“AI is not meant to take over decision-taking roles and replace humans in these tasks,” Delbecq said. “Instead, embedding AI into existing business processes improves the decisions made at every single step. For business owners, AI offers a deep understanding of the markets and its consumers and predicts specific actions’ consequences. Enterprises assisted with AI considerably reduce the risk associated with any strategic decision. New business processes were also not possible before, including but not limited to new interaction patterns like chatbots.”

What’s an excellent example of an enterprise machine learning workflow?

“There is no AI without proper data infrastructure,” Delbecq said. “The first step is to centralize the data management and its governance: no more data silos, no more do-you-know-where-I-can-find-this-information type of question. Enterprises must know what piece of information is being used by whom, since when, till when, and for which purpose. Next, data scientists must have the tools to quickly build models and extract meaningful insights from the data. More importantly, they must have access to deployment spaces in which all the models will be indexed, versioned, documented, and accessible like any other data of the company. The deployment of machine learning models into production environments is still the most challenging part of today’s journey. Once the data scientists have developed the logic, companies must deploy it on a large scale and connect it to existing business processes.”

With the increased usage of data science, ML, AI, what are the professions that will undergo a significant change?

“Everyone will benefit from AI-infused business processes,” Delbecq said. “It increases the likelihood of making better decisions and properly ranks confidence in those decisions. Let’s start with Business Analysts that will benefit from rapid answers to the WHY question that will drive WHAT outcomes. We can move to people responsible for a business that can better answer HOW questions using an optimized decision-making process. The list is endless.”

Most of the time, rules that are calculated through machine learning are not convertible back to human understandable format (“black box”). Is it just a matter of trust for companies, or should we always explain machine learning algorithms?

“Machines reply with two distinct answers; ‘I think it is this, and I am that X percent confident that it may also be this’,” Delbecq said. “They will guide a better decision process that sometimes requires automated work as part of the business process or where humans can help. Many of the AI techniques are easily explainable. Some are more difficult when the math behind the scene abstracts the problem too much. However, there are ways to explain the decision point through explainable techniques. They are as good as any deterministic technique we use in traditional software engineering.” 

The implications of ML, DS, and AI are challenging for business cultures when AI and Machine Learning are introduced into business operations. How would you advise companies to develop more trust in these technologies?

“There are a few ways that companies evolve and change,” Delbecq said. “One is through regulations that impose a particular behavior on companies. Typically, this does not end with a company getting compliant, but it instead creates new opportunities. For example, the introduction of Compliance and Risk allowed CDOs to have a clear view of the enterprise’s data layout to stay compliant. Later we saw clear trends that they got better to serve the business line by exposing more data by growing into self-serve and shop for data as they become more confident in interactions.” 

“Another way we see change is to experience the real benefits from understanding new use-cases and business processes. This might be by improving on the existing ones or increasing only the reaction time. AI is driving into this space a lot of change, considering that access to AI and computers capable of carrying AI gets more and more affordable over the last ten years. ML and Data Science have been part of our life for the last 50 years, but getting it more mainstream did pose challenges in cost and skills. These barriers are getting removed fast.”

“As such good governance/compliance and experiencing the benefits will play a significant role in people to adopt and trust AI. From a governance and monitoring perspective, making sure that models are fair, explainable, robust, well documented, and that a company can ensure lineage is key.”

Please share one of the most successful use cases from your work, and tell us why you consider it a success?

“For one of our customers in retail, we performed a customer segmentation, automated the entire process, and connected it to the marketing process that surveys the customers and collects more information about their preferences,” Delbecq said. “We consider it a success as they are currently migrating the code to production.”

And that’s key. In addition to AI becoming prevalent in every business, and not just those that naturally lean towards it because they come from the tech startup culture, not only do we need to make it accessible, but it needs to produce results and stay in production. It seems that we’re well on our way there.

