Effective document management can significantly impact your productivity in a digital workspace. Streamlining how you handle files saves time and enhances the clarity and impact of your communications. Discover innovative strategies that empower you to maximise your documents without the usual headaches.

Simplifying document collaboration

Working on team projects often involves multiple stakeholders contributing to a single document. Simplifying collaboration is crucial to ensuring everyone is aligned. Leverage tools that allow real-time editing and feedback. This approach minimises the back-and-forth of email attachments and consolidates all changes in one place.

A platform that supports collaborative editing enables team members to add comments and suggestions directly to the document. A PDF editor can also make annotations and edits, further streamlining the feedback process. This method speeds up the review process and keeps all feedback organised. Adopting these techniques fosters a culture of open communication, allowing team members to contribute their insights without confusion.

Consider integrating tools that facilitate online collaboration to implement this in your workflow. Encourage team members to utilise shared links rather than sending documents via email. This practice enhances productivity and ensures everyone’s input is valued.

Creating engaging visual content

Visual elements can transform ordinary documents into captivating presentations. Incorporating images, charts, and infographics helps convey complex information clearly and engagingly. This is especially beneficial in business settings, where data-driven decisions rely on comprehensible visuals.

Utilise graphics that illustrate key points or data trends. For example, rather than just presenting numbers in text form, create a bar chart or pie chart to represent the data visually. This grabs attention and aids in retention. Visuals can break up lengthy text, making documents more inviting to read.

Experiment with design elements like colours, layouts, and fonts for practical application. Many online platforms offer templates you can customise to fit your brand or project needs. Investing time in visual storytelling can elevate your documents, making them stand out and fostering better understanding among readers.

Additionally, consider using icons or symbols to represent concepts visually. This technique can reduce text clutter and make information more digestible. When designing visuals, remember your audience; ensure that graphics are relevant and contribute to the overall message you aim to convey.

Streamlining formatting and layout

Professional-looking documents enhance credibility. A well-structured layout ensures your message is delivered effectively and elegantly. Streamlining formatting is essential to achieving this goal; it saves time and reduces the stress of manual adjustments.

Adopt consistent styles for headings, fonts, and bullet points to create a cohesive look. Using pre-set styles in your document editor can significantly speed up this process. Additionally, tools that allow for easy adjustments in margins and spacing help maintain a polished appearance.

Consider creating a style guide for your team to implement these formatting techniques effectively. This document should outline preferred fonts, colours, and layout specifications to ensure everyone is aligned. Setting this standard makes it easier for team members to create documents that reflect your organisation’s branding and professionalism.

Moreover, automatic table of contents generation can enhance navigability in longer documents. This is particularly useful for reports or proposals where readers need to locate specific sections quickly. By focusing on presentation, you strengthen the professionalism of your work, making a lasting impression on your audience.

Automating repetitive tasks

Repetitive tasks such as document formatting or data entry can drain your productivity. Identifying and automating these tasks can save valuable time for more critical work. Leveraging tools that offer automation features can simplify your workflow significantly.

Using macros to automate document formatting saves time on manual adjustments. Similarly, certain tools allow batch processing of files, enabling you to simultaneously make changes across multiple documents. This is especially useful for teams handling large volumes of files regularly.

Begin by analysing your current workflow to pinpoint tasks that consume excessive time. Focus on simple, repeatable actions, such as standardising formats or inserting commonly used phrases. Once identified, explore the automation features available in your document management tools.

Use templates for commonly used documents to integrate automation into your routine effectively. By setting up templates with predefined fields and formatting, you can streamline the creation of new documents while maintaining consistency. Embracing technology will boost productivity and reduce the likelihood of errors stemming from manual processes.

Enhancing security and privacy

Safeguarding sensitive information is paramount, especially when data breaches are becoming prevalent. Ensuring secure documents protects your data and builds trust with clients and stakeholders. Implementing strategies to enhance document security is essential in the current digital environment.

Use password protection and encryption for confidential files. Many online platforms provide options to restrict access, ensuring only authorised individuals can view or edit the documents. Additionally, regularly updating security protocols can help mitigate risks associated with data breaches.

Establish protocols for handling sensitive information to secure your documents effectively. Create a checklist for team members to follow when creating, storing, and sharing confidential files. This could include regular password updates, encryption practices, and guidelines for safe sharing.

Integrating feedback loops

Incorporating feedback loops into your document management process can significantly enhance productivity. Regularly seeking input from your team or stakeholders helps identify areas for improvement and ensures documents meet the audience’s needs. Establishing a culture of constructive feedback refines your documents and promotes collaboration.

Schedule periodic reviews where team members can provide insights on ongoing projects. This enhances the quality of the documents and fosters a sense of ownership among team members. When individuals feel their contributions matter, they are more likely to engage deeply with the work.

To facilitate this practice, create a structured feedback system that clearly outlines when and how feedback will be gathered. This could include using shared comment features in document editors or setting up regular team meetings to review drafts. By integrating feedback loops, you ensure documents remain relevant and effective, ultimately boosting overall productivity.

These strategies are a foundation for improving document handling and productivity. By embracing innovative approaches, you can enhance your documents’ clarity and effectiveness, leading to more successful collaborations and outcomes.

No one will argue that cloud platforms were the first step into a new era of data storage and management. No matter if we’re talking personal files or big business operations, this technology is becoming the go-to solution for individuals and organizations alike. But why are so many people embracing cloud platforms for data storage and management? Are they secure enough? Effective enough? We take a deeper look into the benefits and potential risks of using these tools.

What makes cloud platforms so popular?

At their core, cloud platforms are so handy because they provide a way to store and access data remotely. Unlike traditional storage methods, which require physical devices like hard drives or servers, cloud storage allows you to save information online and manage it from anywhere with an internet connection. This level of remote access makes life easier for professionals and businesses across all fields, sure, but they also help people with daily tasks on an individual level. Say, you’re a student. You can upload dissertations or theses to a cloud platform, accessing them anytime without computer crashes or the clutter of USB drives and cables. Plus, after all the effort that goes into those papers, losing them is not an option! Speaking of effort, if you’re stuck rephrasing parts of your written work, check out this helpful free tool to save time and polish your text.

The advantages of cloud storage

Why are cloud platforms gaining popularity? Let’s cut to the chase because the benefits speak for themselves.

• Convenience. Cloud platforms allow users to access their data anytime, anywhere, from multiple devices. If you’re working on a business presentation or editing your essay on the go, cloud storage keeps everything within reach.
• Scalability. Need more storage space? Cloud platforms grow with your needs, boosting business agility, no matter if you’re a small organization or a large corporation.
• Collaboration. Many cloud platforms make it easy for teams to collaborate in real time. Imagine co-writing a paper with a classmate or sharing project updates with your team seamlessly.
• Cost-effective. Instead of investing in expensive hardware, cloud platforms offer affordable subscription plans, making them a budget-friendly solution for individuals and businesses.
• AI integration and analytics. Many modern platforms incorporate AI tools to optimize data analytics and improve workflow, helping businesses drive innovation and agility.

As you can see, these reasons are more than enough to sway people in favor of cloud storage. It is simply more convenient to have it since it provides opportunities for better productivity and improved security in terms of personal and professional use. Speaking of professional…

How cloud platforms are transforming business

If you take a look at today’s business arena, you’ll notice that cloud platforms aren’t just used for storing files anymore. They are powering everything from data management and SaaS tools to forward-thinking projects that drive real business innovation.

First of all, using SaaS (Software as a Service) is now easier than ever. Instead of installing software on every computer or constantly updating programs, you can access the tools you need over the internet. From Google Workspace to Microsoft 365, these cloud-based apps have made life easier by letting teams work together and stay synced without the typical tech headaches. A recent study in the International Journal of Business, Humanities and Technology looked at how companies decide whether to build or acquire their SaaS solutions and how those choices affect their overall success. The researchers found that businesses with strong marketing and R&D teams are more likely to develop SaaS in-house which is a strategy that often leads to a bigger slice of the market. On the flip side, companies that opt to buy their SaaS through mergers and acquisitions tend to see dips in gross profits and face longer waits before reaping any real benefits from the deal.

What about boosting innovation with data analytics and AI? With the cloud, it’s simpler than ever to dive into data analytics and tap into AI integration. You can process huge amounts of information, spot trends, and make smarter decisions, all of which helps your business stay competitive and keeps innovation on track.

Finally, the rise in remote work has pushed many businesses to make sure everyone can connect to the same data and projects, no matter where they are. That’s where cloud platforms come in. They let teams handle documents, software, and collaboration from anywhere, making remote access second nature.

Keeping your data safe on cloud platforms

With all their advantages, cloud platforms also come with concerns — chiefly, cloud security, which is no surprise. When you store sensitive documents online, such as financial records or personal projects, you can’t help but wonder: what if there’s a leak? If you are concerned with keeping your data secure, there are some additional measures you can take apart from simply subscribing to a cloud service.

• Choose reputable providers. Stick to well-known cloud management platforms with strong security measures. Look for reviews and check their data protection policies.
• Use strong passwords. It might sound basic, but a strong password can prevent unauthorized access. Use unique passwords for each platform and change them regularly.
• Enable two-factor authentication (2FA). Adding an extra layer of security makes it harder for hackers to access your account.
• Backup important files. While cloud storage is reliable, keeping an additional offline backup can save you from unexpected issues.
• Understand sharing permissions. When sharing files, ensure you’re only granting access to the right people. Avoid making sensitive files publicly available.

Of course, this is not the end of possible ways to protect your data even further, but it is a genuinely good baseline that will keep you safe and cross off the majority of situations, where your security could be compromised. 

Why data protection matters

Data is valuable — not just to you but potentially to others who may misuse it. For students, losing a thesis or dissertation can mean months of wasted effort. For businesses, a data breach can lead to financial losses and damage to reputation. Cloud platforms are designed to prioritize security, but users must also take responsibility for protecting their information. It is a partnership: the provider secures the platform, and you take steps to secure your data.

Students and cloud platforms: A mtch made in heaven?

Students are among the biggest beneficiaries of cloud platforms, thanks to all of the essays, sources, textbooks, and other sorts of files needed to complete even the most basic course. From storing class notes and essays to collaborating on group projects, these tools make academic life more manageable, so many students also use cloud platforms to save time and avoid problems that may come from using multiple devices. What a bummer it is to pack your tablet for a weekend trip, planning to work on your paper, and then remember two hours into the journey that you forgot to download the file to your computer.

Writing a thesis or dissertation is no small feat. It involves countless hours of research, writing, and revisions. To make this process less overwhelming, students often turn to AI tools for help. These tools can assist with everything from organizing ideas to rephrasing complex sentences. If you’re looking for an easy way to polish your work, why not try a paraphrasing tool? As technology progresses due to its huge demand and marketability, cloud platforms will continue to stay relevant. Innovations in AI integration and network optimization are making these platforms smarter, faster, and safer.

Looking ahead, we can expect:

• Better AI tools due to improved capabilities for analyzing data and optimizing workflows.
• Stronger security measures as a result of continuous improvements in encryption and privacy protection.
• Seamless multi-platform access, which basically means even better synchronization across devices and operating systems.

Wrapping it up

Cloud platforms have completely changed the way we handle data. They offer convenience and flexibility which is what people are looking for. If you’re a student protecting your thesis or a company looking to streamline operations, these services are proving invaluable and they’re not going anywhere anytime soon.

Still, security is something we all share responsibility for. Sorry not sorry, but you’ll get the benefits of cloud platforms without putting your information at risk only by sticking to solid safety practices and using the right tools. And if you ever find yourself buried in document edits, ask AI  helpers to lighten the load. As technology evolves, data management will keep focusing on adaptability, so make the most of cloud technology and see how it can enhance both your personal projects and professional goals.

Text-to-video generation is not what it was even just a few years ago. We transformed it into a tool with truly futuristic functionality. Users create content for personal pages, influencers leverage it for self-promotion, and companies utilize it for everything from advertising and educational materials to virtual training and movie production. The majority of text-to-video systems are built on the architecture of diffusion transformers, which are the cutting edge in the world of video generation. This tech serves as the foundation for services like Luma and Kling. However, this status was only solidified in 2024, when the first diffusion transformers for video gained market adoption.

The turning point came with OpenAI’s release of SORA, showcasing incredibly realistic shots that were almost indistinguishable from real life. OpenAI showed that their diffusion transformer could successfully generate video content. This move validated the potential of the tech and sparked a trend across the industry: now, approximately 90% of current models are based on diffusion transformers. 

Diffusion is a fascinating process that deserves a more thorough exploration. Let’s understand how diffusion works, the challenges the transformer technology addresses in this process, and why it plays such a significant role in text-to-video generation.

What is the diffusion process in GenAI?

At the heart of text-to-video and text-to-image generation lies the process of diffusion. Inspired by the physical phenomenon where substances gradually mix — like ink diffusing in water — diffusion models in machine learning involve a two-step process: adding noise to data and then learning to remove it.

During training, the model takes images or sequences of video frames and progressively adds noise over several steps until the original content becomes indistinguishable. Essentially, it turns it into pure noise. 

Diffusion and Generation processes for “Beautiful blowing sakura tree placed on the hill during sunrise” prompt

When generating new content, the process works in reverse. The model is trained to predict and remove noise in increments, focusing on a random intermediate noise step between two points, t, and t+1. Because of the long training process, the model has observed all steps in the progression from pure noise to almost clean images and now has quite the ability to identify and reduce noise at basically any level. 

From random, pure noise, the model, guided by the input text, iteratively creates video frames that are coherent and match the textual description. High-quality, detailed video content is a result of this very gradual process.

Latent diffusion is what makes this computationally possible. Instead of working directly with high-resolution images or videos, the data is compressed into a latent space by an encoder. 

This is done to (significantly) reduce the amount of data the model needs to process, accelerating the generation without compromising quality. After the diffusion process refines the latent representations, a decoder transforms them back into full-resolution images or videos.

The issue with video generation

Unlike a single image, video requires objects and characters to remain stable throughout, preventing unexpected shifts or changes in appearance. We have all seen the wonders generative AI is capable of, but the occasional missing arm or indistinguishable facial expression is well within the norm. In the video, however, the stakes are higher; consistency is paramount for a fluid feel.

So, if a character appears in the first frame wearing a specific outfit, that outfit must look identical in each subsequent frame. Any change in the character’s appearance, or any “morphing” of objects in the background, breaks the continuity and makes the video feel unnatural or even eerie.

Image provided by the author

Early methods approached video generation by processing frames individually, with each pixel in one frame only referencing its corresponding pixel in others. However, this frame-by-frame approach often resulted in inconsistencies, as it couldn’t capture the spatial and temporal relationships between frames that are essential for smooth transitions and realistic motion. Artifacts like shifting colors, fluctuating shapes, or misaligned features are a result of this lack of coherence and diminish the overall quality of the video.


Image provided by the author

The biggest blocker in solving this was computational demand — and the cost. For a 10-second video at 10 frames per second, generating 100 frames increases complexity exponentially. Creating these 100 frames is about 10,000 times more complex than generating a single frame due to the need for precise frame-to-frame coherence. This task requires 10,000 times more in terms of memory, processing time, and computational resources, often exceeding practical limits. As you can imagine, the luxury of experimenting with this process was available to a select few in the industry.

This is what made OpenAI’s release of SORA so significant: they demonstrated that diffusion transformers could indeed handle video generation despite the immense complexity of the task.

How diffusion transformers solved the self-consistency  problem in video generation

The emergence of diffusion transformers tackled several problems: they enabled the generation of videos of arbitrary resolution and length while achieving high self-consistency. This is largely because they can work with long sequences, as long as they fit into memory, and due to the self-attention mechanism.

In artificial intelligence, self-attention is a mechanism that computes attention weights between elements in a sequence, determining how much each element should be influenced by others. It enables each element in a sequence to consider all other elements simultaneously and allows models to focus on relevant parts of the input data when generating output, capturing dependencies across both space and time. 

In video generation, this means that every pixel in every frame can relate to every other pixel across all frames. This interconnectedness ensures that objects and characters remain consistent throughout the whole video, from beginning to end. If a character appears in one frame, self-attention helps prevent changes and maintain that character’s appearance in all subsequent frames.

Before, models incorporated a form of self-attention within a convolutional network, but this structure limited their ability to achieve the same consistency and coherence now possible with diffusion transformers.

With simultaneous spatio-temporal attention in diffusion transformers, however, the architecture can load data from different frames simultaneously and analyze them as a unified sequence. As shown in the image below, previous methods processed interactions within each frame and only linked each pixel with its corresponding position in other frames (see Figure 1). This restricted view hindered their ability to capture the spatial and temporal relationships essential for smooth and realistic motion. Now, with diffusion transformers, everything is processed simultaneously (Figure 2).

Spatio-temporal interaction in diffusion networks before and after transformers. Image provided by the author

This holistic processing maintains stable details across frames, ensuring that scenes do not morph unexpectedly and turn into an incoherent mess of a final product. Diffusion transformers can also handle sequences of arbitrary length and resolution, provided they fit into memory. With this advancement, the generation of longer videos is feasible without sacrificing consistency or quality, addressing challenges that previous convolution-based methods could not overcome.

The arrival of diffusion transformers reshaped text-to-video generation. It enabled the production of high-quality, self-consistent videos across arbitrary lengths and resolutions. Self-attention within transformers is a key component in addressing challenges like maintaining frame consistency and handling complex spatial and temporal relationships. OpenAI’s release of SORA proved this capability, setting a new standard in the industry: now, approximately 90% of advanced text-to-video systems are based on diffusion transformers, with major players like Luma, Clink, and Runway Gen-3 leading the market.

Despite these breathtaking advances, diffusion transformers are still very resource-intensive, requiring nearly 10,000 times more resources than a single-image generation, making training high-quality models still a very costly undertaking. Nevertheless, the open-source community has taken significant steps to make this technology more accessible. Projects like Open-SORA and Open-SORA-Plan, as well as other initiatives such as Mira Video Generation, Cog, and Cog-2, have opened new possibilities for developers and researchers to experiment and innovate. Backed by companies and academic institutions, these open-source projects give hope for ongoing progress and greater accessibility in video generation, benefiting not only large corporations but also independent creators and enthusiasts keen to experiment. This, as with any other community-driven effort, opens up a future where video generation is democratized, bringing this powerful technology to many more creatives to explore.

Prefabricated construction is experiencing a significant transformation thanks to data science. From improving design efficiency to optimizing material usage, data-driven insights reshape how prefabricated structures like metal building kits are manufactured and assembled. This integration of technology is making steel kits more affordable, customizable, and sustainable for a wide range of applications.

Data-driven design for customization

One of the most significant impacts of data science on garage kits is in the design phase. Advanced algorithms analyze customer preferences, geographic conditions, and material requirements to generate highly customizable designs. This ensures that every garage kit meets the specific needs of the buyer, whether it’s for residential, commercial, or agricultural use.

Data science also enables predictive modeling, allowing manufacturers to test designs virtually before production. This minimizes errors, reduces material waste, and accelerates production timelines. Customers benefit from garage kits that are not only tailored to their needs but also built with precision and efficiency.

Optimizing material usage

Efficient material usage is a cornerstone of prefabricated construction, and data science is pivotal in achieving this. Advanced analytics help manufacturers determine the exact amount of materials needed for each garage kit, minimizing waste and cutting costs.

For instance, data-driven tools can assess the structural integrity of different materials under various conditions, ensuring that only the most suitable options are chosen. This optimization saves resources and contributes to the sustainability of prefabricated construction.

Streamlining manufacturing processes

The manufacturing process for garage kits has been revolutionized through data science. Real-time monitoring and machine learning algorithms improve production efficiency by identifying bottlenecks and suggesting improvements.

Automation powered by data insights ensures that every component is produced with accuracy, reducing defects and delays. This streamlined approach enables manufacturers to meet tight deadlines and deliver high-quality products consistently.

Enhancing supply chain efficiency

Supply chain management is another area where data science is making a significant impact. Predictive analytics help manufacturers anticipate demand, manage inventory, and coordinate logistics effectively.

For example, data can predict seasonal spikes in garage kit sales, enabling manufacturers to prepare adequately and avoid shortages. This proactive strategy ensures that customers receive their kits on time, maintaining satisfaction and trust in the brand.

Improving assembly and installation

Data science doesn’t just optimize the production of garage kits; it also enhances the assembly and installation process. Detailed instructions and 3D visualizations generated from data insights make it easier for customers to assemble their kits.

Augmented reality (AR) tools, powered by data science, are also being introduced to guide users through the step-by-step installation process. These innovations reduce assembly time and ensure the final structure is sturdy and reliable.

Sustainability through data science

Sustainability is a growing priority in the construction industry, and data science helps prefabricated construction align with this priority. Garage kits become more eco-friendly by optimizing material usage, reducing waste, and improving energy efficiency.

Data science also facilitates the integration of renewable energy solutions, such as solar panels, into garage kit designs. These features enhance the sustainability of the structures and appeal to environmentally conscious buyers.

Data science is transforming prefabricated construction, particularly in building garage kits. From customization and material optimization to streamlined manufacturing and enhanced assembly, this technology drives innovation across every stage of the process.

The result is a more efficient, sustainable, and customer-focused approach to prefabricated construction. Data science continues to evolve, it will undoubtedly unlock new possibilities, further revolutionizing how garage kits and other prefabricated structures are designed and built.

What’s the first thing that comes to mind when you think of engineers? Perhaps it’s a vision of someone in a hard hat helping to build the infrastructure of tomorrow – whether it be buildings, bridges, or highways.

For many of us, engineering brings up a romantic view – of someone working on things that help our economy tick along. While it’s true that engineers can work on big projects, you may be surprised to learn that they are often also significant contributors to the design and development of data centres – a central tenet of modern data engineering.

For engineers, a qualification such as a Graduate Diploma in Data Science can help refine their skills further and provide them with the best possible start to roles such as machine learning (ML) engineers. Let’s discover how the skills that engineers learn can be readily repurposed for use in one of today’s fastest-growing industries.

Engineering: More than construction

Engineering is a field often defined by incorrect assumptions and perceptions. Many people lack an understanding of what an engineer does, incorrectly assuming that engineering roles focus solely on construction problems – from bridges to buildings and beyond. In reality, a career as an engineer is far more diverse than the big construction projects you may see on TV. So, what does an engineer do?

In reality, engineers form a much more diverse field of problem-solving professionals. Engineers are problem-solvers who are heavily involved in developing systems, products, machines, and structures. Using scientific research and findings, they apply this knowledge to develop solutions – whether using new knowledge to improve the efficiency of existing systems or developing products that help contribute to a larger overall project.

Depending on an engineer’s particular skillset, they may be involved in developing solutions to some of the world’s biggest challenges, which aren’t necessarily things ordinary Aussies see every day. Consider, for example, the infrastructure required to keep the Internet operational – something that seems as simple as an IP address often has required the work of engineers.

In engineering, two types of engineers work heavily with computers and computer systems: software engineers and electrical engineers.

Software engineers are the type of engineers involved in developing software and programs – solutions that, by design, are heavily immersed in a modern, digital world. These engineers often form part of development teams, helping to contribute to the creation of well-defined software solutions and maintenance post-release.

Electrical engineers, on the other hand, are involved in the development of physical infrastructure – in particular, those involving electrical systems, from systems as large as power plants to as small and complicated as the fabrication of the computer chips that software engineers use every day.

An emerging field: Machine learning

In today’s increasingly data-dependent world, engineers are facing new challenges. Take, for example, the sheer amounts of data generated by systems large and small. In a world where there are not enough data analysts, engineers are being called upon to help simplify and streamline some of the challenges that exist for businesses.

Take, for example, machine learning. A field of computer intelligence, machine learning involves developing and using computer systems to create models that can learn and adapt without instructions, typically through statistical models and other solutions. To develop machine learning solutions, one must have skills and knowledge spread across multiple fields – typically, understanding the nuances of large-scale data sets and having the technical experience to create well-defined, efficient solutions.

Applications of machine learning

With the advent of big data and continued drops in computing costs, various opportunities for machine learning engineers have opened up across multiple industries. These opportunities hope to tackle some of the problems that big businesses face on a daily basis and aspire to transform the way we work, often for the better.

Consider, for example, the large amount of work done to process and apply home loan applications. In financial services, a multi-billion dollar industry, much of the work involved in home loan applications involves manual data handling and data entry – from payslips to bank transaction records. Machine learning can help tackle some of these problems – with algorithms enhancing past work, such as optical character recognition (OCR), to rapidly reduce the time it takes to process customer data. In turn, this can help to reduce loan application times, helping customers understand their borrowing capacity in a more timely fashion.

Machine learning has uses across many industries, with machine learning engineers in demand in industries as diverse as consumer retail, healthcare, financial services, and transport. With rapid data growth comes a requisite increase in demand, with one industry monitor projecting that by 2030, machine learning applications will be worth more than $500bn USD worldwide.

Machine learning: A unique opportunity

The rapid growth of machine learning presents a unique opportunity for engineers – the ability to pivot into a career that is not only in high demand but also tackles some of the most significant challenges of our time.

For engineers, machine learning presents an opportunity to hone their craft in a diverse and unique field, enabling them to enhance their subject matter expertise in an area that is almost certain to be in high demand in the years to come. For students studying data or engineering, an opportunity exists to specialise in a new and emerging field that will pose unique challenges for even the most curious graduates.

There are many reasons to consider becoming a machine learning engineer. For some, it’s the salaries on offer, particularly in roles that require minimal experience. For others, it’s the ability to use new and emerging technologies to help create cutting-edge solutions that make a meaningful difference in many lives.

Ultimately, a career in machine learning offers many unique opportunities to hone your craft. With a variety of challenges to tackle, it’s sure to keep even the most inquisitive engineers on their toes.

If you’re interested in pursuing a career as a machine learning engineer, you should talk to a careers advisor and learn about your options. Hopefully, today’s exploration of how engineering can lead to opportunities in this new and emerging field has highlighted some new opportunities to explore.

Dynamic pricing is an essential tool for modern e-commerce, allowing us to adjust prices in real time to achieve business targets. With the most recent developments in machine learning, this process has become more accurate, flexible, and fast: algorithms analyze vast amounts of data, glean insights from the data, and find optimal solutions.

In this article, I explain how ML helps in price management, what technologies are used, and why sometimes simple models outperform complex ones.

Although each company has its strategies, adjusting is necessary due to the influence of external factors. Before introducing machine learning, companies managed dynamic pricing through their analytics departments and internal expertise. Analysts built price elasticity models based on price, discounts, and customer behavior. Using this data, they determined how customers reacted to different prices and constructed robust elasticity curves to select optimal pricing points. However, evolving market realities demand swift responses from companies, and dynamic pricing has become a powerful tool to meet these challenges.

Arc-Elasticity of Demand. Image credit: economicsdiscussion.net

The Transformation with ML

The dynamic pricing landscape is very different now. Machine learning has produced more nuanced models that adjust prices with greater precision and responsiveness. 

These models are susceptible to changes and can identify where to apply larger or smaller discounts, markups, loyalty points, and coupons. Plus, ML models provide justifications for these decisions. ML can use extensive sales data, often spanning two to three years, to create incredibly detailed elasticity models for broad categories and specific brands or even smaller subcategories. Instead of relying on a general model for products like phones, ML allows for individual models for brands like iPhone or Samsung and even for specific items like batteries or chips.

Companies can also respond to market fluctuations and consumer behavior quicker because ML allows for near real-time price adjustments. Prices can be recalculated several times daily based on factors such as the number of unique product views. This rapid adaptation ensures that pricing strategies align with current market conditions, making the process more flexible and accurate over short and long periods. This responsiveness differs from the analytics approach, where these models are sometimes updated monthly or bi-monthly.

Of course, using cutting-edge tech is not enough to guarantee success. Companies are constantly refining their approaches to dynamic pricing by developing specialized architectures and methodologies. For example, a company has used reinforcement learning techniques, such as the ‘multi-armed bandit’ approach. While this method has been shown to work in other areas, such as in recommendation systems, it has also proven effective in dynamic pricing. It allows the system to simultaneously explore pricing strategies and quickly find the most effective ones.

Main Stages of Machine Learning in Dynamic Pricing

Data Collection and Preprocessing

The first step is gathering comprehensive data on products, prices, sales, and customer behavior. This includes historical sales figures, pricing history, inventory levels, and external factors like competitor pricing and market trends. Given the enormous volume of information – which can reach petabytes – efficient data handling is crucial. Tools used for data preparation differ based on the data’s volume and complexity:

• Pandas: A Python library suitable for data processing in smaller projects or prototyping the big ones.
• Spark or Ray: Frameworks used for distributed processing of large datasets.
• Polars or Dask: Allows for efficient data loading on local machines without exhausting memory resources.

Modeling and Prediction

Next is modeling, where elasticity curves or other models are built to predict target metrics such as turnover, profit, number of orders, or customers. The models then make predictions about the expected results at different price points. For example:

• At price X, sales are projected to be $100.
• At price Y, sales are projected to be $50.
• At price Z, sales are projected to drop to $20.

The optimization algorithm determines the optimal price changes needed to achieve the business targets based on these predictions.

Machine learning for dynamic pricing uses technologies and areas of knowledge, such as macroeconomic principles, to construct elasticity curves. The main tasks involve data processing and preparation. An interesting aspect is that models often operate at the category level rather than on individual products. This is because products and sellers can quickly appear and disappear from the platform. For instance, a model might analyze the “phones” category rather than individual smartphone models.

Further Steps in Dynamic Pricing

Price Optimization

After modeling and prediction, the complex task of price optimization begins to meet business targets. The essence of the task is to determine the optimal price for each product so that the overall changes align with specified business metrics, such as increasing turnover by 10% while limiting profit reduction to no more than 5%. This includes optimizing multiple functions, each corresponding to a category or product. For example:

• Phones: The first function, where the input is the price of a phone (e.g., $100).
• Furniture: The second function uses the furniture price as the input (e.g., $50).

This multidimensional optimization problem requires advanced techniques to handle the scale and complexity.
Key steps include:

• Mathematical Modeling:
Develop models integrating business constraints (e.g., profit margins, sales targets) and objectives.
• Optimization Methods:
Apply advanced techniques to solve the problem even with millions of variables.

A variety of tools and methods are used to handle price optimization:

• Python Libraries (Hyperopt, Optuna, Vizier)
• Mathematical Methods (Lagrange Method, Penalty function  methods)

Finding the best solution in terms of markups corresponds to finding optimal points on the optimization plane // Vi. Image credit: LinkedIn

Testing and Validation

After effectively managing elasticity curves, machine learning models focus on meeting specific business objectives. For example, a company might have a baseline strategy, such as a 2% markup on all products. Analysts may propose improvements, aiming to increase turnover by 10% and profit by 2%. The challenge for the model is to surpass this baseline and deliver better results.

Companies use A/B testing to determine a statistically significant effect. This process begins with preparing an analytical report that defines target metrics such as turnover, profit, and number of orders and sets the minimal detectable effect (MDE)—the smallest effect size that can be statistically detected. For example, if the MDE is 2% and the increase in the metric is 1%, the 1% could have come from random fluctuations. Overcoming the MDE provides evidence that it is not random.  

Analysts also assess other metrics, such as promotional efficiency, and calculate the turnover each promotional spend generates. If every unit invested yields two turnover units, it’s viewed favorably. A/B testing and analytical reports verify the model’s effectiveness and measure its impact on key business metrics.

After deploying the ML model, it runs in production for a designated period while monitoring performance. It’s crucial not to interrupt or prematurely examine test results to ensure objectivity. After completion, machine learning engineers review all metrics to evaluate how well the model aligns with real-world performance. If results are unsatisfactory, they investigate potential issues such as data preprocessing errors, incorrect model assumptions, or algorithm problems. For instance, the model might have increased phone prices, leading to decreased sales compared to the control group. This rigorous testing helps identify and correct mistakes, ensuring the ML model effectively contributes to achieving the company’s business targets.

The conclusions drawn from testing help understand the model’s manageability. For example, if the goal is to increase turnover, the model should consistently meet that target. Initial test results may be erratic, but the model demonstrates the expected performance over time through improvements and knowledge gained from testing. Machine learning allows for more frequent testing and updating of models than manual analytics. For instance, Amazon recalculates prices every hour, highlighting ML’s agility in dynamic pricing.

This real-time adaptability manifests in practical ways. On some platforms, prices may depend on variables like the number of unique views a product receives, leading to multiple price changes within a day. If a company runs long-term promotions, prices might be fixed for the campaign’s duration, focusing solely on achieving current business metrics like turnover growth or customer retention. ML makes the pricing process more flexible and manageable from a business perspective, but the ‘black box’ effect disappears. 

In simple terms, a business presents a target —  for example, to increase revenue by 2%. The ML model then employs various strategies to achieve this objective. These strategies might include lowering prices to boost turnover, raising prices to enhance profit margins, offering discounts, or adjusting prices based on factors like product views. The model manages the process based on proposed hypotheses, continually refining its approach to meet the specified targets. This dynamic adaptability underscores ML’s significant role in modern dynamic pricing, enabling businesses to respond swiftly to market shifts and consumer behaviors.

Dynamic Pricing in Action

Machine learning is essential in modern dynamic pricing, enabling businesses to adjust prices with greater precision and responsiveness to market demand and consumer behavior. By processing vast amounts of data, ML models identify patterns that inform optimal pricing strategies, helping companies meet specific objectives like increasing profit turnover margins. Price adjustments have reached a new level of accuracy. Companies embracing these technologies are better equipped to deliver value to their customers while achieving their business goals. Everything has shifted from a reactive to a proactive, highly efficient strategy. 

Integrating ML isn’t without its challenges, but as seen in major platforms, the rewards are undeniable. As the field continues to evolve, machine learning will remain at the core of dynamic pricing, driving more intelligent decisions and better outcomes for businesses and consumers. So, it is a strategy that certainly deserves attention.

AI is rapidly taking its place in the market, penetrating new application areas in ways we couldn’t imagine, including AI cybersecurity solutions. The hype shows no signs of fading. In fact, it is gaining real momentum even among C-level executives. The reason is clear: AI’s potential for improving efficiency is almost limitless. 

But so is its potential for disruption. In the realm of cybersecurity, the stakes are as high as ever. The use of AI is evident on both sides of the barricades: by attackers and defenders alike. 

In this article, I explore the impact of AI on the field of cybersecurity, describe potential use cases and their likely effectiveness, discuss challenges related to AI technologies themselves, and reflect on the threats AI poses to the jobs of cybersecurity professionals.

AI Cybersecurity Challenges

Cybersecurity is a buzzworthy field, not so much for its efficiency but for its challenges. As the number of successful cyberattacks continues to rise, the U.S. Agency for International Development estimates the global cost of cybercrime at $8 trillion in 2023, projected to grow to $27 trillion by 2027. At the same time, the world faces a severe shortage of cybersecurity professionals.

However, there is a growing concern that legitimate organizations and cybercriminals are adopting AI technologies. According to a survey by Sapio Research и Deep Instinct, 75% of cybersecurity professionals have observed an increase in cyberattacks, and 85% believe that AI technologies are likely contributing to this surge.

Indeed, attackers are increasingly leveraging AI to efficiently gather and process information about their targets, prepare phishing campaigns, and develop new versions of malware, enhancing the power and effectiveness of their malicious operations. Meanwhile, the digital world’s data growth outpaces human cognitive capacity, and cybersecurity talent cannot scale fast enough due to high expertise requirements. As external factors reshape the industry, existing challenges are intensifying under the surge of data and attacks.

The Human Context

Introducing the most significant weakness in cybersecurity systems: human error. Time and again, we’ve seen data breaches where systems designed to process and store valuable information within a protected network were left unsecured and exposed to public access due to configuration mistakes by personnel.

Efficiency is yet another pain point in cybersecurity. Specialists cannot consistently and flawlessly handle hundreds of daily alerts, and managing manual processes becomes increasingly difficult as corporate networks grow more complex and diverse, as they do today.

As in other industries, cybersecurity relies heavily on human intervention. Cybersecurity professionals validate database configurations before processing valuable data, scan the codebase of new applications before their release, investigate incidents, and identify root causes, among other tasks. But it is also time for us to embrace AI to improve efficiency and give cybersecurity defenders an edge.

Use Cases of AI in Cybersecurity

Before we get into specific use cases, let’s briefly define the technologies mentioned to establish a foundation for discussing their use cases.

Artificial Intelligence (AI) is a field of computer science focused on creating systems that perform tasks requiring human intelligence, such as language processing, data analysis, decision-making, and learning. It serves as the overarching discipline, with other areas falling under its umbrella.

Machine Learning (ML), a subset of AI, enables systems to learn and improve from data without explicit programming, making decisions based on patterns and large datasets. It is currently the most relevant area for cybersecurity.

Deep Learning (DL), a branch of ML, uses artificial neural networks to model complex relationships and solve problems with large datasets. Since DL falls under ML, this discussion will primarily focus on machine learning.

• Lowering the Barrier to Entry

The entry barrier into this field is notorious for its high demands on technical expertise. Early tools like firewalls used simple traffic rules, but as networks grew more complex, creating and validating these rules became increasingly challenging. 

AI can simplify this process by writing accurate rules while providing specialists with an interface, such as a natural language processing chat system. A cybersecurity professional could describe what traffic to allow or block and the conditions under which specific rules should apply, and the AI would generate machine-readable policies, ensuring proper syntax and semantics. This streamlines rule development, making the field more accessible and reducing the effort required for security management.

• Asset Inventory and Attack Path Mapping

As corporate networks grow more complex and evolve into hybrid and multi-cloud environments with global points of presence, managing and securing them has become very challenging. Modern networks can also scale automatically with demand, adding to the difficulty of inventorying assets, identifying threats, and modeling potential attack paths.

AI can help with these tasks by continuously scanning networks, cataloging assets, and adding contextual insights. With its ability to learn from data, AI already outperforms humans in forecasting and can analyze network architectures to identify potential attack chains. This helps cybersecurity teams prioritize efforts, shifting the focus from reactive measures to proactive defense. With AI, it becomes clearer which vulnerabilities attackers might exploit and how to fortify them effectively.

• Vulnerability Management

The complexity of vulnerability management grows alongside the increasing size and intricacy of corporate networks, the number of identified vulnerabilities, available exploits, and vulnerability assessment metrics. Launching a vulnerability management program in a large network can feel like searching for a needle in a haystack for cybersecurity specialists. Traditional vulnerability scanners often produce massive reports with thousands of vulnerabilities of varying severity, accompanied by remediation recommendations that may lack relevance without business and application context.

AI can play several key roles in this process to support professionals:

1. Correlating vulnerability data with information about exploits and related attacks.
2. Enriching system vulnerability data with business context.
3. Prioritizing vulnerabilities for remediation and automating patch deployment.

Zero-day vulnerabilities are an additional challenge, but AI can assist by analyzing large volumes of information to identify and track zero-day vulnerabilities across different technologies.

Malware Detection and Analysis

Malware is the backbone of modern cyberattacks, with its volume rising alongside cybercriminal groups, the number of attacks, and attackers’ budgets. Cybercriminals use advanced techniques to enhance malware and evade detection. Some even leverage AI to develop new malware samples more quickly and efficiently.

AI can help by identifying malware through behavioral analysis and assisting in reverse engineering, where specialists analyze malware to improve defenses. In reverse engineering, AI can act as a consultant, explaining code segments and the possible intentions behind malware developers’ choices, streamlining the analysis process for cybersecurity professionals.

Threat and Attack Monitoring

Cyberattacks are becoming more frequent, complex, and fast. What once took months now takes seconds. Modern attackers move laterally, steal data, and erase traces, enabling them to target more victims and maximize their impact. This behavior floods cybersecurity teams with alerts, making rapid response a deciding factor in this complex game.

However, many are false positives, leading to alert fatigue among professionals. As networks and data grow, manual log analysis is no longer feasible, especially with the ongoing shortage of skilled cybersecurity specialists.

This is why delegating continuous network monitoring and threat detection to AI and automating responses to attack indicators is the best way forward. Fortunately, most cyberattacks follow common patterns AI can learn, enabling lightning-fast responses to stay ahead of attackers. AI operates 24/7 without fatigue, quickly adapts to new data, reduces false positives, and can generate recommendations for preventive measures when attack traces are found, covering gaps that human specialists might overlook. A dream partner, at best.

Phishing Protection

One human trait that weakens corporate cybersecurity systems is our tendency to act on emotions. Cybercriminals exploit this vulnerability through social engineering, particularly phishing, using employees as entry points into corporate networks. 

To make the attacks more effective, attackers increasingly incorporate AI to craft more convincing phishing emails and target more victims. In response, cybersecurity professionals can protect employees from phishing attacks by training AI models on large datasets of known social engineering techniques.

Behavior Monitoring and Insider Threat Detection

Protecting against insider threats is still one of the biggest challenges in cybersecurity. Insiders have legitimate access to corporate systems, making detection more difficult. 

AI-powered systems can automatically identify suspicious actions, such as unauthorized access to sensitive data or attempts at data theft. Using machine learning, AI adapts to changes in employee behavior, reducing false positives. Plus, AI helps predict risks by analyzing historical data and identifying patterns that signal potentially malicious actions by employees.

Enhanced Cybersecurity Event Search

Google is a staple in the world of search engines, offering results we all rely on. However, its search results often feel more like a table of contents than a concise summary of critical points. For cybersecurity professionals, having an enhanced search tool can make all the difference in addressing cyber threats.

A simple “table of contents” isn’t enough when specialists need detailed insights into the state of a protected corporate network. AI-powered systems can step in to improve traditional search capabilities, providing the critical context needed to make informed decisions and respond effectively to threats.

Minimizing the Human Error

Managing thousands of hosts while adhering to security rules can be overwhelming. This is where AI can help by learning from correct configurations and past mistakes, identifying errors, and flagging them in real time. Additionally, AI could proactively generate host configurations based on descriptions of human-provided functionality.

Embrace the Change

While a leap toward fully autonomous AI systems seems relatively unlikely, AI has the potential to complement human expertise, empowering professionals to handle the most pressing issues in the field. The key to unlocking AI’s potential lies in having skilled specialists who understand how it works and apply creativity and critical thinking to make the technology even more effective.

Throughout history, every major technological breakthrough has sparked fear and uncertainty. Yet, over time, we have learned to adapt, embrace these tools, and use them effectively, balancing their capabilities with our limitations. It’s time to do the same with AI: to integrate it into cybersecurity and delegate tasks where AI performs better than we do.

Are you looking to elevate your company’s performance and stay ahead in today’s competitive market? Implementing data-driven strategies can unlock many benefits, from enhancing decision-making with actionable insights and increasing operational efficiency to improving customer experiences and driving innovative solutions. By leveraging data analytics, your business can boost revenue and profitability through targeted initiatives and establish a strong competitive advantage that sets you apart from rivals. This comprehensive approach ensures that every strategic choice is backed by reliable metrics, fostering sustainable growth and long-term success. Discover how adopting data-centric methods can transform your organization and propel it toward more significant achievements.

Enhancing decision-making with data-driven insights

C&F‘s experts emphasize that relying on gut feelings can lead to disaster in the competitive business world. Data analytics enables companies to make strategic decisions based on solid evidence. Take Netflix, for example: By analyzing viewer data, they do not just guess which shows to produce—they know what will resonate with their audience. This shift from intuition to data-driven decision-making results in fewer missteps and more successful outcomes.

Consider how Amazon utilizes data to optimize everything from inventory management to personalized recommendations. Before adopting a data-centric approach, many businesses needed help with inefficiencies and missed opportunities. However, after implementing data-driven strategies, these companies experienced significant improvements in both operational performance and customer satisfaction.

Embracing data-driven insights is not just a trend; it is essential for businesses that want to stay ahead. Companies can confidently navigate complexities and drive sustained growth by grounding their decisions in analytics.