Meet Ann-Elise Delbecq on November 18-20 at DN Unlimited Conference, Europe’s biggest data science virtual gathering. At the conference, you will be able to hear from IBM’s leading experts on AI, machine learning, data science & more.

Register to get a free pass here.

• The DN Unlimited Conference will take place online for the first time this year
• More than 100 speakers from the fields of AI, machine learning, data science, and technology for social impact, including from The New York Times, IBM, Bayer, and Alibaba Cloud
• Fully remote networking opportunities via a virtual hub

The DN Unlimited Conference will take place online for the first time this year.

The Data Natives Conference, Europe’s biggest data science gathering, will take place virtually and invite data scientists, entrepreneurs, corporates, academia, and business innovation leaders to connect on November 18-20, 2020.

The conference’s mission is to connect data experts, inspire them, and let people become part of the equation again. With its digital networking platform, DN Unlimited expects to reach a new record high with 5000+ participants. Visitors can expect keynotes and panels from the industry experts and a unique opportunity to start on new collaborations during networking and matchmaking sessions.

In 2019, the sold-out Data Natives conference gathered over 3000 data, technology professionals and decision-makers from over 30 countries, including 29 sponsors, 45 community and media partners, and 176 speakers.The narrative of DN Unlimited Conference 2020 focuses on assisting the digital transformation of businesses, governments, and communities by offering a fresh perspective on data technologies – from empowering organizations to revamp their business models to shedding light on social inequalities and challenges like Climate Change and Healthcare accessibility.

Data science, new business models and the future of our society

In spring 2020, the Data Natives community of 80.000 data scientists mobilised to tackle the challenges brought by the pandemic – from the shortage of medical equipment to remote care – in a series of Hackcorona and EUvsVirus hackathons. Through the collaboration of governments such as the Greek Ministry for Digital Governance, institutions such as the Charité and experts from all over Europe, over 80 data-driven solutions have been developed. DN Unlimited conference will continue to facilitate similar cooperation.

The current crisis demonstrates that only through collaboration, businesses can thrive. While social isolation may be limiting traditional networking opportunities, we are more equipped than ever before to make connections online.

The ability to connect to people and information instantly is so common now. It’s just the beginning of an era of even more profound transformation. We’re living in a time of monumental change. And as the cloud becomes ambiguous, it’s literally rewriting entire industries

Gretchen O’Hara, Microsoft VP; DN Unlimited & HumanAIze Open Forum speaker.

The crisis has called for a digital realignment from both companies and institutions. Elena Poughia, the Founder of Data Natives, perceives the transformation as follows:

It’s not about deploying new spaces via data or technology – it’s about amplifying human strengths. That’s why we need to continue to connect with each other to pivot and co-create the solutions to the challenges we’re facing. These connections will help us move forward

Elena Poughia, the Founder of Data Natives

The DN Unlimited Conference will bring together data & technology leaders from across the globe – Christopher Wiggins (Chief Data Scientist, The New York Times), Lubomila Jordanova (CEO & Founder, Plan A), Angeli Moeller (Bayer AG, Head Global Data Assets), Jessica Graves (Founder & Chief Data Officer, Sefleuria) and many more will take on the virtual stages to talk about the growing urge for global data literacy, resources for improving social inequality and building a data culture for agile business development. 

On stage among others:

• Erika Cheung, Executive Director, Ethics in Entrepreneurship
• Cory Doctorow, Science Fiction Author, Activist, and Journalist.
• Whurley (William Hurley), Eisenhower Fellow, a Senior Member of the Institute of Electrical and Electronics Engineers (IEEE)
• Alistair Croll, Founder, Solve for Interesting
• Clare Jones, Chief Commercial Officer, what3words
• Mark Turrell, Orcasci, CEO
• und many more …

If you are a Data Scientist wondering what companies could have the most career opportunities or an employer looking to hire the best data science talent but aren’t sure what titles to use in your job listings — a recent report using Diffbot’s Knowledge Graph could hold some answers for you.