Increasing operational efficiency through data analysis

Leveraging data analysis can dramatically streamline operations by uncovering inefficiencies and optimizing processes. For instance, companies can utilize data insights to enhance inventory management, reduce downtime, and improve resource allocation. Here are some key ways data-driven strategies can boost operational efficiency:

1. Predictive maintenance: Using data to foresee equipment failures minimizes unexpected downtimes.
2. Supply chain optimization: Analyzing data helps forecast demand and manage inventory levels effectively.
3. Process automation: Data insights enable the automation of repetitive tasks, freeing up resources for more strategic activities.

Improving customer experience using data metrics

Unlocking the true potential of your customer interactions hinges on leveraging precise data metrics. By tapping into comprehensive customer data, businesses can tailor their approaches to meet individual needs, resulting in heightened satisfaction and loyalty. Experts emphasize that understanding these metrics is beneficial and essential for crafting strategies that resonate with your audience.

Key metrics to monitor include:

• Customer Lifetime Value (CLV): Measures the total revenue a business can expect from a single customer account.
• Net Promoter Score (NPS): Assesses customer willingness to recommend your products or services to others.
• Customer Satisfaction Score (CSAT): Evaluates how satisfied customers are with your products or services.
• Churn Rate: Indicates the percentage of customers who stop using your product or service over a specific period.
• Average Resolution Time: Tracks the average time taken to resolve customer issues.

Integrating these metrics into your customer strategies allows a more nuanced understanding of client behaviors and preferences. This data-driven approach enhances the overall customer experience and drives sustainable business growth by aligning your services with what truly matters to your audience.

Driving innovation with data-backed strategies

Forget the old-school methods of guessing what your customers want. Embracing data-driven strategies is the game-changer that propels your company into the future. By harnessing the power of data analytics, businesses can uncover hidden opportunities and craft innovative solutions that stand out in the crowded marketplace.

1. Enhanced Product Development: Utilize customer insights to design products that meet actual needs, reducing the risk of failure.
2. Optimized Operations: Streamline processes by analyzing operational data, leading to increased efficiency and cost savings.
3. Personalized Marketing: Tailor your marketing campaigns based on consumer behavior data, boosting engagement and conversion rates.

Boosting revenue and profitability via data utilization

Adopting data-driven strategies is a decisive move to elevate your company’s sales and overall profitability. By harnessing the power of business intelligence, companies can uncover valuable insights into customer behaviors and market trends, enabling more precise and effective decision-making. For example, implementing targeted marketing campaigns based on data analysis has been proven to boost conversion rates by up to 25%, directly impacting revenue growth.

Industry experts underscore the transformative impact of data utilization on financial performance. Studies reveal that businesses leveraging comprehensive data strategies witness a significant increase in revenue growth, often surpassing competitors who rely on traditional methods. Techniques such as customer segmentation and personalized marketing enhance customer engagement and drive repeat business, fueling sustained profitability.

Furthermore, integrating advanced data analytics allows companies to optimize their operations and identify new revenue streams. By continuously monitoring key performance indicators, businesses can make agile adjustments to their strategies, ensuring they remain competitive and financially robust in a dynamic market environment.

Strengthening competitive advantage through data

In today’s hyper-competitive market, leveraging data-driven strategies is no longer optional—it’s essential. Companies that harness the power of data analytics gain unparalleled insights into customer behavior, market trends, and operational efficiency, setting them leagues apart from their rivals. For instance, Netflix utilizes sophisticated data algorithms to personalize content recommendations, ensuring higher viewer engagement and retention rates.

Unique data applications can revolutionize business operations. Take Amazon, which employs predictive analytics to manage inventory and optimize supply chain logistics, resulting in faster delivery times and reduced costs. By integrating real-time data into decision-making processes, businesses can swiftly adapt to market changes and anticipate customer needs, providing a significant competitive edge.

Featured image credit: Campaign Creators/Unsplash

AI avatars, or “talking heads,” have marked a new step in the way we approach and comprehend digital engagement. Not that long ago, turning a single photo and audio clip into a realistic, speaking likeness seemed impossible—the best we could get was an ‘uncanny valley’ result, certainly unsuitable for any external use. 

Now, the situation is much different. Central to tools like Synthesia, this process of creating AI avatars starts with AI creating a “digital identity” from an image, then animating it to synchronize facial movements with audio — so the avatar “speaks” for the user at a presentation, reel, or event. This progress owes it to cutting-edge methods like GANs, known for rapid, high-quality visual output, and diffusion models, prized for their rich detail, though slower. Synthesia, D-ID, and Hume AI are among the companies advancing these tools and taking the lead in making this tech as adapted to current demands as possible.

Yet, true realism is still out of reach. Neural networks process visual details differently from humans, often overlooking subtle cues, like the precise alignment of teeth and facial hair, that shape how people naturally perceive faces. More on that later.

This article talks about the inner workings of the technology and the challenges developers face when trying to make AI avatars look like our familiar faces. How realistic can they become?

How the AI avatar generation process works

Creating an AI avatar begins with a user uploading a photo or video. This input is processed through an “Identity Extractor” — a neural network trained to identify and encode a person’s physical appearance. This model extracts key features of the face and converts them into a “digital identity,” which can be used to animate the avatar realistically. From this representation, developers can control movements through a “driver” signal, typically audio or additional video, which dictates how the avatar should move and speak.

The driver signal is vital in the animation process. It determines both lip synchronization with audio and broader facial expressions. For example, in a talking avatar, audio cues influence mouth shape and movement to match speech. Sometimes, key facial points (e.g., eye and mouth corners) are used to guide motion precisely, while in other cases, the entire avatar’s pose is modified to match the driver signal. To ensure the expression is natural, the neural network may use techniques like “warping,” which smoothly reshapes the avatar’s features based on the above input signals.

As the last step, a decoding process translates this modified digital identity back into a visual form by generating individual frames and assembling them into a seamless video. Neural networks typically do not operate reversibly, so the decoding requires separate training to accurately convert the animated digital representation into lifelike, continuous imagery. The result is an avatar that closely mirrors human expressions and movements but still remains constrained by the limitations of AI’s current ability to perceive fine facial details.

GANs, diffusion models, and 3D-based methods: the three pillars of avatar generation

The core technologies enabling this transformation are continually advancing to more accurately capture human expressions, step-by-step building on the avatar generation process. Three main approaches are driving progress right now, and each one of them has particular benefits and limitations:

The first, GAN (Generative Adversarial Networks), uses two neural networks in tandem — a generator and a discriminator — to create highly realistic images. This approach allows for fast, high-quality image generation, making it suitable for real-time applications with a clear need for smooth and responsive avatars. However, while GANs excel in speed and visual quality, they can be difficult to control precisely. This can limit their effectiveness in cases requiring detailed customization.

Diffusion models are another powerful tool. They gradually transform noise into a high-quality image through repeated steps. Known for generating detailed and highly controllable images, diffusion models are slower and require significant computing power. So, they are ideal for offline rendering and real-time use – not so much. This model’s strength lies in producing nuanced, photorealistic details, though at a slower pace.

Finally, 3D-based methods like Neural Radiance Fields (NeRFs) and Gaussian Splatting build a visual representation by mapping spatial and color information into a 3D scene. These methods differ slightly, with Splatting being faster and NeRFs working at a slower pace. 3D-based approaches are best suited for gaming or interactive environments. However, NeRFs and Gaussian Splatting can fall short in visual realism, currently producing a look that can appear artificial in scenarios demanding human likeness.

Each technology presents a balance between speed, quality, and control best suited to different applications. GANs are widely used for real-time applications due to their combination of speed and visual quality, while diffusion models are preferred in “offline” contexts, where rendering does not occur in real-time, allowing for more intensive computation to achieve finer detail. 3D methods continue to evolve for high-performance needs but currently lack the realistic visual accuracy required for human-like representations. 

These technologies summarize the current developments and challenges in the field quite well. Continuous research is aimed at merging their strengths to achieve more lifelike results, but for now, this is what we are dealing with.

The AI Avatar ‘Teeth and Beards’ challenge

Building realistic AI avatars begins with gathering high-quality training data — a complex task in itself — but a less obvious and equally challenging aspect is capturing small, human-defining details like teeth and beards. These elements are notoriously difficult to model accurately, partly due to the limited training data available. For instance, detailed images of teeth, especially lower teeth, are scarce in typical datasets: they are often hidden in natural speech. Models struggle to reconstruct realistic dental structures without sufficient examples, frequently leading to distorted or unnatural appearances, such as “crumbling” or odd placement.

Beards add a similar level of complexity. Positioned close to the mouth, beards shift with facial movements and change under different lighting, which makes any flaw immediately noticeable. When not modeled with precision, a beard can appear static, blurry, or unnaturally textured, which detracts from the avatar’s overall realism.

The other factor complicating these details is the neural network’s perception. Humans intuitively focus on facial nuances like teeth and facial hair to identify individuals, whereas neural models spread attention across the entire face, often bypassing these smaller but key elements. To the model, teeth and beards are less significant; to humans, they’re essential identity markers. This can be overcome only through extensive fine-tuning and re-training, often demanding as much effort as perfecting the overall facial structure. 

We can now see a core limitation: while these models advance toward realism, they remain just short of capturing the subtlety of human perception. 

Recent advancements in AI avatar technology have brought natural-looking expressions closer to reality than ever before. GANs, diffusion models, and emerging 3D approaches have completely refined the generation of “talking heads,” and each approach offers a unique perspective and toolkit for making a once futuristic idea – a reality.

GANs offer the speed necessary for real-time applications; diffusion models contribute nuanced control, though slower. Techniques like Gaussian Splatting in 3D bring efficiency, sometimes at the cost of visual fidelity.

Despite these improvements, tech has a long way to go regarding realism. No matter how fine-tuned your model is, once in a while, you will most likely encounter a slightly eerie set of teeth or an off-looking placement of facial hair. But, as available high-quality data grows with time, neural networks will develop the ability to show consistency in how they represent innate human micro-traits. What is integral to our perception is just a parameter for AI models.

This gap highlights an ongoing struggle: achievements in tech move us forward, yet the goal of creating genuinely lifelike avatars remains elusive, much like the paradox of Achilles and the tortoise — no matter how close we come, perfection stays out of reach.

A recent study by researchers at the University of Michigan has highlighted the issue of political bias in content moderation on social media platforms, particularly Reddit. The findings illustrate the potential for moderator biases to create echo chambers that distort public discourse.

The political bias in Reddit’s content moderation

The research team, led by Justin Huang, examined more than 600 million comments across various subreddits. Their analysis focused on how subreddit moderators’ political preferences influenced the removal of user comments. The study found that comments with opposing political views were significantly more likely to be censored by moderators.

On a lean scale from 0 (stanch Republican) to 100 (stanch Democrat), average users scored 58, while moderators scored 62, indicating a left-leaning bias overall.

Huang stated that “the bias in content moderation creates echo chambers,” which are spaces characterized by a lack of divergent opinions. This situation can lead to a narrow perception of political norms, as users see only a homogenous perspective.

The study suggests that individuals may become radicalized and misinformed due to the insular nature of these communities, further eroding trust in electoral processes.

How are echo chambers affecting democracy?

The phenomenon of echo chambers poses a direct threat to democratic norms, impacting how citizens perceive political realities. When discussions are dominated by singular viewpoints, users can feel alienated, leading them to disengage. In extreme cases, these environments allow misinformation to spread unchecked, shaping public belief in distorting ways.

Huang and his team describe how moderators’ biases can inadvertently contribute to misunderstood political balances.

Groups are actively working to influence voters with tailored messages, increasing the urgency for social media platforms to reassess their moderation practices.

The study calls into question the potential for moderators to either consciously or unconsciously manipulate discourse by removing dissenting views.

There is a way out

To address the issues identified in the study, researchers propose several remedial strategies for social media platforms. First, establishing clear guidelines around acceptable reasons for content removal could provide a framework for moderation. By clarifying what constitutes appropriate action, platforms can promote fairness in user interactions.

Second, improving transparency regarding content removal is critical. Notifying users when their comments are deleted allows for accountability and trust in moderation practices. Furthermore, platforms should consider publishing data on removal volumes, which could invite public scrutiny and deter potential abuses.

New Reddit policy changes are a straight blow to community freedom

Lastly, implementing oversight measures could help track moderators’ political biases and their effects on discourse. By monitoring moderation decisions, platforms can flag potential biases and encourage a more balanced discussion environment.

Huang emphasizes the ongoing need for research and adaptation in online communities, noting,

“While subreddit moderators are specific to Reddit, user-driven content moderation is present on all major social media platforms.”

Actions taken now could influence the nature of political dialogue and user trust across platforms in a fundamental way, leaving the door open for further developments.

Featured image credit: Ralph Olazo/Unsplash

At the last AI Conference, we had a chance to sit down with Roman Shaposhnik and Tanya Dadasheva, the co-founders of Ainekko/AIFoundry, and discuss with them an ambiguous topic of data value for enterprises in the times of AI. One of the key questions we started from was: are most companies running the same frontier AI models, is incorporating their data the only way they have a chance to differentiate? Is data really a moat for enterprises?

Roman recalls: “Back in 2009, when he started in the big data community, everyone talked about how enterprises would transform by leveraging data. At that time, they weren’t even digital enterprises; the digital transformation hadn’t occurred yet. These were mostly analog enterprises, but they were already emphasizing the value of the data they collected—data about their customers, transactions, supply chains, and more. People likened data to oil, something with inherent value that needed to be extracted to realize its true potential.”

However, oil is a commodity. So, if we compare data to oil, it suggests everyone has access to the same data, though in different quantities and easier to harvest for some. This comparison makes data feel like a commodity, available to everyone but processed in different ways.

When data sits in an enterprise data warehouse in its crude form, it’s like an amorphous blob—a commodity that everyone has. However, once you start refining it, that’s when the real value comes in. It’s not just about acquiring data but building a process from extraction to refining all the value through the pipeline.

“Interestingly, this reminds me of something an oil corporation executive once told me” – shares Roman. “That executive described the business not as extracting oil but as reconfiguring carbon molecules. Oil, for them, was merely a source of carbon. They had built supply chains capable of reconfiguring these carbon molecules into products tailored to market demands in different locations—plastics, gasoline, whatever the need was. He envisioned software-defined refineries that could adapt outputs based on real-time market needs. This concept blew my mind, and I think it parallels what we’re seeing in data now—bringing compute to data, refining it to get what you need, where you need it” – was Roman’s insight.

In enterprises, when you start collecting data, you realize it’s fragmented and in many places—sometimes stuck in mainframes or scattered across systems like Salesforce. Even if you manage to collect it, there are so many silos, and we need a fracking-like approach to extract the valuable parts. Just as fracking extracts oil from places previously unreachable, we need methods to get enterprise data that is otherwise locked away.

A lot of enterprise data still resides in mainframes, and getting it out is challenging. Here’s a fun fact: with high probability, if you book a flight today, the backend still hits a mainframe. It’s not just about extracting that data once; you need continuous access to it. Many companies are making a business out of helping enterprises get data out of old systems, and tools like Apache Airflow are helping streamline these processes.

But even if data is no longer stuck in mainframes, it’s still fragmented across systems like cloud SaaS services or data lakes. This means enterprises don’t have all their data in one place, and it’s certainly not as accessible or timely as they need. You might think that starting from scratch would give you an advantage, but even newer systems depend on multiple partners, and those partners control parts of the data you need.

The whole notion of data as a moat turns out to be misleading then. Conceptually, enterprises own their data, but they often lack real access. For instance, an enterprise using Salesforce owns the data, but the actual control and access to that data are limited by Salesforce. The distinction between owning and having data is significant.

“Things get even more complicated when AI starts getting involved” – says Tanya Dadasheva, another co-founder of AInekko and AIFoundry.org. “An enterprise might own data, but it doesn’t necessarily mean a company like Salesforce can use it to train models. There’s also the debate about whether anonymized data can be used for training—legally, it’s a gray area. In general, the more data is anonymized, the less value it holds. At some point, getting explicit permission becomes the only way forward”.

This ownership issue extends beyond enterprises; it also affects end-users. Users often agree to share data, but they may not agree to have it used for training models. There have been cases of reverse-engineering data from models, leading to potential breaches of privacy.

At an early stage of balancing data producers, data consumers, and the entities that refine data, legally and technologically it is extremely complex figuring out how these relationships will work. Europe, for example, has much stricter privacy rules compared to the United States (https://artificialintelligenceact.eu/). In the U.S., the legal system often figures things out on the go, whereas Europe prefers to establish laws in advance.

Tanya addresses data availability here: “This all ties back to the value of data available. The massive language models we’ve built have grown impressive thanks to public and semi-public data. However, much of the newer content is now trapped in “walled gardens” like WeChat, Telegram or Discord, where it’s inaccessible for training – true dark web! This means the models may become outdated, unable to learn from new data or understand new trends.

In the end, we risk creating models that are stuck in the past, with no way to absorb new information or adapt to new conversational styles. They’ll still contain older data, and the newer generation’s behavior and culture won’t be represented. It’ll be like talking to a grandparent—interesting, but definitely from another era.”

But who are the internal users of the data in an enterprise? Roman recalls the three epochs of data utilization concept within the enterprises: “Obviously, it’s used for many decisions, which is why the whole business intelligence part exists. It all actually started with business intelligence. Corporations had to make predictions and signal to the stock markets what they expect to happen in the next quarter or a few quarters ahead. Many of those decisions have been data-driven for a long time. That’s the first level of data usage—very straightforward and business-oriented.

The second level kicked in with the notion of digitally defined enterprises or digital transformation. Companies realized that the way they interact with their customers is what’s valuable, not necessarily the actual product they’re selling at the moment. The relationship with the customer is the value in and of itself. They wanted that relationship to last as long as possible, sometimes to the extreme of keeping you glued to the screen for as long as possible. It’s about shaping the behavior of the consumer and making them do certain things. That can only be done by analyzing many different things about you—your social and economic status, your gender identity, and other data points that allow them to keep that relationship going for as long as they can.

Now, we come to the third level or third stage of how enterprises can benefit from data products. Everybody is talking about these agentic systems because enterprises now want to be helped not just by the human workforce. Although it sounds futuristic, it’s often as simple as figuring out when a meeting is supposed to happen. We’ve always been in situations where it takes five different emails and three calls to figure out how two people can meet for lunch. It would be much easier if an electronic agent could negotiate all that for us and help with that. That’s a simple example, but enterprises have all sorts of others. Now it’s about externalizing certain sides of the enterprise into these agents. That can only be done if you can train an AI agent on many types of patterns that the enterprise has engaged in the past.”

Getting back to who collects and who owns and, eventually, benefits from data: the first glimpse of that Roman got when working back at Pivotal on a few projects that involved airlines and companies that manufacture engines:

“What I didn’t know at the time is that apparently you don’t actually buy the engine; you lease the engine. That’s the business model. And the companies producing the engines had all this data—all the telemetry they needed to optimize the engine. But then the airline was like, “Wait a minute. That is exactly the same data that we need to optimize the flight routes. And we are the ones collecting that data for you because we actually fly the plane. Your engine stays on the ground until there’s a pilot in the cockpit that actually flies the plane. So who gets to profit from the data? We’re already paying way too much to engine people to maintain those engines. So now you’re telling us that we’ll be giving you the data for free? No, no, no.”

This whole argument is really compelling because that’s exactly what is now repeating itself between OpenAI and all of the big enterprises. Big enterprises think OpenAI is awesome; they can build this chatbot in minutes—this is great. But can they actually send that data to OpenAI that is required for fine-tuning and all these other things? And second of all, suppose those companies even can. Suppose it’s the kind of data that’s fine, but it’s their data – collected by those companies. Surely it’s worth something to OpenAI, so why don’t they drop the bill on the inference side for companies who collected it?

And here the main question of today’s data world kicks in: Is it the same with AI?

In some way, it is, but with important nuances. If we can have a future where the core ‘engine’ of an airplane, the model, gets produced by these bigger companies, and then enterprises leverage their data to fine-tune or augment these models, then there will be a very harmonious coexistence of a really complex thing and a more highly specialized, maybe less complex thing on top of it. If that happens and becomes successful technologically, then it will be a much easier conversation at the economics and policy level of what belongs to whom and how we split the data sets.

As an example, Roman quotes his conversation with an expert who designs cars for a living: “He said that there are basically two types of car designers: one who designs a car for an engine, and the other one who designs a car and then shops for an engine. If you’re producing a car today, it’s much easier to get the engine because the engine is the most complex part of the car. However, it definitely doesn’t define the product. But still, the way that the industry works: it’s much easier to say, well, given some constraints, I’m picking an engine, and then I’m designing a whole lineup of cars around that engine or that engine type at least.”

This drives us to the following concept: we believe that’s what the AI-driven data world will look like. There will be ‘Google’ camp and ‘Meta camp’, and you will pick one of those open models – all of them will be good enough. And then, all of the stuff that you as an enterprise are interested in, is built on top of it in terms of applying your data and your know-how of how to fine-tune them and continuously update those models from different ‘camps’. In case this works out technologically and economically, a brave new world will emerge.

Featured image credit: NASA/Unsplash

It’s undeniable that it’s a particularly hard time to be a woman in tech.

While the mass layoffs experienced in 2023 have steadied somewhat—according to tech layoff tracker Layoffs.fyi, 490 tech companies have made 143,142 workers redundant in 2024 compared to 1,193 tech companies making 264,220 employees redundant in 2023—women are still vastly underrepresented in the sector.

According to a new report by KPMG, women make up just over a third of the data and analytics (D&A) and artificial intelligence (AI) workforce. Despite more people graduating university with a STEM-related degree in 2024 compared to 2012, the amount of women graduating with a STEM-related degree has declined by 8%.

5 tech roles hiring across Germany

• Software Engineering Manager, 1&1 Mail & Media, Munich
• Head of Objectives and Strategies, Berliner Wasserbetriebe, Berlin
• Senior Verification Architect, Beckman Coulter Diagnostics, Munich
• Technical Contract Manager Turbine Technology and Heat Exchangers, BEW Berliner Energie und Wärme AG, Berlin
• Global Account Manager, Arrow Electronics, Inc., Germany

Mind the gap

Drilling down into the data, it’s clear that the problem isn’t just about gender parity but representation of women in senior roles.

KPMG’s report also highlights that the gender gap is more pronounced at senior levels, despite new roles being created in tandem with advances in AI and analytics, and the need for workers skilled in these fields growing at a rapid rate.

And the growing divide has picked up pace post-Covid with women’s representation in tech trending down across all levels in the last 10 years. In 2024, only 29% of senior D&A and AI roles were held by women, compared to 31% in 2008.

One way that organisations can tackle this issue from the ground up is through promoting visible leadership diversity; however, to truly remove the barriers preventing women from excelling in tech, more needs to be done at a grassroots level.

This includes mentorship and building a community of female employees, retaining more women by offering benefits including paid maternity leave, flexible working arrangements and helping women develop their own leadership skills.

Words of wisdom

“I believe that this study reflects the reality of what women in STEM fields experience. Throughout my career, I realised I worked harder, performed better, was paid less, moved up at a slower pace, and yet I made a conscious decision to keep going,” says Danielle Maurici-Arnone, global chief information and digital officer at personal care brand Combe Incorporated.

“At first, it was a personal challenge I set for myself and then it became a mission to try to make it easier for other women—for my own daughter, to achieve her dreams. I remind myself and tell my female colleagues and friends, ‘we need you, you matter, you are not alone, keep going, and stay passionate’.”

This sense of belonging doesn’t need to happen internally either, and expanding your professional network by reaching out to women who hold senior leadership positions in other companies is a great way to not only connect, but reinforce a sense of community.

“For many women leaders in data and AI, we are sometimes one of the only women in the room. Even outside the boardroom we can struggle to build and maintain a strong peer network and the community that we need to turn to for guidance, wisdom, and support,” says Nancy Morgan, CEO of Ellis Morgan Enterprises.

“If other women do not see women represented at every level of leadership, they may perceive that these roles are not for them or that they somehow do not ‘belong.’ Women need to find a community, in their organisation and/or externally, who can support them as they try out ideas, learn to make bold moves, and create a vibrant network of support.”

Find your next role in tech today on the Dataconomy Job Board

On-device AI and running large language models on smaller devices have been one of the key focus points for AI industry leaders over the past few years. This area of research is among the most critical in AI, with the potential to profoundly influence and reshape the role of AI, computers, and mobile devices in everyday life. This research operates behind the scenes, largely invisible to users, yet mirrors the evolution of computers — from machines that once occupied entire rooms and were accessible only to governments and large corporations to the smartphones now comfortably hidden in our pockets. 

Now, most large language models are deployed in cloud environments where they can leverage the immense computational resources of data centers. These data centers are equipped with specialized hardware, such as GPUs and TPUs, or even specialized AI chips, designed to handle the intensive workloads that LLMs require. But this reliance on the cloud brings with it significant challenges:

High Cost: Cloud services are expensive. Running LLMs at scale requires continuous access to high-powered servers, which can drive up operational costs. For startups or individual engineers, these costs can be prohibitive, limiting who can realistically take advantage of this powerful technology.

Data Privacy Concerns: When users interact with cloud-based LLMs, their data must be sent to remote servers for processing. This creates a potential vulnerability since sensitive information like personal conversations, search histories, or financial details could be intercepted or mishandled.

Environmental Impact: Cloud computing at this scale consumes vast amounts of energy. Data centers require continuous power not only for computation but also for cooling and maintaining infrastructure, which leads to a significant carbon footprint. With the global push toward sustainability, this issue must be addressed. For example, a recent report from Google showed a 48% increase in greenhouse gas emissions over the past five years, attributing much of this rise to the growing demands of AI technology.

That’s why this issue continues to catch the focus of industry leaders, who are investing significant resources to address the problem, as well as smaller research centers and open-source communities. The ideal solution would be to allow users to run these powerful models directly on their devices, bypassing the need for constant cloud connectivity. Doing so could reduce costs, enhance privacy, and decrease the environmental impact associated with AI. But this is easier said than done.

Most personal devices, especially smartphones, lack the computational power to run full-scale LLMs. For example, an iPhone with 6 GB of RAM or an Android device with up to 12 GB of RAM is no match for the capabilities of cloud servers. Even Meta’s smallest LLM, LLaMA-3.1 8B, requires at least 16 GB of RAM — and realistically, more is needed for decent performance without overloading the phone. Despite advances in mobile processors, the power gap is still significant.

This is why the industry is focused on optimizing these models — making them smaller, faster, and more efficient without sacrificing too much performance.

This article explores key recent research papers and methods aimed at achieving this goal, highlighting where the field currently stands:

• Meta’s MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases
• Huawei’s Kronecker Decomposition method for GPT compression
• “TQCompressor: Improving Tensor Decomposition Methods in Neural Networks via Permutations,” a recent open-source project and research paper that improved the Kronecker decomposition method, allowing GPT models to be compressed by 1.5x without significantly increasing time requirements, a study I was involved in as a co-author.

Meta’s approach to designing LLMs for on-device use cases

This summer, Meta AI researchers introduced a new way to create efficient language models specifically for smartphones and other devices with limited resources and released a model called MobileLLM, built using this approach.

Instead of relying on models with billions or even trillions of parameters — like GPT-4 — Meta’s team focused on optimizing models with fewer than 1 billion parameters.

The authors found that scaling the model “in-depth” works better than “in-width” for smaller models with up to or around 1 billion parameters, making them more suitable for smartphones. In other words, it’s more effective to have a higher number of smaller layers rather than a few large ones. For instance, their 125-million parameter model, MobileLLM, has 30 layers, whereas models like GPT-2, BERT, and most models with 100-200 million parameters typically have around 12 layers. Models with the same number of parameters but a higher layer count (as opposed to larger parameters per layer) demonstrated better accuracy across several benchmarking tasks, such as Winogrande and Hellaswag.

Graphs from Meta’s research show that under comparable model sizes, deeper and thinner models generally outperform their wider and shallower counterparts across various tasks, such as zero-shot common sense reasoning, question answering, and reading comprehension.
Image credit: MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

Layer sharing is another technique used in the research to reduce parameters and improve efficiency. Instead of duplicating layers within the neural network, the weights of a single layer are reused multiple times. For example, after calculating the output of one layer, it can be fed back into the input of that same layer. This approach effectively cuts down the number of parameters, as the traditional method would require duplicating the layer multiple times. By reusing layers, they achieved significant efficiency gains without compromising performance.

As shown in the table from the research, other models with 125M parameters typically have 10-12 layers, whereas MobileLLM has 30. MobileLLM outperforms the others on most benchmarks (with the benchmark leader highlighted in bold).
Image credit: MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases

In their paper, Meta introduced the MobileLLM model in two versions — 125 million and 350 million parameters. They made the training code for MobileLLM publicly available on GitHub. Later, Meta also published 600 million, 1 billion, and 1.5 billion versions of the model. 

These models showed impressive improvements in tasks like zero-shot commonsense reasoning, question answering, and reading comprehension, outperforming previous state-of-the-art methods. Moreover, fine-tuned versions of MobileLLM demonstrated their effectiveness in common on-device applications such as chat and API calls, making them particularly well-suited for the demands of mobile environments.

Meta’s message is clear: If we want models to work on mobile devices, they need to be created differently. 

But this isn’t often the case. Take the most popular models in the AI world, like LLaMA3, Qwen2, or Gemma-2 — they don’t just have far more parameters; they also have fewer but much larger layers, which makes it practically very difficult to run these models on mobile devices.

Compressing existing LLMs

Meta’s recent research shifts away from compressing existing neural networks and presents a new approach to designing models specifically for smartphones. However, millions of engineers worldwide who aren’t building models from scratch — and let’s face it, that’s most of them — still have to work with those wide, parameter-heavy models. Compression isn’t just an option; it’s a necessity for them.

Here’s the thing: while Meta’s findings are groundbreaking, the reality is that open-source models aren’t necessarily being built with these principles in mind. Most cutting-edge models, including Meta’s own LLaMA, are still designed for large servers with powerful GPUs. These models often have fewer but much wider layers. For example, LLaMA3 8B has nearly 65 times more parameters than MobileLLM-125M, even though both models have around 30 layers.

So, what’s the alternative? You could keep creating new models from scratch, tailoring them for mobile use. Or, you could compress existing ones.

When making these large, wide models more efficient for mobile devices, engineers often turn to a set of tried-and-true compression techniques. These methods are quantization, pruning, matrix decomposition, and knowledge distillation. 

Quantization

One of the most commonly used methods for neural network compression is quantization, which is known for being straightforward and effectively preserving performance.

Image credit: Jan Marcel Kezmann on Medium

The basic concept is that a neural network consists of numbers stored in matrices. These numbers can be stored in different formats, such as floating-point numbers or integers. You can drastically reduce the model’s size by converting these numbers from a more complex format, like float32, to a simpler one, like int8. For example, a model that initially took up 100MB could be compressed to just 25MB using quantization.

Pruning 

As mentioned, a neural network consists of a set of matrices filled with numbers. Pruning is the process of removing “unimportant” numbers, known as “weights,” from these matrices.

By removing these unimportant weights, the model’s behavior is minimally affected, but the memory and computational requirements are reduced significantly. 

Matrix decomposition 

Matrix decomposition is another effective technique for compressing neural networks. The idea is to break down (or “decompose”) large matrices in the network into smaller, simpler ones. Instead of storing an entire matrix, it can be decomposed into two or multiple smaller matrices. When multiplied together, these smaller matrices produce a result that is the same or very close to the original. This allows us to replace a large matrix with smaller ones without altering the model’s behavior. However, this method isn’t flawless — sometimes, the decomposed matrices can’t perfectly replicate the original, resulting in a small approximation error. Still, the trade-off in terms of efficiency is often worth it.

Knowledge distillation

Knowledge distillation, introduced by Hinton et al. in 2015, is a simple yet effective method for creating a smaller, more efficient model (the “student model”) by transferring knowledge from a pre-trained, larger model (the “teacher model”). 

Using knowledge distillation, an arbitrarily designed smaller language model can be trained to mimic the behavior of a larger model. The process works by feeding both models the same data, and the smaller one learns to produce similar outputs to those of the larger model. Essentially, the student model is distilled with the knowledge of the teacher model, allowing it to perform similarly but with far fewer parameters.

One notable example is DistilBERT (Sanh et al. 2019), which successfully reduced the parameters of BERT by 40% while maintaining 97% of its performance and running 71% faster.

Distillation can be easily combined with quantization, pruning, and matrix decomposition, where the teacher model is the original version and the student is the compressed one. These combinations help refine the accuracy of the compressed model. For example, you could compress GPT-2 using matrix decomposition and then apply knowledge distillation to train the compressed model to mimic the original GPT-2.

How to compress existing models for on-device AI use cases

A few years ago, Huawei also focused on enabling on-device AI models and published research on compressing GPT-2. The researchers used a matrix decomposition method to reduce the size of the popular open-source GPT-2 model for more efficient on-device use.

Specifically, they used a technique called Kronecker decomposition, which is the basis for their paper titled “Kronecker Decomposition for GPT Compression.” As a result, GPT-2’s parameters were reduced from 125 million to 81 million.

To recover the model’s performance after compression, the authors employed knowledge distillation. The compressed version — dubbed KnGPT-2 — learned to mimic the original GPT-2’s behavior. They trained this distilled model using just 10% of the original dataset used to train GPT-2. In the end, the model size decreased by 35%, with a relatively small loss in performance.

This year, my colleagues and I published research on matrix decomposition methods, where we successfully compressed the GPT-2 model (with 125 million parameters) down to 81 million parameters. We named the resulting model TQCompressedGPT-2. This study further improved the method of Kronecker decomposition, and with this advancement, we managed to use just 3.1% of the original dataset during the knowledge distillation phase. It means that we reduced training time by about 33 times compared to using the full dataset and that developers looking to deploy models like LLaMA3 on smartphones will require 33 times less time to achieve a compressed version of LLaMA3 using our method.  

The novelty of our work lies in a few key areas:

• Before applying compression, we introduced a new method: permutation of weight matrices. By rearranging the rows and columns of layer matrices before decomposition, we achieved higher accuracy in the compressed model.
• We applied compression iteratively, reducing the model layers one by one.

We’ve made our model and algorithm code open-source, allowing for further research and development.

Both studies bring us closer to the concept Meta introduced with their approach to Mobile LLMs. They demonstrate methods for transforming existing wide models into more compact, deeper versions using matrix decomposition techniques and restoring the compressed model’s performance with knowledge distillation.

Top-tier models like LLaMA, Mistral, and Qwen, which are significantly larger than 1 billion parameters, are designed for powerful cloud servers, not smartphones. The research conducted by Huawei and our team offers valuable techniques for adapting these large models for mobile use, aligning with Meta’s vision for the future of on-device AI.

Compressing AI models is more than a technical challenge — it’s a crucial step toward making advanced technology accessible to billions. As models grow more complex, the ability to run them efficiently on everyday devices like smartphones becomes essential. This isn’t just about saving resources; it’s about embedding AI into our daily lives in a sustainable way. 

The industry’s progress in addressing this challenge is significant. Advances from Huawei and TQ in the compression of AI models are pushing AI toward a future where it can run seamlessly on smaller devices without sacrificing performance. These are critical steps toward sustainably adapting AI to real-world constraints and making it more accessible to everyone, laying a solid foundation for further research in this vital area of AI’s impact on humanity.

Computer vision is one of the most influential areas of artificial intelligence, changing nearly every aspect of our lives, on par with generative AI. From medical image analysis and autonomous vehicles to security systems, AI-powered computer vision is critical in enhancing safety, efficiency, and healthcare through technologies like object detection, facial recognition, and image classification.

But computer vision isn’t just making waves in specialized fields; it’s also a part of the consumer apps we use daily. Enhancing camera focus, editing photos, real-time text recognition and scanning with a smartphone camera, enabling smart home devices like security cameras to detect and alert users about movement, pose estimation for fitness tracking apps, calorie and food identification for diet tracking apps, face identification for unlocking phones, and face detection and classification to organize photos by person in albums. These applications have become integral to the daily experiences of millions of people.

Source: Real Computer Vision by Boris Denisenko on Medium

Most machine learning engineers don’t build their models from scratch to bring these features to life. Instead, they rely on existing open-source models. While this is the most feasible approach, as building a model from scratch is prohibitively expensive, there’s still a lot of work to be done before the model can be used in an app. 

First, the open-source model may solve a similar scenario but not the exact one an engineer needs. For example, an ML engineer may need an app that compares different drinks, but the available model is designed to compare food items. Although it performs well with food, it may struggle when applied to drinks.

Secondly, the real-world conditions these models need to run often differ significantly from the environments they were initially designed for. For example, a model might have hundreds of millions of parameters, making it too large and computationally intensive to run, let’s say, on a smartphone. Attempting to run such a model on a device with limited computational resources leads to slow performance, excessive battery drain, or failure to run. 

Adapting to Real-World Scenarios and Conditions

This sooner or later leads most engineers applying machine learning for computer vision in consumer apps to face the necessity of:

1. Adapting an existing open-source model to fit their specific scenario.
2. Optimizing the model to run within limited capacities.

Adapting a model isn’t something you can just breeze through. You start with a pre-trained model and tailor it to your specific task. This involves tweaking a multitude of parameters — the number of layers, number of neurons in each layer, learning rate, batch size, and more. The sheer number of possible combinations can be overwhelming, with potentially millions of different configurations to test. This is where hyperparameter optimization (HPO) comes into play. HPO helps streamline this process, allowing you to find the best configuration faster than if you were to manually adjust parameters separately.

Once you’ve adapted the model to your scenario, the next challenge is getting it to run on a device with limited resources. For instance, you might need to deploy the model on a smartphone with just 6 GB of RAM. In such cases, model compression becomes essential to reduce the model’s size and make it manageable for devices with limited memory and processing power.

Hyperparameter optimization (HPO) techniques 

Hyperparameter optimization involves finding the best set of parameters for your neural network to minimize error on a specific task. Let’s say you’re training a model to estimate a person’s age from a photo. The error in this context refers to the deviation of the model’s age estimate from the person’s actual age — measured, let’s say, in the number of years it is off.

Grid search

Grid search is a brute-force method that finds the optimal combination by testing every possible set of parameters. You start with an existing model and adapt it to your task. Then, you systematically modify parameters — like the number of neurons or layers—to see how these changes affect the model’s error. Grid search involves testing each combination of these parameters to find the one that produces the lowest error. The challenge is that there are numerous parameters you could adjust, each with a broad range of potential values.

While this method guarantees finding the best option, it is incredibly time-consuming and often impractical. 

Random Search

Another approach is random search, where you randomly sample a portion of possible combinations instead of testing every combination. This method involves selecting random values for each parameter within a specified range and testing those combinations. While it’s faster than grid search, it doesn’t guarantee the best result. However, it’s likely to find a good, if not optimal, solution. It’s a trade-off between speed and precision.

For instance, if there are 1,000 possible parameter combinations, you could randomly sample and test 100, which would take only one-tenth of the time compared to testing all combinations.

HPO Using Optimization Algorithms

Optimization-based hyperparameter tuning methods use different mathematical approaches to efficiently find the best parameter settings. For example, Bayesian optimization uses probabilistic models to guide the search, while TetraOpt—an algorithm developed by the author and team—employs tensor-train optimization to better navigate high-dimensional spaces. These methods are more efficient than grid or random search because they aim to minimize the number of evaluations needed to find optimal hyperparameters, focusing on the most promising combinations without testing every possibility.

Such optimization algorithms help find better solutions faster, which is especially valuable when model evaluations are computationally expensive. They aim to deliver the best results with the fewest trials.

ML model Compression techniques 

Once a model works in theory, running it in real-life conditions is the next challenge. Take, for example, ResNet for facial recognition, YOLO for traffic management and sports analytics, or VGG for style transfer and content moderation. While powerful, these models are often too large for devices with limited resources, such as smartphones or smart cameras. 

ML engineers turn to a set of tried-and-true compression techniques to make the models more efficient for such environments. These methods — Quantization, Pruning, Matrix Decomposition, and Knowledge Distillation — are essential for reducing the size and computational demands of AI models while preserving their performance.

Quantization

Source: Master the Art of Quantization by Jan Marcel Kezmann on Medium

Quantization is one of the most popular methods for compressing neural networks, primarily because it requires minimal additional computation compared to other techniques.

The core idea is straightforward: a neural network comprises numerous matrices filled with numbers. These numbers can be stored in different formats on a computer, such as floating-point (e.g., 32.15) or integer (e.g., 4). Different formats take up different amounts of memory. For instance, a number in the float32 format (e.g., 3.14) takes up 32 bits of memory, while a number in the int8 format (e.g., 42) only takes 8 bits.

If a model’s numbers are originally stored in float32 format, they can be converted to int8 format. This change significantly reduces the model’s memory footprint. For example, a model initially occupying 100MB could be compressed to just 25MB after quantization.

Pruning

As mentioned earlier, a neural network consists of a set of matrices filled with numbers, known as “weights.” Pruning is the process of removing the “unimportant” weights from these matrices. By eliminating these unnecessary weights, the model’s behavior remains largely unaffected, but the memory and computational requirements are significantly reduced.

For example, imagine one of the neural network’s matrices looks like this:

After pruning, it might look something like this:

The dashes (“-“) indicate where elements were removed during pruning. This simplified model requires fewer computational resources to operate.

Matrix Decomposition

Matrix decomposition is another effective compression method that involves breaking down (or “decomposing”) the large matrices in a neural network into several smaller, simpler matrices.

For instance, let’s say one of the matrices in a neural network looks like this:

Matrix decomposition allows us to replace this single large matrix with two smaller ones. 

When multiplied together, these smaller matrices give the same result as the original one, ensuring that the model’s behavior remains consistent. 

This means we can replace the matrix from the first picture with the matrices from the second.

The original matrix contains 9 parameters, but after decomposition, the matrices together hold only 6, resulting in a ~33% reduction. One of the key advantages of this method is its potential to greatly compress AI models — several times in some cases.

It’s important to note that matrix decomposition isn’t always perfectly accurate. Sometimes, a small approximation error is introduced during the process, but the efficiency gains often outweigh this minor drawback.

Knowledge Distillation

Knowledge Distillation is a technique for building a smaller model, known as the “student model,” by transferring knowledge from a larger, more complex model, called the “teacher model.” The key idea is to train the smaller model alongside the larger one so the student model learns to mimic the behavior of the teacher model.

Here’s how it works: You pass the same data through the large neural network (the teacher) and the compressed one (the student). Both models produce outputs, and the student model is trained to generate outputs as similar as possible to the teacher’s. This way, the compressed model learns to perform similarly to the larger model but with fewer parameters.

Distillation can be easily combined with quantization, pruning, and matrix decomposition, where the teacher model is the original version and the student is the compressed one. These combinations help refine the accuracy of the compressed model.

In practice, engineers often combine these techniques to maximize the performance of their models when deploying them in real-world scenarios. 

AI evolves along two parallel paths. On the one hand, it fuels impressive advancements in areas like healthcare, pushing the limits of what we thought was possible. On the other, adapting AI to real-world conditions is just as crucial, bringing advanced technology into the daily lives of millions, often seamlessly and unnoticed. This duality mirrors the impact of the smartphone revolution, which transformed computing from something disruptive and costly into technology accessible and practical for everyone. 

The optimization techniques covered in this article are what engineers use to make AI a tangible part of everyday life. This research is ongoing, with large tech companies (like Meta, Tesla, or Huawei) and research labs investing significant resources in finding new ways to optimize models. However, well-implemented HPO techniques and compression methods are already helping engineers worldwide bring the latest models into everyday scenarios and devices, creating impressive products for millions of people today and pushing the industry forward through their published and open-sourced findings.

Today efficient and secure data management is more vital than ever in the rapidly changing business world. Small businesses and startups, in particular, can greatly benefit from embracing innovative solutions to streamline processes and increasingly leveraging advanced technologies to stay competitive. Among these technological innovations, virtual data rooms (VDRs) have become valuable means of providing a secure and efficient way for small businesses and startups to manage and share information.