According to Glassdoor, a Data Scientist is a person who “utilizes their analytical, statistical, and programming skills to collect, analyze, and interpret large data sets. They then use this information to develop data-driven solutions to difficult business challenges. Data Scientists commonly have a bachelor’s degree in statistics, math, computer science, or economics. Data Scientists have a wide range of technical competencies including: statistics and machine learning, coding languages, databases, machine learning, and reporting technologies.”

DATA SCIENCE COMPANIES: IBM tops the list of employers

Of all the top tech companies, it is no surprise that IBM has the largest Data Science workforce. Amazon and Microsoft have similar amounts of Data Science employees. Despite their popularity, Google and Apple are in the bottom two. Why is this the case? It could have something to do with their attitude to how to attract and retain a data scientist. The report does not clearly mention the reasons for these rankings. 

However, Data Scientists want to work for companies that provide them with the right challenges, the right tools, the right level of empowerment, and the right training and development. When these four come together harmoniously, it provides the right space for Data Scientists to thrive and excel at their jobs in their companies.

TOP FIVE COUNTRIES WITH DATA SCIENCE PROFESSIONALS: USA, India, UK, France, Canada

The United States contains more people with data science job titles than any other country. Glassdoor actually names “Data Scientist as the best job in the United States for 2019.”  After the United States are the following countries in this order:

• India
• United Kingdom
• France
• Canada
• Australia
• Germany
• Netherlands
• Italy
• Spain
• China

China has the least amount of data science job titles at 1,829 compared to the United States’ number of 152, 608. But what is the scenario for Data Scientists in Europe? What is the demand and supply? 

Key findings indicate that demand for Data Scientists far outweighs supply in Europe. The existence of a combination of established corporations and up-and-coming startups have given Data Scientists many great options to choose where they want to work. 

MOST SOUGHT AFTER DATA SCIENCE JOB ROLES: Data Scientist, Data Engineer and Database Administrator.

Among all companies, the most common job roles are Data Scientist, Data Engineer and Database Administrator. Data Scientist is the most common job role among all companies, with Database Administrator coming in at second place. If you remove Database Administrator, you find that Microsoft leads the way in terms of data science employees. This means that the reason for IBM’s lead in its data science workforce could largely be due to its sheer amount of Database Administrators. Unsurprisingly, across every job title in data science, males outnumber females 3:1 or more.  It is also interesting to note that this ratio only exists within the Database Administrator category. At the Data Scientist category, the ratio reads 6:1.

It also comes to no surprise that Data Scientist ranks number 1 in LinkedIn’s Top 10. It has a job score of 4.7, job satisfaction rating of 4.3 with 6,510 open positions paying a median base salary of $108,000 in the U.S. However, it is important to note that these positions do not work in isolation. A move towards Data Science collaboration is increasing the need for Data Scientists who can work alone and in a team as well. By utilizing the strengths of all the different job roles mentioned above, data science projects in companies remain manageable and their goals become more attainable. The main takeaway is that despite the vast amount of job titles, each role brings its own unique expertise to the table. 

DATA COLLECTION AND ANALYSIS

Diffbot is an AI startup whose Knowledge Graph automatically and instantly extracts structured data from any website. After rendering every web page and browser, it interprets them based on formatting, content, and web page type. With its record linking technology, Diffbot found the people currently employed in the data science industry at a point in time to provide an accurate representation of the statistics mentioned in this article. 

Data scientists are constantly challenged with improving their ML models. But when a new algorithm won’t improve your AUC there’s only one place to look: DATA. This guide walks you through six easy steps for data acquisition, a complete checklist for data provider due diligence, and data provider tests to uplift your model’s accuracy. 

Editor’s note: This free guide walks you through six easy steps for data acquisition, a complete checklist for data provider due diligence, and data provider tests to uplift your model’s accuracy.

When trying to improve a model’s accuracy and performance data improvement (generating, testing, and integrating new features from various internal and/or external sources) is time-consuming, difficult, but it could be a major discovery and move the needle much more.