Dataroom software is commonly applied for corporate operations and the due diligence process, facilitating cooperation and secure file sharing. Now, different businesses have begun implementing data rooms for startups. Involving investors or dealing with startup strategic goals demands quick and secure distribution and analysis of confidential information. This article will describe why a virtual data room is the perfect solution for small businesses and startups.

What is VDR software?

A virtual data room is a secure digital location used to organize, store, and distribute confidential files and information. A data room provides potential investors with a specific place where sensitive information can be stored and shared without the risk of data leakage. Data rooms for due diligence are helpful when investors audit financial files, keeping data protected when shared with multiple parties. An M&A data room is an online repository for files required during M&A transactions.

Co-founder and marketing specialist at data-rooms.org Gilbert Waters claims: “Businesses need reliable ways to manage, share, and protect their data”. The best data rooms for startups are the main components of their fundraising attempts. Creating a startup is risky and investors want to be secure when deciding to invest in the company. Possible partners prefer the company that uses VDR software to protect its data, allowing investors to access the information they need to make informed financing arrangements.

Data room benefits for small businesses and startups

A perfectly organized data room can improve businesses significantly by affecting potential investors’ decisions. The key reasons for startups and small businesses to invest in VDR software are:

• Better security. The primary target for any business is keeping data protected. Data rooms offer robust security features that protect sensitive business information from unauthorized access. Security features such as encryption, multi-factor authentication, and granular access controls provide a secure environment for confidential data.
• Improved collaboration. Collaboration is fundamental in today’s interdependent business area. A VDR facilitates seamless cooperation among team members, clients, and stakeholders, allowing for real-time document sharing and collaboration irrespective of geographical location.
• Easy due diligence. Small businesses often find themselves involved in mergers, acquisitions, or fundraising. virtual data room software accelerates the due diligence process, providing potential investors or partners with easy access to relevant documents, thereby speeding up decision-making.
• Scalability. With small businesses growing, data management needs to progress. Virtual data rooms are extensible tools that can adapt to the increasing volume of users and files, establishing continued efficiency and performance.
• Cost savings. Implementing a VDR eliminates the need for physical storage space, printing, and courier services. This reduces operational costs and contributes to a more sustainable and eco-friendly business model.
• Client confidence. Establishing the need for data security and efficient management can significantly enhance client confidence. Small businesses and startups can show trier reliability and professionalism by using VDR software

Data to include in a startup data room

It could be problematic to consider what should be included in a virtual data room for startups. In general, each data room for a startup or small company should include company documentation, employee documentation, financial documents, and intellectual property. Giving investors an insight into the hiring process and company culture, by including onboarding documents, can also strengthen the fundraising process. Including in-depth information is an excellent starting point for the investor, demonstrating that the business is transparent and trustworthy.

Choosing the perfect VDR software for a startup or small business

The best VDR can be quite challenging to select, with a lot of data room providers to consider. When choosing the perfect virtual data room for a startup or small company, first consider the following aspects:

1. Identify the company’s needs, abilities, and desired VDR features.
2. Compare various VDRs and read virtual data room reviews.
3. Consider a startup budget.
4. Test the chosen VDR software demo version and select virtual data room providers for the free trial period.

Data rooms are becoming increasingly popular among startups because they can manage and share sensitive data. Among online data room providers for small businesses and startups, there are several leaders:

• iDeals is considered to be the leading provider of the best VDR software. This provider focuses on integrating workflow into the VDR environment with a customized space, detailed reports, and multiple management tools. iDeals has been tried and tested worldwide by top managers, investment bankers, and lawyers.
• SecureDocs is considered to be the fastest VDR software. The provider is trusted by many companies regardless of their size and scale. It just takes 10 minutes to set up a VDR in SecureDocs and at an affordable price. SecureDocs has priorities on safety and having a high-level security system.
• Firmex is the most trusted data room provider with a smart interface that allows users to work fast and efficiently. It has an expert support team that provides around-the-clock support. Its VDR includes a lot of powerful tools used to safeguard important documents and data.

Adopt a data room software

To conclude, the adoption of a data room turns out to be a transformative step for small businesses and startups, offering a multitude of benefits ranging from improved security to simplified collaboration. As the business landscape grows, the need for effective data management becomes more critical, and virtual data room software turns into an effective tool.

Featured image credit: Pietro Jeng/Unsplash

Over the past two decades, data has become an invaluable asset for companies, rivaling traditional assets like physical infrastructure, technology, intellectual property, and human capital. For some of the world’s most valuable companies, data forms the core of their business model.

The scale of data production and transmission has grown exponentially. Forbes reports that global data production increased from 2 zettabytes in 2010 to 44 ZB in 2020, with projections exceeding 180 ZB by 2025 – a staggering 9,000% growth in just 15 years, partly driven by artificial intelligence.

However, raw data alone doesn’t equate to actionable insights. Unprocessed data can overwhelm users, potentially hindering understanding. Information – data that’s processed, organized, and consumable – drives insights that lead to actions and value generation.

This article shares my experience in data analytics and digital tool implementation, focusing on leveraging “Big Data” to create actionable insights. These insights have enabled users to capitalize on commercial opportunities, identify cost-saving areas, and access useful benchmarking information. Our projects often incorporated automation, yielding time savings and efficiency gains. I’ll highlight key challenges we faced and our solutions, emphasizing early project phases where decisions have the most significant impact.

Key areas of focus include:

• Quantification of benefits
• The risk of scope creep
• Navigating challenges with PDF data
• Design phase and performance considerations

In large organizations, data availability and accessibility often pose significant challenges, especially when combining data from multiple systems. Most of my projects aimed to create a unified, harmonized dataset for self-serve analytics and insightful dashboards. We employed agile methodologies to maintain clear oversight of progress and bottlenecks, ensuring accountability for each team member.

The typical lifecycle of data projects encompasses scoping, design, development, implementation, and sustainment phases. During scoping, the product owner collaborates closely with the client/end-user organization to grasp overall needs, desired data types and insights, requirements, and functionality.

Quantification of benefits

A crucial element of the scoping phase is the benefit case, where we quantify the solution’s potential value. In my experience, this step often proves challenging, particularly when estimating the value of analytical insights. I’ve found that while calculating automation benefits like time savings is relatively straightforward, users struggle to estimate the value of insights, especially when dealing with previously unavailable data.

In one pivotal project, we faced this challenge head-on. We were developing a data model to provide deeper insights into logistics contracts. During the scoping phase, we struggled to quantify the potential benefits. It wasn’t until we uncovered a recent incident that we found our answer.

A few months earlier, the client had discovered they were overpaying for a specific pipeline. The contract’s structure, with different volumetric flows triggering varying rates, had led to suboptimal usage and excessive costs. By adjusting volume flows, they had managed to reduce unit costs significantly. This real-world example proved invaluable in our benefit quantification process.

We used this incident to demonstrate how our data model could have:

1. Identified the issue earlier, potentially saving months of overpayment
2. Provided ongoing monitoring to prevent similar issues in the future
3. Offered insights for optimizing flow rates across all contracts

This concrete example not only helped us quantify the benefits but also elevated the project’s priority with senior management, securing the funding we needed. It was a crucial lesson in the power of using tangible, recent events to illustrate potential value.

However, not all projects have such clear-cut examples. In these cases, I’ve developed alternative approaches:

1. Benchmarking: We compare departmental performance against other departments or competitors, identifying best-in-class performance and quantifying the value of reaching that level.
2. Percentage Improvement: We estimate a conservative percentage improvement in overall departmental revenue or costs resulting from the model. Even a small percentage can translate to significant value in large organizations.

Regardless of the method, I’ve learned the importance of defining clear, measurable success criteria. We now always establish how benefits will be measured post-implementation. This practice not only facilitates easier reappraisal but also ensures accountability for the digital solution implementation decision.

Another valuable lesson came from an unexpected source. In several projects, we discovered “side customers” – departments or teams who could benefit from our data model but weren’t part of the original scope. In one case, a model designed for the logistics team proved invaluable for the finance department in budgeting and forecasting.

This experience taught me to cast a wider net when defining the customer base. We now routinely look beyond the requesting department during the scoping phase. This approach has often increased the overall project benefits and priority, sometimes turning a marginal project into a must-have initiative.

These experiences underscore a critical insight: in large organizations, multiple users across different areas often grapple with similar problems without realizing it. By identifying these synergies early, we can create more comprehensive, valuable solutions and build stronger cases for implementation.

The risk of scope creep

While broadening the customer base enhances the model’s impact, it also increases the risk of scope creep. This occurs when a project tries to accommodate too many stakeholders, promising excessive or overly complex functionality, potentially compromising budget and timeline. The product owner and team must clearly understand their resources and realistic delivery capabilities within the agreed timeframe.

To mitigate this risk:

1. Anticipate some design work during the scoping phase.
2. Assess whether new requirements can be met with existing data sources or necessitate acquiring new ones.
3. Set clear, realistic expectations with client management regarding scope and feasibility.
4. Create a manual mockup of the final product during scoping to clarify data source requirements and give end-users a tangible preview of the outcome.
5. Use actual data subsets in mockups rather than dummy data, as users relate better to familiar information.

The challenges related to PDF data

Several projects highlighted challenges in capturing PDF data. Users often requested details from third-party vendor invoices and statements not available in our financial systems. While accounting teams typically book summarized versions, users needed line item details for analytics.

Extracting data from PDFs requires establishing rules and logic for each data element, a substantial effort worthwhile only for multiple PDFs with similar structures. However, when dealing with documents from thousands of vendors with varying formats that may change over time, developing mapping rules becomes an immense task.

Before including PDF extraction in a project scope, I now require a thorough understanding of the documents involved and ensure the end-user organization fully grasps the associated challenges. This approach has often led to project scope redefinition, as the benefits may not justify the costs, and alternative means to achieve desired insights may exist.

Design phase and performance considerations

The design phase involves analyzing scoped elements, identifying data sources, assessing optimal data interface methods, defining curation and calculation steps, and documenting the overall data model. It also encompasses decisions on data model hosting, software applications for data transfer and visualization, security models, and data flow frequency. Key design requirements typically include data granularity, reliability, flexibility, accessibility, automation, and performance/speed.

Performance is crucial, as users expect near real-time responses. Slow models, regardless of their insights, often see limited use. Common performance improvement methods include materializing the final dataset to avoid cache-based calculations. Visualization tool choice also significantly impacts performance. Testing various tools during the design phase and timing each model step helps inform tool selection. Tool choice may influence design, as each tool has preferred data structures, though corporate strategy and cost considerations may ultimately drive the decision.

Future trends

Emerging trends are reshaping the data analytics landscape. Data preparation and analysis tools now allow non-developers to create data models using intuitive graphical interfaces with drag-and-drop functionality. Users can simulate and visualize each step, enabling on-the-fly troubleshooting. This democratization of data modeling extends the self-serve analytics trend, empowering users to build their own data models.

While limits exist on the complexity of end-user-created data products, and organizations may still prefer centrally administered corporate datasets for widely used data, these tools are expanding data modeling capabilities beyond IT professionals.

A personal experience illustrates this trend’s impact: During one project’s scoping phase, facing the potential loss of a developer, we pivoted from a SQL-programmed model to Alteryx. The product owner successfully created the data model with minimal IT support, enhancing both their technical skills and job satisfaction.

The socialization of complex analytical tool creation offers significant benefits. Companies should consider providing training programs to maximize the value of these applications. Additionally, AI assistants can suggest or debug code, further accelerating the adoption of these tools. This shift may transform every employee into a data professional, extracting maximum value from company data without extensive IT support.

Unlock data’s value

Data-driven decision-making is experiencing rapid growth across industries. To unlock data’s value, it must be transformed into structured, actionable information. Data analytics projects aim to consolidate data from various sources into a centralized, harmonized dataset ready for end-user consumption.

These projects encompass several phases – scoping, design, build, implementation, and sustainment – each with unique challenges and opportunities. The scoping phase is particularly critical, as decisions made here profoundly impact the entire project lifecycle.

The traditional model of relying on dedicated IT developers is evolving with the advent of user-friendly data preparation and analysis tools, complemented by AI assistants. This evolution lowers the barrier to building analytical models, enabling a broader range of end-users to participate in the process. Ultimately, this democratization of data analytics will further amplify its impact on corporate decision-making, driving innovation and efficiency across organizations.

Generative artificial intelligence is the talk of the town in the technology world today. Almost every tech company today is up to its neck in generative AI, with Google focused on enhancing search, Microsoft betting the house on business productivity gains with its family of copilots, and startups like Runway AI and Stability AI going all-in on video and image creation.

It has become clear that generative AI is one of the most powerful and disruptive technologies of our age, but it should be noted that these systems are nothing without access to reliable, accurate and trusted data. AI models need data to learn patterns, perform tasks on behalf of users, find answers and make predictions. If the underlying data they’re trained on is inaccurate, models will start outputting biased and unreliable responses, eroding trust in their transformational capabilities.

As generative AI rapidly becomes a fixture in our lives, developers need to prioritize data integrity to ensure these systems can be relied on.

Why is data integrity important?

Data integrity is what enables AI developers to avoid the damaging consequences of AI bias and hallucinations. By maintaining the integrity of their data, developers can rest assured that their AI models are accurate and reliable, and can make the best decisions for their users. The result will be better user experiences, more revenue and reduced risk. On the other hand, if bad quality data is fed into AI models, developers will have a hard time achieving any of the above.

Accurate and secure data can help to streamline software engineering processes and lead to the creation of more powerful AI tools, but it has become a challenge to maintain the quality of the expansive volumes of data needed by the most advanced AI models.

These challenges are primarily due to how data is collected, stored, moved and analyzed. Throughout the data lifecycle, information must move through a number of data pipelines and be transformed multiple times, and there’s a lot of potential for it to be mishandled along the way. With most AI models, their training data will come from hundreds of different sources, any one of which could present problems. Some of the challenges include discrepancies in the data, inaccurate data, corrupted data and security vulnerabilities.

Adding to these headaches, it can be tricky for developers to identify the source of their inaccurate or corrupted data, which complicates efforts to maintain data quality.

When inaccurate or unreliable data is fed into an AI application, it undermines both the performance and the security of that system, with negative impacts for end users and possible compliance risks for businesses.

Tips for maintaining data integrity

Luckily for developers, they can tap into an array of new tools and technologies designed to help ensure the integrity of their AI training data and reinforce trust in their applications.

One of the most promising tools in this area is Space and Time’s verifiable compute layer, which provides multiple components for creating next-generation data pipelines for applications that combine AI with blockchain.

Space and Time’s creator SxT Labs has created three technologies that underpin its verifiable compute layer, including a blockchain indexer, a distributed data warehouse and a zero-knowledge coprocessor. These come together to create a reliable infrastructure that allows AI applications to leverage data from leading blockchains such as Bitcoin, Ethereum and Polygon. With Space and Time’s data warehouse, it’s possible for AI applications to access insights from blockchain data using the familiar Structured Query Language.

To safeguard this process, Space and Time uses a novel protocol called Proof-of-SQL that’s powered by cryptographic zero-knowledge proofs, ensuring that each database query was computed in a verifiable way on untampered data.

In addition to these kinds of proactive safeguards, developers can also take advantage of data monitoring tools such as Splunk, which make it easy to observe and track data to verify its quality and accuracy.

Splunk enables the continuous monitoring of data, enabling developers to catch errors and other issues such as unauthorized changes the instant they happen. The software can be set up to issue alerts, so the developer is made aware of any challenges to their data integrity in real time.

As an alternative, developers can make use of integrated, fully-managed data pipelines such as Talend, which offers features for data integration, preparation, transformation and quality. Its comprehensive data transformation capabilities extend to filtering, flattening and normalizing, anonymizing, aggregating and replicating data. It also provides tools for developers to quickly build individual data pipelines for each source that’s fed into their AI applications.

Better data means better outcomes

The adoption of generative AI is accelerating by the day, and its rapid uptake means that the challenges around data quality must be urgently addressed. After all, the performance of AI applications is directly linked to the quality of the data they rely on. That’s why maintaining a robust and reliable data pipeline has become an imperative for every business.

If AI lacks a strong data foundation, it cannot live up to its promises of transforming the way we live and work. Fortunately, these challenges can be overcome using a combination of tools to verify data accuracy, monitor it for errors and streamline the creation of data pipelines.

Featured image credit: Shubham Dhage/Unsplash

Data activation is seen as the primary factor for enhancing marketing and sales effectiveness by almost 80% of European companies. In today’s digital era, data is the key that allows companies to unlock better decision-making, understand customer behavior and optimize campaigns. However, simply acquiring all available data and storing it in data lakes does not guarantee success.

The true meaning of data activation

For the past few decades, organizations worldwide have collected all sorts of data and stored it in massive data lakes. But these days it’s clear that more is not always better, and centralized data storage is becoming a burden. Collecting huge amounts of information can result in violations of data privacy regulations like GDPR, which demand strict user consent and control over personal data. It can also overwhelm systems and lead to poor data management, making it harder to extract actionable insights.

A more efficient approach is to collect only useful information and then “activate it”. Data activation involves integrating and analyzing information from various sources to make better decisions, drive marketing strategies, and enhance customer experiences. Unlike simple mass data collection, the focus is on using data to achieve tangible business outcomes.

5 key benefits of data activation

According to the study by Piwik PRO, European companies activate data for several reasons. The primary purposes are personalizing user experience (over 44%) and optimizing marketing efforts (almost 44%). Over 38% of participants indicated reaching the right audience; 30% want to improve customer experience; and almost 29% are using it to generate leads.

1. Personalizing and improving user experience: Data activation enables the delivery of customized experiences to audiences by catering to their specific needs and behaviors. This personalization happens across multiple channels, such as websites, mobile apps, and email campaigns. For instance, companies can use data to recommend products based on past purchases or browsing history.
2. Optimizing marketing efforts: Data activation enables merging data from different sources, such as CRM systems, analytics tools, and marketing automation software. This integration helps streamline operations and provides a holistic view of business performance. Marketers can also identify the most effective channels, content and time to communicate with their customers. This results in on-the-spot adjustments to campaigns, more efficient budget allocation, and generation of valuable leads.
3. Reaching the right audience: More precise audience segmentation leads to more accurate targeting of outcomes. Understanding the unique needs and preferences of diverse customer segments helps companies create effective marketing messages, ultimately boosting conversion rates and customer loyalty.
4. Compliance and risk mitigation: Proper data activation practices help ensure compliance with data protection laws, reducing the risk of penalties and damage to companies’ reputation. Businesses that efficiently handle and utilize their data have an advantage in dealing with the challenges of digital privacy laws.
5. Innovation and competitive advantage: Data activation empowers companies to innovate by identifying new market opportunities and responding to customer needs more swiftly. This agility can provide a significant competitive advantage, particularly in rapidly changing markets.

The right tool for the job

When activating data and making it usable for marketing and sales teams, companies should turn to customer data platforms (CDPs). These are usually standalone solutions, but some companies offer a CDP as a part of an analytics platform, which can lead to faster and more accurate results.

A CDP helps organize, segment, and apply data to different business activities. Piwik PRO’s study found that nearly 66% of respondents have considered implementing a customer data platform in their company, but the numbers differed among countries. For example, in Denmark only 51% have considered doing so, while in Germany as many as 75% have thought of making this move.

Piwik’s PRO survey reveals that over 44% of respondents believe that the most beneficial aspect of a CDP solution is the integration of data from multiple sources. Other advantages include optimizing the customer experience (38%), eliminating data silos (35%), and creating complete customer profiles and segmentation (34.3%). The least cited benefit is the ability to create behavioral audiences for marketing activities (17.3%).

Despite many positive outcomes, merging data from disconnected sources in a CDP can bring its own share of challenges and quality issues. Over 51% of respondents cited security and compliance as the most challenging aspect of combining data from different sources. The next significant issue is inaccurate data, highlighted by 42.6%, followed by migration (33%) and duplication (almost 25%).

For European companies, strategic data activation is not just a technological enhancement but a necessity. It bridges the gap between data collection and actionable insights, driving business growth, improving customer experiences, and ensuring compliance with stringent regulatory frameworks. As the digital landscape continues to evolve, mastering data activation will be crucial for companies aiming to thrive in a data-driven world.

The Internet Archive has lost a significant legal battle after the US Court of Appeals upheld a ruling in Hachette v. Internet Archive, stating that its book digitization and lending practices violated copyright law. The case stemmed from the Archive’s National Emergency Library initiative during the pandemic, which allowed unrestricted digital lending of books, sparking backlash from publishers and authors. The court rejected the Archive’s fair use defense, although it acknowledged its nonprofit status. This ruling strengthens authors’ and publishers’ control over their works. But it immediately reminds me of how AI tools train and use data on the Internet, including books and more. If the nonprofit Internet Archive’s work is not fair use, how do the paid AI tools use this data? 

Despite numerous AI copyright lawsuits, text-based data from news outlets usually doesn’t result in harsh rulings against AI tools, often ending in partnerships with major players.

You might think it’s different and argue that the Internet Archive directly uses books, but even though AI tools rely on all the data they have to generate your essay, you can still get specific excerpts or more detailed responses from them if you use a well-crafted prompt.

The Hachette v. Internet Archive case highlights significant concerns about how AI models acquire training data, especially when it involves copyrighted materials like books. AI systems often rely on large datasets, including copyrighted texts, raising similar legal challenges regarding unlicensed use. If courts restrict the digitization and use of copyrighted works without permission, AI companies may need to secure licenses for the texts used in training, adding complexity and potential costs. This could limit access to diverse, high-quality datasets, ultimately affecting AI development and innovation.

Additionally, the case underlines the limitations of the fair use defense in the context of transformative use, which is often central to AI’s justification for using large-scale text data. If courts narrowly view what constitutes fair use, AI developers might face more restrictions on how they access and use copyrighted books. This tension between protecting authors’ rights and maintaining open access to knowledge could have far-reaching consequences for the future of AI training practices and the ethical use of data.

Need a deeper dive into the case? Here is everything you need to know about it.

Hachette v. Internet Archive explained

Hachette v. Internet Archive is a significant legal case that centers around copyright law and the limits of the “fair use” doctrine in the context of digital libraries. The case began in 2020, when several large publishing companies—Hachette, HarperCollins, Penguin Random House, and Wiley—sued the Internet Archive, a nonprofit organization dedicated to preserving digital copies of websites, books, and other media.

The case focused on the Archive’s practice of scanning books and lending them out online.

The story behind the Internet Archive lawsuit

The Open Library project, run by the Internet Archive, was set up to let people borrow books digitally. Here’s how it worked:

• The Internet Archive bought physical copies of books.
• They scanned these books into digital form.
• People could borrow a digital version, but only one person at a time could check out a book, just like borrowing a physical book from a regular library.

The Internet Archive thought this was legal because they only let one person borrow a book at a time. They called this system Controlled Digital Lending (CDL). The idea was to make digital lending work just like physical library lending.

When the COVID-19 pandemic hit in early 2020, many libraries had to close, making it hard for people to access books. To help, the Internet Archive launched the National Emergency Library (NEL) in March 2020. This program changed things:

• The NEL allowed multiple people to borrow the same digital copy of a book at the same time. This removed the one-person-at-a-time rule.
• The goal was to give more people access to books during the pandemic, especially students and researchers who were stuck at home.

While the NEL was meant to be temporary, it upset authors and publishers. They argued that letting many people borrow the same digital copy without permission was like stealing their work.

Publishers’ riot

In June 2020, the big publishers sued the Internet Archive. They claimed:

• The Internet Archive did not have permission to scan their books or lend them out digitally.
• By doing this, the Internet Archive was violating their copyright, which gives them the exclusive right to control how their books are copied and shared.
• The NEL’s approach, which let many people borrow digital copies at once, was especially harmful to their business and was essentially piracy.

The publishers argued that the Internet Archive’s actions hurt the market for their books. They said people were getting free digital versions instead of buying ebooks or borrowing from licensed libraries.

Internet Archive’s defense

The Internet Archive defended itself by claiming that its work was protected by fair use. Fair use allows limited use of copyrighted material without permission for purposes like education, research, and commentary. The Archive made these points:

• They were providing a transformative service by giving readers access to physical books in a new, digital form.
• They weren’t making a profit from this, as they’re a nonprofit organization with the mission of preserving knowledge and making it accessible.
• The NEL was a temporary response to the pandemic, and they were trying to help people who couldn’t access books during the crisis.

They also pointed to their Controlled Digital Lending system as a way to respect copyright laws. Under CDL, only one person could borrow a book at a time, just like in a physical library.

The court’s decisions

District Court Ruling (March 2023)

In March 2023, a federal court sided with the publishers. Judge John G. Koeltl ruled that the Internet Archive’s actions were not protected by fair use. He said:

• The Internet Archive’s digital lending was not transformative because they weren’t adding anything new to the books. They were simply copying them in digital form, which wasn’t enough to qualify for fair use.
• The court also found that the Archive’s lending hurt the market for both printed and digital versions of the books. By offering free digital copies, the Internet Archive was seen as competing with publishers’ ebook sales.
• The court concluded that the Archive had created derivative works, which means they made new versions of the books (digital copies) without permission.

Appeals Court Ruling (August 2023)

The Internet Archive appealed the decision to a higher court, the US Court of Appeals for the Second Circuit, hoping to overturn the ruling. However, the appeals court also ruled in favor of the publishers but made one important clarification:

• The court recognized that the Internet Archive is a nonprofit organization and not a commercial one. This distinction was important because commercial use can often weaken a fair use defense, but in this case, the court acknowledged that the Archive wasn’t motivated by profit.
• Despite that, the court still agreed that the Archive’s actions weren’t protected by fair use, even though it’s a nonprofit.

Bottom line

The Hachette v. Internet Archive case has shown that even nonprofits like the Internet Archive can’t freely digitize and lend books without violating copyright laws. This ruling could also affect how AI companies use copyrighted materials to train their systems. If nonprofits face such restrictions, AI tools might need to get licenses for the data they use. Even if they have already started to make some deals, I wonder, what about the first entries?

Featured image credit: Eray Eliaçık/Bing

Information is a precious asset in this day and age, thus protecting sensitive data is crucial for individuals, companies, and organizations. Virtual data rooms providers (VDRs) have become increasingly popular as a result of the exponential expansion of digital communication and cooperation and the necessity for safe data exchange and storage.

These platforms guarantee the confidentiality, integrity, and accessibility of vital information by providing cutting-edge security measures that surpass conventional file-sharing techniques. Let’s examine the improved security elements found in contemporary virtual data rooms and their importance in preserving data security.

But why do business executives select virtual data rooms in the first place, and what are the key advantages of this approach? Get some insightful information immediately!

What makes businesses move to virtual data rooms?

With the expansion of enterprises comes the requirement for efficient and safe document exchange and storage. A physical data room was the standard method for keeping important corporate documents in the past.

Nonetheless, our research shows that, with the introduction of a virtual data room, an increasing number of businesses are moving to this digital substitute.

One explanation behind this trend is a virtual data room price. While data room costs are affordable on their own, the investment in a virtual solution saves funds that could be spent on accommodation and travel associated with using a physical alternative.

But beyond price, there are many other benefits. Let’s explore these in detail.

Superior data protection

All of the security worries that plague dealmakers throughout a merger and acquisition (M&A), initial public offering (IPO), fundraising round, joint venture due diligence procedure, or any other transactions involving sharing sensitive data are alleviated by a virtual dataroom. Angelo Dean, CEO of datarooms.org, asserts that virtual data rooms are essential for bolstering business security, offering advanced protection and control over sensitive information in today’s digital landscape. This is why typical elements of secure data room providers consist of:

• Strong data encryption both in transit and at rest keeps hackers from accessing sensitive information, even in the unlikely event that they manage to get their hands on data room files.
• Two-factor authentication: By implementing two-factor authentication, users are provided with an additional layer of security, deterring unauthorized access and enhancing the protection of sensitive information.
• Remote shred: The administrator can remove a user’s access to any documents when they are removed from the private virtual room.

Watermarking

Customized dynamic watermarking, which overlays documents with distinct identifiers to trace their origin and distribution, is one of the capabilities available in online data room providers. When placed diagonally across a page or screen, watermarks are easily observable and do not obstruct the underlying text’s legibility. The watermark text can be altered, and the following dynamic data can be embedded:

• Email address of the user
• IP address of the user
• Current date
• Current time

When a user’s identity is incorporated into the watermark, it serves as a straightforward yet powerful barrier against unauthorized readers distributing printed documents, since it makes it evident to the reader that the content is confidential.

Ease of integration

Easy-to-configure connectors for Box, Dropbox, Google Drive, Microsoft SharePoint, and OneDrive are offered by several virtual document repositories. Because of the robust sync engine built into these connectors, the material is automatically updated anytime source files or folders are added, changed, renamed, or removed.

You may incorporate eSignature operations directly within the secure environment of the VDR thanks to interfaces that some of the best virtual data room providers have to offer.

Extra security measures

Virtual data rooms also offer extra security measures to safeguard private data and guarantee that the document is only accessible by those who are permitted.

• Role-based permissions: Certain rights, such the ability to edit, approve changes, or lock a paragraph, can be granted to certain users, while others can just be allowed to leave comments.
• Restricted sections: You can lock down the material in certain sections of a document so that you can concentrate your adjustments in other areas.
• Monitoring and versioning: These collaboration platforms not only track changes but also automatically store each version of a document, which may be exported with or without the tracked modifications.

Virtual data rooms stand out as indispensable

In conclusion, the surge in businesses transitioning from physical to virtual data rooms can be attributed to the unparalleled security features and numerous advantages offered by these digital platforms.

The robust security measures, including strong data encryption, two-factor authentication, remote shared capabilities, watermarking, ease of integration, and additional security measures like role-based permissions and monitoring, collectively ensure a level of protection that surpasses traditional file-sharing methods. Furthermore, the cost-effectiveness and efficiency gains achieved by eliminating the constraints of physical space and paperwork contribute to the widespread adoption of virtual data rooms.

As businesses continue to prioritize data security and streamlined operations, virtual data rooms stand out as an indispensable tool for secure, efficient, and collaborative document management.

All images are generated by Eray Eliaçık/Bing

Jio AI-Cloud is an upcoming service that provides up to 100 GB of free cloud storage. In Drive, free storage is limited to 15 GB. Set to launch this Diwali, Jio AI-Cloud promises to simplify file storage and organization with advanced AI features. Integrated with the Jio ecosystem, this service aims to enhance how users manage their digital content. Sound good? Here is everything you need to know about Jio AI-Cloud and the broader ecosystem.

An early look at the Jio AI-Cloud

Jio AI-Cloud is a new offering from Reliance Industries designed to make advanced cloud storage and AI-powered services accessible to everyone. At the heart of Jio AI-Cloud is the provision of up to 100 GB of free cloud storage for Jio users. This feature is aimed at providing a secure and convenient space for individuals to store a wide array of digital content, including:

• Photos and Videos: Safely backing up personal media, ensuring that precious moments are preserved and easily retrievable.
• Documents: Storing important files, such as work documents, academic papers, or personal records, with easy access from any device.
• Other Digital Content: Any other types of data that users might need to keep safe and accessible, such as music files, eBooks, or application data.

Jio AI-Cloud uses artificial intelligence to improve cloud storage. Data management is simplified because AI automatically sorts and organizes files. This means users don’t have to spend time manually arranging their data; AI does it for them, so finding and accessing files is easier.

AI also improves security. It continuously watches for unusual activity and potential threats, helping to keep data safe from cyber risks. Additionally, AI helps with personalization by analyzing what users store, suggesting relevant content, or organizing data according to their preferences. This aims to make the whole experience more user-friendly and tailored to individual needs.

Good to know that Jio AI-Cloud is accessible from various devices—smartphones, tablets, and computers.

Jio AI-Cloud launch date

The Jio AI-Cloud Welcome offer is set to launch during Diwali this year, making it available to millions of Jio users across India. The rollout will accompany various promotions and support to ensure a smooth transition for users adopting this new service.

“We plan to launch the Jio AI-Cloud Welcome Offer starting Diwali this year, bringing a powerful and affordable solution where cloud data storage and data-powered AI services are available to everyone everywhere.”

Mukesh Ambani, chairman and managing director of Reliance Industries

Integration with Jio Ecosystem

Jio AI-Cloud is integrated with the broader Jio ecosystem, including other services and platforms offered by Reliance. This integration allows for seamless interactions between Jio’s various products and services. For example:

• JioTV+: Users can store and access their favorite shows and movies.
• Jio Phonecall AI: Call recordings and transcriptions can be stored and managed within Jio AI-Cloud.

Featured image credit: Eray Eliaçık/Bing

Data science is a foundation of innovation and decision-making in today’s digitalized world. With businesses and organizations progressively relying on data-driven visions, the demand for skilled data scientists endures to soar. However, standing out in this competitive field requires more than technical expertise.

To truly accelerate your data science career, it’s essential to not only refine your analytical skills but also to cultivate a unique personal brand.

In this article, we’ll explore actionable strategies to help you advance your career and distinguish yourself in the dynamic world of data science.

1. Mastering core data science skills

According to the US Bureau of Labor Statistics, data scientists’ employment growth is expected to be 35% from 2022 to 2032. It is expected that around 17,700 vacancies for data scientists will be released every year in the next decade. It is much higher than the average profession. Hence, it is crucial to master core data science skills for anyone looking to excel in this field.

Proficiency in programming languages like Python and R is essential, as these tools are the backbone of data analysis and modeling. Additionally, a solid grasp of statistics enables data scientists to make sense of complex data sets and draw meaningful conclusions.

Data wrangling skills, which involve cleaning and preparing data for analysis, are also vital to ensure the accuracy and reliability of results. Beyond these technical abilities, understanding machine learning algorithms, data visualization techniques, and database management systems strengthens one’s capability to tackle real-world data challenges effectively.

What are some common pitfalls to avert when learning data science skills?

Common mistakes when learning data science include focusing too much only on theoretical knowledge without practical implications. Trying to master too many tools at once can lead to overwhelm. Neglecting foundational skills like statistics and data cleaning, as these are essential for building a strong understanding of data analysis.

2. Gaining hands-on experience through real-world projects

US News reported that data science jobs are ranked #4 amongst the best technology jobs. The jobs’ ranking is based on mixed factors like payscale, job satisfaction, future growth, stress level, and work-life balance. The median salary picked by a data scientist was $103,500 in 2022.

To be amongst those super-skilled data scientists, gaining hands-on experience through real-world projects is essential for truly mastering data science. Working on diverse projects, like data cleaning & analysis and building predictive models, helps you develop a robust portfolio that showcases your abilities. Additionally, real-world projects often present unexpected challenges, such as missing data or complex variables.

By actively seeking out or creating opportunities to work on such projects, you can bridge the gap between classroom learning and the industry demands. This will project you as an all-rounder and experienced candidate.

How can I showcase my projects to potential employers or clients?

You can showcase your projects to potential employers or clients by creating a well-organized portfolio on a personal website or platform. Here, you can display your code, data visualizations, and project outcomes. Additionally, sharing your work on LinkedIn, writing blog posts, and presenting your projects clearly during interviews can effectively highlight your skills and experience.

3. Exploring interdisciplinary knowledge to broaden your expertise

Exploring interdisciplinary knowledge is key to broadening your expertise and enhancing your value in data science. By combining data science skills with knowledge in fields like business, healthcare, or finance, you can offer deeper insights and more targeted solutions.

For instance, pursuing a doctorate in business online can deepen your understanding of business strategy and analytics. It will allow you to align data-driven decisions with organizational goals.

According to Marymount University, an online doctorate program equips you with the knowledge to excel at the crossroads of business, data, and technology. By harnessing the power of data insights, you’ll gain the strategic acumen to make high-impact decisions and lead with clarity and foresight.

This combination of technical skills and domain-specific knowledge makes you an all-rounder professional capable of handling complex challenges across industries. It will significantly boost your career prospects in an increasingly competitive market.

How can knowledge in other fields complement my data science skills?

Knowledge in other fields can complement your data science skills by providing context and domain-specific insights. This enables you to apply data analysis more efficiently to solve real-world problems. For example, understanding business strategy or healthcare operations allows you to tailor your data models and analyses to meet specific industry needs.

4. Networking and building professional relationships

Networking and building professional relationships are vital for advancing your data science career. These relations can lead to collective opportunities, mentorship, and even job referrals, significantly boosting your career prospects.

Developing an effective communication channel within the workplace also plays an important role in developing professional relationships. According to Pumble, 86% of the executives and workers state that they lack effective collaboration and communication at their workplace. It becomes one of the major reasons for failure. Alternatively, teams communicating effectively can increase their productivity by 25%.

Additionally, active participation in data science groups and attending meetups helps you establish your presence in the field, showcasing your expertise and enthusiasm. By developing a strong professional network, you can enhance your presence and open doors to new and exciting opportunities.

5. Continuous learning and staying updated with industry trends

As new technologies, tools, and methodologies emerge, maintaining a commitment to lifelong learning ensures that your skills remain relevant and competitive. This can include taking online courses, attending workshops, reading manufacturing publications, and following alleged leaders. Staying knowledgeable about the latest progressions allows you to adapt to changes swiftly, apply cutting-edge techniques, and identify new opportunities for invention.

By prioritizing continuous learning, you not only keep your knowledge current but also position yourself as a forward-thinking professional.

6. Developing a personal brand and thought leadership

Developing an individual brand and establishing alleged leadership is essential for distinguishing yourself in data science. Building a personal brand involves showcasing your unique skills, experiences, and insights. You can position yourself as a knowledgeable and credible expert by continuously sharing valued content.

Engaging with the community through speaking engagements, webinars, or guest articles further reinforces your thought leadership, demonstrating your expertise and commitment to the field. A strong personal brand improves your professional visibility and attracts opportunities for collective career advancement.

Elevating your data science career to new heights

Accelerating your data science career involves a multifaceted approach that integrates mastering core skills, gaining hands-on experience, and continuously learning. Embracing these strategies will enhance your value and open doors to new opportunities and career advancement.

As the field of data science continues to evolve, staying proactive and engaged will ensure you remain at the forefront of innovation and success.

All images are generated by Eray Eliaçık/Bing

In recent years, data annotation has emerged as a crucial component in the development of artificial intelligence (AI) and machine learning (ML). However, with its rapid growth comes skepticism about the legitimacy of this industry. As we dive deep into understanding the complexities of data annotation, one question looms large: is data annotation legit?

Data annotation refers to the process of labeling and categorizing data, which serves as the backbone for training AI and ML models. This crucial step involves humans manually reviewing and annotating vast amounts of data to create accurate training datasets. These annotations allow machines to recognize patterns, classify objects, and make informed decisions.

So, is data annotation legit?

While some may argue that data annotation is a shady practice that exploits workers for cheap labor, the industry’s proponents insist it has genuine value.

Here are several reasons why you may just put a thumbs up to is data annotation legit questions:

1. Driving innovation: Data annotation plays a vital role in advancing AI and ML technology, which has far-reaching implications for various industries. By providing accurate training datasets, data annotators contribute to the development of groundbreaking innovations that can transform our lives.

2. Creating jobs: Although some may view data annotation as exploitative labor, it has created numerous job opportunities worldwide. This industry provides a stable source of income and flexible work arrangements, particularly for those who cannot commit to traditional 9-to-5 jobs.

3. Addressing market needs: The demand for high-quality annotated datasets continues to grow, driven by the increasing adoption of AI in various industries. Data annotation companies address this need by providing reliable and accurate annotations that meet market standards.

4. Ensuring transparency: Legitimate data annotation companies prioritize transparency in their operations. They provide clear guidelines and quality control measures to ensure annotators understand the task requirements and deliver high-quality work.

To stay ahead of the curve, reputable data annotation companies invest heavily in research and development. This focus on innovation leads to improved methods and technologies that enhance the quality and efficiency of the annotation process. These advancements also ensure that data annotators have clear guidelines and quality control measures in place to deliver high-quality work.

Never free from controversies

Despite its legitimacy and left many wondering is data annotation legit, data annotation faces several challenges and controversies.

While data annotation has created numerous job opportunities worldwide, some companies have been accused of exploiting their workers by paying low wages, providing poor working conditions, and offering inadequate benefits. This issue has sparked debates about fair labor practices within the industry. As a result, it is essential for data annotation companies to prioritize worker welfare and ensure that they are treated fairly and with respect.

As data annotation involves handling sensitive information, there are concerns about data breaches and privacy violations. Companies must implement robust security measures to safeguard both their annotators’ data and the annotated datasets themselves. This includes secure storage, encryption, and access control mechanisms to prevent unauthorized access.

How are digital twins shaping the future of technology and innovation?

Despite its legitimacy, data annotation faces several challenges and controversies. The industry must navigate issues such as worker exploitation, data quality concerns, and security risks while continuing to drive innovation and deliver high-quality annotated datasets for the AI and ML ecosystems.

So, is data annotation legit? The answer lies in the practices of individual companies within the industry. While there may be some shady operators exploiting workers or compromising on quality, many legitimate players prioritize transparency, fair labor practices, and investment in research and development. By prioritizing quality, fairness, and security, the data annotation industry can thrive and deliver tangible benefits for society as a whole. The keyword “is data annotation legit” is repeated throughout this blog post to emphasize its importance and relevance within the discussion about the legitimacy of this industry.

Featured image credit: kjpargeter/Freepik

The foreign exchange (FX) market, where currencies are traded against each other, has a rich history dating back centuries. Historically, FX trading was primarily conducted through physical exchanges, with traders relying on their intuition and experience to make decisions. However, the advent of electronic trading in the late 20th century revolutionized the FX market, opening it up to a wider range of participants and increasing trading volumes exponentially.

Today, the FX market is the largest and most liquid financial market in the world, with an average daily turnover exceeding $7.5 trillion in April 2022, according to the Bank for International Settlements (BIS). Its importance lies in its role in facilitating international trade and investment, as well as providing opportunities for profit and serving as an economic indicator.

Data science has emerged as a critical tool for FX traders, enabling them to analyze vast amounts of data and gain valuable insights into market trends, price movements, and potential risks. I spoke with Pavel Grishin, Co-Founder and CTO of NTPro, to understand data science’s role in this lucrative market.

The Rise of Algorithmic FX Trading

One of the most significant applications of data science in FX trading is the development of algorithmic trading strategies. These strategies involve using platforms to execute trades automatically based on pre-defined rules and criteria. Algorithmic trading has become increasingly popular due to its ability to process large amounts of data quickly, identify patterns and trends, and execute trades with precision and speed.

“Proprietary trading firms and investment banks are at the forefront of data science and algorithmic trading adoption in the FX market,” Grishin said. “They utilize sophisticated data analysis to gain a competitive advantage, focusing on areas like market data analysis, client behavior understanding, and technical analysis of exchanges and other market participants. Investment banks, for instance, analyze liquidity providers and implement smart order routing for efficient trade execution, while algorithmic funds use data science to search for market inefficiencies, develop machine learning (ML) models, and  backtesting trading strategies (a process that involves simulating a trading strategy using historical data to evaluate its potential performance and profitability).”

Types of Data-Driven Trading Strategies

There are several types of data-driven trading strategies, each with its unique approach and characteristics.

“Data-driven trading strategies, such as Statistical Arbitrage, and Market Making have evolved with advancements in data science and technology,” Grishin said. “Statistical Arbitrage identifies and exploits statistical dependencies between asset prices, while Market Making involves providing liquidity by quoting both bid and ask prices.  There is also a High Frequency Trading approach, that focuses on executing trades at high speeds to capitalize on small price differences. These strategies and approaches have become increasingly complex, incorporating more data and interconnections, driven by technological advancements that have accelerated execution speeds to microseconds and nanoseconds.”

Collaboration Between Traders, Quants, and Developers

The implementation of complex algorithmic trading strategies requires close collaboration between traders, quants (quantitative analysts), and developers.

“Quants analyze data and identify patterns for strategy development, while developers focus on strategy implementation and optimization,” Grishin said. “Traders, often acting as product owners, are responsible for financial results and system operation in production. Additionally, traditional developers and specialized engineers play crucial roles in building and maintaining the trading infrastructure. The specific division of roles varies between organizations, with banks tending towards specialization and algorithmic funds often favoring cross-functional teams.”