The process of data acquisition can be broken down into six steps:

Hypothesizing – use your domain knowledge, creativity, and familiarity with the problem to try and scope the types of data that could be relevant to your model.

Generating a list of potential data providers – create a shortlist of sources (data partners, open data websites, commercial entities) that actually provide the type of data you hypothesized would be relevant.

Data provider due diligence – an absolute must. The list of parameters below will help you disqualify irrelevant data providers before you even get into the time-consuming and labor-intensive process of checking the actual data.

Data provider tests – set up a test with each provider that will allow you to measure the data in an objective way.

Calculate ROI – once you have a quantified number for the model’s improvement, ROI can be calculated very easily.

Integration and production – The last step in acquiring a new data source for your model is to actually integrate the data provider into your production pipeline.

Get the full guide for free here.

In 2020, companies face an operational environment that is unlike any seen before. Rapid globalization has created fierce competition, and digitalization is driving innovation in every direction. As a result, firms that fall behind are going out of business, and more than half of all Fortune 500 companies have left the marketplace in the past 15 years. 

To compete in this environment, more companies are turning to mergers and acquisitions, joint ventures, and partnerships to promote their products and stay afloat. And intuition is playing a smaller and smaller role in how decisions are made around mergers and acquisitions. As with so many other areas of business nowadays, M&A has become a data-driven process, built upon hard numbers that can help executives and entrepreneurs minimize risk. 

Once management from two firms has expressed interest in potentially partnering or beginning the process of a merger or acquisition, the data teams on both sides of the negotiating table are often charged with facilitating due diligence. Of course, in a business setting that is often highly regulated and privacy-focused, data transparency for M&A due diligence situations can be tricky. Successfully navigating that dynamic is critical to the process and is often the difference between a successful merger and a lost opportunity. 

With this conundrum in mind, here are three priorities for data pros that can lead to an effective data-driven acquisition process. 

Secure yet Thorough Information Acquisition 

While the digital age produces an abundance of data, sharing proprietary or sensitive information can be a challenging aspect of the acquisition process. That’s why many companies are turning to Virtual Data Rooms (VDRs) to share private information in a secure setting. 

VDRs allow bulk uploads of critical information and data sets while ensuring that files aren’t captured, downloaded, or shared. At the same time, it allows for activity tracking and insider analytics that provide specific insights into the process, enabling potential sellers to know who viewed their information, what they saw, and, in some cases, how they felt about the result. As business intelligence company Phocas points out, when facing an acquisition, data teams on the seller’s side need to share more than their own visualizations and reports. Because the buyer’s team will likely want to perform their own analysis as well, you’ll want to provide all of the backing data resources.

When preparing data to inform a merger or acquisition, then, entrepreneurs and their data teams should consider making complete resources available to analysts from all critical decision-makers, including: 

• C-Suite Executives. Providing pivotal data to the top leadership team keeps everyone in the loop and helps the decision making process move more quickly and smoothly. 

• Board of Directors. A board of directors governs most large companies interested in acquisitions. Compiling reports suitable for this team can likewise help expedite requisite approvals.
  
• Human Resource Managers. These leaders often require access to people’s most sensitive information, such as salaries, health insurance plans, and other personal details, which are often analyzed by acquiring companies. This data needs to be carefully monitored and protected. Depending on the locations of the two negotiating parties, employee data may need to be anonymized as well.

Of course, balancing ease-of-use and security is a complicated endeavor, but it’s one that many new platforms are well-equipped to handle. What’s more, while VDRs aren’t the only type of solution for sharing company information during the acquisition process, it’s a unique technological approach that saves time, money, and resources without compromising basic securing and accessibility. 

Analysis and Business Feasibility Assessments

Within the next two years, according to a recent report from Gartner, three-quarters of all data generated by enterprises will be created and processed outside of companies’ cloud servers and data centers. The information that we depend on for agile business and for M&A situations, then, is getting harder to centralize and therefore harder to control.

That’s why, despite the information being readily available, nearly three-quarters of an organization’s data goes unused. When it comes to corporate acquisitions, entrepreneurs are especially eager to convey their capabilities, a message which can quickly become muddled when too much data overwhelms those charged with assessing its consequences. 

Using services like ContractZen, entrepreneurs can produce VDRs and also deploy smart tools for tagging, managing, logging, and reporting data sets. It’s a governance efficiency platform that helps make data-driven acquisitions secure and smooth. 

Ultimately, critical acquisitions will rely on effective analysis that hinges on how effectively stakeholders can make a verifiable data-driven argument for their company’s valuation.

Authenticating the Conclusions

In 2020, everyone is well-aware of the prevalence of misinformation and disinformation, which means that potential buyers and partners aren’t inherently inclined to trust the information provided by entrepreneurs peddling the companies and ideas. 

Especially when teams of shareholders and lawyers are involved, due diligence is pivotal to any M&A. As a result, authentication is increasingly important, as potential buyers want to know that the data truly reflects a company’s standing and potential. 

If you suspect that an insider at your company might share sensitive intellectual property assets with unauthorized parties on the outside, which has the potential to derail a merger, security anomaly services like Code42 can detect and alert you when suspicious activity takes place.

Regardless, sellers need to be aware that there is often a significant due diligence period associated with mergers and acquisitions. However, by providing thorough and detailed records in a safe and accessible environment, sellers can ensure that buyers have all the information that they need to make an informed, speedy decision. 

Closing Thoughts 

In today’s disruptive but innovative digital economy, expect more companies to survive by merging or partnering with other organizations. It’s a healthy process that produces a comprehensive and complete ecosystem that meets customers’ demands. For sellers, it’s a high-stakes game that can make or break their financial future. 

Rather than leaving it up to chance, buyers want insights into their acquisitions, making the process increasingly data-driven and transparent. Those charged with producing and presenting this information can best serve their platforms by effectively preparing for data acquisition, analysis, and authentication. 

The global COVID-19 pandemic has generated a wide variety of responses from citizens, governments, charities, organizations, and the startup community worldwide. At the time of writing, the number of confirmed cases has now exceeded 1,000,000, affecting 204 countries and territories.

From mandated lockdowns to applauding health workers from balconies, a significant number of people are taking this as an opportunity to step up and help in any way they see fit. And this is true of the various tech ecosystems too.

And while some are repurposing their existing startups and businesses to assist with the pandemic response, others are joining an ever-expanding number of hackathons across the globe to come up with fresh ideas and feasible solutions.

One such hackathon, #HackCorona, gathered over 1,700 people, and during the course of the 48-hour long online event, 300 people delivered 23 digital solutions to help the world fight the outbreak. Organized by Data Natives and Hacking Health Berlin, the event was created in record time, a hallmark of people’s response to the situation. There really is no time to waste.

Attracting hackers from 41 countries, the teams worked tirelessly to produce solutions that were useful, viable, and immediately available to help in a multitude of areas affected by the spread of the novel coronavirus. Mentors and jurors from Bayer Pharmaceuticals, Flixbus, MotionLab.Berlin, T-Systems International, Fraunhofer, and more both assisted the teams with their applications, and decided which would win a number of useful prizes.

“We are happy to have created a new community of inspired, talented, and creative people from so many different backgrounds and countries eager to change the course of this critical situation,”  CEO at Data Natives, Elena Poughia, said. “This is exactly the reason why we, at Data Natives, are building and nurturing data and tech communities.” 

Distrik5, born from members of the CODE University of Applied Sciences in Berlin, developed a digital currency that is earned when one of its users provides assistance to the elderly, those that are at the highest risk of dying from COVID-19 and its associated complications. The team won a fast track to join the current incubator cohort at Vision Health Pioneers.