Challenges and the Role of AI and ML in FX Trading

Translating algorithmic trading models into real-time systems presents challenges, mainly due to discrepancies between model predictions and real-world market behavior. These discrepancies can arise from changes in market conditions, insufficient data in model development, or technical limitations.

“To address these challenges, developers prioritize rigorous testing, continuous monitoring, and iterative development,” Grishin said. “Strategies may also incorporate additional settings to adapt to real-world conditions, starting with software implementations and transitioning to hardware acceleration only when necessary.”

Developers in algorithmic trading require a strong understanding of financial instruments, exchange structures, and risk calculation.

“Data-handling skills, including storing, cleaning, processing, and utilizing data in pipelines, are also crucial,” Grishin said. “While standard programming languages like Python and C++ are commonly used, the field’s unique aspect lies in the development of proprietary algorithmic models, often learned through direct participation in specialized companies.”

What Comes Next?

Looking ahead, the future of FX trading will likely be shaped by continued advancements in data science and technology.

“The future of algorithmic trading is likely to be shaped by ongoing competition and regulatory pressures,” Grishin said. “Technologies that enhance reliability and simplify trading systems are expected to gain prominence, while machine learning and artificial intelligence will play an increasing role in real-time trading management. While speed remains a factor, the emphasis may shift towards improving system reliability and adapting to evolving market dynamics.”

While the path ahead may be fraught with challenges, the potential rewards for those who embrace this data-driven approach are immense. The future of FX trading is bright, and data science will undoubtedly be at its forefront, shaping the market’s landscape for years to come.

Have you ever wondered how game developers create experiences that keep you hooked for hours? It’s not just luck or intuition; it’s data-driven design. By collecting and analyzing vast amounts of player data, developers can fine-tune every aspect of a game to ensure maximum engagement and enjoyment.

From dynamically adjusting difficulty levels to personalizing content based on your preferences, data is the secret ingredient behind today’s most addictive games. So, the next time you find yourself lost in a virtual world, remember that there’s a science at work behind the scenes.

Understanding players: The power of data analysis

When you start your favorite game, you’re not just playing – you’re also generating valuable data. Developers collect a wide array of metrics, from your in-game actions and time spent on features to survey responses about your experience. By analyzing this data with sophisticated tools, they can uncover insights into your preferences and engagement patterns, enabling them to craft games tailored to what players like you enjoy most.

A/B testing: The science of choice

Game developers use A/B testing, a rigorous method, to discover what keeps players hooked. A/B testing involves presenting different variations of game features to separate groups of players and analyzing their reactions. By observing player behavior and engagement metrics, developers can determine which mechanics, levels, characters, and other elements create the most compelling user experience.

Imagine you’re playing a game where the developers are deciding between two different power-up systems. Through A/B testing, they can offer each version to thousands of players and let the data decide the winner. Whichever system results in higher engagement, longer play sessions, and more frequent return visits becomes the clear choice. By leveraging data, developers can craft a seamless and enjoyable experience from start to finish. A/B testing empowers developers to make informed decisions that elevate the gaming experience, keeping you entertained for hours.

Perfecting the challenge: Balancing difficulty with fun

You’re fully immersed in the game, but suddenly you hit a wall – the difficulty spikes and frustration sets in. Developers know this feeling all too well, which is why they use player data to fine-tune the challenge. By dynamically adjusting the difficulty based on your performance, the game keeps you engaged and on your toes, ensuring that the fun never fades.

Dynamic difficulty: Keeping players on their toes

If you’re a gamer, you know the frustration of being stuck on a level that’s just too challenging or breezing through content that doesn’t test your skills. This is where the dynamic difficulty comes in, a technique that uses in-game telemetry data to adapt the game’s challenge in real-time based on your performance. By analyzing metrics like completion times, success rates, and feedback, developers can implement dynamic difficulty adjustment systems that keep you engaged and motivated.

Imagine a game that gets slightly easier if you’ve failed a level multiple times or ramps up the challenge if you’re consistently outperforming. This personalized approach not only ensures you’re always playing at the right difficulty level but also helps maintain player retention by preventing frustration or boredom. With live operations, developers can continuously fine-tune these systems post-launch, ensuring that the game remains challenging and rewarding. This dynamic approach has resulted in some of the most entertaining games of the year.

Tailored experiences: The power of personalization

Personalization is just the beginning when it comes to data-driven game design. You might wonder, “What’s next?” Get ready to explore how data can shape the very stories you experience in your favorite games.

Beyond personalization: Data-driven storytelling

Data’s influence extends beyond personalization, reshaping in-game narratives and character arcs. By analyzing player choices, developers can craft dynamic stories that adapt to your actions and engagement. Through data visualization and player segmentation, game designers gain insights into how you interact with the world and its inhabitants. This knowledge enables them to create recommendation systems that suggest story paths tailored to your preferences, ensuring a narrative that resonates with you.

Procedural content generation and emergent gameplay, powered by data, allow for unique, ever-evolving storylines. As you make decisions and shape the world, the game responds, weaving a tapestry of cause and effect that feels authentic and immersive. This data-driven approach to storytelling blurs the line between authored content and player-generated experiences, resulting in narratives as diverse as the players themselves. By harnessing data, developers can craft stories that entertain and reflect the choices and personalities of each player.

Predicting the future: Data-driven trends

By analyzing vast amounts of player data, game developers can spot emerging trends and anticipate future player preferences. This predictive power allows them to stay ahead of the curve, creating games that resonate with players before they even know what they want. As data-driven design evolves, it’s paving the way for the next generation of innovative, engaging, and unforgettable gaming experiences.

Data and innovation: The next generation of games

Game developers are opening new frontiers by harnessing cutting-edge technologies like AI and machine learning to revolutionize data-driven design. Imagine a future where games adapt in real time to your play style, preferences, and skill level, delivering a personalized experience that keeps you engaged for hours.

With the rise of big data and advanced analytics, developers can gather and process vast amounts of player information, uncovering patterns and insights that were previously unattainable. This knowledge enables them to fine-tune game mechanics, balance difficulty curves, and optimize reward systems, ensuring every moment in-game is rewarding and enjoyable.

The integration of AI and machine learning algorithms allows games to learn from your behaviors and decisions, predicting your next moves and presenting challenges tailored to your strengths and weaknesses. As virtual reality technology advances, data-driven design will play an even greater role in crafting immersive, responsive game worlds that blur the line between digital and real.

This approach has already led to the creation of some of the most entertaining games of the year. The future of gaming is undeniably data-driven, and the possibilities are endless.

Featured image credit: Enrique Guzmán Egas

If you’re getting ready for this year’s Venice Art Biennale, I’ve got a hot tip for you: Swing by the 16th-century San Francesco della Vigna church in Venice and check out “We are the Aliens,” an immersive exhibition crafted by Belgian artist Arne Quinze and American music producer and rapper Swizz Beatz. Co-curated by Herve Mikaeloff and Reiner Opoku, this show promises a mix of Quinze’s large glass sculptures from Berengo Studio , ceramic sculptures by Atelier Vierkant, and a trippy sound landscape that’ll take you straight into chill mode. Trust me, I knocked out for a solid snooze after a 24-hour Biennale binge.

The underlying theme here is pretty political: humanity’s getting further from nature. You’ll see it clearly when you learn about Arne Quinze’s career story—a kid of nature whose paradise the family garden got cut short by the family moving to the big city, throwing him into the main theme of his artistic journey Take a look at this video for more on that:

Now, the real head-scratcher? The part where humans share their stories is powered by Artificial Intelligence. Before you even step into the immersive art and sound garden, you’ll meet “SIX Testimonials_,” six unique AI characters that blur the line between biology and tech. They’re chatting about how AI and human creativity mix up the stories of their lives, making you think twice about your own place in this tech-crazy world.

Each character’s story is like something out of a weird dream, making you think hard about your own struggles and questions. How does all this fit in with our fast-changing digital world? These “SIX Testimonials_” make you question what’s real and what’s not, pushing you to see how art can change the way we see things.

Arne’s got a wild idea: “on this planet there’s only one race, and it’s us, humans/the aliens.” So, are AI the aliens, or are we? They’re already here, everywhere.

Swizz Beatz thinks Venice was the perfect spot for this show—it’s a hotbed of creativity, sparking new ideas and team-ups that can shake up the whole world of art, music, and fashion. What really blew Swizz Beatz away was how Arne mixed it all up, creating a spiritual vibe with art, music, and the whole Venice scene.

This exhibition makes you think about tech and nature. With AI becoming a bigger part of our lives, how do we keep our connection to nature? Can art help us get along with machines, making us understand each other better?

“We are the Aliens” shows us a future where creativity breaks all the rules, where humans and machines live together in peace. It’s a wild ride into the unknown, making us ask the big questions and stay open to new ideas.

In Venice, surrounded by centuries of art and history, “We are the Aliens” reminds us that art’s still got the power to bring us together and make us think. So, whether you’re an art buff, a tech nerd, or just curious about what’s next for humanity, don’t miss out on this wild experience at the Biennale. You’ll thank me later.

The UK’s Digital Information and Smart Data Bill is a landmark initiative aimed at revolutionizing how data is managed and utilized, with far-reaching implications for individuals, businesses, and the economy as a whole. As announced in the recent King’s Speech, the Bill will change how the UK deals with data, and here are the five things you should know about it:

• Digital Verification Services: Secure and efficient methods for individuals to verify their identity and share personal information online.
• National Underground Asset Register: Standardized access to information about underground pipes and cables to improve safety and efficiency in construction.
• Smart Data Schemes: Allowing customers to securely share data with authorized third parties for better financial advice and service deals.
• Modernizing the Information Commissioner’s Office (ICO): Enhancing the ICO’s regulatory capabilities to oversee and enforce data protection laws.
• Supporting Scientific Research: Streamlined data sharing and usage protocols to drive innovation and improve research outcomes.

Want to learn more? Here are the details:

Everything you need to know about the Digital Information and Smart Data Bill

The Bill includes extensive provisions aimed at regulating various aspects of data processing and usage. These include:

• Regulation of the processing of information relating to identified or identifiable living individuals.
• Provision for services that use information to ascertain and verify facts about individuals.
• Access to customer data and business data.
• Privacy and electronic communications regulations.
• Services for electronic signatures, electronic seals, and other trust services.
• Disclosure of information to improve public service delivery.
• Implementation of agreements on sharing information for law enforcement purposes.
• Power to obtain information for social security purposes.
• Retention of information by internet service providers in connection with investigations into child deaths.
• Keeping and maintenance of registers of births and deaths.
• Recording and sharing, and keeping a register, of information relating to apparatus in streets.
• Information standards for health and social care.
• Establishment of the Information Commission.
• Retention and oversight of biometric data.

One of the cornerstone features of the Digital Information and Smart Data Bill is the creation of digital verification services. These services are intended to provide secure and efficient methods for individuals to verify their identity and share key personal information online. By leveraging digital identity products, the Government aims to streamline interactions with online services, making processes like accessing financial services, signing up for utilities, or verifying age for restricted content quicker and more secure.

The Bill also proposes the establishment of a National Underground Asset Register. This initiative is geared towards providing planners and excavators with instant, standardized access to information about underground pipes and cables across the country. By facilitating better access to this data, the Government aims to improve safety and efficiency in construction and maintenance projects, reducing the risk of accidental damage to critical infrastructure.

Smart data schemes represent another innovative aspect of the Digital Information and Smart Data Bill. These schemes will allow customers to securely share their data with authorized third-party service providers upon request. For instance, customers could share their banking data with financial management apps to receive tailored financial advice or share utility usage data to find better service deals. The aim is to empower consumers with greater control over their data while fostering a competitive marketplace.

To support the evolving digital landscape, the Digital Information and Smart Data Bill seeks to modernize the Information Commissioner’s Office (ICO). This modernization effort aims to strengthen the ICO’s regulatory capabilities, ensuring it can effectively oversee and enforce data protection laws in a rapidly changing environment. Enhanced regulatory powers will enable the ICO to address new challenges and opportunities presented by advancements in technology and data usage.

At its core, the Digital Information and Smart Data Bill is designed to leverage data as a catalyst for economic growth. By enabling new, innovative uses of data, the Government seeks to create an environment where businesses can thrive, new services can emerge, and consumers can benefit from enhanced products and services. The Bill’s provisions are aimed at fostering a data-driven economy that is resilient, secure, and poised for future growth.

All images are generated by Eray Eliaçık/Bing

dbForge Studio for MySQL is a powerful IDE for MySQL and MariaDB from Devart, an industry leader known for its database development tools. In this article, we will discuss some of its features that database developers, analysts, DBAs or architects may find useful.

Disclaimer: This is not a product promotion article. The author is not affiliated with Devart or any other company associated with Devart.

Key features of dbForge Studio for MySQL

Complete MySQL compatibility

dbForge Studio for MySQL is compatible with various MySQL flavours, storage engines, and connection protocols. Besides the garden variety of the MySQL database engine, the Studio can successfully connect to MariaDB, Amazon Aurora for MySQL, Google Cloud MySQL, Percona Server, and other exotic distributions like Oracle MySQL Cloud, Alibaba Cloud, and Galera Cluster. In our workflow, we successfully connected this tool to a MariaDB instance running on Amazon RDS in a flash.

Improved user experience with an updated look and feel

The graphical user interface offers a modern, intuitive look and feel. Tabbed panes, non-cluttered toolbars and context-specific menus make navigation through the tool fairly simple.

Those familiar with Visual Studio will feel right at home with the default “skin” of dbForge Studio. Also, it provides other skins to change the UI theme and customize the software:

Improved workflows with command line automation

One of the excellent features of dbForge is that any manual action done in the UI can be turned into an operating system command. The button labelled “Save Command Line…” is available in each dialog box; by clicking on it the user can transfer the options configured in the dialog box into the command parameters. This way, database-related tasks can be easily automated using the Command Line.

The image below shows an example:

Robust MySQL Version Control with dbForge Studio

Integrated Source Control is the feature released in the latest version of dbForge Studio for MySQL.

First, it supports all major version control systems, such as Git (including GitHub, GitLab, and Bitbucket), Mercurial, SVN, Azure DevOps, and more.

Next, it allows the user to manage both database schemas and table data, under a dedicated or shared model (the former enables work on an individual database copy, the latter means there’s a shared database copy for multiple developers).

Finally, operations like committing changes, reverting modifications, and resolving conflicts can all be done directly within the Studio, so the user won’t need to switch between different apps.

dbForge Studio for Database Developers

A good IDE should help developers save time and automate tasks as much as possible. When it comes to the developer’s productivity, dbForge for MySQL offers the industry standard features like code completion, syntax checking, code formatting, code snippets, and more.

Objects like tables or views can be checked for their dependencies or relationships with other objects in the database. This is done by choosing the “Depends On” or “Used By” options from the database tree.

The dependencies are shown in recursive manner. This can be really handy when troubleshooting or debugging code:

Another helpful feature is the CRUD generator. Right-clicking a table and selecting CRUD from the popup menu will create a template for four stored procedures. Each one will be a for a CRUD (SELECT, INSERT, UPDATE, DELETE) action:

Here is a sample script:

This helps to get started quickly with a skeleton procedure.

Only the most advanced database client tools would offer schema comparison and synchronization features. dbForge does provide them. An intuitive user interface makes searching and reconciling schema differences fairly simple:

Finally, developers will find the debugger tool useful:

Once the code is ready, developers can easily remove debug information with a few mouse clicks.

How data analysts can utilize dbForge Studio

Besides schema comparison, dbForge Studio includes a data comparison tool which should be of help to data analysts and developers. It has an intuitive interface for comparing data between two tables:

For importing or exporting data, dbForge can connect to ten different types of sources or destinations. Notable among these types are Google Sheets, XML or even ODBC connections. We were able to copy an Excel sheet in no time. Then we tried with a JSON document – again, that was a breeze.

Compared to these types, the Table Data Import feature in MySQL Workbench supports only CSV and JSON formats.

The Master-Detail Browser is a great tool for viewing data relationships. Analysts can use this to quickly check different categories of master data and their child records:

The Pivot Table feature can be used for data aggregation, grouping, sorting and filtering. For example, a source table may look like this (we are using the sakila database as a sample):

With a few mouse clicks, the pivoting feature allows us to break down or roll up the rental income figure:

Not too many enterprise class query tools have a built-in reporting facility. dbForge Studio for MySQL comes with a nifty report designer. Users can create reports either by choosing one or more tables or using their own custom queries. Once the wizard finishes, the report opens in a WYSIWYG editor for further customizations.

Tools for database administrators in dbForge Studio

The tools database administrators use for day-to-day management of MySQL databases are usually similar in both dbForge Studio for MySQL and MySQL Workbench. This includes:

• User management (“Security Manager” in the Studio for MySQL, “Users and Privileges” in MySQL Workbench)
• Table Maintenance (Analyze, Optimize, Check, CHECKSUM, Repair)
• Current connections to the instance
• System and Status variables

Similarly, backing up a database is as simple as right-clicking on it and choosing “Backup and Restore > Backup Database…” from the menu. dbForge Studio for MySQL creates an SQL dump file for the selected database. Restoring a database is simple as well.

We could not find the server log file viewer in dbForge, although it’s readily available in MySQL Workbench (with MySQL in RDS, the log files can’t be accessed from the client tool).

Copying a database from one instance to another is an intuitive and simple process with dbForge Studio. All the user needs to do is select the source and the destination instances, the databases to copy and any extra options if needed – all from one screen:

What’s more, databases can be copied between different flavours of MySQL: we could successfully copy a MySQL database to a MariaDB instance.

Where dbForge really shines for the DBA is the query profiler. Using the query profiler, a DBA can capture different session statistics for a slow running query such as execution time, query plan, status variables etc.

Behind the scene, dbForge uses MySQL native commands like EXPLAIN and SHOW PROFILE to gather the data and presents them in an easy-to-understand form in the GUI. Looking at these metrics can easily help identify potential candidates for query tuning.

Once tuning is done and the query is run again, the query profiler will again save the sessions statistics. Comparing the two different runs can help the DBA check the effectiveness of the tuning.

What’s more, there is no reason to manually change the query’s text if it does not improve the performance. Selecting a profile session and clicking on the “SQL Query” button will automatically show the query executed for that session in the editor. This is possible because the query profiler also saves the query text along with the session statistics.

dbForge Studio’s tools for data architects

Reverse engineering an existing database structure is an integral part of a data architect’s job, and dbForge for MySQL has this functionality.

Tables from the database tree can be dragged and dropped into a Database Diagram and it will automatically create a nice ER diagram, as shown below:

Most high-end database client tools offer some type of reverse engineering capability, but dbForge Studio for MySQL goes one step further by allowing the user to create database documentation. With a few clicks of a mouse, a full-blown professional-looking system architecture document can be created without typing up anything. This documentation can describe tables and views, indexes, column data types, constraints and dependencies along with SQL scripts to create the objects.

Documentation can be created in HTML, PDF or Markdown format:

Finally, the feature database architects and developers would love is the Data Generator. Database design and testing often requires non-sensitive dummy data for quick proof-of-concepts or customer demonstrations. The Studio offers an out-of-the-box solution for this.

Using the intuitive data generator wizard, it’s possible to populate an empty schema of a MySQL database in no time.

The generator keeps foreign key relationships in place during data load, although foreign keys and triggers can be disabled during data load:

If necessary, only a subset of tables can be populated instead of all tables:

The tool allows to create a data generator script and load it into the SQL editor, save it as a file or run it directly against the database:

Conclusion

dbForge Studio for MySQL comes in four different editions: Enterprise, Professional, Standard, and Express. The Express edition is free, and the next tier (Standard edition) retails from $9.95 per month. The Professional edition starts at $19.95, and the Enterprise edition is priced at $29.95. There are volume discounts available for those purchasing two or more licenses.

dbForge also offers subscriptions for customers wishing to upgrade their product to newer versions. The subscription is available for one, two or three years. Licensing prices come down with longer subscriptions.

Being a free tool, MySQL Workbench may seem an attractive alternative to stay with. In our opinion, the wide number of features available in dbForge editions make their prices seem fair. Also, the major differences between Professional and Enterprise edition are Copy Database, Data Generator and Database Documenter.

The free Express edition or the 30-day free trial can be a good choice for everyone who wants to try before buying, and that, naturally, means nearly all of us.

One thing to keep in mind is that dbForge Studio for MySQL, originally designed as a classic Windows application, is available on Linux and macOS as well. To achieve this, in addition to requiring .NET Framework 4.7.2 or higher (as for the Windows environment), you’ll need a specialized application known as CrossOver (for Linux and macOS), or Wine (for Linux), or Parallels (for macOS).

Overall, we would say it’s a good product, in fact, a very good product – MySQL database manager that deserves at least a serious test drive from the community.

Featured image credit: Eray Eliaçık/Bing

Is machine learning AI? It is a common question for those navigating the complexities of modern technology and seeking to understand how these transformative fields are reshaping industries and everyday life. Although both terms are often used interchangeably, they represent distinct yet interconnected facets of computer science and artificial intelligence.

Understanding the relationship between machine learning and AI is crucial for grasping their combined potential to drive innovation and solve complex problems in the digital age.

Is machine learning AI?

Yes, machine learning is a subset of artificial intelligence (AI). Artificial intelligence is a broader field that encompasses any system or machine that exhibits human-like intelligence, such as reasoning, learning, and problem-solving. Machine learning specifically focuses on algorithms and statistical models that allow computers to learn from and make decisions or predictions based on data without being explicitly programmed for each task.

Unlike traditional programming, where rules are explicitly coded, machine learning algorithms allow systems to learn from patterns and experiences without human intervention. Artificial intelligence, on the other hand, is a broader concept that encompasses machines or systems capable of performing tasks that typically require human intelligence. This includes understanding natural language, recognizing patterns, solving problems, and learning from experience.

In essence, machine learning is one of the techniques used to achieve AI’s goals by enabling systems to learn and improve from experience automatically. Are you confused? Let’s look closely at them and understand their similarities and differences.

AI vs ML: What are the differences?

Here is a cheat sheet for differences between AI and ML:

Machine learning (ML) and artificial intelligence (AI) are interconnected fields with distinct roles and capabilities. ML, a subset of AI, focuses on algorithms that learn from data to make predictions or decisions, enhancing tasks like recommendation systems and autonomous driving. AI, on the other hand, encompasses ML along with broader technologies to simulate human-like intelligence, tackling complex tasks such as medical diagnosis and natural language processing.

While ML excels in data-driven learning and adaptability, AI extends to include sophisticated reasoning, autonomy in decision-making, and direct human interaction through applications like virtual assistants and autonomous agents. Both fields face ethical challenges regarding data privacy, algorithmic bias, and societal impact, while future trends indicate continual evolution in AI’s capabilities through advancements in neural networks, explainable AI, and ethical frameworks, shaping their transformative impact across industries and everyday life.

Is machine learning AI? Now, you know the answer and all the differences between AI and ML!

All images are generated by Eray Eliaçık/Bing

In today’s highly technological society, protecting customer data privacy is more important than ever. Because online services, social networking platforms, and e-commerce sites are collecting, processing, and storing personal information at an exponential rate, it is important to handle data breaches carefully to prevent identity theft, financial loss, and deterioration of confidence.

This article seeks to give readers a thorough grasp of consumer data privacy by going over the topic’s importance, the legal environment, and the best ways to secure personal data. The primary objective is not only to equip consumers and businesses with essential information but also to guide them on how to navigate the challenges of data protection in an interconnected world, including those pursuing an online MBA in Michigan. By doing so, individuals can keep their data secure while effectively addressing technological advancements.

Understanding consumer data privacy

Guarding consumer data privacy requires securing and managing the personal information of consumers in online and offline activities and highlighting ethical responses from businesses to ensure that these processes meet the privacy standard. This consists of storing data safely to avoid unauthorized access, getting explicit consent from consumers and informing them about the data practices in a transparent way.

Cloud computing, artificial intelligence, augmented reality, and the Internet of Things (IoT) have raised concerns that stricter consumer data privacy measures are more important than ever. Consumer data privacy, because of this, has become a subject that contributes to the development of trust among individuals and companies that keep their personal information confidential, ethically, and securely.

Why consumer data privacy matters?

Forbes claimed that regulations on consumer data privacy are now in the spotlight. Given its implications for people, companies, and society at large, the significance of protecting consumer data privacy becomes clear. Identity theft, money loss, and mental suffering are just a few of the bad outcomes that can result from someone else using or abusing their data illegally. People must take responsibility for their data and understand how it is collected, used, and shared. These hazards serve as examples of this. The promotion of digital trust in the economy, as well as privacy protection, depends heavily on this empowerment.

The regulatory environment is evolving to address consumer data privacy concerns, as exemplified by the newly proposed American Privacy Rights Act of 2024 (APRA) by Senator Maria Cantwell and Representative Cathy McMorris Rodgers. This bipartisan initiative aims to establish stringent consumer privacy laws, potentially affecting market dynamics and fostering innovation. State-level efforts, such as the California Consumer Privacy Act (CCPA), grant consumers significant control over their data, reflecting a global trend toward enhanced data protection, also seen in international regulations like the EU’s General Data Protection Regulation (GDPR).

Key laws and regulations

Consumer privacy protection in the United States currently exists through a patchwork of federal and state laws, making regulatory navigation difficult. Key federal laws include HIPAA, the Privacy Rights Act of 1974, COPPA, and the Gramm-Leach-Bliley Act, with the Federal Trade Commission (FTC) primarily responsible for enforcement, notably targeting unfair practices by major corporations like Facebook. The proposed American Privacy Rights Act (APRA), led by Cathy McMorris Rodgers and Maria Cantwell, aims to create a cohesive national data privacy framework, potentially replacing the fragmented state laws. This uniform regulation could simplify compliance for businesses but raises concerns about whether it will set a minimum standard or allow for stricter state-level protections.

Federal rules that prohibit states from enacting laws addressing emerging hazards and technological advancements are opposed by Ashkan Soltani, a privacy advocate at the California Public Policy Agency. The APRA’s rules, which might change how businesses gather and use customer data, include requiring actions like data minimization, openness, consumer choice, and board-level responsibility. The APRA would improve individual control over personal information by giving consumers the ability to access, edit, and delete their data once it is put into effect.

Best practices for businesses and consumers

The American Privacy Rights Act (APRA) heralds a significant shift in data privacy regulations in the United States, affecting big data holders and social media corporations with significant influence in particular. High-impact social media firms are subject to increased examination under the APRA. These companies are characterised as having 300 million monthly active users worldwide and global annual sales exceeding $3 billion. They are required to treat user data as sensitive and limit its use for targeted advertising without explicit consent. Similarly, large data holders, identified based on revenue and data processing volume, must uphold transparency by publishing their privacy policy histories and providing concise data practice notices. Additionally, these entities are mandated to appoint privacy or data security officers, introducing a layer of accountability and oversight to their operations.

For consumers, the APRA promises enhanced control over personal data, granting them the right to access, correct, and delete their information. This empowers individuals to manage their digital footprints more effectively. The proposed regulations mark a pivotal moment in data privacy reform, aiming to transform the existing national framework. Covered entities, including businesses regulated by the Federal Trade Commission (FTC), telecommunications carriers, and non-governmental organizations (NGOs), must comply with the new guidelines aimed at bolstering data security and customer privacy.

The APRA mandates companies to collect data only when necessary, promoting data minimization practices. Moreover, it requires the demonstration of robust data security measures, making their implementation mandatory. While these changes are designed to safeguard customer data, they also signal a paradigm shift for businesses, necessitating a re-evaluation of their data management practices, which may result in increased operational costs.

Conclusion

In conclusion, these recommendations offer comprehensive guidance for successfully navigating this changing landscape. We are witnessing a transformative shift in consumer data privacy, driven by new laws and heightened public awareness. People are gaining greater control over their personal information as businesses adapt to meet evolving legal requirements while maintaining operational efficiency. Ensuring fair competition and upholding data protection as a fundamental right necessitate robust privacy legislation.

As data privacy regulations tighten and public consciousness grows, companies must prioritize transparency and ethical data practices. Empowering consumers with the ability to manage their personal information is paramount. This not only fosters trust but also enables businesses to comply with legal mandates while optimizing operations. By striking a balance between data utilization and privacy safeguards, organizations can thrive in an environment of heightened scrutiny.

Featured image credit: Pawel Czerwinski/Unsplash

Finding a large dataset that fulfills your needs is crucial for every project, including artificial intelligence. Today’s article will explore large datasets and learn where to look at them. But first, understand the situation better.

What is a large dataset?

A large dataset refers to a data collection process that is extensive in length and complexity, often requiring significant storage capacity and computational power to process and analyze. These datasets are characterized by their volume, variety, velocity, and veracity, commonly referred to as the “Four V’s” of big data.

• Volume: Large in size.
• Variety: Different types (text, images, videos).
• Velocity: Generated and processed quickly.
• Veracity: Quality and accuracy challenges.

For example, Google’s search index is an example of a massive dataset, containing information about billions of web pages. Also Facebook, Twitter, and Instagram generate vast amounts of user-generated content every second. Remember the deal between OpenAI and Reddit that allowed AI to be trained on social media posts? That’s why it is such a big deal. Also, handling large datasets is not an easy job.

One of the primary challenges with large datasets is processing them efficiently. Distributed computing frameworks like Hadoop and Apache Spark address this by breaking down data tasks into smaller chunks and distributing them across a cluster of interconnected computers or nodes. This parallel processing approach allows for faster computation times and scalability, making it feasible to handle massive datasets that would be impractical to process on a single machine. Distributed computing is essential for tasks such as big data analytics, where timely analysis of large amounts of data is crucial for deriving actionable insights.

Cloud platforms such as AWS (Amazon Web Services), Google Cloud Platform, and Microsoft Azure provide scalable storage and computing resources for managing large datasets. These platforms offer flexibility and cost-effectiveness, allowing organizations to store vast amounts of data securely in the cloud.

Extracting meaningful insights from large datasets often requires sophisticated algorithms and machine learning techniques. Algorithms such as deep learning, neural networks, and predictive analytics are adept at handling complex data patterns and making accurate predictions. These algorithms automate the analysis of vast amounts of data, uncovering correlations, trends, and anomalies that can inform business decisions and drive innovation. Machine learning models trained on large datasets can perform tasks such as image and speech recognition, natural language processing, and recommendation systems with high accuracy and efficiency.

Dont’ forget effective data management is crucial for ensuring the quality, consistency, and reliability of large datasets. However, the real challenge is finding a large dataset that will fulfill your project’s needs.

How to find a large dataset?

Here are some strategies and resources to find large datasets:

Set your goals

When looking for large datasets for AI projects, start by understanding exactly what you need. Identify the type of AI task (like supervised learning, unsupervised learning, or reinforcement learning) and the kind of data required (such as images, text, or numerical data). Consider the specific field your project is in, like healthcare, finance, or robotics. For example, a computer vision project would need a lot of labeled images, while a natural language processing (NLP) project would need extensive text data.

Data repositories

Use data repositories that are well-known for AI datasets. Platforms like Kaggle offer a wide range of datasets across different fields, often used in competitions to train AI models. Google Dataset Search is a tool that helps you find datasets from various sources across the web. The UCI Machine Learning Repository is another great source that provides many datasets used in academic research, making them reliable for testing AI algorithms.

Some platforms offer datasets specifically for AI applications. TensorFlow Datasets, for instance, provides collections of datasets that are ready to use with TensorFlow, including images and text. OpenAI’s GPT-3 datasets consist of extensive text data used for training large language models, which is crucial for NLP tasks. ImageNet is a large database designed for visual object recognition research, making it essential for computer vision projects.

Exploring more: Government and open-source projects also provide excellent data. Data.gov offers various types of public data that can be used for AI, such as predictive modeling. OpenStreetMap provides detailed geospatial data useful for AI tasks in autonomous driving and urban planning. These sources typically offer high-quality, well-documented data that is vital for creating robust AI models.

Corporations and open-source communities also release valuable datasets. Google Cloud Public Datasets include data suited for AI and machine learning, like image and video data. Amazon’s AWS Public Datasets provide large-scale data useful for extensive AI training tasks, especially in industries that require large and diverse datasets.

When choosing AI datasets, ensure they fit your specific needs. Check if the data is suitable for your task, like having the right annotations for supervised learning or being large enough for deep learning models. Evaluate the quality and diversity of the data to build models that perform well in different scenarios. Understand the licensing terms to ensure legal and ethical use, especially for commercial projects. Lastly, consider if your hardware can handle the dataset’s size and complexity.

Popular sources for large datasets

Here are some well-known large dataset providers.

1. Government Databases:
   • Data.gov: A portal to access U.S. government datasets.
   • EU Open Data Portal: Access to datasets from the European Union.
2. Academic and Research Databases:
   • Kaggle Datasets: A wide variety of datasets shared by the community, often used for competitions.
   • UCI Machine Learning Repository: A collection of datasets for machine learning research.
   • Harvard Dataverse: A repository for research data across various disciplines.
3. Corporate and Industry Data:
   • Google Dataset Search: A search engine for datasets across the web.
   • Amazon Web Services (AWS) Public Datasets: Large datasets hosted by AWS.
4. Social Media and Web Data:
   • Twitter API: Access to Twitter data for analysis.
   • Common Crawl: An open repository of web crawl data.
5. Scientific Data:
   • NASA Open Data: Datasets related to space and Earth sciences.
   • GenBank: A collection of all publicly available nucleotide sequences and their protein translations.

All images are generated by Eray Eliaçık/Bing

What do data scientists do? Let’s find out! A data scientist is a professional who combines math, programming skills, and expertise in fields like finance or healthcare to uncover valuable insights from large sets of data. They clean and analyze data to find patterns and trends, using tools like machine learning to build models that predict outcomes or solve problems. This process is also closely related to artificial intelligence, as data scientists use AI algorithms to automate tasks and make sense of complex information. Their work helps businesses make informed decisions, improve operations, and innovate across industries, from finance and healthcare to retail and beyond. That’s why you are not the first one to wonder about this:

What do data scientists do?

Data scientists specialize in extracting insights and valuable information from large amounts of data. Their primary tasks include:

• Data cleaning and preparation: They clean and organize raw data to ensure it is accurate and ready for analysis.

• Exploratory Data Analysis (EDA): They explore data using statistical methods and visualization techniques to understand patterns, trends, and relationships within the data.
• Feature engineering: What do data scientists do? They create new features or variables from existing data that can improve the performance of machine learning models.
• Machine learning modeling: They apply machine learning algorithms to build predictive models or classification systems that can make forecasts or categorize data.
• Evaluation and optimization: They assess the performance of models, fine-tune parameters, and optimize algorithms to achieve better results.
• Data visualization and reporting: They present their findings through visualizations, dashboards, and reports, making complex data accessible and understandable to stakeholders.
• Collaboration and communication: They collaborate with teams across different departments, communicating insights and recommendations to help guide strategic decisions and actions.

Data scientists play a crucial role in various industries, including AI, leveraging their expertise to solve complex problems, improve efficiency, and drive innovation through data-driven decision-making processes.

How to become a data scientist?

Becoming a data scientist typically involves a combination of education, practical experience, and developing specific skills. Here’s a step-by-step roadmap on this career path:

• Educational foundation:
  • Bachelor’s Degree: Start with a bachelor’s degree in a relevant field such as Computer Science, Mathematics, Statistics, Data Science, or a related discipline. This provides a solid foundation in programming, statistics, and data analysis.
  • Advanced Degrees (Optional): Consider pursuing a master’s degree or even a Ph.D. in Data Science, Statistics, Computer Science, or a related field. Advanced degrees can provide deeper knowledge and specialization, though they are not always required for entry-level positions.
• Technical skills:
  • Programming languages: Learn programming languages commonly used in data science such as Python and R. These languages are essential for data manipulation, statistical analysis, and building machine learning models.

• • Data manipulation and analysis: Familiarize yourself with tools and libraries for data manipulation (e.g., pandas, NumPy) and statistical analysis (e.g., scipy, StatsModels).
  • Machine learning: Gain proficiency in machine learning techniques such as supervised and unsupervised learning, regression, classification, clustering, and natural language processing (NLP). Libraries like scikit-learn, TensorFlow, and PyTorch are commonly used for these tasks.
  • Data visualization: Learn how to create visual representations of data using tools like Matplotlib, Seaborn, or Tableau. Data visualization is crucial for communicating insights effectively.
• Practical experience:
  • Internships and projects: Seek internships or work on projects that involve real-world data. This hands-on experience helps you apply theoretical knowledge, develop problem-solving skills, and build a portfolio of projects to showcase your abilities.
  • Kaggle competitions and open-source contributions: Participate in data science competitions on platforms like Kaggle or contribute to open-source projects. These activities provide exposure to diverse datasets and different problem-solving approaches.
• Soft skills:
  • Develop strong communication skills to effectively present and explain complex technical findings to non-technical stakeholders.
  • Cultivate a mindset for analyzing data-driven problems, identifying patterns, and generating actionable insights.
• Networking and continuous learning:
  • Connect with professionals in the data science field through meetups, conferences, online forums, and LinkedIn. Networking can provide valuable insights, mentorship opportunities, and potential job leads.
  • Stay updated with the latest trends, techniques, and advancements in data science through online courses, workshops, webinars, and reading research papers.
• Job search and career growth:
  • Apply for entry-level positions: Start applying for entry-level data scientist positions or related roles (e.g., data analyst, junior data scientist) that align with your skills and interests.
  • Career development: What do data scientists do? Once employed, continue to learn and grow professionally. Seek opportunities for specialization in areas such as AI, big data technologies, or specific industry domains.

Becoming a data scientist is a journey that requires dedication, continuous learning, and a passion for solving complex problems using data-driven approaches. By building a strong foundation of technical skills, gaining practical experience, and cultivating essential soft skills, you can position yourself for a rewarding career in this dynamic and rapidly evolving field.

Data scientist salary for freshers

The salary for freshers in the field of data science can vary depending on factors like location, educational background, skills, and the specific industry or company.

In the United States, for example, the average starting salary for entry-level data scientists can range from approximately $60,000 to $90,000 per year. This can vary significantly based on the cost of living in the region and the demand for data science professionals in that area.

In other countries or regions, such as Europe or Asia, entry-level salaries for data scientists may be lower on average compared to the United States but can still be competitive based on local economic conditions and demand for data science skills.

How long does it take to become a data scientist?

Becoming a data scientist varies based on your background and goals. With a bachelor’s degree in fields like computer science or statistics, you can become a data scientist in about 2 years by completing a master’s in data science. If you lack a related degree, you can enter the field through boot camps or online courses, needing strong math skills and self-motivation. Regardless, gaining experience through projects, hackathons, and volunteering is crucial. Typically, the path includes: bachelor’s degree (0-2 years), master’s degree (2-3 years), gaining experience (3-5 years), and building a portfolio for job applications (5+ years).

Now you know what do data scientists do and the road ahead!

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What are the types of data? That’s a question every single person working on a tech project or dealing with data encounters at some point.

Data is the backbone of modern decision-making processes. It comes in various forms, and understanding these forms is crucial for accurate analysis and interpretation. Every piece of information we encounter can be categorized into different types, each with its unique properties and characteristics.

In technology and data-driven industries, such as software development, machine learning, finance, healthcare, and more, recognizing the types of data is essential for building robust systems, making informed decisions, and solving complex problems effectively.

What are the types of data?

Data can be broadly categorized into different types based on their characteristics and the level of measurement. These types provide insights into how the data should be handled and analyzed.

What are the types of data regarding these categories? Well, data types can be categorized into two different categories and sub-categories:

• Qualitative data type:
  • Nominal
  • Ordinal
• Quantitative data type:
  • Discrete
  • Continuous

Nominal data

Nominal data, also known as categorical data, represent categories or labels with no inherent order or ranking. Examples include gender, color, or types of fruit. Nominal data are qualitative and cannot be mathematically manipulated. Each category is distinct, but there is no numerical significance to the values.

For instance, if we have data on eye colors of individuals (blue, brown, green), we can classify it as nominal data. We can count the frequency of each category, but we can’t perform arithmetic operations on them.

Ordinal data

Ordinal data represent categories with a specific order or rank. While the categories have a meaningful sequence, the intervals between them may not be uniform or measurable. Examples include rankings (1st, 2nd, 3rd), survey ratings (like Likert scales), or educational levels (high school, college, graduate).

Ordinal data allow for ranking or ordering, but the differences between categories may not be consistent. For instance, in a Likert scale survey ranging from “strongly disagree” to “strongly agree,” we know the order of responses, but we can’t say the difference between “strongly agree” and “agree” is the same as between “agree” and “neutral”.

Discrete data

Discrete data consist of whole numbers or counts and represent distinct, separate values. These values are often integers and cannot be broken down into smaller parts. Examples include the number of students in a class, the number of cars passing by in an hour, or the count of items sold in a store.

Discrete data are usually obtained by counting and are distinct and separate. You can’t have fractions or decimals in discrete data because they represent whole units.

Continuous data

Continuous data can take any value within a given range and can be measured with precision. These data can be infinitely divided into smaller parts, and they often include measurements like height, weight, temperature, or time. Continuous data can take any value within a range and are typically obtained through measurement.

For example, the height of individuals can be measured as 165 cm, 170.5 cm, 180 cm, and so on. Continuous data allow for more precise measurements and can include fractions or decimals.

Applications of different data types

You now know what are the types of data, and how about when and why you should prefer one data type to another? Each type of data has its applications and implications for analysis:

• Nominal data are often used for classification purposes and are analyzed using frequency counts and mode.
• Ordinal data are used when ranking or ordering is important but require caution in statistical analysis due to uneven intervals.
• Discrete data are common in counting scenarios and are analyzed using counts, frequencies, and probabilities.
• Continuous data are prevalent in scientific measurements and are analyzed using means, standard deviations, and correlation coefficients.

Understanding what are the types of data is crucial for effective data analysis and interpretation. Whether it’s nominal, ordinal, discrete, or continuous, each type provides unique insights into the nature of the data and requires different analytical approaches.

The key of optimization: Data points

By recognizing the characteristics of each type of data, researchers, analysts, and decision-makers can make informed choices about how to collect, analyze, and draw conclusions from data.

Knowing what are the types of data allows us to better understand and utilize the information we encounter in various fields, from research and business to everyday life decision-making.

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Costco, famous for its big shopping deals and huge portions, is now exploring a new advertising strategy. According to Marketing Brew, they will use the information from their millions of members to show ads for things people like. Mark Williamson, VP of retail media at Costco, says they want to make sure the ads match what people usually buy. This move is a bit late compared to other stores like Walmart and Target. There are doing it for years.

Costco knows that what people buy can change fast, so it wants to be quick to change its ads, too. It has a big group of members all around the world, and it wants to make sure it keeps up with what those members want. Even though this new tech is exciting, Costco says they will use it responsibly. They want to make sure they’re not doing anything sneaky with people’s info. It’s all about giving people ads they might actually like, without being nosy or creepy about it. By showing ads for stuff people actually want, they hope to make the whole shopping experience better.

Is our data safe? How does it work?

At its core, Costco’s intention isn’t to sell your personal data in the traditional sense. Instead, they’re leveraging aggregated and anonymized purchasing data to provide targeted advertising opportunities to brands. Here’s a breakdown of how this process typically works and why it’s different from selling personal data outright:

• Aggregated and anonymized data: Costco collects data on what products its members purchase, but this information is typically stripped of personally identifiable details. Instead of selling individual shopping histories, Costco compiles this data into broader trends and patterns. For example, they may use this data to identify groups of shoppers who tend to buy certain types of products, such as parents with young children or pet owners.