Homenauts created a participatory directory of resources to help maintain strong mental health while isolating. Polypoly.eu developed Covid Encounters, a mobile app to track exposure and alert citizens without compromising privacy. HacKIT_19 created a solution that uses data to help you make better decisions with self-reported symptoms. 

In total, eight teams created winning solutions that are viable and instantly applicable to the crisis. And #HackCorona is just one of many such examples around the world.

“The solutions created were a good mixture of ‘citizen first’ solutions with the aim to assist people with limited technology,” Poughia said. “However, what really stood out to me was that we need more data scientists working closely with epidemiologists to predict and understand the current outbreak.”

Poughia warns that we mustn’t slow down now, or become complacent.

“I think it is admirable to see institutions, academic universities, incubators, and accelerators joining in to support the projects,” Poughia said.

“What we need is immediate action and immediate support to keep the momentum going. Volunteers should continue to come together to help but we also need the support of governments, companies, startups, and corporations, so that we can accelerate and find immediate solutions.”

Data Natives is now bringing the #HackCorona concept to Greece. With the support of the Greek Ministry of Digital Governance, Hellenic eHealth and innovation ecosystems and co-organised by GFOSS and eHealthForum, the second edition of HackCorona aims to find creative, easily scalable, and marketable digital solutions. Its aim is to help hospitals manage the supply and demand chain, provide real-time information for coronavirus hotlines, offer telehealth solutions allowing doctors to care for patients remotely, use data to create an extensive mapping, create symptom checkers, and more. 

HackCoronaGreece is currently gathering teams of data scientists, entrepreneurs, technology experts, designers, healthcare professionals, psychologists, and everyone who is interested in contributing for a weekend-long hacking marathon which will conclude on Monday, April 13th with a closing ceremony. Applications are closing on April 10th at 23:59 Central European Time.

Head of Marketing for TechBBQ, and co-organizer of Hack the Crisis DK, Juliana Geller explained the motivation behind creating hackathons at times of need.

“It’s the potential of getting people of all walks of life together to create solutions to a problem that affects all of us,” Geller said. “By doing that for this particular challenge, we can prove it is possible to do it for all the other challenges we face as a society.”

Hack the Crisis is, in fact, not one hackathon, but an entire series that have been set up to find solutions pertaining to COVID-19. Hack the Crisis Norway ran for 48 hours on March 27, 2020, and was won by a team that used 3D printing technology to put visors in the hands of medical staff on site, saving time and reducing the supply chain dramatically.

Of course, bringing people together to create apps, products, and services is one thing, but getting to market quickly enough to make a difference is an entirely different proposition. Almost every hackathon I looked at when researching this article has built deliverability into the judging criteria, so that those who can put the solution into the hands of those that need it are rewarded.

“One of our judging criteria is actually that the solution is delivered as an MVP by the end of the Hackathon and had the potential to be developed into a go-to-market product quickly”, Geller said. “Besides for the ‘saving lives solutions,’ which are obviously the most urgent, we want to see ideas to help the community and help businesses, and it is already clear that those will be affected for a much longer period. So we are positive that the solutions will indeed make a difference.”

Hack the Crisis was originally created by Garage48 AccelerateEstonia, and other Estonian startups, but it has become an entire hackathon community, determined to not only support the efforts against the novel coronavirus, but to supporting other hackathon creators.

Anyone can organize a hackathon and post it on the Hack the Crisis website, which at the time of writing has 46 hackathons listed in over 20 countries. Geography, of course, it not important at this time, since every hackathon is being run remotely, but it does illustrate how global the response is, and how everyone, everywhere, is looking to solve the biggest COVID-19 challenges.

“It is a worldwide movement,” Geller said. “And on April 9-12, 2020, there’ll be a Global Hack. But that is not where it stops, absolutely not. We want to generate solutions that will have value after this crisis, that can actually become a startup and keep benefiting the community later on.”