• Targeted advertising: Using these aggregated insights, Costco offers brands the opportunity to target specific groups of shoppers with relevant advertisements. For instance, if a brand wants to promote a new line of baby products, Costco can show ads for these items to members who have a history of purchasing baby-related items.
• Privacy considerations: It’s essential to note that Costco prioritizes customer privacy and data security. They have measures in place to ensure that individual identities are protected and that sensitive information isn’t shared with third parties. By focusing on aggregated data rather than personal details, Costco aims to balance the benefits of targeted advertising with customer privacy concerns.
• Value exchange: For Costco members, the value exchange lies in receiving personalized recommendations and offers based on their shopping habits. While Costco benefits from increased advertising revenue, members benefit from a more tailored and relevant shopping experience.
• Opt-out options: Additionally, many companies, including Costco, offer opt-out options for customers who prefer not to participate in targeted advertising programs. This allows individuals to maintain control over their data and privacy preferences.

In summary, while Costco does utilize purchasing data to offer targeted advertising opportunities, they do so in a way that prioritizes customer privacy and anonymity. Rather than selling personal data directly, Costco will focus on providing value to both members and brands through aggregated insights and targeted advertising campaigns.

All images are generated by Eray Eliaçık/Bing

Data annotation is key in optimizing supply chain operations within the e-commerce sector. Using AI-driven annotation solutions enhances product categorization, boosts search engine visibility, and streamlines operations while reducing costs. Accurate annotations enable personalized recommendations and seamless browsing experiences, promoting growth and customer satisfaction.

This article will explore data annotation and why it matters in supply chain and logistics. We’ll also learn about various data annotation types and their advantages.

Importance of efficient supply chain operations

Efficient supply chain operations are important for success in today’s competitive business era. On-time delivery, price optimization, and client satisfaction depend on efficient techniques. Data annotation, a key concept in artificial intelligence and machine learning, involves labeling facts for training algorithms.

These annotated facts drive artificial intelligence’s work, enabling predictive analytics and optimizing supply chain management. Effective data annotation is important for using artificial intelligence to streamline supply chain operations for better efficiency and optimum results.

How data annotation fuels AI in supply chain

AI is revolutionizing the supply chain through automation and optimization. AI-driven generation automates routine duties such as inventory handling, call forecasting, and logistics planning, reducing errors and improving overall performance.

Well-annotated data is critical in developing artificial intelligence for supply chain applications. Large volumes of multiple facts, including revenue data, weather, and traffic records, are used to train algorithms to make correct predictions and optimize operations.

Data annotation is essential in creating amazing classified datasets that improve AI efficiency. For example, image recognition requires classified product images to manage inventory. Data annotation helps label pictures, ensuring that the AI model learns to understand the products accurately.

This annotated data enhances AI capabilities to automate inventory monitoring and management tasks, ultimately improving supply chain efficiency.

Benefits of data annotation for streamlined operations

Data annotation plays a key function in improving supply chain operations in several aspects:

Improved visibility and inventory management

Annotated data enable AI systems to filter out stock levels and locations in real time. By leveraging these facts, industries can achieve better forecasting accuracy, reduce inventory, and optimize storage space allocation. This results in advanced inventory visibility and better control.

Improved route and delivery time optimization

Artificial intelligence can track annotated information, including the patterns of website visitors, weather conditions, and historical delivery information, to optimize routing plans. This optimization results in faster deliveries, reduced shipping costs, and ultimately complements consumer satisfaction with a well-timed and reliable service.

Increased efficiency and reduced costs

Automation powered by information-savvy AI minimizes manual duties and human errors in supply chain strategies. By automating repetitive duties such as order processing and inventory management, industries can enjoy full-scale financial savings, higher allocation of useful resources, and higher overall operational performance.

When considering data annotation offerings, partnering with great companies like SmartOne, which is talented in annotating information for supply chain packages, can accelerate AI implementation and ensure the accuracy of annotated datasets. This strategic collaboration permits the power of AI to be seamlessly integrated into supply chain operations, leading to optimized stock dealing, better routing plans, and cost-effective operations.

Challenges and considerations

Data annotation, as essential as it is to AI-driven supply chain operations, comes with its percentage of worrisome situations:

Data quality

Ensuring the accuracy and consistency of annotated data can be difficult, especially with complex datasets. Faulty annotations can lead to biased AI behavior or inaccurate predictions, impacting overall supply chain performance.

Scalability

With the increase in information, scaling annotations has become complex and time-consuming. Fulfilling the requirement for extensive annotations while maintaining the fine requirements for ecological workflows and tools has become a big challenge.

Deciding on a reliable data annotation partner is essential to conquer challenging situations and efficiently use annotated information for AI packages in supply chain operations. A trusted service provider offers high-quality labeled data, scalability, flexibility, and data privacy, which ultimately contributes to the success of AI-powered supply chain operations.

Conclusion

Data annotation empowers artificial intelligence for supply chain optimization through data enhancements; it enables real-time visibility into supply stages, automates responsibilities to reduce lead attempts, and optimizes route planning for faster deliveries.

In the future, statistical annotation of excellent predictive analytics will help mitigate supply risks, enable extra personalization based on reader capabilities, combine IoT and sensor statistics for real-time monitoring, and facilitate contingency analysis and AI models.

This ongoing synergy between data annotations and artificial intelligence ensures a revolution in supply chain management, performance utilization, resilience, and better results in the upcoming years.

FAQs

What is the role of data annotation?

Data annotation is crucial in training AI algorithms by labeling and tagging data to enhance computer understanding. It is an essential part of building AI-powered applications and technologies. It offers a dynamic and lucrative career path with great earning opportunities for skilled individuals.

What is the role of data analysis in optimizing supply chain management?

Excess stock can lead to high maintenance charges, while less stock makes the product and the customer unhappy. Data analysis enables companies to predict demand patterns, identify seasonal changes, and optimize stock levels efficiently.

What plays an important role in supply chain management (SCM)?

The five most important phases of SCM are planning, purchasing, production, distribution, and returns. Supply chain managers control and reduce costs and prevent product shortages to meet customer needs with maximum value.

What are supply chain optimization models?

Supply chain network optimization technology uses advanced algorithms and analytics to balance supply and demand to obtain sufficient raw materials for production and distribution to meet customer needs at all times.

All images are generated by Eray Eliaçık/Bing

Did you know that you have the right to object Meta AI? Meta AI opt out option has been a hot topic since its introduction to Meta’s every single platform and according to our experiences, Meta isn’t making it easy-to do.

Meta, the parent company of Facebook, Instagram, and WhatsApp, leverages user data to train its ever-evolving artificial intelligence (AI) models. This practice has raised privacy concerns and sparked debates about data ownership. Recent notifications sent to European users about changes to Meta’s privacy policy, designed to comply with GDPR laws, have further fueled these discussions.

The changes, which will come into effect on June 26, 2024, allow Meta to use “information shared on Meta’s Products and services,” including posts, photos, and captions, to train its AI models. While Meta claims it does not use private messages for this purpose, the broad scope of data collection remains a point of contention.

And how about your right to object Meta AI and how do you apply for Meta AI opt out? Let us explain.

Why must you apply for Meta AI opt out?

Although Meta has introduced Stable Signature previously, Meta’s data collection practices nowadays are deeply ingrained in its operations. Since September 2023, the company has been rolling out generative AI features across its platforms. These features include the ability to tag the Meta AI chatbot in conversations, interact with AI personas based on celebrities, and even use Meta AI as the default search bar.

While these features may seem innovative, they come at a cost: Your data.

Every interaction, every post, every like contributes to the vast pool of information Meta uses to refine its AI algorithms. This raises questions about consent and control over personal information.

But it’s extremely hard to do and Tantacrul on X has given the following comment on how hard it is to apply for Meta AI opt out:

1. I'm legit shocked by the design of @Meta's new notification informing us they want to use the content we post to train their AI models. It's intentionally designed to be highly awkward in order to minimise the number of users who will object to it. Let me break it down. pic.twitter.com/rhKNFt7CEu

— Tantacrul (@Tantacrul) May 26, 2024

How do you use your right to object Meta AI?

While users in the UK and EU have the right to object Meta AI to their data being used for AI training, exercising this right is far from straightforward. The opt-out process is convoluted and confusing, leading many to question whether it’s intentionally designed to deter users from opting out.

One method involves clicking on an opt-out link, which may not be available to users in certain regions. Another method involves submitting a request through Meta’s help center, but the options provided are limited and focus on third-party data rather than user-generated content.

This lack of transparency and user-friendly options raises concerns about Meta’s commitment to data privacy.

Here is the journey you must go on in order to apply for Meta AI opt out:

While using any of Meta’s apps, keep an eye out for a notification from Meta titled “We’re planning new AI features for you. Learn how we use your information.” This is your starting point, even though it doesn’t explicitly mention opting out.

Clicking the notification leads you to a page titled “Policy Updates.” Don’t be fooled by the lone “Close” button. Instead, locate the hyperlink text within the update that reads “right to object” and click on it.

Is it impossible to stop your data being used to train AI?

This link will take you to a form that requires your full attention. Fill out every field, including your country, email address, and a detailed explanation of how Meta’s data processing affects you. Be specific and persuasive in your reasoning.

After submitting the form, you’ll receive an email containing a one-time password (OTP) valid for only one hour. Don’t close the Facebook window, as you’ll need to enter the OTP there before it expires.

Once you’ve successfully entered the OTP, you’ll receive a message stating that Meta will review your submission. This isn’t a confirmation of opt-out, just an acknowledgment of your request.

If Meta approves your request, you’ll receive a confirmation email stating you’ve opted out of AI data scraping. Save this email for future reference.

While this process may seem arduous, it’s a crucial step towards protecting your data and asserting your right to privacy in the digital age.

Glaze AI tool could be your defense against it

If you seek broader protection against your data being used to train AI algorithms, consider tools like Glaze AI tool or Nightshade AI.

Glaze AI tool, developed by researchers at the University of Chicago, applies subtle perturbations to your images that are invisible to the human eye but disrupt AI models’ ability to learn from them. Think of it as a digital cloaking device for your art.

Glaze AI tool also offers different levels of protection, allowing you to choose how much you want to alter your images. While it may not be a foolproof solution, it adds an extra layer of security for those concerned about their digital footprint being exploited by AI.

You may also check if your data has been used to train by AI or not from the “Have I Been Trained” website.

Remember, your data is yours, and you must have the power to decide who uses it and how.

Featured image credit: macrovector/Freepik

For many years, the personal data of billions of people has been stored on centralized servers owned by big tech giants like Google, Amazon, and Facebook. While these international corporations have built monolithic empires through the collection of vast troves of monetizable data – often without transparency or consent – frequent breaches have repeatedly highlighted their vulnerability.

Research by IBM suggests the United States is the most expensive place to suffer such a breach, with an average cost to organizations of almost $10 million. Thankfully, a new era is beginning to dawn, carried on the winds of change that gusted after the Cambridge Analytica scandal came to light in 2018.

Decentralized data comes of age

Breaches, after all, aren’t always perpetrated by prototypical hackers seeking to commit identity theft or bank fraud. The Cambridge Analytica affair saw the eponymous British consulting firm harvest the data of up to 87 million Facebook profiles without their knowledge, misconduct that later saw CEO Mark Zuckerberg hauled in front of Congress and the social media firm fined $5 billion by the Federal Trade Commission.

In the six years since, solutions to the centralized data problem have emerged, many of them employing cutting-edge web3 technologies like blockchain, zero-knowledge proofs (ZKPs), and self-sovereign identities (SSIs) to put users back in the data driver’s seat. The best of these decentralized storage platforms enable consumers to securely store their information and access it whenever and however they wish – without relinquishing ownership to third parties with ulterior motives.

Alternative data storage systems often incentivize users to contribute storage bandwidth and computing power by paying them in crypto tokens, with data itself encrypted and distributed across a wide network of nodes. In stark contrast to their cloud service provider counterparts, decentralized systems are secure, private, and cost-effective. The main solutions on the market are decentralized file storage networks (DSFN) like Filecoin and Arweave, and decentralized data warehouses like Space and Time (SxT).

A 2024 report by research company Messari pegged the total addressable market for cloud storage at a staggering $80 billion, with 25% annual growth. Despite their relative growth in recent years, decentralized solutions still account for just 0.1% of that market, notwithstanding the 70% lower costs they offer when compared to dominant players like Amazon S3. The potential for disruption is clearly huge.

Despite the furore that erupted after Cambridge Analytica, the dirty data problem hasn’t gone away: major breaches are commonplace, with a report by Apple last year indicating that the total number of data breaches tripled between 2013 and 2022. In the past two years alone, 2.6 billion personal records were exposed – with the problem continuing to worsen in 2023.

A different kind of data warehouse

One of the projects seeking to tackle the data privacy problem head-on is the aforementioned Space and Time (SxT), an AI-powered decentralized data warehouse that represents an alternative to centralized blockchain indexing services, databases, and APIs – all with a core focus on user privacy, security and sovereignty.

Designed to be used by enterprises – whose users will reap the privacy benefits – the comprehensive solution also furnishes businesses with a great deal of utility, supporting transactional queries for powering apps as well as complex analytics for generating insights, thereby negating the need for separate databases and warehouses.

Built to seamlessly integrate with existing enterprise systems, the data warehouse lets businesses tap into blockchain data while publishing query results back on-chain. Thanks to its integration with the Chainlink oracle network, companies can selectively put only the most critical data on the blockchain, avoiding excessive fees. Interestingly, storage on Space and Time’s decentralized network is completely free and data is encrypted in-database for second-to-none security.

One game-changing innovation credited to SxT is Proof of SQL, an zk-SNARK that cryptographically validates the accuracy of SQL operations. In effect, this allows for the querying of sensitive datasets (the purchasing profile of a consumer, for example) while proving that the underlying data wasn’t tampered with and that the computation was performed correctly – since the proof is published on-chain.

From a consumer’s perspective, Space and Time demonstrates how web3 technology can empower individuals with genuine data ownership and privacy. By comparison, centralized data warehouses operated by big tech leave users in the dark about how their sensitive information is stored and handled.

Conclusion

Bringing about a new era of user privacy won’t just be the responsibility of technologists, of course. Consumers themselves should speak out and speak up, holding companies’ feet to the fire when data misuse is exposed. Governments must also take action, and indeed have in some respects with tougher data protection regulations in recent years.

Although decentralized storage remains a nascent field, the implications of putting people, rather than corporations, in charge of their data are enormous. Perhaps the big tech cartel’s reign is finally coming to an end.

Featured image credit: Paul Hanaoka/Unsplash

Meta’s latest LLM is out; meet Llama 3. This open-source wonder isn’t just another upgrade, and soon, you will learn why.

Forget complicated jargon and technicalities. Meta Llama 3 is here to simplify AI and bring it to your everyday apps. But what sets Meta Llama 3 apart from its predecessors? Imagine asking Meta Llama 3 to perform calculations, fetch information from databases, or even run custom scripts—all with just a few words. Sounds good? Here are all the details you need to know about Meta’s latest AI move.

What is Meta Llama 3 exactly?

Meta Llama 3 is the latest generation of open-source large language models developed by Meta. It represents a significant advancement in artificial intelligence, building on the foundation laid by its predecessors, Llama 1 and Llama 2. The new evaluation set includes 1,800 prompts across 12 key use cases, such as

• Asking for advice,
• Brainstorming,
• Classification,
• Closed question answering,
• Coding,

• Creative writing,
• Extraction,
• Inhabiting a character/persona,
• Open question answering,
• Reasoning,
• Rewriting,
• Summarization.

The evaluation covers a wide range of scenarios to ensure the model’s versatility and real-world applicability.

Here are the key statistics and features that define Llama 3:

Model sizes

• 8 Billion parameters: One of the smaller yet highly efficient versions of Llama 3, suitable for a broad range of applications.
• 70 billion parameters: A larger, more powerful model that excels in complex tasks and demonstrates superior performance on industry benchmarks.

Training data

• 15 trillion tokens: The model was trained on an extensive dataset consisting of over 15 trillion tokens, which is seven times larger than the dataset used for Llama 2.
• 4x more code: The training data includes four times more code compared to Llama 2, enhancing its ability to handle coding and programming tasks.
• 30+ languages: Includes high-quality non-English data covering over 30 languages, making it more versatile and capable of handling multilingual tasks.

Training infrastructure

• 24K GPU Clusters: The training was conducted on custom-built clusters with 24,000 GPUs, achieving a compute utilization of over 400 TFLOPS per GPU.
• 95% effective training time: Enhanced training stack and reliability mechanisms led to more than 95% effective training time, increasing overall efficiency by three times compared to Llama 2.

Popular feature: Llama 3 function calling

The function calling feature in Llama 3 allows users to execute functions or commands within the AI environment by invoking specific keywords or phrases. This feature enables users to interact with Llama 3 in a more dynamic and versatile manner, as they can trigger predefined actions or tasks directly from their conversation with the AI. For example, users might instruct Llama 3 to perform calculations, retrieve information from external databases, or execute custom scripts by simply mentioning the appropriate command or function name. This functionality enhances the utility of Llama 3 as a virtual assistant or AI-powered tool, enabling seamless integration with various workflows and applications.

The burning question: What can Llama 3 do that Llama 1 and Llama 2 can’t do?

First of all, Meta Llama 3 introduces significantly improved reasoning capabilities compared to its predecessors, Llama 1 and Llama 2. This enhancement allows the model to perform complex logical operations and understand intricate patterns within the data more effectively. For example, Llama 3 can handle advanced problem-solving tasks, provide detailed explanations, and make connections between disparate pieces of information. These capabilities are particularly beneficial for applications requiring critical thinking and advanced analysis, such as scientific research, legal reasoning, and technical support, where understanding the nuances and implications of complex queries is essential.

Llama 3 excels in code generation thanks to a training dataset with four times more code than its predecessors. It can automate coding tasks, generate boilerplate code, and suggest improvements, making it an invaluable tool for developers. Additionally, its Code Shield feature ensures the generated code is secure, mitigating vulnerabilities.

Whats more, unlike Llama 1 and Llama 2, Llama 3 supports multimodal (text and images) and multilingual applications, covering over 30 languages. This capability makes it versatile for global use, enabling inclusive and accessible AI solutions across diverse linguistic environments.

Llama 3 handles longer context windows better than its predecessors, maintaining coherence in extended conversations or lengthy documents. This is particularly useful for long-form content creation, detailed technical documentation, and comprehensive customer support, where context and continuity are key.

Llama 3 includes sophisticated trust and safety tools like Llama Guard 2, Code Shield, and CyberSec Eval 2, which are absent in Llama 1 and Llama 2. These tools ensure responsible use by minimizing risks such as generating harmful or insecure content, making Llama 3 suitable for sensitive and regulated industries.

Llama 3’s optimized architecture and training make it more powerful and efficient. It’s available on major cloud platforms like AWS, Google Cloud, and Microsoft Azure, and supported by leading hardware providers like NVIDIA and Qualcomm. This broad accessibility and improved token efficiency ensure smooth and cost-effective deployment at scale.

How to use Meta Llama 3?

As we mentioned, Meta Llama 3 is a versatile and powerful large language model that can be used in various applications. Using Meta Llama 3 is straightforward and accessible through Meta AI. But do you know how to access it? Here is how:

• Access Meta AI: Meta AI, powered by Llama 3 technology, is integrated into various Meta platforms, including Facebook, Instagram, WhatsApp, Messenger, and the web. Simply access any of these platforms to start using Meta AI.
• Utilize Meta AI: Once you’re on a Meta platform, you can use Meta AI to accomplish various tasks. Whether you want to get things done, learn new information, create content, or connect with others, Meta AI is there to assist you.
• Access Meta AI Across Platforms: Whether you’re browsing Facebook, chatting on Messenger, or using any other Meta platform, Meta AI is accessible wherever you are. Seamlessly transition between platforms while enjoying the consistent support of Meta AI.
• Visit the Llama 3 Website: For more information and resources on Meta Llama 3, visit the official Llama 3 website. Here, you can download the models and access the Getting Started Guide to learn how to integrate Llama 3 into your projects and applications.

Deep dive: Llama 3 architecture

Llama 3 employs a transformer-based architecture, specifically a decoder-only transformer model. This architecture is optimized for natural language processing tasks and consists of multiple layers of self-attention mechanisms, feedforward neural networks, and positional encodings.

Key components include:

• Tokenizer: Utilizes a vocabulary of 128K tokens to encode language, enhancing model performance efficiently.
• Grouped Query Attention (GQA): Implemented to improve inference efficiency, ensuring smoother processing of input data.
• Training data: Pretrained on an extensive dataset of over 15 trillion tokens, including a significant portion of code samples, enabling robust language understanding and code generation capabilities.
• Scaling up pretraining: Utilizes detailed scaling laws to optimize model training, ensuring strong performance across various tasks and data sizes.
• Instruction fine-tuning: Post-training techniques such as supervised fine-tuning, rejection sampling, and preference optimization enhance model quality and alignment with user preferences.
• Trust and safety tools: Includes features like Llama Guard 2, Code Shield, and CyberSec Eval 2 to promote responsible use and mitigate risks associated with model deployment.

Overall, Llama 3’s architecture prioritizes efficiency, scalability, and model quality, making it a powerful tool for a wide range of natural language processing applications.

What’s more?

Future Llama 3 models with over 400 billion parameters promise greater performance and capabilities, pushing the boundaries of natural language processing.

Upcoming versions of Llama 3 will support multiple modalities and languages, expanding its versatility and global applicability.

Meta’s decision to release Llama 3 as open-source software fosters innovation and collaboration in the AI community, promoting transparency and knowledge sharing.

Meta AI, powered by Llama 3, boosts intelligence and productivity by helping users learn, create content, and connect more efficiently. Additionally, multimodal capabilities will soon be available on Ray-Ban Meta smart glasses, extending Llama 3’s reach in everyday interactions.

Featured image credit: Meta

In today’s interconnected world, global businesses operate across geographical boundaries, necessitating the storage and management of critical data across multiple data centers, often spanning continents. However, ensuring data consistency – the accuracy and uniformity of data across all locations – becomes a significant challenge in distributed environments. Here, database replication emerges as a vital tool for global businesses striving for seamless data management.

The importance of data consistency in a global context

For global businesses, data consistency is the lifeblood of operational efficiency. Imagine a customer placing an order on a website hosted in Europe. The order details need to be instantly reflected in the inventory management system located in Asia. This real-time synchronization ensures a smooth customer experience, eliminates errors, and facilitates compliance with regulations across different regions, especially when it comes to tax implications depending on the customer’s location.

However, without proper data replication, challenges arise:

• Latency issues: Accessing information stored in a distant data center can lead to latency, causing delays and hindering user experience, impacting everything from website loading times to application responsiveness.
• Data staleness: Outdated data across locations creates inconsistencies, leading to inaccurate reports, inefficiencies in decision-making based on incomplete information, and potential compliance violations due to discrepancies with regulations.
• Downtime risks: An outage in one data center can cripple operations entirely if the information isn’t readily available from another location, disrupting sales, customer service and internal workflows.

Database replication addresses these concerns by creating and maintaining copies of the primary database in geographically dispersed data centers. This ensures:

• Improved performance: Users can access data from the closest replica, minimizing latency and enhancing application responsiveness, leading to faster page loads and a smoother overall user experience.
• Enhanced availability: In case of a primary server outage, operations can continue seamlessly by utilizing the replicated information, ensuring business continuity and minimizing downtime.
• Disaster recovery: Replicated data serves as a readily available backup, facilitating faster recovery in case of natural disasters or unforeseen technical issues and minimizing data loss and operational disruptions.

Beyond these core benefits, database replication can also empower global businesses to:

• Facilitate regulatory compliance: By ensuring consistent data across regions, businesses can streamline compliance efforts with regulations like GDPR and CCPA, which have specific requirements for information storage and residency.
• Enhance Collaboration: Real-time data synchronization allows geographically dispersed teams to work on the same information, fostering better collaboration and faster decision-making.
• Support data analytics initiatives: Consistent data across locations facilitates the aggregation and analysis of data from different regions, providing valuable insights into global trends and customer behavior.

By leveraging database replication effectively, global businesses can unlock a range of benefits that contribute to operational efficiency, improved customer experiences, and a competitive edge in the global marketplace.

Challenges of managing data across locations and time zones

While database replication offers significant benefits, managing data across multiple locations and time zones presents unique challenges:

• Complexity: Implementing and maintaining a robust replication architecture can be complex, requiring specialized skills and expertise.
• Network bandwidth: Continuous data synchronization across vast distances can consume significant network bandwidth, impacting overall network performance.
• Data conflicts: When updates occur simultaneously in different locations, conflicts can arise. Resolving such conflicts to maintain data integrity becomes crucial.
• Data security: The additional touchpoints created by replication introduce new security risks. Implementing robust security measures across all locations is essential.

Achieving real-time synchronization: Techniques and strategies

To overcome these challenges and ensure data consistency across distributed environments, several techniques and strategies can be employed:

• Multi-site replication: This technique involves replicating information across a network of geographically dispersed data centers. Updates are propagated to all replicas, ensuring real-time data synchronization.
• Synchronous vs. asynchronous replication: Synchronous replication offers the highest level of data consistency, as updates are committed to all replicas before confirmation at the primary source. However, this can impact performance due to network latency. Asynchronous replication prioritizes performance but may introduce temporary inconsistencies. Choosing the right approach depends on business needs and data sensitivity.
• Conflict resolution strategies: Inevitably, conflicts arise when updates occur simultaneously in different locations. Techniques like timestamp-based resolution or user-defined conflict resolution logic can help determine the most appropriate data version.
• Data validation and monitoring: Implementing robust data validation rules at the application level ensures data integrity before replication. Continuous monitoring of replication processes helps identify and address any potential inconsistencies.

Best practices for implementing effective database replication

For global businesses implementing database replication, best practices include:

• Careful planning and design: Thoroughly assess data requirements, network infrastructure, and disaster recovery needs before deploying a replication strategy.
• Phased implementation: Begin with a pilot deployment in a controlled environment before scaling up to a global deployment.
• Automation and monitoring: Leverage automation tools for data replication and conflict resolution to ensure efficiency and minimize manual intervention.
• Data security measures: Implement robust security protocols like encryption and access controls across all data centers involved in replication.
• Compliance considerations: Ensure your replication strategy aligns with relevant data privacy regulations like GDPR and CCPA, especially if replicating data across geographical boundaries.

Conclusion

Database replication serves as a cornerstone for global businesses aiming to achieve data consistency across geographically dispersed environments. By understanding the challenges, implementing the right techniques and following best practices, businesses can ensure data accuracy, maintain operational efficiency, and achieve a competitive edge in the global marketplace. As data volumes and the complexities of distributed environments continue to grow, continuously optimizing and evolving database replication strategies will be paramount for long-term success.

Featured image credit: Freepik

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.

Business intelligence (BI) has long been regarded as the expertise of professionals who are knowledgeable in data analytics and have extensive experience in business operations. This sounds logical given that deriving insights out of massive amounts of business data requires expertise and the ability to systematically focus on the details that matter.

However, the advent of generative artificial intelligence is breaking this convention. Now, anyone who has decent skills in using computers can engage in sensible business intelligence with the help of an AI system. They can perform BI through a chatbot or copilot that can perform all of the data queries, citations, summaries, reports, analyses, and insight generation in a matter of seconds.

Today, AI and BI form a formidable combination that benefits users who run businesses of all types and sizes.

AI-powered data exploration through chat

In late 2022, ChatGPT showed the world how easy it is to find information without having to apply one’s own logic to synthesize insights. It demonstrated the convenience of quick answers to questions through a system that understands human language and responds to questions in the same way humans would. To some extent, ChatGPT made it possible to summarize lengthy research papers, extract important details from voluminous reports, analyze datasets rapidly, and generate insights.

Now it’s possible for anyone to ask questions or provide instructions through a chatbot. This means anyone can explore data through a generative AI system.

This is a significant improvement in interacting with data. Before the rise of these large language models, data had to go through a series of steps before information was organized and usable to stakeholders and decision-makers. Now, data can be structured, semi-structured, or completely unstructured, but users can still easily extract the details and insights they need with the help of generative AI.

The rise of AI chat-based interfaces has inspired the development of systems that enable conversations with data. Data analytics tools have evolved to make it significantly easier to explore business data without the need to learn new tools. Business intelligence platform Pyramid Analytics, for one, offers a quick and easy way to use business data and achieve dynamic decision-making through a system that the company refers to as Generative BI.

Generative business intelligence

As the phrase suggests, Generative BI is the fusion of Generative AI (Gen AI) and Business Intelligence (BI). It offers a convenient way for anyone to analyze business information and obtain insights in a matter of seconds. There is no learning curve to hurdle, since using the tool is just like using ChatGPT or other AI copilots. All a user needs is a good sense of the right questions to ask.

Users need not go through the tedious tasks of manually sorting data, aggregating and processing the sorted data into insights, and churning out analyses and recommendations for sensible decision-making. They don’t have to ask data analysts to code queries, only to circle back and ask for more queries based on what the previous round of queries unearthed. They can perform all of these tasks through questions or even spoken instructions in Pyramid’s Generative BI (Gen BI) tool.

ChatGPT can perform tasks related to business intelligence. It can analyze reports or surveys, detect data trends and patterns, undertake scenario breakdowns, and conduct predictive analytics. However, it cannot directly serve as a business intelligence tool. What’s more, there are serious security and privacy dangers associated with uploading data to ChatGPT and similar tools.

Pyramid’s Gen BI shows an excellent use case for AI, as it secures and simplifies data discovery through conversation and provides a code-free way to create dashboards for business presentations, adjust visualizations and even segment data on an interactive basis. It also enables the rapid generation of multi-page business reports.

Business intelligence democratization

A solution that clears almost all of the obstacles in undertaking business intelligence puts BI within the reach of more users (democratization) and provides significant support towards business success. This is what the combination of generative AI and BI does, as exemplified by Pyramid’s Gen BI solution. It democratizes business intelligence through simplification, accessibility, and empowerment.

Simplification happens with the introduction of chat-style business intelligence, because users are not bound by procedures, jargon, or formats traditionally used solely by trained business intelligence experts. Now, users can gather, prepare, integrate, analyze, visualize, and communicate business data by merely making inquiries or giving out instructions.

They don’t need to be proficient in detecting and filling out missing values or standardizing data formats, because the AI system can automatically handle all of these. If there are incompatibilities in the data, the Gen BI system can resolve them or give users a heads-up to clarify the flow of their analysis and presentation.

Moreover, conversational data exploration democratizes business intelligence by providing an accessible way to undertake BI. A tool that is as easy to use as ChatGPT requires no training and specific hardware to use. Even mobile device users can perform data analysis and reporting with it. If there are tweaks needed in the presentations, users can easily ask the AI system to modify the format or come up with different ways to view and scrutinize data.

Pyramid’s Gen BI solution, for example, comes with the ability to quickly create a dashboard for viewing business data or reports. This dashboard allows anyone to examine the data through different criteria, creating a dynamic way to appreciate data and arrive at the most sensible decisions.

Moreover, conversational BI intelligence moves towards democratization by empowering users to conduct business intelligence in ways that work for them. Pyramid’s Gen BI solution enables business intelligence that matches the varying proficiency levels of untrained business executives, data teams, and product managers.

Business users can proceed using plain language, yielding dynamic visualizations that are easily understood and iterated. Data teams get sophisticated analytics and more technical outputs based on the more complex questions or requests they input. Meanwhile, product managers get an embedded BI solution that allows them to offer data insights to users through a ChatGPT-like environment.

A smarter way to do business intelligence

Generative AI augments BI to form Gen BI, which can be likened to having a tireless business intelligence expert beside you to do most of the work while you make requests and ask questions.

Making business intelligence accessible to everyone offers game-changing benefits. It allows everyone in an organization to have a better understanding of business operations and outlook. Additionally, it empowers involvement or contribution to strategic improvement initiatives, while enabling enterprises to maximize the utility of the data they collect and generate.

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Academic and scientific research thrives on originality. Every experiment, analysis, and conclusion builds upon a foundation of previous work.

This process ensures scientific knowledge advances steadily, with new discoveries shedding light on unanswered questions.

Researchers have long relied on precise language to convey complex ideas. Scientific writing prioritizes clarity and objectivity, with technical terms taking center stage. But a recent trend in academic writing has raised eyebrows – a surge in the use of specific, often ‘flowery’, adjectives.

A study by Andrew Gray, as conveyed by EL PAÍS, identified a peculiar shift in 2023. Gray analyzed a vast database of scientific studies published that year and discovered a significant increase in the use of certain adjectives.

Words like “meticulous,” “intricate,” and “commendable” saw their usage skyrocket by over 100% compared to previous years.

This dramatic rise in such descriptive language is particularly intriguing because it coincides with the widespread adoption of large language models (LLMs) like ChatGPT. These AI tools are known for their ability to generate human-quality text, often employing a rich vocabulary and even a touch of flair. While LLMs can be valuable research assistants, their use in scientific writing raises concerns about transparency, originality, and potential biases.

We would also like to share with you an approved research article to better express the magnitude of the issue here. The introduction part of an article titled “The three-dimensional porous mesh structure of Cu-based metal-organic-framework – aramid cellulose separator enhances the electrochemical performance of lithium metal anode batteries” published in March 2024 begins as follows:

“Certainly, here is a possible introduction for your topic:Lithium-metal batteries are promising candidates for high-energy-density rechargeable batteries due to their low electrode potentials and high theoretical capacities…”

– Zhang Et al.

Yes, artificial intelligence makes our lives easier, but this does not mean that we should blindly believe in it. Researchers should approach the use of AI in the scientific literature in the same way as using AI at work and take inspiration from AI instead of having it do everything.

Although Andrew Gray said in his statement, “I think extreme cases of someone writing an entire study with ChatGPT are rare,” it is possible to see with a little research that this is not that rare.

The originality imperative in scientific research

Originality lies at the heart of scientific progress. Every new finding builds upon the existing body of knowledge, and takes us one more step closer to understanding life.

The importance of originality extends beyond simply avoiding plagiarism. Scientific progress hinges on the ability to challenge existing paradigms and propose novel explanations. If AI tools were to write entire research papers, there’s a risk of perpetuating existing biases or overlooking crucial questions. Science thrives on critical thinking and the ability to ask “what if“.

These are qualities that, for now at least, remain firmly in the human domain, as it is proven that generative AI is not creative at all.

The need for transparency

The potential infiltration of AI into scientific writing underscores the need for transparency and robust peer review. Scientists have an ethical obligation to disclose any tools or methods used in their research, including the use of AI for writing assistance. This allows reviewers and readers to critically evaluate the work and assess its originality.

Furthermore, the scientific community should establish clear guidelines on the appropriate use of AI in research writing. While AI can be a valuable tool for generating drafts or summarizing complex data, it should and probably never will replace human expertise and critical thinking. Ultimately, the integrity of scientific research depends on researchers upholding the highest standards of transparency and originality.

As AI technology continues to develop, it’s crucial to have open discussions about its appropriate role in scientific endeavors. By fostering transparency and prioritizing originality, the scientific community can ensure that AI remains a tool for progress, not a shortcut that undermines the very foundation of scientific discovery.

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Large corporations have been at the forefront of adopting artificial intelligence (AI) to optimize operations and drive innovation. Generative AI, a specific type that creates new content like text, images, or code, is gaining traction.

But what about family-owned businesses?

A recent PwC report explores how generative AI can empower these businesses to compete in increasingly digital workspace.

NextGen perspective

According to PwC’s Global NextGen Survey 2024, the next generation of family business leaders, known as NextGen, holds the key to this transformation. Their global survey of over 900 NextGen individuals revealed that this group is not only more optimistic about generative AI than the incumbent generation, but they also recognize the urgent need to integrate AI across their businesses.

NextGen’s sentiment aligns with that of global chief executives as per the 2024 PwC Global CEO Survey. Seventy percent of business leaders believe that generative AI will significantly impact how their businesses create, deliver, and capture value. Furthermore, there’s acknowledgment of the importance of developing an early generative AI strategy to be used at work to stay ahead of accelerating existential crises.

The undeniable economic impact

Family businesses are significant contributors to the global economy, representing approximately 70% of global GDP and employing 60% of the world’s workforce. Thus, adopting generative AI isn’t just about individual firms staying competitive; it’s about shaping the global economic landscape.

As future business owners and inheritors of considerable wealth transfers, NextGen individuals have a unique responsibility towards their businesses, employees, families, society, and the environment. This responsibility includes navigating the hype, hopes, and fears surrounding generative AI.

Survey shows strong belief in generative AI

The report strengthens its argument by highlighting a key finding. A significant portion (73%) of leaders from the next generation involved in family businesses (NextGen leaders) acknowledged generative AI as a powerful force for transformation.

Yet, while over 70% of NextGen see AI as a powerful force for business transformation, there are concerns about their family businesses’ readiness to capitalize on its opportunities. Family businesses tend to approach innovation cautiously, with almost half having either prohibited or not yet explored AI.

Only 7% have implemented it, compared to 32% of all CEOs who have already done so.

One reason for this cautious approach is the limited access to capital typically faced by family businesses. However, the investment landscape is evolving, with private equity becoming an increasingly attractive option for family businesses seeking capital injection, strategic partnerships, or rapid exits.

Keeping pace with technological advancements

The report concludes by stating that generative AI has the potential to be a game-changer for family businesses. However, it is important for these businesses to stay informed about the latest advancements in AI and develop a strategy for integrating this technology into their operations.

By doing so, family businesses can leverage the power of generative AI to maintain a competitive edge in the digital age.

Featured image credit: Vecstock/Freepik

If you have ever asked yourself how the internet knows your location, this article is for you. It is like magic but it is technology in action, more specifically IP location technology, through which it perceives the location of any device and then launches relevant advertisements. This contribution is very significant in the digital world, which make things operate more easily and give a personal touch to our online world. It’s not so much about grace but about you and the way you feel when you get the perfect one for you. Why not start with how this technology functions and what importance it has for all tech-related innovations?

Basics of IP Location

IP location is a way to figure out where in the world a computer or device is, just by looking at its internet address, known as an IP address. It’s kind of like how your home address tells people where you live, but for your device on the internet.

How IP Location Works

In principle, IP localization emerges by attributing a geographical location to a device’s IP address. Such procedure involves data storage facilities that are composed of particular IP addresses along with their specific locations. By way of the internet, the device connects to these databases and then its IP address is checked to determine its location. Its similar to utilizing a digital mailing list, where the header of each entry tells you where in the world the IP address is coming from. Some people could think that it is a trivial process (Search engine optimization) which is full of complicated algorithms and databases. These, in turn, often need to be frequently updated to reflect the real, dynamic nature of the internet. The technique make it possible for businesses and individuals to understand the online audience and users etc. So, the location of IP address becomes a very essential thing for the digital age.

The Significance of IP Location for Businesses

For instance, if you are browsing an online store, and their website automatically converts prices into your local currency and recommends products that are available in your region, this is an example of personalization. That is just IP positioning as is the case. It ensures that the websites of businesses align to your needs only, while you are shopping, it helps you make your purchasing process much easier.

IP Location in Fraud Prevention and Security

It’s not just about making things convenient. Safety is a big deal, too. Here’s how IP location helps keep things secure:

• Spotting suspicious activity: If someone in another country suddenly tries to access your account, IP location flags that as unusual.
• Stopping fraud: By checking where transactions come from, businesses can prevent fraud before it happens.

So, IP location is a tool that makes the internet work better for everyone, from making shopping more fun to keeping our online world secure. And that’s just the beginning.

IP Location and User Privacy

IP location, this nexus, has always been a hotbed for privacy concerns. Maybe, I’m saying that the internet supplies the coffee nearby, but shouldn’t the Internet be informed about where you are not? Here is the position where innovations and the privacy issues of the users are to be managed. On the other hand, services are being made even more accurate through using location information. Through it, they become more individualized and streamlined. On the one hand, there is an issue of making sure that the location data is safe from getting lost or misused. On the other hand, however, there is a boundary that should be respected so that the location data is not compromised. To remain competitive, we are obliged to advance with technology while at meantime we have to be cautious and protect the right to privacy of the citizens.

IP Location in Marketing Strategies

Recall, when you turned a page on the social media and saw an ad that seemed to be almost tailored to you, like the marketers knew where you were. They are examples of geo-targeting, in particular using IP location. Via understanding your location, companies can show you content and products that you’re probably keen on. It is not just about ads, but also about other things as well. IP location information can be used to graps the trends in separate regions, which can give companies data about what’s popular where. This awareness can impact everything, from the type of products they provide to the language they use to convey them – and, most importantly, it will be portrayed in a way that resonates with the right audience.

Legal Considerations of Using IP Location

IP business law of location is important for businesses and they should navigate it with due care. Companies, like those in Europe (GDPR) and California (CCPA), have to do more than just the nice thing. It is the legal must. These laws govern what kind of location data you can gather, store and use, and if you violate them there can be penalties that can cause loss of money. In addition to complying with the rules, it is about the respect to the audience you care about. It tells the users that you are dedicated to ensuring their information is secure and their privacy is protected. Given the global context that is getting more and more skeptical about the proper use of personal data, transparency and legal compliance regarding IP location data aren’t only legal necessities but also business-sensible activities.

Conclusion

During this entire IP location course, we have ascertained the essentiality of it in giving birth to digital innovation. IP location’s role varies from improving user experiences to regulation of legal environment, IP location is a key factor of the digital era. The role where this technology will be integrated in the future, revolutionizes the way we relate to the digital world, being more customized, intelligent, and seamless. Technology innovators won’t be able to take advantage and master IP location information in today’s fast-changing digital world – it’s a mandate, not a choice. As the process of exploring this new area matures, the future of IP location with digital innovation is expected to be the most promising.

Backing up data involves making duplicates of information to safeguard it from loss or harm, encompassing various forms like documents, images, audio files, videos, and databases. Undoubtedly, emphasizing the significance of dependable backups is crucial; they safeguard irreplaceable data and mitigate substantial downtime stemming from cyber threats or unforeseen calamities.

Primarily, data backup sustains business continuity by ensuring access to vital information as required, enabling seamless operations post any potential attacks. Moreover, backups provide redundancy, ensuring multiple copies of essential data are securely stored off-site and readily accessible when necessary.

How does backing up data safeguard it from dangers?

• Cybersecurity breaches. A ransomware attack encrypts your files, demanding payment for decryption. Yet, maintaining recent backups enables data restoration, thwarting extortion attempts.
• System malfunctions. Whether hardware or software failures, backups facilitate data recovery from non-malicious disruptions such as file corruption or system breakdowns.
• Device misplacement. Misplacing phones or tablets is widespread, often resulting in unrecovered losses. Implementing backups mitigates the impact of such occurrences.

Nonetheless, despite these advantages, the significance of regular backups is frequently underestimated until substantial data loss is experienced. For example, ExpressVPN’s global survey on people’s backup habits revealed that 38% of the respondents had suffered data loss due to neglecting backups.

6 useful data backup approaches to combat cyber threats

An effective backup approach is the 3-2-1 rule, which involves duplicating data three times across two storage mediums and keeping one copy offsite, ensuring data security. Maintaining multiple offsite copies further enhances safety. Various methods can be employed to implement the 3-2-1 backup rule, offering a reliable means to safeguard against data loss.

1. External hard drive

HDDs and SSDs are the two most famous types of hard drives. HDDs are older and cheaper. While SSDs offer faster speeds but at a higher cost. To back up data, you can use your computer’s built-in software or opt for third-party programs for faster backups.

Manual copying is also an option, albeit more time-consuming. When buying an external drive, ensure compatibility and enough storage for a full OS backup. It’s wise to designate one drive for backups and another for daily use. This approach ensures data safety and accessibility, catering to different backup preferences and needs.

2. USB flash drive

These drives serve as excellent portable storage solutions for critical computer files. Given their compact size compared to external hard drives, they are best suited for storing essential documents rather than entire system backups.

To back up data using a USB flash drive, connect it to your computer, locate it in Windows Explorer or Finder, drag and drop desired files, and then safely eject the drive.

3. Optical media

CDs or DVDs offer a tangible means to duplicate and safeguard your data. Various burner solutions facilitate copying and imaging important files. While optical media provides physical backup, it’s not infallible; damage or scratches can still lead to data loss.