But there are also groups that are forgoing the traditional hackathon format and are coming up with solutions created in WhatsApp, Telegram, and Facebook Messenger group chats. One such chat was created by Paula Schwarz, fondatrice of the Cloud Nation and founder of Datanomy.Today.

By bringing together like-minded people, and through constant curation of the chat and calls to action to incentivize members to come up with solutions, Schwarz has created a pseudo-hackathon that never ends.

One such solution is Meditainer, which helps get important supplies to those in need. It’s a simple solution, but one that was created quickly and effectively. 

Meditainer is a project very close to Schwarz’ heart. “My grandfathers started a medical company shortly after the second world war,” she said. “This is why I have very good connections in the pharmaceutical sector.”

“Since I had mandates from the United Nations to organize the data of 25 cities and I watched the supply chains of the United Nations fall apart, I realized that right now is the time to leverage my network and the background of my family, together with sophisticated usage of data in order to provide next-level healthcare innovation for the people,” Schwarz said.

So how does it work? 

“Meditainer works directly with governments and strong institutional partners inside and around the United Nations to close supply gaps in healthcare through our effective public-private partnerships,” Schwarz said. “It operates as a distributor of thermometers, smart corona tests and apps that will hopefully help to reduce the spread of the virus.”

So whether you organize a hackathon, participate in one, or create your own “mastermind group” on a messaging platform, there’s one thing that is for sure – you’re making a difference and you’re aiding those in need, when they need it the most.

The benefits for society are obvious, and the growth you’ll witness by getting involved in some way is also extremely apparent.

“I’m grateful to be working with so many active masterminds and I look forward to getting to know key players in the industry even better,” Schwarz said.

The startup industry, and those connected to it, have really stepped up at a time when it is needed the most, and long may that spirit continue.

Five experts went live with educational sessions at our community site DN Club and told us about technology in times of coronavirus. Where are we heading in the crisis and how can the tech community contribute to finding solutions? Even though we are moving towards difficult times, there might be some light at the end of the tunnel. This crisis could make open-source movements flourish and could even fix our healthcare systems. 

Birds can be heard chirping loud, as Mark Turrell (CEO at Orcasci, Founder of unDavos) talks to the Data Natives online community from his garden. A squirrel might even jump on his head at any moment, he warns. In this idyllic scene from his home quarantine it might not seem so at first sight, but the entrepreneur, author and contagion expert is worried. And that says a lot, coming from a man who also used to be a spy in Libya and Syria. “We are living in a very unusual time”, he says.

Turrell was in Davos this year when the coronavirus crisis broke loose in Wuhan. He became alarmed when he learned that the Chinese government had closed Wuhan. “A city of 16 million people, to just shut it down, that is weird”, he tells. “And then I saw, this virus has properties that will make it extremely hard to suppress and extremely hard to defeat.”

It’s important to know what we are up against. That’s why he advises everyone to play the game “Plague Inc.”. He takes out his phone, as he tells the Data Natives community that we are now the virus and our purpose is to destroy humanity. “So we give it a name and we give it certain properties”, he says, selecting to make the virus airborne. “Then the symptoms. If I want to wipe out the earth, I want the virus to give no symptoms, so people won’t notice it.” 

That’s how you win the game as a virus and that’s exactly how the coronavirus behaves. It might be the key to why the coronavirus became a pandemic: it spreads through infected persons who don’t have any symptoms yet or won’t get them at all. “On the third day of being infected a person becomes infectious, but only on the fifth day usually symptoms start to appear”, tells Turrell.  

Time is ticking

The problem is, we still know very little about the disease that now mutates every thirty days. “So what first came out of China isn’t necessarily experienced by what is now experienced in the US or in Germany”, he tells.” In China, 90% of the cases had fever first. That’s why China measured temperatures when you walked around because for them it is a good early warning sign.” But in Germany, most cases started with coughing. 

There is still a lot to get to know about the virus, but the time is ticking. Most likely, the virus will become more benign as it mutates, but it could also become more dangerous. “It is plausible that some mutations will become more lethal. Or give more serious symptoms, for example, to the younger generation”, he says. But a cure is still far from being available. According to Turrell, it will take at least 18 months to get to a safe and effective vaccine, 2,5 years at worst. 

If we keep business closed and ourselves locked, that could have detrimental consequences to the economy, he worries. With no immunity in society, the virus can spread any time we get out of our isolation. Businesses might open and close again, uncertain economies will spiral into bankruptcies and layoffs. The 2008 financial crisis could be a bliss compared to what we are looking at, he warns: 

“Central banks are already pulling out the big guns now and what is it, day twelve? What options do we have left?” 

Mark Turrell

A Facebook post from a senior doctor in New York gave him an idea. After being sick from the coronavirus for two weeks, she wrote how happy she was to now be immune, so she could focus completely on curing patients. “My concept is safe infection and recovery”, he says. In his plan, healthy volunteers would get infected under medical supervision and then monitored whether they are on track for recovery, or need special treatment. “In this way, you can immunize the population quickly.” 

So far as we know, the chances of infection with the virus developing into a serious situation are slim for a person under 50, with no previous diseases. According to Turrell, his plan would be a chance to create group immunity in societies fast. A prolonged economic crisis may kill more people than the virus itself. “I understand there are medical concerns”, he says.

“But we are looking at the end of days at the moment. Where there will be practically no businesses standing.” 

Mark Turrell

Difficulties to comprehend 

In another DN Club webinar, an expert in digital transformation Bart de Witte explores with his two guests how data science can help societies during the pandemic. Simone Bianco is genomics and AI researcher at IBM, Tamas David-Barrett is a Behavioural Scientist. 

David-Barrett started the online conversation with his observation that humans seem to be struggling a great deal to managing the coronavirus crisis.

“We are not good at two things: exponential curve and risk”, he tells. “We don’t have a feel for it.” 

Tamas David-Barrett

That’s why the response in many places in the world was delayed. When in China all hell broke loose, there was very little worry in Europe that it might come to that side of the world as well. 

“Even today, it’s happening in Italy and France, but in the UK for example, people keep hesitating and hesitating with the response. It is almost like they don’t believe it until the death rates are going up”, he tells. 

“We have wasted all that time. We have a real problem imagining this process.” 

Bianco also thinks countries around the world could have had a much better uniform response. That’s because we could see such a pandemic coming, we even knew that a respiratory disease would likely cause it.

“Preparedness should be ensured at the level of governments and large bodies. But this situation now requires the efforts of multiple institutions. Scientists need to be at the forefront.”

Simone Bianco

Because of the late response, a lot of combined efforts are needed to overcome our challenges. “We need people”, he says. 

“The ideal task force to advise governments would be people with strong mathematics and computational skills. They can accelerate the creation of public measures, vaccines, and drugs. So we can control these pandemics more quickly.”

Online community to the rescue 

But one amazing advantage of this age is that it stands strong online. We are all able to contribute to the challenges we are facing from our homes. Online data science communities are a source of power that shouldn’t be underestimated. For example, bioinformatics is the backbone of drug development. The data science community can contribute to the development of new vaccines and drugs, using sources like Kaggle, where over 26.000 papers about the coronavirus are available. 

This time of crisis shows how very crucial open-source is. Researching pandemics in recent weeks, Bart de Witte read how people bought religious from the church, in the hope that it would cure them of the plague.  “A lack of knowledge hurts society”, says De Witte. 

“This pandemic might be a chance for us to realise we need to open up information.” 

Bart De Witte

David-Barrett agrees that these efforts are hugely important, and says Academic institutions became what they are today, in their efforts to push out theology from the field of science. “But maybe this is the time to go away with this 200-year-old structure”, he tells.

 “Maybe this is the moment to use this outpouring of collaborations against a common enemy, to somehow reach academia.” 

Tamas David-Barrett