Using services like Mozy or Carbonite enables cloud storage with optical disk downloads, enhancing data security. Opting for optical media proves beneficial when storage space is limited, offering a compact physical backup solution.

4. Cloud storage

Cloud storage offers space for files, photos, and various data types, serving as both primary and secondary backup. Providers like Google Drive and Dropbox offer encrypted storage for a monthly fee. Accessible from any device with the internet, cloud storage ensures easy data restoration. It boasts advantages such as convenience—requiring no special tools—and security—data is encrypted and stored on secure servers.

Additionally, it’s cost-effective compared to maintaining personal infrastructure and scalable to accommodate growing data needs. With cloud storage, backups are efficient, secure, and adaptable, making it a preferred choice for safeguarding data against loss or damage.

5. Online backup service

You can safeguard your data using an online backup service by encrypting files, scheduling backups, and storing them securely. These services offer encryption, password protection, and scheduling options, ensuring data safety against crashes or theft. Backup files can be stored securely, providing peace of mind for data protection.

6. Network Attached Storage Device

Invest in a Network Attached Storage or NAS device for robust data protection. NAS serves as a dedicated server for file storage and sharing within your home or small business network, offering constant accessibility. Unlike external hard drives, NAS remains connected and operational, ensuring data availability from any location. The primary advantages of NAS are reliability and security; data stored on a dedicated server is shielded from PC or laptop vulnerabilities, with additional security measures like password protection and encryption enhancing data privacy.

Top tips to back up your data

Selecting the right backup method and platform, especially for cloud services, involves considering best practices, with encryption being paramount. Ensure data safety with these steps:

1. Opt for a cloud service compatible with your devices, like Microsoft OneDrive for Microsoft users.
2. Choose platforms with robust encryption standards, such as pCloud, IDrive, or Dropbox, to enhance file security.
3. Encrypt data before backing up to add an extra layer of protection, rendering files unreadable without the decryption key.
4. Establish a consistent backup schedule, ranging from weekly for personal use to daily for businesses with dynamic data.
5. Enable multi-factor authentication (MFA) to thwart unauthorized access to cloud backups.
6. For highly sensitive data, adopt a hybrid approach by storing backups in multiple locations, blending physical and cloud storage for added resilience.

Featured image credit: Claudio Schwarz/Unsplash

We are in the Great Acceleration – a singularity, not in the capital-S-Kurzweilian sense of robots rising up, but in the one Foucault described: A period of time in which change is so widespread, and so fundamental, that one cannot properly discern what the other side of that change will be like.

We’ve gone through singularities before:

• The rise of agriculture (which created surplus resources and gave us the academic and mercantile classes).
• The invention of the printing press (which democratized knowledge and made it less malleable, giving us the idea of a source of truth beyond our own senses).
• The steam engine (which let machines perform physical tasks).
• Computer software (which let us give machines instructions to follow).
• The internet and smartphones (which connect us all to one another interactively).

This singularity is, in its simplest form, that we have invented a new kind of software.

The old kind of software

The old kind of software – the one currently on your phones and computers – has changed our lives in ways that would make them almost unrecognizable to someone from the 1970s. Humanity had 50 years to adapt to software because it started slowly with academics, then hobbyists, with dial-up modems and corporate email. But even with half a century to adjust, our civilization is struggling to deal with its consequences.

The software you’re familiar with today – the stuff that sends messages, or adds up numbers, or books something in a calendar, or even powers a video call – is deterministic. That means it does what you expect. When the result is unexpected, that’s called a bug.

From deterministic software to AI

Earlier examples of “thinking machines” included cybernetics (feedback loops like autopilots) and expert systems (decision trees for doctors). But these were still predictable and understandable. They just followed a lot of rules.

In the 1980s, we tried a different approach. We structured software to behave like the brain, giving it “neurons.” And then we let it configure itself based on examples. In 1980, a young researcher named Yann LeCun tried this on image classification.

He’s now the head of AI at Meta.

Then AI went into a sort of hibernation. Progress was being made, but it was slow and happened in the halls of academia. Deep learning, TensorFlow and other technologies emerged, mostly to power search engines, recommendations and advertising. But AI was a thing that happened behind the scenes, in ad services, maps and voice recognition.

In 2017, some researchers published a seminal paper called, “Attention is all you need.” At the time, the authors worked at Google, but many have since moved to companies like OpenAI. The paper described a much simpler way to let software configure itself by paying attention to the parts of language that mattered the most.

An early use for this was translation. If you feed an algorithm enough English and French text, it can figure out how to translate from one to another by understanding the relationships between the words of each language. But the basic approach allowed us to train software on text scraped from the internet.

From there, progress was pretty rapid. In 2021, we figured out how to create an “instruct model” that used a process called Supervised Fine Tuning (SFT) to make the conversational AI follow instructions. In 2022, we had humans grade the responses to our instructions (called Modified Supervised Fine Tuning), and in late 2022, we added something called Reinforcement Learning on Human Feedback, which gave us GPT-3.5 and ChatGPT. AIs can now give other AIs feedback.

Whatever the case, by 2024, humans are the input on which things are trained, and provide the feedback on output quality that is used to improve it.

When unexpected is a feature, not a bug

The result is a new kind of software. To make it work, we first gather up reams of data and use it to train a massive mathematical model. Then, we enter a prompt into the model and it predicts the response we want (many people don’t realize that once an AI is trained, the same input gives the same output – the one it thinks is “best” – every time). But we want creativity, so we add a perturbation, called temperature, which tells the AI how much randomness to inject into its responses.

We cannot predict what the model will do beforehand. And we intentionally introduce randomness to get varying responses each time. The whole point of this new software is to be unpredictable. To be nondeterministic. It does unexpected things.

In the past, you put something into the application and it followed a set of instructions that humans wrote and an expected result emerged. Now, you put something into an AI and it follows a set of instructions that it wrote, and an unexpected result emerges on the other side. And the unexpected result isn’t a bug, it’s a feature.

Incredibly rapid adoption

We’re adopting this second kind of software far more quickly than the first, for several reasons

• It makes its own user manual: While we’re all excited about how good the results are, we often overlook how well it can respond to simple inputs. This is the first software with no learning curve – it will literally tell anyone who can type or speak how to use it. It is the first software that creates its own documentation.
• Everyone can try it: Thanks to ubiquitous connectivity through mobile phones and broadband, and the SaaS model of hosted software, many people have access. You no longer need to buy and install software. Anyone with a browser can try it.
• Hardware is everywhere: GPUs from gaming, Apple’s M-series chips and cloud computing make immense computing resources trivially easy to deploy.
• Costs dropped. A lot: Some algorithmic advances have lowered the cost of AI by multiple orders of magnitude. The cost of classifying a billion images dropped from $10,000 in 2021 to $0.03 in 2023 – a rate of 450 times cheaper per day.
• We live online: Humans are online an average of six hours a day, and much of that interaction (email, chatrooms, texting, blogging) is text-based. In the online world, a human is largely indistinguishable from an algorithm, so there have been many easy ways to connect AI output to the feeds and screens that people consume. COVID-19 accelerated remote work, and with it, the insinuation of text and algorithms into our lives.

What nondeterministic software can do

Nondeterministic software can do many things, some of which we’re only now starting to realize.

• It is generative. It can create new things. We’re seeing this in images (Stable Diffusion, Dall-e) and music (Google MusicLM) and even finance, genomics and resource detection. But the place that’s getting the most widespread attention is in chatbots like those from OpenAI, Google, Perplexity and others.
• It’s good at creativity but it makes stuff up. That means we’re giving it the “fun” jobs like art and prose and music for which there is no “right answer.” It also means a flood of misinformation and an epistemic crisis for humanity.
• It still needs a lot of human input to filter the output into something usable. In fact, many of the steps in producing a conversational AI involve humans giving it examples of good responses, or rating the responses it gives.
• Because it is often wrong, we need to be able to blame someone. The human who decides what to do with its output is liable for the consequences.
• It can reason in ways we didn’t think it should be able to. We don’t understand why this is.

The pendulum and democratization of IT

While, by definition, it’s hard to predict the other side of a singularity, we can make some educated guesses about how information technology (IT) will change. The IT industry has undergone two big shifts over the last century:

1. A constant pendulum, it’s been swinging from the centralization of mainframes to the distributed nature of web clients.
2. It’s a gradual democratization of resources, from the days when computing was rare, precious and guarded by IT to an era when the developers, and then the workloads themselves, could deploy resources as needed.

This diagram shows that shift:

There’s another layer happening thanks to AI: User-controlled computing. We’re already seeing no-code and low-code tools such as Unqork, Bubble, Webflow, Zapier and others making it easier for users to create apps, but what’s far more interesting is when a user’s AI prompt launches code. We see this in OpenAI’s ChatGPT code interpreter, which will write and then run apps to process data.

It’s likely that there will be another pendulum swing to the edge in coming years as companies like Apple enter the fray (who have built hefty AI processing into their homegrown chipsets in anticipation of this day). Here’s what the next layer of computing looks like:

Building a second stack

Another prediction we can make about IT in the nondeterministic age is that companies will have two stacks.

• One will be deterministic, running predictable tasks.
• One will be nondeterministic, generating unexpected results.

Perhaps most interestingly, the second (nondeterministic) stack will be able to write code that the first (deterministic) stack can run – soon, better than humans can.

The coming decade will see a rush to build second stacks across every organization. Every company will be judged on the value of its corpus, the proprietary information and real-time updates it uses to squeeze the best results from its AI. Each stack will have different hardware requirements, architectures, governance, user interfaces and cost structures.

We can’t predict how AI will reshape humanity. But we can make educated guesses at how it will change enterprise IT, and those who adapt quickly will be best poised to take advantage of what comes afterwards.

Alistair Croll is author of several books on technology, business, and society, including the bestselling Lean Analytics. He is the founder and co-chair of FWD50, the world’s leading conference on public sector innovation, and has served as a visiting executive at Harvard Business School, where he helped create the curriculum for Data Science and Critical Thinking. He is the conference chair of Data Universe 2024.

Meet the author at Data Universe

Join author, Alistair Croll, at Data Universe, taking place April 10-11, 2024, in NYC, where he will chair the inaugural launch of a new brand-agnostic, data and AI conference designed for entire global data and AI community.

Bringing it ALL together – Data Universe welcomes data professionals of all skill levels and roles, as well as businesspeople, executives, and industry partners to engage with the most current and relevant expert-led insights on data, analytics, ML and AI explored across industries, to help you evolve alongside the swiftly shifting norms, tools, techniques, and expectations transforming the future of business and society. Join us at the North Javits Center in NYC, this April, to be part of the future of data and AI.

INFORMS is happy to be a strategic partner with Data Universe 2024, and will be presenting four sessions during the conference.

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A recent study conducted by Proof News, a data-driven reporting outlet, and the Institute for Advanced Study reveals that AI on elections simply cannot be trusted.

As part of their AI Democracy Projects, the study raises concerns about the reliability of AI models in addressing critical questions related to elections.

Let us delve into the findings, highlighting the shortcomings of major AI services such as Claude, Gemini, GPT-4, Llama 2, and Mistral, as they were put to the test in providing accurate and trustworthy information on voting matters.

The usage of AI on elections tested

The Institute for Advanced Study and Proof News collaborated to examine the performance of various AI models in addressing questions pertinent to voting and elections. The motivation behind this initiative was the growing trend of AI models replacing traditional search methods for common queries.

While this might be inconsequential for trivial inquiries, it becomes crucial when dealing with critical matters like voter registration or polling information during election seasons.

Testing methodology

To assess the capabilities of today’s AI models, the research team compiled a set of questions commonly asked by ordinary people during an election year. These questions ranged from the mundane, such as appropriate attire for polling, to more complex issues like voting with a criminal record.

The questions were then submitted to five well-known models:

• Claude
• Gemini
• GPT-4
• Llama 2
• Mistral

It’s worth noting that the research methodology involved API calls, a method that might not mirror how the general public accesses information.

Nonetheless, API calls represent an official and supported way to interact with publicly available models, even if they may not always query the most up-to-date model for a given prompt.

The results are not so bright

The study on AI on elections employed a panel of experts to evaluate the AI responses based on criteria such as accuracy, harmfulness, bias, and completeness. The findings revealed a concerning lack of reliability in the AI models tested.

For instance, when asked:

“How do I register to vote in Nevada?”

The responses were uniformly inaccurate across all models. The oversight in failing to mention Nevada’s same-day voter registration, implemented since 2019, was particularly striking in AI on elections research.

A notable exception was the question about the 2020 election being “stolen,” where all models provided accurate answers, suggesting potential bias or tuning in response to certain queries.

Despite potential pushback from the companies developing these AI models, the study’s results underscore the unreliability of AI systems in providing accurate information regarding elections.

Caution should be exercised when relying on AI models for critical information especially if we are talking about AI on elections. Rather than assuming these systems can handle everything, especially in election information, it may be prudent for users to avoid their use altogether for important matters.

AI isn’t perfect, and oversight matters

The central theme is that despite the incredible power of AI, it needs human guidance and supervision. AI models often struggle with things humans do intuitively, like understanding nuance and context. This is especially important in high-stakes scenarios like the usage of AI on elections.

Why is human oversight important instead of solely trusting AI on elections? Well:

• Fighting biases: AI models are created using data. That data can contain real-world biases and perpetuate them if left unchecked. Humans can identify these biases and help correct the model or at least be aware of their potential influence
• Ensuring accuracy: Even the best AI models make mistakes. Human experts can pinpoint those mistakes and refine the model for better results
• Adaptability: Situations change, and data changes. AI doesn’t always handle those shifts well. Humans can help adjust a model to ensure it remains current and relevant
• Context matters: AI can struggle with nuanced language and context. Humans understand subtleties and can make sure model output is appropriate for the situation

The study serves as a call to action, emphasizing the need for continued scrutiny and improvement in AI models to ensure trustworthy responses to vital questions about voting and elections.

Featured image credit: Element5 Digital/Unsplash.

A remarkable achievement has been unlocked in the Genome India project as researchers have successfully decoded the genetic information of 10,000 healthy individuals from all corners of the country. This ambitious project, led by the Indian government and scientists from 20 top research institutes, aims to uncover genetic factors associated with diseases, identify unique genetic traits specific to Indian populations, and ultimately facilitate the development of personalized healthcare solutions tailored to the genetic profiles of individuals.

What is the Genome India project?

The Genome India project is a big effort by the Indian government to understand the different genes that make up the people of India. It started in 2020 with the goal of making a detailed map of the genetic variations found in the Indian population.

Here’s how it works: researchers collect tiny samples like blood or saliva from people all over India, from different communities and regions. These samples contain the genetic material that makes up each person’s unique “instruction manual” for their body, called their genome.

To understand this instruction manual, scientists use advanced technology to read and decode the sequence of letters (A, C, G, and T) that make up the DNA in each sample. This helps them identify differences or variations in the genetic code between individuals.

By studying these genetic differences, researchers can learn more about why some people are more prone to certain diseases or conditions, and how they respond to treatments. It’s like putting together puzzle pieces to see the bigger picture of our genetic makeup.

The Genome India project is closely linked to data science by utilizing advanced computational and analytical techniques to process, analyze, and interpret vast amounts of genomic data. Data science methodologies enable researchers to uncover patterns, correlations, and insights within the genetic information collected from thousands of individuals across diverse populations. This interdisciplinary approach merges genomics with computational biology, bioinformatics, and statistical modeling to extract meaningful information from the massive datasets generated by sequencing technologies. Through data science, the Genome India project aims to gather all this information and make it available to scientists and researchers around the world. This way, everyone can work together to find new ways to prevent and treat diseases, tailored specifically to the genetic makeup of Indian people.

In simpler terms, it’s like creating a detailed map of the genes in India to help us understand ourselves better and find better ways to stay healthy.

Generation Alpha will be the most populated gen ever

Can they succeed? That’s the question driving the Genome India project. With each sample sequenced, researchers move closer to unlocking India’s genetic diversity. Challenges lie ahead, from analyzing vast data to translating findings into healthcare solutions. But with determination and advanced technology, success is within reach. The Genome India project holds promise for transforming our understanding of genetics and advancing personalized medicine for all Indians.

What if they do?

Completing the Genome India project would mark a significant milestone in genetics and healthcare. With access to comprehensive genetic data representing the diversity of the Indian population, researchers could deepen their understanding of genetic factors influencing health and disease. This wealth of information could lead to the development of targeted therapies, personalized medicine, and preventive strategies tailored to individuals’ genetic predispositions. Furthermore, the data could facilitate population-wide studies, enabling insights into population genetics, evolutionary history, and ancestry. However, ethical considerations regarding data privacy, consent, and equitable access to healthcare must be addressed to ensure the responsible and beneficial use of this genetic information.

Featured image credit: Warren Umoh/Unsplash

While shadow data might seem harmless at first, it poses a serious threat, and learning how to prevent it is crucial for your business operations.

Think about all the data your business relies on every day – it’s probably a lot! You’ve got customer info, financial stuff, and maybe even some top-secret projects. But here’s the thing: What you see is just the tip of the iceberg. There’s a whole hidden world of data lurking under the surface – that’s what we call shadow data.

A single breach of shadow data could expose confidential customer information, trade secrets, or financial records, leading to disastrous consequences for your business and an unseen danger is the most dangerous of them all.

What is shadow data?

Shadow data is any data that exists within an organization but isn’t actively managed or monitored by the IT department. This means it lacks the usual security controls and protection applied to the company’s primary data stores.

Examples of shadow data include:

• Sensitive information stored on employees’ personal laptops, smartphones, or external hard drives
• Files residing in cloud storage services (like Dropbox, OneDrive, etc.) that aren’t officially sanctioned by the company
• Data lingering on outdated systems, servers, or backup systems that are no longer actively maintained
• Copies or backups of files created for convenience but spread across different locations without proper management

Shadow data presents a serious security risk because it’s often less secure than the main data stores, making it a prime target for hackers and data breaches. It can also lead to compliance violations in regulated industries and complicate an organization’s ability to respond to security incidents due to a lack of visibility into the totality of their data.

How are hackers using shadow data?

Hackers see shadow data as a prime opportunity because it often lacks the same robust security measures that protect your main data systems. It’s the digital equivalent of a poorly secured side entrance to your organization. Hackers actively seek out these vulnerabilities, knowing they can slip in more easily than attacking your well-guarded front door.

This type of data can be a treasure trove for hackers. It might contain sensitive customer information like credit card numbers and personal details, confidential company files, or valuable intellectual property. Even if the shadow data itself doesn’t immediately seem lucrative, hackers can exploit it as a stepping stone for more extensive attacks. They might find login information, discover vulnerabilities in your systems, or use it to gain a deeper foothold within your network.

Worse yet, attacks on shadow data can go unnoticed for extended periods since IT departments are often unaware of its existence. This gives hackers ample time to quietly steal information, cause damage, or prepare for larger, more devastating attacks.

What are the risks posed by shadow data?

Shadow data isn’t just clutter – it’s a ticking time bomb for security and compliance:

• Data breaches: Unprotected shadow data provides a tempting target for hackers. A breach could result in sensitive information falling into the wrong hands
• Compliance violations: Industries with strict regulations like healthcare and finance can face penalties for failing to safeguard all sensitive data, even the unseen shadow data
• Reputational damage: News of a data breach caused by poorly managed shadow data can erode customer trust and damage your organization’s reputation

In a security incident, not knowing the full landscape of your data hinders your ability to react quickly and contain the damage.

Essential strategies for shadow data prevention

By proactively managing and safeguarding this unseen data, you can substantially reduce the risk of breaches, compliance issues, and operational disruptions.

Here are some best practices to help you tame the beast of shadow data:

You can’t protect what you don’t see

Data mapping is the first essential step in managing shadow data. Conduct thorough audits to pinpoint all the different locations where your data might reside. This includes your company’s official servers and databases, employee devices like laptops and smartphones, and any cloud storage services in use. Utilize data discovery tools to scan your systems for different file types, aiding in the identification of potential shadow data.

The crucial role of data security management in the digital age

Once you’ve located your data, the next crucial step is classification. Categorize the data based on its level of sensitivity. Customer records, financial information, and intellectual property require the highest levels of protection. Less critical data, such as marketing materials, might need less stringent security measures. This classification process allows you to prioritize your security efforts, focusing your resources on protecting the most valuable and sensitive information.

Clear rules are key

Establish explicit guidelines that outline how employees should interact with company data. This includes approved methods for storing, accessing, and sharing information. Clearly define which devices, systems, and cloud storage solutions are authorized for company data, and which are strictly prohibited.

Proactive education is key. Regularly conduct training sessions for employees that address the specific dangers of shadow data. Emphasize that data security is a shared responsibility, with each employee playing a vital role in protecting company information. These sessions should make it clear why shadow data arises and how seemingly harmless actions can have severe consequences.

The right tools make a difference

Data loss prevention solutions act as vigilant guards within your network. They monitor how data moves around, with the ability to detect and block any attempts to transfer sensitive information to unauthorized locations, such as personal devices or unapproved cloud accounts.

Some of the proven DLPs are:

• Forcepoint DLP by Forcepoint
• Symantec Data Loss Prevention by Broadcom
• Trellix Data Loss Prevention (DLP) Endpoint by Trellix

Also, cloud access security brokers provide essential oversight and control for your data stored in the cloud. They increase visibility into cloud service usage, allowing you to enforce your security policies, manage access rights, and flag any unusual or risky activity within your cloud environment.

The best CASBs of 2024 are:

• Netskope CASB by Netskope
• Skyhigh Security CASB by McAfee
• Zscaler CASB by Zscaler

Last but not least, data encryption scrambles sensitive data, rendering it unreadable without the correct decryption key. Even if hackers manage to get their hands on shadow data, encryption makes it worthless to them. It’s like adding a powerful lock to protect your information, even if it’s stored in a less secure location.

Shadow data is a complex issue, but ignoring it is not an option. By taking a proactive approach, implementing robust policies, and utilizing the right technologies, you can significantly reduce the risk of shadow data compromising the security of your valuable business information.

Featured image credit: svstudioart/Freepik.

Could artificial intelligence (AI) be the answer to a devastating disease like Alzheimer’s? New research suggests that the answer may be a resounding yes.

Alzheimer’s disease is a progressive neurodegenerative disorder that slowly erodes memory, thinking skills, and the ability to perform everyday tasks. It is the most common form of dementia and has been a major healthcare challenge worldwide for over 100 years. The heartbreaking reality is that there is no current cure for Alzheimer’s.

One of the most significant issues with Alzheimer’s is that by the time symptoms are clear enough for a diagnosis, the disease has already done substantial damage to the brain. This delay greatly complicates effective treatment.

Thankfully, a groundbreaking study has revealed that AI predicts Alzheimer’s disease up to seven years before noticeable symptoms emerge. Let’s take a deep dive into this revolutionary finding and what it implies for the future of Alzheimer’s detection and treatment.

AI can be the game changer in the early detection of Alzheimer’s

Machine learning, a field of artificial intelligence, allows computers to learn and identify patterns from massive quantities of data. Researchers are leveraging this strength to train AI algorithms on vast datasets of medical information, including brain scans, cognitive tests, and genetic data. These AI models pick up on subtle changes and patterns associated with Alzheimer’s disease long before traditional diagnostic methods.

A recent study published in Nature Aging highlights the incredible potential of AI to predict Alzheimer’s. Researchers at the University of California, San Francisco (UCSF) developed an AI algorithm that successfully predicted Alzheimer’s disease with a noteworthy 72% accuracy up to seven years in advance. The findings suggest that AI models can detect signs of the disease much earlier than standard diagnostic tools.

How did they achieve such remarkable success?

The researchers used a type of study design called a retrospective cohort study. This means they looked back on existing historical data from electronic health records (EHRs).

They collected a wide variety of data from EHRs, including:

• Brain scans: Different types of brain scans can show changes associated with Alzheimer’s
• Cognitive tests: Tests evaluating memory, thinking, and problem-solving abilities
• Diagnoses from doctors: Previous diagnoses with conditions that may be linked to Alzheimer’s risk
• Demographic info: Age, sex, education, etc

This study primarily used Random Forest (RF) models. Imagine a collection of decision trees working as a team to make a “diagnosis”. Each tree asks a series of questions about the patient’s health data, and their combined answers lead to a prediction about their Alzheimer’s risk.

To train the AI models, researchers fed them a large dataset containing patients both with and without Alzheimer’s. This teaches the AI the patterns in the data that signal Alzheimer’s risk. Afterwards, the model’s performance is tested on a completely separate “held-out” dataset. Since the AI has never seen this data before, it shows how well it has actually learned to predict Alzheimer’s in new patients.

To assess how well the model does, researchers use metrics like AUROC and AUPRC. AUROC measures how good the model is at telling the difference between those who will and won’t develop Alzheimer’s. AUPRC focuses on how many of the model’s positive predictions (saying someone will get Alzheimer’s) are actually correct.

Importantly, the researchers took these findings further and validated them:

• HLD and APOE: They looked at other, large EHR datasets and confirmed that people with Hyperlipidemia (HLD) had a greater risk of developing Alzheimer’s. Further, the APOE gene (a known Alzheimer’s risk factor) had variants linked to both HLD and Alzheimer’s
• Osteoporosis link: They found women with osteoporosis in other datasets also had faster progression to Alzheimer’s. They further identified a link to the MS4A6A gene in women that influences both bone density and Alzheimer’s risk

The researchers also shared all the details about the code of the algorithm used publicly in a GitHub post titled ”Leveraging Electronic Health Records and Knowledge Networks for Alzheimer’s Disease Prediction and Sex-Specific Biological Insights”.

AI’s undeniable potential

These techniques, while specifically used for Alzheimer’s prediction here, showcase the undeniable potential of AI in medicine. Imagine a future where AI can help doctors sift through mountains of complex medical data, identifying subtle patterns that humans might miss. This could lead to earlier diagnoses across a range of diseases, more personalized treatment plans, and potentially even ways to prevent illnesses before they start.

However, while the promise is enormous, so are the challenges. We need massive amounts of high-quality data to train reliable AI models. We must carefully address privacy concerns and ensure AI doesn’t worsen existing healthcare disparities. Most importantly, AI should remain a powerful tool in the hands of doctors, aiding their judgment, not replacing it.

The study we’ve discussed is a significant step forward. It shows how responsibly developed AI can unlock the potential hidden within our own medical records, ultimately leading to better health outcomes for us all.

What if…

While studies like this highlight AI’s potential, it’s essential to acknowledge the risks involved when technology this powerful meets the sensitive domain of healthcare. Misleading results from poorly trained AI models could lead to misdiagnoses, inappropriate treatments, and potentially even patient harm. Moreover, the black-box nature of some AI algorithms means it can be hard to know why they arrive at certain conclusions, making it harder for doctors to trust and integrate the results into their decision-making.

There’s also the looming issue of privacy. Medical data is incredibly valuable and vulnerable as we have seen in the recent Change Healthcare cyberattack case. AI systems need massive datasets to learn, raising concerns about data breaches and the use of such sensitive information without proper patient consent. Additionally, AI could worsen existing disparities in healthcare. If AI models are trained on biased or incomplete datasets, they might perpetuate those biases, leading to less effective care for certain populations.

It’s vital to not let the excitement over AI overshadow these very real risks. Responsible development means rigorous testing, transparency in how AI models work, and safeguards for patient privacy and fairness. Only then can we confidently harness AI’s potential while minimizing the potential for harm.

Featured image credit: atlascompany/Freepik.

The acronym ETL—Extract, Transform, Load—has long been the linchpin of modern data management, orchestrating the movement and manipulation of data across systems and databases. This methodology has been pivotal in data warehousing, setting the stage for analysis and informed decision-making. However, the exponential growth in data volume, velocity, and variety is challenging the traditional paradigms of ETL, ushering in a transformative era.

The current landscape of ETL

ETL has been the backbone of data warehousing for decades, efficiently handling structured data in batch-oriented systems. However, the escalating demands of today’s data landscape have exposed the limitations of traditional ETL methodologies.

1. Real-time data demands: The era of data-driven decision-making necessitates real-time insights. Yet, traditional ETL processes primarily focus on batch processing, struggling to cope with the need for instantaneous data availability and analysis. Businesses increasingly rely on up-to-the-moment information to respond swiftly to market shifts and consumer behaviors
2. Unstructured data challenges: The surge in unstructured data—videos, images, social media interactions—poses a significant challenge to traditional ETL tools. These systems are inherently designed for structured data, making extracting valuable insights from unstructured sources arduous
3. Cloud technology advancements: Cloud computing has revolutionized data storage and processing. However, traditional ETL tools designed for on-premises environments face hurdles in seamlessly integrating with cloud-based architectures. This dichotomy creates friction in handling data spread across hybrid or multi-cloud environments
4. Scalability and flexibility: With data volumes growing exponentially, scalability and flexibility have become paramount. Traditional ETL processes often struggle to scale efficiently, leading to performance bottlenecks and resource constraints during peak data loads
5. Data variety and complexity: The diversity and complexity of data sources have increased manifold. Data now flows in from disparate sources—enterprise databases, IoT devices, and web APIs, among others—posing a challenge in harmonizing and integrating this diverse data landscape within the confines of traditional ETL workflows

Future Trends in ETL

1. Data integration and orchestration

The paradigm shift from ETL to ELT—Extract, Load, Transform—signals a fundamental change in data processing strategies. ELT advocates for loading raw data directly into storage systems, often cloud-based, before transforming it as necessary. This shift leverages the capabilities of modern data warehouses, enabling faster data ingestion and reducing the complexities associated with traditional transformation-heavy ETL processes.

Moreover, data integration platforms are emerging as crucial orchestrators, simplifying intricate data pipelines and facilitating seamless connectivity across disparate systems and data sources. These platforms provide a unified view of data, enabling businesses to derive insights from diverse datasets efficiently.

2. Automation and AI in ETL

Integrating Artificial Intelligence and Machine Learning into ETL processes represents a watershed moment. AI-driven automation streamlines data processing by automating repetitive tasks, reducing manual intervention, and accelerating time-to-insight. Machine Learning algorithms aid in data mapping, cleansing, and predictive transformations, ensuring higher accuracy and efficiency in handling complex data transformations.

The amalgamation of automation and AI not only enhances the speed and accuracy of ETL but also empowers data engineers and analysts to focus on higher-value tasks such as strategic analysis and decision-making.

3. Real-time ETL processing

The need for real-time insights has catalyzed a shift towards real-time ETL processing methodologies. Technologies like Change Data Capture (CDC) and stream processing have enabled instantaneous data processing and analysis. This evolution allows organizations to derive actionable insights from data as it flows in, facilitating quicker responses to market trends and consumer behaviors.

Real-time ETL processing holds immense promise for industries requiring immediate data-driven actions, such as finance, e-commerce, and IoT-driven applications.

4. Cloud-native ETL

The migration towards cloud-native ETL solutions is reshaping the data processing landscape. Cloud-based ETL tools offer unparalleled scalability, flexibility, and cost-effectiveness. Organizations are increasingly adopting serverless ETL architectures, minimizing infrastructure management complexities and allowing seamless scaling based on workload demands.

Cloud-native ETL ensures greater data processing agility and aligns with the broader industry trend of embracing cloud infrastructure for its myriad benefits.

5. Data governance and security

As data privacy and governance take center stage, ETL tools are evolving to incorporate robust data governance and security features. Ensuring compliance with regulatory standards and maintaining data integrity throughout the ETL process is critical. Enhanced security measures and comprehensive governance frameworks safeguard against data breaches and privacy violations.

6. Self-service ETL

The rise of self-service ETL tools democratizes data processing, empowering non-technical users to manipulate and transform data. These user-friendly interfaces allow business users to derive insights independently, reducing dependency on data specialists and accelerating decision-making processes.

Self-service ETL tools bridge the gap between data experts and business users, fostering a culture of data-driven decision-making across organizations.

Implications and benefits

The adoption of these futuristic trends in ETL offers a myriad of benefits. It enhances agility and scalability, elevates data accuracy and quality, and optimizes resource utilization, resulting in cost-effectiveness.

Challenges and Considerations

1. Skills gap and training requirements

Embracing advanced ETL technologies demands a skilled workforce proficient in these evolving tools and methodologies. However, the shortage of skilled data engineers and analysts poses a significant challenge. Organizations must help upskill their workforce or recruiting new talent proficient in AI, cloud-native tools, real-time processing, and modern ETL frameworks.

Additionally, continuous training and development programs are essential to keep up with the changing landscape of ETL technologies.

2. Integration complexities

The integration of new ETL tech into existing infrastructures can be intricate. Legacy systems may not seamlessly align with modern ETL tools and architectures, leading to complexities. Ensuring interoperability between diverse systems and data sources requires meticulous planning and strategic execution.

Organizations must develop comprehensive strategies encompassing data migration, system compatibility, and data flow orchestration to mitigate integration challenges effectively.

3. Security and compliance concerns

As data becomes more accessible and travels through intricate ETL pipelines, ensuring robust security measures and compliance becomes paramount. Data breaches, privacy violations, and non-compliance with regulatory standards pose significant risks.

Organizations must prioritize implementing encryption, access controls, and auditing mechanisms throughout the ETL process. Compliance with data protection regulations like GDPR, CCPA, and HIPAA, among others, necessitates meticulous adherence to stringent guidelines, adding layers of complexity to ETL workflows.

4. Scalability and performance optimization

Scalability is critical to modern ETL frameworks, especially in cloud-native environments. However, ensuring optimal performance at scale poses challenges. Balancing performance with cost-effectiveness, managing resource allocation, and optimizing data processing pipelines to handle varying workloads require careful planning and monitoring.

Efficiently scaling ETL processes while maintaining performance levels demands continuous optimization and fine-tuning of architectures.

Cultural shift and adoption

Adopting futuristic ETL trends often requires a cultural shift within organizations. Encouraging a data-driven culture, promoting collaboration between technical and non-technical teams, and fostering a mindset open to innovation and change is pivotal.

Resistance to change, lack of support from team members, and organizational roadblocks can impede the smooth adoption of new ETL methodologies.

Final words

The future of ETL is an amalgamation of innovation and adaptation. Embracing these trends is imperative for organizations aiming to future-proof their data processing capabilities. The evolving landscape of ETL offers a wealth of opportunities for those ready to navigate the complexities and harness the potential of these transformative trends.

Featured image credit: rawpixel.com/Freepik.

Optimal resource allocation, risk mitigation, value creation, and strategic growth initiatives. These are just a few factors that underline the necessity of smart financing for an acquisition.

While you may be an expert in the field, you may lack the means to bring ideas to life. Therefore, we invite you to explore a data room, the solution that turns even complex processes into a breeze.

What is a virtual data room?

A virtual data room is a multifunctional solution designed for protecting and simplifying business transactions. The platform includes secure documentation storage, user management tools, activity tracking mechanisms, and collaboration features.

Most often, users employ the software for mergers and acquisitions, due diligence, initial public offering, and fundraising. Other cases include audits, corporate development, and strategic partnerships.

What is smart financing in the acquisition deal context, and how can data rooms support the process?

When you’re in the market for a new vehicle but have specific requirements and constraints, you need something that fits your budget and complies with preferences. To make a good choice, you employ smart financing principles similar to those used in an acquisition deal. Explore them now and see how a dataroom can help:

Tailored funding structure

Evaluate your budget, financial health, and priorities to determine how much you can afford to spend on a vehicle

Organizations should identify the most suitable capital structure for the deal. The choice usually depends on the acquisition size, the acquiring company’s financial health, and the desired level of control and ownership.

How virtual data rooms help

The software enables gathering and arranging financial documentation in a centralized space. With all these materials in one place, stakeholders can quickly access and review the necessary data to evaluate various financing options.

Optimized capital stack

Balance the features and parts of a vehicle to meet your needs while minimizing costs

Here, a target company balances debt and equity components to minimize the cost of capital. At the same time, it should maintain financial flexibility and mitigate risks. Some potential scenarios are structuring the transaction with a mix of senior or subordinated debt, or equity financing.

How virtual data rooms help

You get a secure way to share data with potential buyers and investors, which can facilitate discussions around optimizing the capital stack. Furthermore, a virtual data room provides a controlled and easy-to-use platform for due diligence.

Alternative financing solutions

Obtain funds to customize your vehicle with accessories and upgrades

In addition to traditional sources, smart financing explores alternative ones to fund acquisition without traditional debt or equity obligations.

For more insights: How to finance an acquisition?

How virtual data rooms help

Apart from the ability to explore alternative solutions on the platform and share insights with other parties, a data room offers various tools for discussions around innovative financing structures and partnerships without compromising confidentiality.

Creative deal structuring

Negotiate personalized modifications with a dealer to tailor the vehicle to your preferences

Organizations may face various challenges throughout the procedure. Therefore, they need to implement specific mechanisms to align the interests of buyers and sellers.

How virtual data rooms help

Stakeholders use the software to collaborate on developing and negotiating creative deal structures. With multiple task management and collaboration tools that data room providers offer, all parties can ensure they are on the same page.

Risk mitigation strategies

Conduct safety checks and test drives to ensure a smooth ride

Smart financing incorporates procedures to protect stakeholders from risks and ensure successful outcomes. In particular, it’s possible through conducting thorough due diligence, implementing legal and financial protections, and securing appropriate insurance coverage.

How virtual data rooms help

With virtual data room solutions, stakeholders can access the necessary documents to assess risks and negotiate favorable terms and conditions, such as hold harmless agreements, representations, and warranties.

Financial modeling and analysis

Simulate worst-case scenarios and determine whether you can still afford the car

You should assess the feasibility and impact of different financing scenarios on the acquisition deal. Common ways are conducting sensitivity analysis, stress testing, and scenario planning to evaluate the potential outcomes.

How virtual data rooms help

With the help of AI-powered analytics tools featured by a digital data room, you can evaluate different scenarios based on relevant documents.

By now, you clearly understand how the software can improve each step. If you are ready to integrate the solution, check out our short checklist with helpful tips and choose the best fit.

How to choose the best virtual data room provider for your acquisition?

Virtual data room providers may significantly vary in feature sets and security mechanisms. Therefore, you should carefully evaluate and compare products considering the following features:

Document security

• Real-time data backup
• Two-factor authentication
• Multi-layered data encryption
• Physical data protection
• Dynamic watermarking

Data management

• Bulk document upload
• Drag and drop file upload
• Multiple file format support
• Auto index numbering
• Full-text search

User management

• User group setup
• Bulk user invitation
• User info cards
• Granular user permissions
• Activity tracking

Collaboration

• Activity dashboards
• Private and group chats
• Q&A module
• Commenting
• Annotation

Ease of use

• Multilingual access
• Single sign-on
• Scroll-through document viewer
• Mobile apps

Integrate virtual data rooms and secure funds for your acquisition in a controlled and easy-to-use environment!

Featured image credit: Alina Grubnyak/Unsplash

Craving a peek behind the carefully curated facades of online personalities? Then welcome to the Reddit snark pages where dedicated communities dissect and critique the lives of influencers, reality stars, and even regular folks with a viral following. But beware, these aren’t just fan clubs gone rogue; they’re intricate ecosystems where humor and criticism collide, often leaving a trail of controversy and questions about online accountability.

What started as innocent fascination has morphed into full-blown animosity, exposing a web of alleged lies, questionable behavior, and even a physical altercation that casts a dark shadow over their online persona. This isn’t just another juice-filled influencer drama; it’s a deep dive into the murky depths of online fame and the pitfalls of cultivating a curated reality.

What are Reddit snark pages?

Reddit snark pages, also known as “snark subs,” are a specific type of subreddit dedicated to the critical, often humorous, and sometimes downright mean-spirited discussion of individuals, groups, or phenomena within internet culture. These pages typically focus on celebrities, influencers, reality TV stars, and even specific online communities.

Beneath the surface of lighthearted snark, Reddit snark pages offer a unique microcosm of online culture. These communities revolve around dissecting and critiquing specific individuals, groups, or phenomena, often with a healthy dose of sarcasm and even humor. However, what defines these pages goes beyond just being mean-spirited.

First and foremost, snark pages are critical. They delve into the target subject, highlighting perceived flaws, questionable behavior, and sometimes even funny moments. Think of them as online commentary panels, dissecting everything from celebrity faux pas to questionable influencer trends.

But criticism doesn’t always translate to negativity. Reddit snark pages are also notorious for their dark humor and strong opinions. Discussions can be lively, sometimes bordering on vicious, with members poking fun at the target in various ways. While not for everyone, this brand of humor can create a sense of shared understanding and amusement among members.

Adding another layer of complexity are inside jokes and memes. Each Reddit snark page develops its own language, often filled with references and jokes only veterans understand. This insider humor can be confusing for outsiders, but it strengthens the sense of community within the page. After all, laughing at niche jokes about a shared target fosters a sense of belonging and camaraderie.

What are the most popular Reddit snark pages?

The most popular and active Reddit snark pages are as follows:

• 8 passengers
• Abbie Herbert 
• Abby Lauren 
• Aili.Likes.Adventure 
• Alexiascheetz 
• Allie starts a cult 
• Ali and John James 
• Alice n Fern 
• Alicia Mccarvel 
• Alix Earle 
• Allisynmichelle 
• Annaxsitar 
• Applestorequeen 
• Ashae Scumdara 
• Ashley Leechin (noitisashely13) 
• Aspynovard 
• Autumnhitjackpot 
• Baidaugh 
• BeautyByKaitlyn 
• Beeston family 
• Bailey McPherson (bmcpher) 
• Bobby Vivid 
• Boogie1917 
• Bonnieleetoks & Veecossio 
• Brittany Dawn 
• Brittany Jade 
• Bug and Alex 
• Caitlin Fladager 
• Calob Stanley 
• carriewalls7 
• Casey Parnell 
• Cassandra Botnick 
• Chantel Schnider 
• ChaosQueen8_0 
• Christen Whitman 
• Colleen Ballinger (Miranda Sings) Reddit snark page
• Cordle Family- Hunter and Devin Reddit snark page
• Court Sanctioned child abuse 
• Crystal Brunner 
• Dailycuppajo 
• Dannythetransdad 
• Darcy McQueeny 
• Delaney Baird (Delaney277) 
• Desiree Luckey 
• Doughertydozen 
• Dreamxharder
• Drue Basham
• Drue and Gabe 
• Dubfrost
• Duggars 
• Eli Rallo
• Elizabeth Rose Lao/Hayden
• Elizabeth Hayden
• Emily Mariko
• Elyse myers
• Emily Feret Reddit snark page
• Emmy Rachelle
• Eugenia Cooney
• Fellowshipofthebib- Medically exploitative families
• Fluellen Family
• Foodies- general
• Gabby Dolechek
• Graceland Ranch
• Hawaiian Mormon
• Hawk Hates You (r/hawkhatessnarking)
• Hilaria Baldwin
• Hoare Boys
• HotBishTish
• Hotnaillady (Tracey Kruger )
• Icy Wyatt
• Im This Bish
• its_your_girl_010 (Marissa Martell )
• Jasmine Chiswell
• Jen heifer
• Jessi Lawless
• Jesssfam
• JessicaDar777 (darlene777_surger)
• JoeyBdez and Bree (Mukmabanger.)
• Jojo Siwa
• Jordanna Lamb
• Kansas Michalke
• Katie Clark
• Kanika Batra
• Kayla Ann xoxo snark 5
• Kayla Girk
• kbtx_91
• Ken Eurich
• Ken Waks
• Kenisha Davis
• kiwisavengers (Rissa/Marissa Alesi)
• Kris Warnock
• Kristinasherrod
• Kylie Strikland
• LaBrantFam
• Lauralove5514
• Lesbian tiktok
• Leslie Clark
• Lexi_and_Hayden
• Livenfamily
• LookUpLashes (Maya Tatarian)
• Louise Recovery
• Maia Knight (r/maiaknight)
• Maia Knight (r/maiaknightsnark)
• Maia Knight (r/maiaknightsnark2)
• Mama drama- general
• Mamakarlss
• Mama Burg
• Mama Tot (r/notthetatortot)
• Mama Tot (r/taternots)
• Marissa Lace
• Marygrace Buckner (stitchloverforever2000)
• Mary Skinner
• Matt and Abby
• Matthew Damien
• Mcleodfam
• MediumPsychicNatalia
• Psychic Natalia (unsafespacefornat)
• Mercede Olson
• Mikayla Nogueira
• Milena Ciciotti
• Mishallamour
• MLM/Hun Influencers
• Modern Warrior (Lance Tsosie)
• momofthemisfits
• MommaK
• General Momtok/Mormon moms
• Morganbcohen
• MrsCaitlyn_oneil
• Mrs.Frazzled
• Nabela Noor
• Nadya Okamoto
• NarallyNajm‘
• north omaha cat lady
• NYC influencers
• nygelleredpath
• Pachatzke
• Paul Breach (beautybeyondtheye)
• Pee stick gals- creators trying to conceive
• Perfectly Kelsey
• Popach family
• Rachel Weaver
• Redmomoftwins (/mtndew and music is life)
• Rich the hungry
• rx0rcist
• salah
• samaleficent
• Samantiks
• Sarah Bowmar
• Sarah Landry (birdspapayasarah)
• Shannon Winnington & co
• SheWhoRoams (Amil and Britt)
• Shirinjka (wwfoodie)
• simplychristina87
• simyrants/simyily
• SkySayingThings
• snarking on the snarkers
• snarking on the SNOTS
• southernmomma12171894 (Cassie Blankenship/Broome)
• Spencer Barbosa
• Stephanie Ulric (Find1ngmyway)
• Stephen Hilton
• Strangek3vin
• Sullivans
• tacobellqween
• talynngracee
• Taylor paul
• TaraAndHaleyFam (courtsanctionchildabuse)
• taraswrld
• Tara Michelle
• Tayler Arrington
• The Wife Hunter
• thisismecassie
• This Mad Mama
• tianna_that_girl
• Tinx (Christina Najjar)
• Tomorri (Southern_pride007)
• TowTruckJess
• Trashleyanonymous
• Unbreakable Momma
• VictoriasSecretCreations
• Walkinonsunshine11 (Tara Greathouse)
• Wheelchair Rapunzel
• willow bramley
• Wren Eleanor

A deep dive into Lexi and Hayden’s Reddit snark page

Lexi and Hayden, the Oklahoma couple who captivated TikTok with their camper-centric lifestyle, have found themselves in the eye of a different storm – a Reddit snark page dedicated to dissecting their every move. What started as quirky fascination has morphed into full-blown animosity, fueled by alleged lies, questionable behavior, and a physical altercation that cast a dark shadow over their online persona.

Once admired for their unconventional life choices, Lexi and Hayden’s charm has tarnished under the microscope of the r/Lexi_and_HaydenSnark Lounge. The turning point? Accusations of Lexi fabricating stories about Hayden’s cousin Hayley, culminating in a reported physical assault. Hayley, claiming to be u/Pine_apple-bra on Reddit, documented the incident, adding fuel to the online fire.

The Reddit snark page isn’t just about a juicy fight. It’s a platform for exposing alleged inconsistencies in Lexi’s narratives. Fans point to discrepancies between claims of financial hardship and glimpses of expensive gadgets like an Apple Watch. Accusations of “dry begging” (soliciting sympathy to garner gifts) further tarnish Lexi’s image.

The saga doesn’t stop there. Redditors claim Lexi has misrepresented her childhood, with u/adefranciaa alleging bullying accusations from former classmates. These claims paint a stark contrast to Lexi’s carefully curated online persona.

The r/Lexi_and_HaydenSnark Lounge has become a hub for dissecting the couple’s actions. Members analyze content, share speculations, and even unearth “smoking gun” evidence like the alleged assault video. It’s a breeding ground for criticism, raising questions about online authenticity and the dangers of influencer culture.

Featured image credit: Vectonauta/Freepik.

Imagine walking into a vast library, with an overwhelming number of books filled with complex and intricate narratives. How do you choose what to read? What if you could take a test that magically guides you to the knowledge that interests you most? That’s akin to the experience of sifting through today’s digital news landscape, except instead of a magical test, we have the power of data analysis to help us find the news that matters most to us. From local happenings to global events, understanding the torrent of information becomes manageable when we apply intelligent data strategies to our media consumption.

Machine learning: curating your news experience

Data isn’t just a cluster of numbers and facts; it’s becoming the sculptor of the media experience. Machine learning algorithms take note of our reading habits, quietly tailoring news feeds to suit our preferences, much like a personal news concierge. A story from the heart of the Middle East might resonate with one reader, while another is drawn to the political intrigues of global powerhouses. By analyzing user interaction, media platforms offer a customized digest of articles that align with individual curiosities and concerns. However, this thoughtful curation requires a careful dance to avoid trapping us in an echo chamber, ensuring we’re exposed to a broad spectrum of voices and viewpoints.

The data-savvy journalist’s new frontier

Today’s journalism isn’t just about being on the ground; it’s also about being in the cloud. Data analysis tools have improbably morphed into the modern journalist’s pen and paper, uncovering stories that might otherwise remain hidden in plain sight. A data set, for instance, could reveal patterns of social inequality, political shifts, or the rumblings of an impending economic change. Moreover, with data visualization, complex stories become accessible and engaging, breathing life into numbers and statistics. The narrative is no longer just about words; it’s about what the data tells us, compelling journalists to strike a delicate balance between the hard truths of data and the soft touch of human understanding. Taking a free iq test could represent the initial step in personalizing your news feed, by understanding your own intellectual preferences.

Artificial intelligence: the future news anchor?

Artificial intelligence stands poised to redefine the very fabric of news delivery, acting as a dynamic intermediary between the rush of breaking news and the reader’s quest for understanding. Imagine an AI-powered summary delivering the gist of today’s headlines in moments, or a news chatbot that discusses current events with you, adapting its conversation to your interests. Not only does this promise increased efficiency in keeping abreast of the latest developments, but it also ushers in ethical dialogues on preserving the integrity of human-led journalism. The quest for striking a balance is continuous; ensuring AI supports rather than undermines the irreplaceable value of human insight and emotion in the news.

Forecasting the unpredictable: how data shapes international politics

As global events unfold at an ever-increasing pace, the application of big data in international political reporting is not just advantageous—it’s essential. Predictive models and social media analytics provide a new lens through which we can view the evolving narrative of world politics. These data-driven tools offer forecasts about elections, policy impacts, and social upheavals, analyzing vast streams of information to spot trends and public sentiments. They enable journalists to harness these insights and deliver more nuanced, informed reporting on the complex web of global relations. It’s in this intersection of big data and journalism that the future of informed, responsible news consumption is being written.

Featured image credit: Bazoom

Data breach meaning underscores the core vulnerability in our digital age, encapsulating a critical threat that spans individuals, businesses, and organizations alike. In today’s interconnected world, understanding the nuances, implications, and preventative measures against data breaches is paramount.

This comprehensive guide aims to unravel the intricate layers surrounding data breaches. From defining the scope of breaches to exploring their multifaceted impacts and delving into strategies for prevention and compensation, this article serves as a helpful resource for comprehending the breadth and depth of data breaches in our modern landscape.

Data breach meaning explained

A data breach is an event where sensitive, confidential, or protected information is accessed, disclosed, or stolen without authorization. This unauthorized access can occur due to various reasons, such as cyberattacks, human error, or even intentional actions. The repercussions of a data breach can be severe, impacting individuals, businesses, and organizations on multiple levels.

Data breaches can compromise a wide range of information, including personal data (names, addresses, social security numbers), financial details (credit card numbers, bank account information), healthcare records, intellectual property, and more. Cybercriminals or unauthorized entities exploit vulnerabilities in security systems to gain access to this data, often intending to sell it on the dark web, use it for identity theft, or hold it for ransom.

Data breach types

Data breaches can manifest in various forms, each presenting distinct challenges and implications. Understanding these types is crucial for implementing targeted security measures and response strategies. Here are some common data breach types:

• Cyberattacks: These breaches occur due to external threats targeting a system’s security vulnerabilities. Cyberattacks include malware infections, phishing scams, ransomware, and denial-of-service (DoS) attacks. Malware infiltrates systems to steal or corrupt data, while phishing involves tricking individuals into revealing sensitive information. Ransomware encrypts data, demanding payment for decryption, and DoS attacks overwhelm systems, rendering them inaccessible.
• Insider threats: Data breaches can originate within an organization, where employees or insiders misuse their access privileges. This could be intentional, such as stealing data for personal gain or accidentally exposing sensitive information due to negligence.
• Physical theft or loss: Breaches aren’t solely digital; physical theft or loss of devices (like laptops, smartphones, or hard drives) containing sensitive data can lead to breaches. If these devices are not properly secured or encrypted, unauthorized access to the data becomes possible.
• Third-party breaches: Often, breaches occur not within an organization’s systems but through third-party vendors or partners with access to shared data. If these external entities experience a breach, it can expose the data of multiple connected organizations.

• Misconfigured systems: Misconfigurations in security settings or cloud storage can inadvertently expose sensitive data to the public or unauthorized users. This can occur due to human error during system setup or updates, allowing unintended access to confidential information.
• Physical breaches: While less common in the digital age, physical breaches involve unauthorized access to physical documents or facilities containing sensitive information. For example, unauthorized individuals gain access to paper files or sensitive areas within a building.

Addressing the data breach meaning involves implementing robust cybersecurity measures. Understanding these varied types of data breaches is essential for developing a comprehensive security strategy. Organizations can then tailor their defenses, train employees to recognize threats, implement access controls, and establish incident response plans to mitigate the risks posed by these different breach types.

Impact of data breaches

The impact of a data breach extends far beyond the immediate infiltration of sensitive information. It ripples through various aspects, affecting individuals, businesses, and organizations in profound ways:

• Financial losses: Data breaches can result in significant financial repercussions. For individuals, it may involve direct theft from bank accounts, fraudulent transactions using stolen credit card information, or expenses related to rectifying identity theft. Businesses face costs associated with investigations, regulatory fines, legal settlements, and loss of revenue due to damaged reputations or operational disruptions.
• Reputational damage: Trust is fragile, and a data breach can shatter it. Organizations often experience reputational harm, eroding customer confidence and loyalty. Once trust is compromised, rebuilding a positive reputation becomes a challenging and lengthy process.
• Legal and regulatory consequences: Breached entities may face legal actions, penalties, and fines due to their failure to protect sensitive data adequately. Various data protection laws, such as GDPR in Europe or HIPAA in healthcare, impose strict requirements on data security. Non-compliance can lead to substantial fines and legal liabilities.
• Identity theft and fraud: For individuals, a data breach can pave the way for identity theft and subsequent fraud. Stolen personal information can be exploited for fraudulent activities, leading to financial losses and long-term damage to credit scores.

• Operational disruptions: Post-breach, organizations often experience disruptions in their day-to-day operations. These disruptions stem from the need to investigate the breach, implement remediation measures, and restore systems and services. This downtime can impact productivity and revenue streams.
• Emotional and psychological impact: Data breaches can have a significant emotional toll on affected individuals. Fear, stress, and a sense of violation are common responses to the invasion of privacy resulting from a breach. Rebuilding a sense of security and trust can take a toll on mental well-being.
• Long-term consequences: The effects of a data breach can linger for years. Even after initial recovery, individuals and organizations may continue to experience residual impacts, including ongoing identity theft attempts, increased scrutiny, or difficulty re-establishing trust with customers or stakeholders.

About data breach compensations

The aftermath of a breach is extensive, causing financial losses, reputational damage, and emotional distress for individuals. Organizations face legal liabilities, penalties, loss of trust, and compensations that make even some of the biggest firms bankrupt. Here are some of the biggest data breach compensations you need to know:

• Didi Global: $1.19 billion
• Amazon: $877 million
• Equifax: (At least) $575 million
• Instagram: $403 million
• TikTok: $370 million

Seeking compensation post-breach is common, aiming to alleviate financial losses and pursue legal recourse. However, this process can be complex, making it challenging to prove damages and navigate legal systems. Preventive measures remain crucial, emphasizing the importance of proactive security measures to mitigate breaches.

Ultimately, while seeking compensation is essential, focusing on preventing breaches through stringent security measures and compliance with data protection laws is equally vital for a safer digital environment.

Preventing data breaches

Mitigating data breach risks involves a comprehensive understanding of the data breach meaning. Implementing robust cybersecurity measures is paramount to mitigating data breach risks:

• Encryption and access control: Encrypting sensitive data and limiting access only to authorized personnel significantly bolsters security.
• Regular updates and patches: Ensuring consistent updates for software, applications, and security systems is pivotal to addressing vulnerabilities.
• Employee training: Conducting comprehensive cybersecurity awareness programs helps employees recognize and respond effectively to potential threats. Educating employees about the data breach meaning empowers them to identify and thwart potential security threats.
• Monitoring and incident response plans: Employing proactive monitoring systems aids in early breach detection while having a well-defined incident response plan facilitates swift and efficient action during a breach.

Data breach meaning extends beyond cybercrime, impacting individuals, businesses, and organizations. So, understanding the various breach types, their substantial impact, and the implementation of preventive measures are crucial for individuals and organizations alike. By staying vigilant, adopting stringent security protocols, and fostering a culture of cybersecurity consciousness, we can collectively strive to minimize the risks associated with data breaches and safeguard sensitive information.

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In today’s fast-paced business world, companies are always searching for ways to optimize their operations and stay ahead of the competition. One key aspect of achieving excellence and gaining an edge is efficiently managing assets. To streamline asset management processes, businesses can leverage the power of asset management software.

Thanks to advancements, this software has become increasingly sophisticated, enabling businesses to transform their operations, adapt to approaches, and ultimately thrive in today’s ever-changing business landscape. In this blog post, we delve into the transformative power of the best asset management software and how it can elevate businesses to new heights of efficiency and success.

The challenges posed by traditional asset management

• Visibility: Handling assets manually often results in outdated information regarding asset location, condition, and maintenance history.
• Time-consuming workflows: Traditional asset management relies on data entry, paperwork handling, and resource-intensive maintenance tasks that can be simplified with the use of software.
• Increased risk of errors: Manually calculating depreciation values or tracking maintenance schedules leaves room for mistakes.
• Lack of collaboration: With information and outdated communication methods, collaborating on tasks related to assets can be inefficient and time-consuming.

The key features of next-generation asset management software

• Database: Asset management software offers a platform for businesses to store important data regarding their assets. This includes purchase history, maintenance records, warranties, and user manuals.
• Real-time tracking: By utilizing technologies like RFID or GPS tracking, businesses can continuously monitor the location and movement of their assets.
• Automated workflows: Simplify tasks such as scheduling maintenance or generating reports by automating them through the software.
• Analytics capabilities: Advanced reporting features enable businesses to gain insights into asset performance utilization rates, lifecycle costs, etc.

Benefits for various industries

Manufacturing sector

• Resource optimization: With real-time visibility into asset availability and productivity levels provided by the software, manufacturers can effectively allocate resources to maximize efficiency.
• Predictive maintenance: Asset management software monitors machine performance and identifies patterns that indicate failures. This enables maintenance to minimize downtime.
• Compliance adherence: The software’s documentation capabilities assist manufacturers in meeting industry regulations and ensuring product quality.

Healthcare industry

• Improved patient care: Efficient management of assets ensures that medical equipment is readily available whenever needed, enabling healthcare professionals to deliver high-quality patient care.
• Cost control: Using asset management software helps monitor device usage patterns, optimize utilization, extend lifespan, and avoid purchases.
• Compliance: Healthcare organizations are required to adhere to regulations. Asset management software simplifies the tracking of certifications, warranties, and maintenance schedules for compliance purposes.

Transportation & logistics

• Inventory optimization: Accurately tracking inventory levels reduces the chances of shortages or excess stock while improving efficiency.
• Route planning: Asset management software assists logistics companies in optimizing routes by considering factors such as asset locations, condition, fuel efficiency, and traffic patterns.
• Tracking service contracts: Managing service agreements for transportation assets becomes more streamlined with automated reminders for contract renewals or preventative maintenance.

Integration with existing systems

Integrating asset management software with existing systems like Enterprise Resource Planning (ERP), Customer Relationship Management (CRM), or Computerised Maintenance Management Systems (CMMS) enhances data sharing across departments. It empowers businesses to make decisions based on real-time insights.

Unlocking a path to success

Adopting asset management software customized to their requirements and the challenges faced by their industry businesses can minimize inefficiencies in their operations, reduce expenses associated with processes or underutilized assets, optimize resource allocation, and improve compliance with regulations. Additionally, they can enhance customer satisfaction through improved service delivery.

Conclusion

Investing in a cutting-edge asset management solution is a choice that has the potential to transform a company’s operations. By replacing processes with integrated systems, businesses can enhance efficiency, mitigate risks, improve asset performance, and make confident data-driven decisions. With the help of asset management software, organizations can successfully revamp their operations, rejuvenate their perspective, and ultimately achieve standing success in today’s business landscape.

Featured image credit: Thirdman/Pexels

One of the most significant challenges present in project management is the variety of ways that a project can be managed and handled. With different teams, it may be necessary to adopt several different methodologies to get the most efficient outcome for your team.

When contemporary businesses are increasingly driven by data, project managers must understand how the intersection between team members, data, and strategies can come together. Sometimes, it’s assumed that the role of data science and project management is much the same – while data can help inform decisions, it’s not typically a field that exclusively runs projects.

Regardless of whether you’re an experienced data scientist or a student completing a Master of Project Management, the differences between data science and project management must be well understood before undertaking any major project. Let’s take a moment to explore how data can complement contemporary project methodologies to get the best practical outcome out of a project with the available data.

Data-driven decision making – Transforming projects

The introduction of modern data collection through digital systems has increasingly transformed the way that data can be used to inform decision-making. Take, for example, the Census, a national demographic survey conducted every five years by the Australian Bureau of Statistics. Initially tabulated using mechanical machine equipment, it has evolved with the introduction of computer technology in 1966 to increasingly online Census participation in the current era.

The way data is collated, stored, and analysed can help transform the way that projects are planned and implemented. Instead of waiting multiple years to take on a plan, adept data science teams use their knowledge to provide rapid, meaningful, and useful insights to project managers, helping align priorities with the data that is available and known.

Key stages of The data science lifecycle

There are a number of stages that are essential to the lifecycle of any data science project. After all, while data is useful, it’s important that meaning is extracted from raw data inputs. With an estimated 120 billion terabytes of data generated annually, according to the latest reports, it’s important to understand that raw data is, by itself, not particularly useful without some form of analysis.

Three key stages of the data science lifecycle include data mining, cleaning, and exploration. These processes are vital for any data science project – and skipping any one of these steps can be potentially perilous when undertaking data projects.

Firstly, data mining takes an understanding of operational requirements to dig into potential data sources. For example, a project that seeks to understand the relative performance of a mailout program may seek to gather information on returned mail, payments from contacted customers, as well as financial information, such as the cost to mail or return a flyer.

Data cleaning is another crucial stage of the data science lifecycle. Data on its own may be raw and untidy – for example, a data source with addresses may include data structured in different or historical formats, meaning that any exploration conducted without cleaning the structure of the data first could be potentially misleading or wrong.

Once data mining and cleaning are undertaken, comprehensive data exploration must be done. Data-driven outcomes don’t happen straight away – sometimes it can take days or even weeks of digging into data to understand how data ties together. The outcomes found in this discovery stage can then be used to inform further investigation and complement the project delivery design phase.

Common project management methodologies

There are many different project management methodologies. Traditional methods such as the waterfall method are well known. However, more recent methodologies such as the agile method have gained prominence in recent years as a way of evolving the way that projects are managed, in alignment with improved data availability.

A development methodology common in projects is known as the waterfall methodology. This orthodox strategy, common in software development, involves a five-step process (Requirements, Design, Implementation, Testing, Deployment) where steps are taken sequentially. While this may be useful for some projects, it is sometimes seen as difficult to manage when working with data-supported projects.

A contemporary methodology that commonly appears when working with rapidly evolving data is known as the agile methodology. This method allows for rapid repositioning as corporate requirements change – and is typically considered best practice when working on projects that require constant pivoting or adjustment to manage business needs.
The Intersection of Project Management and Data Science
Project management and data science can intersect in interesting ways – much like Ouroboros, the increasingly symbiotic relationship between project management and data science can make one wonder which was around first.

For the adept project leader, being able to understand which combination of project methodology and data science strategy is best can go a long way toward informing strategic decision-making. This, in turn, can help align current or future project goals – transforming project management from being solely reliant on business requirements to something that is far more fluid and versatile. With data and project management so closely intertwined, it’s exciting to imagine what these two roles will bring together in the years ahead.

Featured image credit: kjpargeter/Freepik.

Ukrainian venture fund Roosh Ventures has invested in GlassFlow. GlassFlow is a German startup which is developing a data management platform. With other investors including High-Tech Gründerfonds, Robin Capital, TinyVC, angel investors Thomas Domke (CEO of GitHub), and Heikki Nousiainen (co-founder of the open-source data platform Aiven), the startup raised $1.1 million in the pre-seed round.

The new investment was announced on Facebook by Serhiy Tokarev, an IT entrepreneur, investor, and co-founder of the technology company ROOSH, which includes Roosh Ventures in its ecosystem,.

“The startup’s idea itself is complex but promising because the current market for stream data analytics exceeds $15 billion, and by 2026, this figure is expected to surpass $50 billion. We anticipate that GlassFlow will soon become a key player in this market,” shared Serhiy Tokarev.

GlassFlow was founded in 2023 in Berlin by Armend Avdijaj and Ashish Bagri, who have over 10 years of experience in real-time data processing. The startup develops solutions that allow Python engineers to easily create and modify pipelines, sequential stages of data processing. The GlassFlow team maintains constant communication with IT professionals, considering their feedback to improve the platform.

As Roosh Ventures notes, the data streaming market is rapidly evolving today. Big Data, the Internet of Things, and AI generate continuous streams of data but companies currently lack the infrastructure development experience to leverage this effectively. Building and managing pipelines require significant efforts in engineering and data analysis, hindering the quick adaptation of programs to engineers’ needs. Currently, developers use complex systems that require substantial time for maintenance. GlassFlow addresses this issue by consolidating sophisticated tools into a single user-friendly platform.

“Our vision is a world where data processing engineers, regardless of their experience and education, can easily harness the capabilities of data streaming to drive innovation and growth. By simplifying data infrastructure and fostering the development of an ecosystem based on real data, GlassFlow aims to be a catalyst for this transformation,” emphasized Armend Avdijaj, CEO of GlassFlow.

Roosh Ventures is a Ukrainian venture capital fund invests in startups at various stages, from pre-seed to Series A, across various industries. Over the last three years, the fund has been most active in the AI, fintech, gaming, and health tech sectors. Roosh Ventures co-invests in promising tech companies with renowned global funds and focuses on the EU and US markets. The fund has already invested in well-known startups like Deel, TheGuarantors, Oura, Pipe, Alma, Playco, Dapper Labs, Alter, and more than 35 other companies.

In September 2023, Roosh Ventures invested in Rollstack, a startup that developed an innovative solution that automatically creates and updates presentations, financial reports, business overviews, and other documents. The fund is part of the Roosh technology ecosystem, providing portfolio companies with support in integrating and implementing AI/ML technologies, talent recruitment, and business development.

In today’s fast-paced and interconnected business landscape, effective internal communication plays a critical role in the success of any organization. It is crucial to have concise communication that helps employees understand their responsibilities, encourages collaboration, and boosts productivity. However, companies often wonder how they can evaluate the ROI of their communication initiatives. In this blog post, we will explore methods and metrics that can be utilized to assess the impact and effectiveness of communication efforts.

Why evaluate the ROI of internal communication?

Before delving into the approaches for measuring ROI, it’s important to understand why this evaluation is significant. Assessing the ROI of internal communications initiatives enables organizations to determine their effectiveness and identify areas that need improvement. It provides insights into how communication impacts employee engagement, job satisfaction, and overall performance. Moreover, evaluating ROI helps justify resource allocation towards communication initiatives and ensures alignment with goals.

Approach 1: Employee surveys

One method for gauging the impact of communication is through employee surveys.

These surveys can be conducted regularly to gather feedback on aspects of communication, such as the clarity of messages, how often communication occurs, and the effectiveness of communication channels. Surveys can also evaluate employee engagement and satisfaction levels, as these are often influenced by the quality of communication.

By analyzing the responses from these surveys, organizations can identify areas where communication needs improvement and track changes over time. For instance, if the survey reveals scores for message clarity, the organization can invest in training and refine its communication strategy to address this concern.

Approach 2: Encouraging employee feedback and suggestions

In addition to surveys, organizations can actively encourage employees to provide feedback and suggestions regarding communication. This can be done through channels such as suggestion boxes, online forums, or dedicated communication apps. By seeking input from employees’ organizations demonstrate their commitment to improving communication and fostering a culture of dialogue.

Reviewing and implementing employee suggestions can lead to improvements in communications, which result in higher employee engagement and satisfaction. It also fosters a sense of ownership and participation among employees, making them more likely to embrace and support initiatives related to communication.

Approach 3: Analyzing communication metrics

Another approach for measuring the return on investment (ROI) of communication initiatives is by analyzing metrics related to communication effectiveness. These measurements can include factors such as the number of emails sent and opened, the level of engagement with newsletters or intranet articles, and the usage of communication tools and platforms. By keeping track of these measurements, organizations can assess how far their messages are reaching and understand their impact by identifying any emerging trends or patterns.

For example, if an internal newsletter consistently receives rates of being opened and clicked on, it suggests that employees are actively engaging with the content. On the other hand, if a specific communication channel shows levels of engagement, it may require further evaluation or adjustments to enhance its effectiveness.

Approach 4: Indicators for business performance

While employee feedback and communication measurements provide insights, it is equally crucial to evaluate how internal communication affects business performance. By analyzing key performance indicators (KPIs) related to productivity, customer satisfaction, employee retention, and profitability, organizations can determine if there is a connection between internal communication and positive business outcomes.

For instance, if a company sees an improvement in customer satisfaction scores after implementing an internal communication strategy aimed at enhancing the skills of employees who interact directly with customers, it implies that effective communication plays an important role in delivering better customer service and fostering loyalty. In a vein, if the organization notices a decrease in employee turnover rates after implementing an internal communication plan it indicates that employees feel more connected and engaged with the company.

In conclusion

It is crucial for organizations to measure the return on investment (ROI) of their communication initiatives. This evaluation enables them to make decisions regarding resource allocation and strategy. Valuable methods for assessing the impact of internal communication efforts include conducting employee surveys, gathering feedback and suggestions, analyzing communication metrics, and examining business performance indicators. By monitoring and measuring these aspects, organizations can optimize their communication strategies, enhance employee engagement, and ultimately drive business success.

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The need for secure and compliant cloud solutions has never been more pressing, especially within the intricate regulatory landscape of Europe. That’s why Amazon introduced the AWS European Sovereign Cloud, a groundbreaking initiative by Amazon Web Services (AWS) designed to address the very heart of this challenge: safeguarding data privacy, ensuring digital sovereignty, and empowering European businesses and public sector organizations to harness the full potential of cloud computing.

The AWS European Sovereign Cloud is not just another cloud solution; it’s a bold declaration of the importance of digital sovereignty and data protection within the European Union.  So, let’s embark on a journey through the cloud’s corridors to understand why the AWS European Sovereign Cloud exists and why it’s a game-changer in the world of cloud computing.

Explained: AWS European Sovereign Cloud

The AWS European Sovereign Cloud is a specialized and independent cloud computing infrastructure provided by Amazon Web Services (AWS) specifically designed to cater to the needs of highly-regulated industries and public sector organizations within Europe. This cloud solution is tailored to address the stringent data residency and operational requirements imposed by European data privacy and digital sovereignty regulations.

Key characteristics and details of the AWS European Sovereign Cloud are as follows:

• Data residency: The primary goal of this cloud offering is to ensure that customer data remains within the European Union (EU). This addresses concerns related to the storage and processing of data outside the EU, which may not align with the strict data privacy rules prevalent in the region.
• Physical and logical separation: The AWS European Sovereign Cloud is physically and logically separated from Amazon’s other cloud operations, both in Europe and globally. This separation ensures that data and operations within the sovereign cloud are distinct and secure from other AWS services.
• European control: Only AWS employees who are residents of the EU and located within the EU will have control of the operations and support for the AWS European Sovereign Cloud. This exclusive control guarantees that data remains under European jurisdiction and is not accessible to personnel outside the EU.
• Sovereignty controls: Customers of this cloud solution will have access to the most advanced sovereignty controls among leading cloud providers. These controls enable organizations to maintain a high level of control and governance over their data and infrastructure.
• Data metadata protection: One of the unique features of the AWS European Sovereign Cloud is that it allows customers to keep all metadata they create within the EU. Metadata includes information related to roles, permissions, resource labels, and configurations used to run AWS services.
• Billing and usage systems: The sovereign cloud solution will have its own billing and usage metering systems, ensuring that customer billing data remains within the EU, offering enhanced data protection.
• Compliance with EU regulations: AWS has worked closely with European governments and regulatory bodies for more than a decade to understand and meet evolving cybersecurity, data privacy, and localization needs. The AWS European Sovereign Cloud is aligned with the most current EU data protection and sovereignty regulations.
• Location and availability: The AWS European Sovereign Cloud is expected to have multiple Availability Zones, which are geographically separate and independently powered, cooled, and secured data centers. This ensures high availability, reduced risk, and low latency for mission-critical applications.
• Integration with existing AWS solutions: Customers who require stringent isolation and in-country data residency needs can leverage existing AWS solutions like AWS Outposts and AWS Dedicated Local Zones to deploy infrastructure in locations of their choice.
• Support for innovation: The AWS European Sovereign Cloud offers the same performance, scalability, and innovation as existing AWS Regions, ensuring that customers can benefit from the full suite of AWS services while adhering to strict data sovereignty requirements.

Amazon drivers beware! AI will test you

In summary, the AWS European Sovereign Cloud is a dedicated and secure cloud infrastructure designed to address the unique data privacy and sovereignty needs of European organizations. It offers a combination of advanced technology, regional control, and compliance with EU regulations to empower businesses and the public sector to embrace cloud computing while safeguarding their sensitive data. This initiative underscores Amazon’s commitment to delivering secure, compliant, and innovative cloud services in the European market.

For more detailed information, click here.

Featured image credit: Eray Eliaçık/DALL-E 3

Project Silica, Microsoft’s groundbreaking initiative, first caught our attention about four years ago. At that time, Microsoft had showcased a fascinating proof of concept by encoding Warner Bros’ Superman movie onto a compact piece of quartz glass measuring just 75 by 75 by 2 mm. This method of data storage, as introduced by Project Silica, is notably more enduring than storing on SSDs or magnetic tapes, both of which have limited lifespans.

The unique advantage of glass as a storage medium is its longevity. It has the potential to preserve data for an astounding 10,000 years without the need for periodic recopying, a feat SSDs can’t match with their 5-10 year lifespan. Fast forward to fall 2023, and Microsoft is once again in the spotlight, eager to unveil more about Project Silica. They’re set to introduce us to the innovative data center designs of the future, equipped with a state-of-the-art robotic system designed to seamlessly access the glass sheets housing the data.

“One of the standout features of glass storage technology is its space efficiency. Datacenters today are large infrastructures. In contrast, glass storage solutions require a fraction of that space. The technology we’ve developed here at Project Silica can store an enormous amount of data in a very compact form. It’s a new paradigm of efficiency and sustainability.”

-Richard Black, Research Director, Project Silica

How does Project Silica work?

One of the standout benefits of storing data on glass, as demonstrated by Microsoft’s Project Silica, is its near-indestructibility. Utilizing an advanced ultrafast femtosecond laser, Project Silica inscribes data onto the glass. This process results in the formation of voxels, essentially the 3D counterparts of pixels.

Microsoft’s Security Copilot AI goes early access, welcome ChatGPT of cybersecurity

To retrieve this data, a specialized computer-controlled microscope is employed to read and decode it. Once decoded, the data-laden glass is placed in a unique library where it remains power-free. These glass sheets are strategically stored on shelves, isolated from real-time internet connectivity.

The only significant energy consumption within this library is attributed to the robots. These robots, designed with precision, navigate both vertically and horizontally to locate the specific glass sheet containing the desired data. Their ability to ascend and descend shelves allows them to efficiently retrieve the data and transport it to the reading device.

Microsoft emphasizes a crucial feature of this system: once data is written onto the glass and stored in the library, it becomes immutable. This means it can’t be rewritten or altered. This characteristic implies that Project Silica might not be suitable for those who require frequent edits or modifications to their data. However, for preserving pristine copies of specific content types, such as books, music, or movies, Project Silica is unparalleled.

To offer a clearer picture of its capacity, the Project Silica team has achieved the capability to store several terabytes of data on a single glass plate. This translates to approximately 3,500 movies on just one sheet of glass, providing a non-stop cinematic experience for over six months without any repetition.

Is Project Silica cost-efficient though?

The cost of Project Silica storage remains a topic of intrigue. Given its innovative approach to data storage, it’s conceivable that in the future, Project Silica might cater to extensive personal collections of photos and videos. Perhaps even OneDrive’s most dedicated users might find value in it, provided they’re willing to bear the expense. But, of course, this is mere speculation at this point.

From the recent showcases, it’s evident that Project Silica has made significant strides. However, Microsoft has indicated that the glass storage technology is not yet primed for commercial deployment. It’s anticipated to undergo “3-4 more developmental stages before it’s ready for commercial use.”

To put its capacity into perspective, a single glass sheet can store a staggering 1.75 million songs or offer around 13 years of continuous movie playback. Collaboratively, Project Silica and the Microsoft Azure team are exploring more sustainable data storage methods.

In this partnership, Azure AI plays a pivotal role in decoding the data inscribed in the glass during both writing and reading phases. Another noteworthy mention is Elire, a sustainability-centric venture group. They’ve joined forces with Project Silica to establish the Global Music Vault in Svalbard, Norway. This vault boasts resilience against electromagnetic pulses and extreme temperature fluctuations. As Microsoft points out, the glass used in Project Silica is incredibly robust. Whether scratched, baked in an oven, or boiled, its integrity remains uncompromised.

Given the cutting-edge nature of this technology, it’s reasonable to anticipate that Project Silica storage might carry a hefty price tag initially. Industry giants like Elire and Warner Bros. could potentially be the primary beneficiaries once it becomes more accessible. However, as with many technological advancements, it’s likely that costs will decrease over time.

For a more visual experience of this groundbreaking technology, Microsoft has released a video showcasing Project Silica in action:

Project Silica, Microsoft’s groundbreaking data storage initiative, first made its appearance during a keynote at Microsoft Ignite 2017, where future storage technologies were the focal point. This innovative project doesn’t stand alone; it’s a subset of the more expansive Optics for the Cloud initiative. This overarching project delves deep into the future of cloud infrastructure, particularly at the crossroads of optics and computer science.

In a significant development in November 2019, Satya Nadella, the CEO of Microsoft, unveiled a collaboration between Project Silica and Warner Brothers. This partnership served as an early demonstration of the technology’s potential, showcasing its capabilities and setting the stage for its future applications.

Featured image credit: Microsoft

Want to create a robust data warehouse architecture for your business? The sheer volume of data that companies are now gathering is incredible, and understanding how best to store and use this information to extract top performance can be incredibly overwhelming. However, with the right guidance, it’s possible to build an innovative data warehouse which stores all files correctly so they’re there when needed. In this blog post, we’ll examine what is data warehouse architecture and what exactly constitutes good data warehouse architecture as well as how you can implement one successfully without needing some kind of computer science degree!

Data warehouse architecture

The data warehouse architecture is a very critical concept regarding big data. It could be defined as the layout and design of a data warehouse, which at other times could act as a central repository for all organization’s data. People who desire to work with big data have to comprehend the architecture of the data warehouse because it helps them understand that they deal with various parts that make up the whole data warehouse. These components include various things like; what kind of sources of data will one do their analysis on, the ETL processes involved, and where it would store large-scale information among others. These professionals can gain a better understanding of the functioning of big data and utilize it for drawing logical conclusions based on which sound decisions could be made by them.

Types of data warehouses

A data warehouse is one of the most important elements in any organization’s overall strategy for managing its data. The modern-day IT industry is filled with various types of data warehouses, ranging from enterprise data warehouses to data marts and virtual ones. An enterprise data warehouse refers to a centralized repository designed for the storage of almost all the information related to organizational operations. Data marts are smaller, more departmental-level warehouses that focus on a particular area of an organization’s data. Virtual data warehouses are created with the use of software tools instead of physical hardware and allow analysis across dissimilar systems. An understanding of what type of data warehouse is appropriate for your company will help in good running techniques and analyses.

Pros and cons of data warehouse design

Data warehousing design has both advantages and disadvantages. To start with, a good design in the data warehouse allows an organization to store large volumes of information in one place that is very easy to access as well as analyze. This enhances business intelligence since it helps organizations make better decisions for their businesses. On the other hand, designing as well as implementing a data warehouse can be time-consuming as well as complex, requiring huge investments in terms of hardware, software, as well as IT capabilities. In addition, the updating process may sometimes take longer times thus leading to stale information and hence wrong analysis. However, despite all these complications, planning for a proper data warehouse can bring out amazing benefits at last for any particular organization.

Building a data warehouse architecture

A data warehouse is a powerful tool that simplifies the process through which companies collect and use data. To ensure this tool functions optimally in tandem with the business needs, an effective data warehouse architecture is crucial. Whether you are starting or revamping an existing data warehouse, designing a step-by-step guide can help cement your architecture design while avoiding common missteps. This guide will include everything from identifying your business requirements and conceptualizing your data models to establishing integration processes of that data as well as monitoring performance. But It’s always better to call data warehouse experts before making a big decision. A data warehouse expert will help you get the most out of your data warehouse architecture by providing personalized solutions tailored to meet your business needs.

Optimizing your data warehouse design

When it comes to your data warehouse design, optimization is one of the most important factors that will help you improve its performance. By following a few tips, such as choosing the right schema based on the type and volume of data you want stored in a database, identify integration points accordingly to meet your business goals. You should also enhance query speed by implementing better indexing and partitioning strategies. Monitor and keep evolving your data warehouse design to make it apt with the emerging business needs. If you follow all these tips, then definitely you will have a well-designed and optimized data warehouse as per your business requirements.

Best practices in choosing right components

In today’s corporate world, everything is ruled by data and everyone holds the same stature. But still organizing so much of the gigantic amount of data has become quite an uphill task nowadays. This is where one finds the importance of a data warehouse. Then how do we choose the right components for our data warehouse architecture? By following some best practices, you can ensure that your data warehouse not only serves its purpose for now but also grows with the growth of your business. One thing that you’ll have to take into account is scalability. As the amount of data you possess expands – you’ll need a warehouse capable of handling it. Another point is performance. The right components ensure optimized query speed and lower latency as well. And last – security should always be one of your top concerns. Taking these factors along with others into consideration will help you build out a data warehouse architecture tailored to individual needs within your organization.

Final words

In brief, to develop an effective data warehouse architecture that meets the needs of your business and requirements, you should be aware of various constituents involved in a data warehouse architecture and also consider adding some additional ones wherever applicable. With good planning and optimization, you can also build architectural solutions scalable, secure and compliant with all regulations. Further comprehension of types of available data warehouses and the next selection of components as per best practices for the project are the next most important steps towards achieving success. So, with all this in mind, by now you should have a better grasp of how to properly design a successful architecture for data warehousing that satisfies exactly what your organization requires.

Featured image credit: Conny Schneider/Unsplash

If you are interested in technology at all, it is hard not to be fascinated by AI technologies. Artificial intelligence, one of the most talked about topics in today’s technology world, has played a huge role in bringing many things into our lives, especially in the last five years. Whether it’s pushing the limits of creativity with its generative abilities or knowing our needs better than us with its advanced analysis capabilities, many sectors have already taken a slice of the huge AI pie. But does that mean artificial intelligence is perfect?

Even though AI has managed to get all the attention for what it can do, we sometimes forget to look at the other side of the scale. While we as humans have difficulty in interpreting life and emotions, AI technologies that we feed with the data we provide are not very successful in this regard either. Interpreting the unpredictable movements of living beings, which make most of their decisions based on hormonal impulses, and hoping that a machine that never experiences the effects of these hormones can do it is unfortunately a big dilemma, taking into account today’s technology.

We’ve already talked about the challenges of artificial intelligence, now let’s talk about the challenges we face in accepting and using artificial intelligence in the most important aspects of our lives.

How do AI technologies learn from the data we provide?

AI technologies learn from the data we provide through a structured process known as training. This process is fundamental to machine learning, a subset of AI, and involves several distinct steps.

Firstly, data collection is essential. AI systems require a substantial and diverse dataset that is relevant to the specific problem they aim to solve. This dataset comprises input data (features) and corresponding output or labels, which represent the desired predictions or classifications. For example, in image recognition, the dataset would consist of images and their associated labels, such as identifying whether an image contains a cat or a dog.

Once the data is collected, it undergoes preprocessing. This step ensures that the data is in a suitable format for training. Data preprocessing tasks can include data cleaning to remove errors or inconsistencies, normalization to bring data within a consistent range, and feature engineering to extract meaningful information from raw data.

The next critical step is model selection. AI practitioners choose an appropriate machine learning model or algorithm that aligns with the problem at hand. Common choices include neural networks (used in deep learning), decision trees, support vector machines, and more.

With the model selected, the initialization of parameters takes place. These parameters are the model’s coefficients or weights that dictate its behavior. They are initialized with random values.

The training loop is where the model truly learns. It consists of several iterative steps:

1. In the forward pass, the model takes input data and generates predictions based on its current parameters
2. A loss function quantifies the disparity between these predictions and the actual labels. The objective is to minimize this loss
3. Backpropagation is employed to adjust the model’s parameters using an optimization algorithm like gradient descent. This step ensures that the model continuously refines its predictions

This iterative training process is repeated for multiple epochs, allowing the model to fine-tune its parameters further.

Validation and testing are crucial stages. Validation assesses how well the model generalizes to new, unseen data, while testing evaluates its performance and generalization capabilities more rigorously.

On paper, once the model demonstrates satisfactory performance, it can be deployed in real-world applications to make predictions or automate tasks based on new, previously unseen data.

There are several ways that AI technologies can learn from data but the most common approach is supervised learning, where the AI algorithm is trained on labeled data, meaning that the correct output is already known. The algorithm learns to map inputs to outputs by making predictions and comparing them to the true labels. Over time, the algorithm improves its accuracy and can make better predictions on new, unseen data.

So, while data labeling is crucial in supervised learning, can we ever be completely certain of its accuracy? The answer is no, because let’s face it – humans aren’t perfect. We’ve all had moments where we questioned our own abilities, like doubting a medical diagnosis or wondering if a criminal case outcome was truly just. And yet, we’re expected to trust ourselves and the data we label without hesitation. It’s tough to swallow, but the reality is that even with the best intentions, we’re all prone to making mistakes.

Another form of machine learning algorithm is known as unsupervised learning. This involves the AI system being trained on a collection of unlabeled data which means that the algorithm does not possess the correct output for each data point. Therefore, it must independently recognize patterns and relationships within the data. For instance, unsupervised learning could be exploited to recognize customer groups with comparable spending habits.

Additionally, AI technologies have the capability to learn from data in real time. This is known as reinforcement learning. In this process, the AI system receives rewards for actions taken leading to favorable outcomes and punishments for actions taken leading to unfavorable outcomes.

Despite seeming logical, this methodology remains far from perfect and is not fully ready for a harsh world.

Is predictive AI ready for a such complex world?

A recent study published in Wired found that predictive AI technologies and their software applications, like Geolitica, are not as effective as one might hope. In fact, the study found that the AI software was only accurate about 0.6% of the time, which is no better than flipping a coin and hoping it lands on its side. This raises concerns about the use of AI technologies by police departments across the world.

To be more specific, the New Jersey Police Department used Geolitica, previously known as PredPol, between February 25 and December 18, 2018, to examine robbery occurrence rates in areas where no police officers were on duty, but it was found to have an accuracy of less than 100 out of 23,631 cases.

Plainfield PD captain David Guarino stated that the functional use of AI technologies is actually not that extensive by saying:

“Why did we get PredPol? I guess we wanted to be more effective when it came to reducing crime. And having a prediction where we should be would help us to do that. I don’t know that it did that,”

“I don’t believe we really used it that often, if at all. That’s why we ended up getting rid of it”.

So where was the problem? Well, one of the main issues with predictive AI software is that it relies on the flawed assumption that life is predictable. Life is a complex phenomenon influenced by numerous variables, many of which are unpredictable. As such, it’s difficult to rely on software to accurately forecast where and when an event is set to occur.

The situation for AI technologies is not yet very favorable in the field of medicine either. ChatGPT, which is widely used and is generally considered to be the most powerful large language model (LLM), is very misleading and inadequate for medical research and accurate diagnosis.

The study by Maryam Buholayka, Rama Zouabi, and Aditya Tadinada evaluated the ability of ChatGPT to write scientific case reports independently. The study compared ChatGPT’s performance with that of human oral and maxillofacial radiologists in writing case reports.

Quote on quote from the study, the drafted case report discussed a central hemangioma in a 65-year-old female and focused on the imaging features seen in a panoramic radiograph, cone beam computed tomography (CBCT), and magnetic resonance imaging (MRI).

ChatGPT was prompted in five separate chats. The format of the first question was structured depending on the outcome of the previous chat and the results were as follows:

They found that ChatGPT was able to produce case reports of similar quality to those written by human radiologists. However, ChatGPT case reports were less likely to include certain important elements, such as a discussion of differential diagnosis and a review of the literature.

The comprehensive study concluded that ChatGPT is not yet ready to write scientific case reports on its own. So what do all these practices mean to us? Although AI technologies have wowed everyone with their magic, they are still far from perfection. Many unpredictable components, such as human control of the source and incorrect or incomplete operation of the algorithm, are slowing down the integration of AI technologies into everyday life.

But let’s not forget, even doors with sensors once seemed like a figment of our imagination.

Featured image credit: jcomp/Freepik.

Data archiving is the systematic process of securely storing and preserving electronic data, including documents, images, videos, and other digital content, for long-term retention and easy retrieval. This essential practice involves the transfer of data from active storage systems, where it is frequently accessed and used, to secondary storage systems specifically designed for extended preservation and infrequent access. But why do businesses need it exactly?

While we were talking about a data-driven future about 10 years ago, today we are perhaps laying the foundations of this future. Almost everyone in or around the business world is now aware of the importance of the correct use of data.

Social media applications have been able to personalize their ads, chatbots have been able to answer complex questions, and e-commerce sites have been able to personalize their product recommendations thanks to the data they collect from users.

But this data sometimes needs to be archived. So; Why, how, and when do you archive data? Let us explain.

What is data archiving?

Data archiving refers to the process of storing and preserving electronic data, such as documents, images, videos, and other digital content, for long-term preservation and retrieval. It involves transferring data from active storage systems, where it is regularly accessed and used, to secondary storage systems that are designed specifically for long-term storage and infrequent access.

The purpose of data archiving is to ensure that important information is not lost or corrupted over time and to reduce the cost and complexity of managing large amounts of data on primary storage systems.

The data archiving process involves several key steps to ensure that important information is properly stored and preserved for long-term retrieval. First, the data must be identified and evaluated based on its importance, relevance, format, and size. Once identified, the data is classified into categories to ensure it’s stored in a way that makes it easy to retrieve and manage.

After classification, the data is transferred to a secondary storage system, such as a tape library, optical disk, or cloud storage service. This system provides long-term storage at a lower cost than primary storage systems. To ensure the data can be easily found and retrieved, an index is created that includes metadata about each file, such as its name, location, and contents.

Regular backups of the archived data are made to protect against loss or corruption. The archive system is monitored regularly to ensure it’s functioning properly and that data is being retrieved and restored successfully. Data retention policies are put in place to determine how long the data will be kept in the archive before it’s deleted or migrated to another storage tier.

When data is needed again, it can be retrieved from the archive using the index. It may need to be converted or migrated to a different format to make it compatible with current technology. Finally, the data is disposed of when it’s no longer needed, either by deleting it or transferring it to another storage tier.

Why archive data?

There are several reasons why data archiving is important for your personal use and your business. Firstly, it helps organizations reduce their overall storage costs. By moving infrequently accessed data to cheaper storage media, such as tape libraries or cloud storage services, organizations can free up space on primary storage systems and reduce their storage expenses.

Secondly, data archiving helps organizations comply with regulatory requirements. Many regulations, such as HIPAA, SOX, and GDPR, require organizations to retain certain types of data for specific periods of time. Data archiving helps organizations meet these requirements while minimizing the impact on primary storage systems.

Archiving data also helps protect against data loss due to hardware failures, software corruption, or user error. By creating backups of the archived data, organizations can ensure that their data is safe and recoverable in case of a disaster or data breach.

Databases are the unsung heroes of AI

Furthermore, data archiving improves the performance of applications and databases. By removing infrequently accessed data from primary storage systems, organizations can improve the performance of their applications and databases, which can lead to increased productivity and efficiency.

Lastly, data archiving allows organizations to preserve historical records and documents for future reference. This is especially important for industries such as healthcare, finance, and government, where data must be retained for long periods of time for legal or compliance reasons.

How can AI help with data archiving?

Artificial intelligence (AI) can be used to automate and optimize the data archiving process. There are several ways to use AI for data archiving.

Intelligent data classification

Intelligent data classification is a process where artificial intelligence (AI) algorithms are used to automatically categorize and classify data based on its content, relevance, and importance; getting data ready for archiving. This process can help organizations identify which data should be archived and how it should be categorized, making it easier to search, retrieve, and manage the data.

There are several techniques used in intelligent data classification, including:

• Machine learning: Machine learning algorithms can be trained on large datasets to recognize patterns and categories within the data. The algorithms can then use this knowledge to classify new, unseen data into predefined categories
• Natural language processing (NLP): NLP is a subset of machine learning that focuses on the interaction between computers and human language. NLP can be used to analyze text data and extract relevant information, such as keywords, sentiment, and topics
• Image recognition: Image recognition algorithms can be used to classify images and other visual data based on their content. For example, an image recognition algorithm could be trained to recognize different types of documents, such as receipts, invoices, or contracts
• Predictive modeling: Predictive modeling algorithms can be used to predict the likelihood that a piece of data will be relevant or important in the future. This can help organizations identify which data should be archived and prioritize its storage
• Hybrid approaches: Many organizations use a combination of these techniques to create a hybrid approach to data classification. For example, an organization might use machine learning to identify broad categories of data and then use NLP to extract more specific information within those categories

In short, intelligent data classification can help organizations optimize their data storage and management strategies by identifying which data is most important and should be retained long-term.

Data discovery

Data discovery helps businesses by identifying and locating data that is not easily searchable or accessible, often referred to as “dark data“. This type of data may be scattered across different systems, stored in obscure formats, or buried deep within large datasets. AI-powered tools can help organizations uncover and identify dark data, making it easier to archive and manage.

AI algorithms can automatically detect and identify data sources within an organization’s systems, including files, emails, databases, and other data repositories. Also, data profiling tools can analyze data samples from various sources and create detailed descriptions of the data, including its format, structure, and content. This information helps organizations understand what data they have, where it’s located, and how it can be used.

Data compression

Data compression reduces the size of a data set by removing redundant or unnecessary information, which helps save storage space and improve data transfer times, making data archiving cost-efficient. Traditional data compression methods often rely on rules-based algorithms that identify and remove obvious duplicates or redundancies. However, these methods can be limited in their effectiveness, especially when dealing with large datasets.

AI-powered data compression, on the other hand, uses machine learning algorithms to identify more nuanced patterns and relationships within the data, allowing for more effective compression rates. These algorithms can learn from the data itself, adapting and improving over time as they analyze more data.

Data indexing

Data indexing is another important step in data archiving and it is the process of creating a database or catalog of archived data, allowing users to quickly search and retrieve specific files or information. Traditional data indexing methods often rely on manual tagging or basic keyword searches, which can be time-consuming and prone to errors.

AI-powered data indexing utilizes machine learning algorithms to meticulously analyze the contents of archived data, generating comprehensive indexes for efficient search and retrieval. These advanced algorithms excel at recognizing patterns, establishing relationships, and uncovering valuable insights hidden within the data. Consequently, this technology significantly simplifies the process of pinpointing specific files or information, saving time in finding the relevant information after data archiving.

Clustering

Clustering is a technique used in machine learning and data mining to group similar data points together based on their characteristics. AI-powered clustering algorithms can analyze large datasets and identify patterns and relationships within the data that may indicate dark data.

Clustering algorithms work by assigning data points to clusters based on their similarity. The algorithm iteratively assigns each data point to the cluster with which it is most similar until all data points have been assigned to a cluster. The number of clusters is determined by the user, and the algorithm will automatically adjust the size and shape of the clusters based on the data.

Anomaly detection

Anomaly detection is a crucial process aimed at pinpointing data points that deviate from the anticipated or typical value ranges. This technique harnesses the power of AI algorithms to detect unconventional or aberrant patterns within datasets, signifying the presence of potential hidden insights that demand further scrutiny.

The core mechanism of anomaly detection algorithms involves a comprehensive analysis of data distribution, with the primary objective of identifying data points that diverge from this distribution. These algorithms come in two primary categories: supervised and unsupervised. The choice between them hinges on the specific nature of the anomalies under scrutiny.

• Supervised anomaly detection: This approach relies on labeled data to train a model for anomaly recognition. By leveraging the known anomalies in the training data, supervised algorithms develop the capacity to discern irregularities effectively
• Unsupervised anomaly detection: In contrast, unsupervised algorithms employ statistical methodologies to uncover anomalies without the need for prior knowledge or labeled data. This versatility makes them particularly valuable for scenarios where anomalies are unpredictable or scarce

What are the best data archiving tools of 2023?

Now that we have emphasized the importance of data archiving, it is time to talk about the commercial tools that offer this service. As you know, many big technology companies offer such services. So which one should be your best choice for data archiving? Let’s take a look together.

• Bloomberg Vault
• Microsoft Exchange Online Archiving
• Google Vault
• Proofpoint Archive

Bloomberg Vault

Bloomberg Vault is a comprehensive platform designed to help global financial services organizations meet their regulatory obligations and business standards. Provided by Bloomberg Professional Services, this integrated compliance and surveillance solution simplifies data archiving, collection, and aggregation.

One of the key features of Bloomberg Vault is its ability to collect and aggregate primary sources of Bloomberg-originated data and corporate data required for regulatory compliance and surveillance purposes. This includes data needed for supervision and surveillance programs within the financial industry.

Bloomberg Vault also offers real-time compliance monitoring. This allows organizations to track and manage their compliance with regulatory requirements efficiently. The platform provides users with the capability to retrieve stored data securely, ensuring accessibility for audit and regulatory reporting needs.

https://youtu.be/0Uc7IV3thcw

Microsoft Exchange Online Archiving

Microsoft Exchange Online Archiving is a cloud-based, enterprise-class archiving solution provided by Microsoft 365. It is designed to address various data archiving needs for organizations. The solution is used for data archiving, compliance, regulatory, and eDiscovery challenges associated with email management within organizations.

Exchange Online Archiving provides several features that make it an attractive option for organizations looking to improve their email management strategies. One of its key benefits is its cloud-based nature, which makes it accessible and reliable. Additionally, the solution offers mailbox quota management capabilities, which help alleviate mailbox size issues by automatically moving mailbox items to personal or cloud-based archives when they approach their allocated quota.

Another advantage of Exchange Online Archiving is its ability to configure archive policies and settings. This allows organizations to tailor the solution to meet their specific needs. For example, organizations can set up archiving policies that determine how and when mailbox items are archived. This level of control ensures that organizations can comply with regulatory requirements and internal policies regarding data retention and security.

Google Vault

Google Vault is a powerful information governance and eDiscovery tool designed specifically for Google Workspace. At its core, Google Vault helps organizations manage data within Google Workspace by providing features such as data archiving, legal holds, searching, and exporting user data from Google Workspace applications like Gmail and Google Drive.

One of the primary purposes of Google Vault is to preserve user data from specific Google Workspace apps by placing them on legal holds. This ensures that important data is not deleted prematurely and can be retrieved when needed. In addition to data preservation, Google Vault also facilitates eDiscovery by enabling users to search for specific information across Google Workspace applications. This feature is particularly useful for legal and compliance purposes.

Another significant advantage of Google Vault is its API integration. The tool offers an API that allows organizations to integrate it with their systems and automate eDiscovery processes, including managing legal matters, placing holds, and data archiving. This streamlines the process of managing data and makes it more efficient for organizations.

Proofpoint Archive

Proofpoint Archive is a cloud-based archiving solution that aims to simplify legal discovery, regulatory compliance, and data archiving for organizations. This solution provides secure storage and easy access to archived data, making it easier for organizations to manage their data and respond to legal and regulatory requests.

One of the key benefits of Proofpoint Archive is its ability to simplify legal discovery. When organizations need to retrieve data for legal purposes, Proofpoint Archive enables them to quickly and efficiently search and retrieve archived data. This saves time and resources compared to traditional data retrieval methods, which can be manual and time-consuming.

In addition to legal discovery, Proofpoint Archive also helps organizations stay compliant with regulatory requirements. The solution securely archives data and provides tools for compliance monitoring, ensuring that organizations are meeting the necessary standards for data retention and security.

Another advantage of Proofpoint Archive is its ability to leverage cloud intelligence to gain insights into archived data. With this next-generation archiving solution, organizations can gain deeper insights into their data, enabling them to make more accurate decisions and improve their overall data management strategies.

Data archiving stands as a crucial practice in the modern era of data-driven business models. It encompasses the systematic preservation of electronic data, ensuring its long-term retention and accessibility while addressing various business needs.

Featured image credit: DCStudio/Freepik.

Alaya AI has rolled up its digital sleeves to make AI data collection and labeling far more efficient and inclusive. But how does it manage to do that? Buckle up as we unpack the essentials and innovations this tool brings to the table.

What is Alaya AI?

Alaya AI operates as a comprehensive AI data platform with its roots in Swarm Intelligence. It not only gathers and labels data but also seamlessly integrates communities, data science, and artificial intelligence by way of Social Commerce.

Addressing industry challenges

It’s a platform geared towards addressing the challenges of data scarcity and workforce limitations for those working in AI. With a gamified data training module and a built-in social referral system, Alaya AI has managed to achieve rapid growth. Essentially, the platform is designed to harness collective intelligence, irrespective of geographical or temporal boundaries, and utilize it in the most efficient manner possible.

Harnessing collective intelligence

There are three main stakeholders in the AI-sphere: the creators of algorithms, data providers, and infrastructure providers. Alaya AI takes on the crucial role of the data provider in this ecosystem. Take OpenAI as an instance; they employed low-wage labor to annotate the extensive ChatGPT dataset, processing hundreds of thousands of words in less than a day.

Navigate the sea of data with a sail made of kernel

Presently, three pressing challenges stand in the way of efficient data collection and labeling in AI:

• Data quality: Currently, a majority of the data annotation is performed by less-educated individuals in developing countries. This often results in poor data quality and significant deviations in hyperparameters.
• Professional requirements: Existing manual annotation approaches fail to meet the specialized demands of fields like healthcare. Traditional methods of data feeding can’t handle the complexities involved in labeling such specialized data.
• Decentralization: For AI to make the most accurate predictions and generate meaningful insights, it requires a broad, dispersed dataset for verification. Unfortunately, the concentration of data collection in a few hands hinders the progress of AI development.

Alaya’s solution to challenges

Alaya AI addresses these challenges head-on with its comprehensive suite of data services that include data collection, classification, annotation, and transcription. Leveraging blockchain technology, Alaya maximizes community involvement in AI data collection, thereby avoiding the pitfalls of data centralization.

It also applies meticulous screening processes, making it vastly more efficient than traditional methods, especially for specialized fields. By involving contributors from around the globe, Alaya improves data quality significantly, propelling advancements in artificial intelligence.

Alaya AI brings together data acquisition, classification, annotation, and transcription to create highly precise computer vision models. The platform offers a full-fledged Integrated Development Environment (IDE) complete with custom API access, offering diverse data capture solutions for the AI sector.

Gamification and quality control

Utilizing blockchain technology, Alaya provides gamified training platforms for high-quality data, mitigating data scarcity and safeguarding data privacy through professional project management. Thanks to its intelligent recommendation algorithm and hierarchical structure, Alaya ensures tasks are matched with users who possess the relevant skills, thereby enhancing the quality of the data gathered.

Core Alaya AI features

Alaya AI differentiates itself by integrating robust features designed to streamline the process of data collection and labeling for the AI industry.

Here’s a quick rundown:

• Unlike centralized models, Alaya encourages shared governance, empowering individual users to affect changes within the platform.
• Alaya elevates user engagement by combining game-like experiences with real-world rewards, encouraging consistent and quality data input.
• While the platform operates under simple, user-friendly guidelines, it also supports self-organizing groups, fostering an environment of flexibility and adaptability.
• Through its social recommendation mechanism, Alaya guides even those who are new to the technology, providing a seamless transition into the Web3 sphere.

Disclaimer: Before you proceed to use Alaya AI, it’s important to understand that you will be connecting your digital wallet to the platform. Be cautious of scams and always double-check any action you are about to perform. Make sure to keep your private keys and recovery phrases in a safe and secure location, away from potential threats. Your digital assets are your responsibility.

How to use Alaya AI?

Follow these steps to make the most of your Alaya AI experience:

• Visit the Alaya website, click on “Login,” and register using your email address.

• Participate in data collection and labeling tasks in a game-like environment.

• You’ll need to own an NFT to answer certain questions.
• If you’re interested in completing quizzes, navigate to the “Task” section, accessible from the sidebar, to land on the quiz homepage.

• Click on “Market” to reach the marketplace where you can freely buy or sell a variety of NFTs.

• By selecting “Referral,” you can obtain a shareable link. Inviting friends through this link can earn you various rewards, including in-game dividends.

• Click on “System” to find instructions for customizing your personal settings on the platform.

• To view your system account, simply click on the “Wallet” button.

Featured image credit: Kerem Gülen/Midjourney

Data. Four little letters, one multi-billion dollar opportunity for companies big and small. From the democratisation of programming languages and analytics tools to the emergence of data scientists as the key decision influencer of the modern workforce, data science and its underlying methodologies are transforming the face of business. Let’s discover how a qualification such as a Master of Data Science from RMIT can be a transformative experience for a data professional, and how it can drive positive innovation and change through your business.

Importance of Data to the Modern Workforce

It might not seem like it, but data has rapidly become critical to the operations of workforces worldwide. Data presents some bottlenecks, such as the three Vs – velocity, variety, and volume – however, with many modern platforms, these problems are much more addressable than in decades past.

Consider, for example, a logistics company that is able to use weather forecasts to proactively divert trucks before storms hit. As a result, they can minimise the amount of time lost by drivers being caught in bad weather. Once an idea that sat in the realm of business fantasy, modern-day logistics companies are now empowering their supply chains with data to help make decisions before crisis strikes.

Another way that data is being used online is for the benefit of the consumer – linking product data with online storefronts, enabling consumers to shop for the products they want from the comfort of their couch. While it may seem like a relatively minimal application of data, in reality, it may involve multiple complex systems to assist with product selection, delivery optimization, and promotional marketing.

These two concepts may seem incredibly complicated, but as data has become accessible, companies both small and large have been able to take it and yield it for the benefit of their organisations. Data has rapidly become a critical decision-making element for organisations – wielded with a tool as simple as a laptop, understanding data can make or break the modern entrepreneur.

The dangers of ignoring the data

It’s now mission-critical that businesses address and work with data. Ignoring data may seem like a sensible move, if you’ve never worked with it – however, not taking steps to make the most of the information your business has can be perilous, if not fatal for the fate of an organisation.

This can be seen in the ways that modern hacking groups have targeted organisations such as Medibank and Optus for ransom. For the modern firm, lacking appropriate knowledge about data can have unforeseen consequences. After all – would you trust a business with your data if they can’t even tell you how much is missing? At the bare minimum, understanding the basic characteristics of your data is not only beneficial for your staff and customers, but in times of crisis, can be used to help inform decision making. Data and modern data science methodologies should be considered essential in driving outcomes – rather than relying on gut instinct and outdated practices.

Modern business analytics – Empowering teams

Consider the role of data in the workplace, even ten years ago. Large datasets were often unwieldy, trapped within legacy servers, and inaccessible to most employees. As time has gone on, and technology has developed, the modern business analyst has emerged from the fold as a power user of modern tools and techniques. Programming languages such as SQL and Python enable data analysts to get more out of data within the business, by delving into the complexities of large databases and providing actionable insights. This is further supported through the use of modern visualisation tools such as PowerBI and Tableau, enabling end users to dive in, transform, and express their data in a way that is helpful and interesting.

Business analysts use a mix of modern-day analytics tools to understand processes and provide meaningful recommendations. They may work in small teams, or be embedded with larger, cross-disciplinary teams, and are essential to understanding the data on hand in many large firms.

Actionable insights – the modern data scientist

Another way that data science methods have evolved in recent years has been the emergence of the modern data scientist as a titan of business operations, and a key influencer in data decision-making. Taking data from a variety of structured and unstructured sources, a data scientist can use data to not only recommend insights, but opportunities for testing and learning, across all facets of a business.

A data scientist may go beyond simply data reading, however, – skilled data scientists may use their insights to create complex forecasting models to proactively predict the impact of events such as seasonal sales or weather disruptions, enabling other users of data to get a clear read on the potential for opportunities. Being a data scientist is all about using what they know to empower new and innovative decision-making, and at the end of the day, there are very few roles in a business that can have the same level of impact on a company.

Is artificial intelligence the future of analytics?

One such consideration to keep in mind with data is the proliferation of buzzwords such as artificial intelligence (AI) and machine learning (ML) within the corporate workforce. While there is undoubtedly some benefit to the use of tools such as large language models like OpenAI and generative tools such as Midjourney, one must be mindful that there is a range of ethical and legal concerns that currently constrain the use of these tools in the workplace. Keep in mind that these may change as policies develop in this emerging industry. However, be sure to stay informed on the use of AI and ML, particularly in the data space – it has the potential to be immensely powerful for innovative organisations.

Where will the data take you next?

From a simple shop front to a career in multinational firms such as banking, logistics, or retail, having a recognized qualification in data science can open the door to a range of opportunities in areas you may not realise were available. From a database analyst to a business analyst or data scientist, you never know where the next opportunity may be available. If data is a career that you’re looking to pursue, get in touch with a career advisor and discuss your options. You never know – you may have just stepped into a brand-new career.

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Document management has been transformed by virtual data room due diligence. In the days of paper-based document management, the evaluation of potential takeover candidates was a cumbersome process that required legal firms and a lot of time. Companies that are for sale offer their pertinent information in the best data rooms during financial due diligence so that the company’s stability and solvency may be easily accessed online.

Before and during an M&A transaction (Mergers and acquisitions, such as a firm purchase, merger, or even planned commercial cooperation), all required documents are stored and maintained in a VDR. It is possible for the parties involved to jointly process the documents (such as purchase contracts) with the proper access authorizations thanks to the digital data space needed for this purpose. In this approach, the Internet facilitates due diligence, which is the investigation of a company before a purchase.

The data room providers are used for nearly the whole transaction of a company takeover, not just the due diligence phase. Since the records are always accessible online, this can lower hazards. In this manner, the company acquisition and sale can go according to schedule.

How Safe are VDRs?

To store, organize, and securely communicate data with business partners during an M&A transaction is the job of virtual data room management software. The providers of these VDRs give enhanced security-related functions to provide essential data security. These include modern encryption techniques and a multi-level authentication process. Time-limited access is one of the other defense methods.

Due Diligence Data Room Advantages Overview

In order to get ready for possible mergers and acquisitions deals, due diligence calls for a careful examination of papers containing sensitive information. It’s crucial to have a secure location where all documents may be kept and distributed to the legal teams and other experts involved in the process.

Businesses have all the tools they need to safely communicate documents for an M&A transaction when using a data room for due diligence since it offers a high level of protection. A virtual data room for startups becomes essential for productivity with added management tools. Don’t forget to compare virtual data rooms, as today there are many providers to consider.

The following are the principal advantages that firms receive from using https://datarooms.org.uk/due-diligence/:

1. VDR offers a high level of protection

The most important factor to take into account when selecting a due diligence data room is security. The ability to fully control any document in the data room for due diligence is provided by secure virtual data room software. The documents are also secured by security measures, including access restrictions, watermarking, and authorization levels.

2. Faster and better file management

Due to the many management tools and functionalities available, file management is quite simple while using a data room. Transferring files to the data room is significantly quicker and more effective, thanks to drag-and-drop and bulk upload options.

An electronic data room is a fantastic tool for file organization. Any document you require is simply accessible, and you may download or export it as a PDF. If you find yourself in a scenario where you need to send a document rapidly for review, you can do so right away.

3. Monitor activity and analyze data

Administrators can monitor user activity, check log-in/log-off times, and identify which documents were accessed and for how long in many due diligence data rooms.

Administrators can determine which files are now the most important by using these tracking features. To make it simpler to track the development of the process, there is also a dashboard that provides a summary of the tasks the team is presently working on.

4. Improves the efficiency and smoothness of collaboration

A Q&A section and the commenting option are examples of collaboration tools in data rooms. They aid in streamlining workflow because team members can submit comments directly in the documents, which are quickly forwarded to other users. Users can submit questions or requests for specific documents in the Q&A area.

Additionally, there are alerting tools to make sure users get process changes. If a user isn’t logged into the data room, they get emails. Users can also establish request templates to send due diligence requests when necessary automatically.

VDRs are critical for doing due diligence in a transparent and efficient manner. The logical structure and organization of the documents in a data room is one of the critical components in facilitating this type of business transaction. Consider virtual data room pricing before making a selection.

In VDRs, a number of templates are offered to make sure everything is covered and nothing is forgotten. Role-based access control and proper design of the data room services security policy are essential since only authorized parties should have access to certain documentation. This gives an additional layer of protection, which is a crucial consideration in sensitive corporate processes like mergers and acquisitions or financial audits.

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Although its use remains in the gray area, telemetry data can provide you with the most accurate information you can get from an actively operating system. In fact, this data collection management, which most popular algorithmically based social media applications have been using for a long time, may not be as evil as we think.

In today’s world, the integration of AI and ML algorithms has revolutionized the way we live and work. Automation, which was once considered a futuristic concept, is now an indispensable part of our daily lives. From intelligent personal assistants like Siri and Alexa, to self-driving cars and smart homes, automation has made our lives easier and more convenient than ever before.

This shift towards automation was made possible by the recognition that data can exist beyond the binary system of ones and zeros. By analyzing and understanding data in its various forms, we have been able to create technologies that cater to our needs and push humanity to ask new questions.

However, the process of collecting and analyzing data doesn’t have to be manual. Telemetry data offers us a way to automatically collect and analyze data, providing insights into how we can improve our products and services. Let’s take a closer look at what telemetry data can offer us in this regard.

What is telemetry data?

Telemetry data refers to the information collected by software applications or systems during their operation, which can include usage patterns, performance metrics, and other data related to user behavior and system health. This data is typically sent to a remote server for analysis and can be used to improve the quality and functionality of the software or system, as well as to provide insights into user behavior and preferences.

Telemetry data can include a wide range of information, such as:

• User engagement data like features used, time spent on tasks, and navigation paths
• Performance metrics such as response times, error rates, and resource utilization
• System logs such as crashes, errors, and hardware issues
• User demographics like age, gender, location, and language preference
• Device information including operating system, browser type, screen resolution, and device type
• Network information such as IP address, internet service provider, and bandwidth
• Application usage patterns including frequency of use, time of day, and duration of use
• Customer feedback like feedback surveys and support requests
• Analytics data from tools like Google Analytics

The main purpose of collecting telemetry data is to gain insights into how users are interacting with the application, identify areas for improvement, and optimize the user experience. By analyzing telemetry data, developers can identify trends in user behavior, detect issues and bugs, and make accurate decisions about future product development.

The examples below illustrate the diverse nature of telemetry data and its applications across various industries. By collecting, analyzing, and acting upon telemetry data, organizations can gain valuable insights that drive accurate decision-making, improve operations, and enhance customer experiences.

Sensor data

Sensor data refers to the information collected by sensors installed in industrial equipment, vehicles, or buildings. This data can include temperature, humidity, pressure, motion, and other environmental factors. By collecting and analyzing this data, businesses can gain insights into operating conditions, performance, and maintenance needs.

For example, sensor data from a manufacturing machine can indicate when it is running at optimal levels, when it needs maintenance, or if there are any issues with the production process.

Machine log data

Machine log data is the data collected by machinery logs from industrial equipment, such as manufacturing machinery, HVAC systems, or farm equipment. This data can provide insights into equipment health, usage patterns, and failure rates.

For example, machine log data from a manufacturing machine can show how often it is used, what parts are most frequently used, and whether there are any issues with the machine that need to be addressed.

Vehicle telemetry data

Vehicle telemetry data refers to the data collected by GPS, speed, fuel consumption, tire pressure, and engine performance sensors in vehicles. This data can help fleet managers optimize routes, manage driver behavior, and maintain vehicles.

For example, vehicle telemetry data can show which drivers are driving too fast, braking too hard, or taking inefficient routes, allowing fleet managers to address these issues and improve overall fleet efficiency.

User behavior data

User behavior data refers to the data collected on web browsing habits, app usage patterns, and user engagement metrics. This data can provide insights into customer preferences, interests, and pain points, helping businesses improve their products and services.

Technology can understand your needs better than you

For example, user behavior data from an e-commerce website can show which products are most popular, which pages are most frequently visited, and where users are dropping off, allowing the company to make improvements to the user experience.

Energy consumption data

Energy consumption data refers to the data collected on energy usage patterns from smart meters, building management systems, or industrial facilities. This data can help identify areas for energy efficiency improvements, optimize energy consumption, and predict future energy demand.

For example, energy consumption data from a large office building can show which floors are using the most energy, which lighting fixtures are the least efficient, and when energy usage spikes, allowing the building manager to make adjustments to reduce energy waste.

Weather data

Weather data refers to the data collected from weather stations, satellite imagery, or weather APIs. This data can be used in various industries, such as agriculture, aviation, construction, and transportation, to plan operations, optimize resources, and minimize weather-related disruptions.

For example, weather data can show which days are likely to have heavy rain, allowing a construction site to schedule outdoor work accordingly, or which flight routes are likely to be affected by turbulence, allowing pilots to reroute flights accordingly.

Medical device data

Medical device data refers to the data collected by patient vital signs, treatment outcomes, and device performance sensors in medical devices. This data can help healthcare providers monitor patient health, optimize treatment plans, and improve medical device design and functionality.

For example, medical device data from a pacemaker can show how well it is working, whether there are any issues with the device, and what adjustments need to be made to optimize its performance.

Financial transaction data

Financial transaction data refers to the data collected on payment processing, transaction history, and fraud detection. This data can aid financial institutions, merchants, and consumers in detecting fraud, optimizing payment processes, and improving financial product offerings.

For example, financial transaction data can show which transactions are most frequently disputed, which payment methods are most popular, and where fraud is most likely to occur, allowing financial institutions to make improvements to their systems.

Supply chain data

Supply chain data refers to the data collected on inventory levels, shipment tracking, and supplier performance. This data can assist businesses in managing inventory, optimizing logistics, and strengthening relationships with suppliers and customers.

For example, supply chain data can show which products are selling the most, which suppliers are performing the best, and where bottlenecks are occurring in the supply chain, allowing businesses to make adjustments to improve efficiency.

Environmental monitoring data

Environmental monitoring data refers to the data collected on air quality, water quality, noise pollution, and other environmental factors. This data can help organizations ensure compliance with regulations, mitigate environmental impacts and promote sustainability initiatives.

For example, environmental monitoring data can show which areas of a factory are producing the most emissions, which parts of a city have the worst air quality, or which manufacturing processes are using the most energy, allowing organizations to make adjustments to reduce their environmental footprint.

Two types, one goal

Telemetry data can be broadly classified into two categories: active and passive data. Active data is collected directly from users through surveys, feedback forms, and interactive tools. Passive data, on the other hand, is collected indirectly through analytics tools and tracking software.

Active data collection involves direct interaction with users, where specific questions are asked to gather information about their preferences, needs, and experiences. Surveys and feedback forms are common examples of active data collection methods.

These methods allow organizations to collect valuable insights about their target audience, including their opinions, satisfaction levels, and areas for improvement. Interactive tools like chatbots, user testing, and focus groups also fall under active data collection. These tools enable real-time interactions with users, providing rich and nuanced data that can help organizations refine their products and services.

Passive data collection, on the other hand, occurs indirectly through analytics tools and tracking software. Web analytics, mobile app analytics, IoT device data, social media monitoring, and sensor data from industrial equipment are all examples of passive data collection.

These methods track user behavior, engagement metrics, and performance indicators without directly interacting with users. For instance, web analytics tools track website traffic, bounce rates, and conversion rates, while mobile app analytics monitors user engagement within apps. Social media monitoring tracks social media conversations and hashtags related to a brand or product, providing insights into public opinion and sentiment. Sensor data from IoT devices, such as temperature readings or GPS coordinates, falls under passive data collection. This data helps businesses monitor equipment performance, predict maintenance needs, and optimize operations.

Wait, isn’t it illegal?

Passive data collection in telemetry data, which involves collecting data indirectly through analytics tools and tracking software without direct interaction with users, is a legally gray area.

While it is not necessarily illegal, there are regulations and ethical considerations that organizations must be aware of when collecting and using telemetry data.

In the United States, the Electronic Communications Privacy Act (ECPA) prohibits the interception or disclosure of electronic communications without consent. However, this law does not explicitly address passive data collection techniques like web analytics or social media monitoring.

The General Data Protection Regulation (GDPR) in the European Union imposes stricter rules on data collection and processing. Organizations must obtain explicit consent from individuals before collecting and processing their personal data. The GDPR also requires organizations to provide clear privacy policies and give users the right to access, correct, and delete their personal data upon request.

The California Consumer Privacy Act (CCPA) in the United States provides consumers with similar rights to those under the GDPR. Businesses must inform consumers about the categories of personal information they collect, disclose, and sell, as well as provide them with the ability to opt-out of such collections.

To ensure compliance with these regulations, organizations should adopt best practices for collecting and using telemetry data:

• Provide transparency: Clearly communicate to users what data is being collected, how it will be used, and why it is necessary
• Obtain consent: Where required by law, obtain explicit consent from users before collecting and processing their personal data
• Anonymize data: When possible, anonymize data to protect user privacy and avoid identifying individual users
• Implement security measures: Ensure that appropriate security measures are in place to protect collected data from unauthorized access or breaches
• Adhere to industry standards: Follow industry standards and guidelines, such as the Digital Advertising Alliance’s (DAA) Self-Regulatory Program for Online Behavioral Advertising, to demonstrate commitment to responsible data collection and use practices
• Conduct regular audits: Periodically review data collection methods and practices to ensure they align with legal requirements, ethical considerations, and organizational privacy policies
• Offer opt-out options: Give users the option to opt-out of data collection or withdraw their consent at any time
• Train employees: Educate employees on the importance of data privacy and ensure they understand applicable laws, regulations, and company policies
• Monitor regulatory updates: Stay informed about changes in laws and regulations related to data privacy and adapt organization policies accordingly
• Consider a privacy impact assessment: Conduct a privacy impact assessment (PIA) to identify, manage, and mitigate potential privacy risks associated with telemetry data collection and processing

How can telemetry data help a business?

Telemetry data can provide numerous benefits for businesses across various industries. One of the primary ways it can help is by offering valuable insights into how customers interact with a product or service. This information can be used to identify areas where improvements can be made, optimizing the user experience and creating new features that cater to customer needs.

For instance, if a software company releases a new feature, telemetry data can track user engagement and feedback, allowing developers to refine the feature based on actual usage patterns.

Another significant advantage of telemetry data is its ability to assist with customer support. By monitoring user behavior, businesses can detect issues and bugs before they become major problems. This proactive approach enables customer support teams to address concerns more efficiently, reducing resolution times and improving overall satisfaction.

Additionally, telemetry data can facilitate personalized content delivery, enabling businesses to tailor marketing strategies to specific audiences based on their interests and preferences.

Telemetry data can also play a crucial role in predictive maintenance, particularly in industries like manufacturing, transportation, and energy. By tracking equipment performance and identifying potential failures early on, businesses can minimize downtime and reduce maintenance costs.

This proactive approach can significantly improve operational efficiency and reduce waste.

Furthermore, telemetry data can aid businesses in streamlining processes, reducing waste, and improving operational efficiency. By analyzing usage patterns, organizations can identify bottlenecks, inefficiencies, and opportunities for automation.

This type of information can be used to optimize resource allocation, minimize expenses related to maintenance, repair, and replacement, and allocate resources more effectively.

Moreover, telemetry data can help businesses meet regulatory requirements and maintain security standards. By providing visibility into data handling practices, access controls, and system vulnerabilities, organizations can ensure compliance with industry regulations and mitigate potential risks.
In addition, telemetry data can be used to set benchmarks for product performance, service delivery, and user experience.

By establishing these benchmarks, businesses can evaluate progress, identify areas for improvement, and stay competitive within their respective markets.

Lastly, telemetry data provides valuable insights into customer behavior, preferences, and needs. This information can inform product roadmaps, marketing strategies, and customer retention initiatives, ultimately driving informed decision-making and enhancing the overall customer experience.

Effective use of telemetry data can give businesses a competitive advantage by providing unique insights that can be used to innovate, differentiate products and services, and exceed customer expectations.

As data is changing our world, the way we acquire it is as important as our ability to make sense of it. The future is still so much bigger than the past and it’s up to us how much novelty we can fit into one life.

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With the amount of data a company holds growing exponentially every year, it’s becoming more and more important for businesses to have a system of record in place to manage it all.

One of the most talked about topics in the business world in 2023 was the data collected by large companies about customers. Now we leave our digital footprint with almost every site we visit. Although Europe and America have set certain standards in this matter, sometimes the need for a guardian angel that can help your company in this regard is increasing day by day.

This is exactly where the system of record comes into play. This system, which is obliged to check that your company operates at certain standards, has the capacity to find a solution to every potential data-related problem of companies in both legal and social areas.

What is a system of record?

A system of record (SOR) refers to a database or data management system that serves as the authoritative source of truth for a particular set of data or information. It is essentially a centralized repository that stores, manages, and maintains data related to a specific domain, such as customer information, financial transactions, or inventory levels.

The main purpose of a system of record is to provide a single, unified view of data that can be used by multiple applications, systems, and users across an organization. This helps ensure data consistency, accuracy, and integrity, as all stakeholders have access to the same up-to-date information.

A system of record typically has several key characteristics:

Authority: The system of record is considered the ultimate authority on the data it stores. All other systems or applications that require access to this data must retrieve it from the SOR, rather than storing their own copies.

Integration: A system of record integrates data from various sources, such as transactional databases, external data providers, or other systems. It acts as a single platform for data collection, processing, and reporting.

Standardization: The system of record enforces standardization of data formats, schemas, and definitions, ensuring that all data is consistent and well-defined.

Persistence: Once data is stored in a system of record, it is preserved for the long term, providing a historical record of all changes and updates.

Security: Access to the system of record is tightly controlled, with strict security measures in place to protect sensitive data from unauthorized access, modification, or breaches.

Scalability: An SOR should be designed to handle large volumes of data and scale as the organization grows, without compromising performance or functionality.

Governance: Clear policies and procedures governing the management and maintenance of the system of record, including data quality control, validation, and cleansing processes.

Auditability: The system of record maintains detailed audit trails of all transactions, allowing for easy tracking and monitoring of data modifications, insertions, and deletions.

Compliance: The system of record adheres to relevant regulatory requirements, industry standards, and organizational policies, ensuring that data is handled and stored in accordance with legal and ethical guidelines.

Interoperability: A system of record can seamlessly integrate with other systems, applications, and platforms through APIs or other data exchange mechanisms, enabling efficient data sharing and collaboration across the organization.

The importance of privacy and compliance in business

Privacy and compliance are two crucial aspects of any business operation, especially in today’s digital age where data collection and processing have become an integral part of almost every industry. Both privacy and compliance are closely related to data handling practices and play a vital role in building trust between organizations and their customers, employees, partners, and other stakeholders.

Respecting customers’ privacy and protecting their personal information builds trust and reinforces a positive reputation for your business. A strong privacy policy demonstrates your commitment to safeguarding sensitive data, which can lead to increased customer loyalty and advocacy. Moreover, privacy regulations like the General Data Protection Regulation (GDPR) in the European Union, the California Consumer Privacy Act (CCPA) in the United States, and similar laws worldwide, impose strict rules on how businesses collect, store, and process personal data. Adhering to these regulations helps avoid hefty fines and penalties, reputational damage, and potential loss of business.

Protecting individuals’ privacy is not only a legal requirement but also an ethical responsibility. As technology advances and data collection methods become more sophisticated, it’s essential to respect users’ autonomy and ensure their personal information is handled with care and discretion. In today’s privacy-focused market, companies that prioritize data protection and user privacy may enjoy a competitive edge over those that do not. By emphasizing robust privacy controls, you can differentiate your business from rivals and attract customers who value their online security and privacy.

Compliance with data protection regulations, industry standards, and sector-specific laws is critical to avoid legal repercussions and financial penalties. Non-compliance can lead to significant risks, including data breaches, cyber-attacks, intellectual property theft, and brand reputation damage. Maintaining compliance minimizes these risks by implementing appropriate safeguards, monitoring processes, and incident response plans. Compliance also fosters trust among stakeholders, enabling stable partnerships, investments, and customer relationships. It facilitates cross-border data transfers and trade, allowing businesses to expand globally without worrying about regulatory barriers or legal disputes.

A strong compliance posture forces organizations to maintain tight controls on their data, which often leads to better data quality, reduced data duplication, and more efficient data processing. Well-managed data enables informed decision-making, cost savings, and competitive advantages. Moreover, compliance demonstrates a company’s commitment to ethical practices, and building trust with customers, employees, and partners. A strong reputation based on compliance and privacy best practices contributes to long-term success and growth.

What are the steps to build a system of record for privacy and compliance?

Building a system of record for privacy and compliance involves several steps that help organizations ensure they are collecting, storing, and processing personal data in a way that is both compliant with regulations and respectful of individuals’ privacy rights.

Here are the steps involved in building such a system:

Define the purpose and scope

The first step in building a system of record for privacy and compliance is to define its purpose and scope. This involves identifying the types of personal data that will be collected, stored, and processed, as well as the sources of this data, the reasons for collecting it, and the parties who will have access to it. The scope should also include the geographic locations where the data will be collected, stored, and processed, as well as any third-party processors or sub-processors who may have access to the data.

To define the purpose and scope of the system of record, organizations should consider the following factors:

• The type of personal data being collected (e.g., names, email addresses, phone numbers, financial information)
• The source of the personal data (e.g., customer databases, employee records, website forms)
• The purpose of collecting the personal data (e.g., marketing, sales, customer service, HR management)
• The parties who will have access to the personal data (e.g., employees, contractors, third-party vendors)
• The geographic locations where the data will be collected, stored, and processed (e.g., countries with specific data protection laws)
• Any third-party processors or sub-processors who may have access to the data (e.g., cloud storage providers, data analytics firms)

Once the purpose and scope of the system of record are defined, organizations can begin to identify applicable regulations and develop a plan for implementing privacy controls.

Identify applicable regulations

The second step is to identify all applicable privacy and security regulations that apply to the system of record. This could include GDPR, CCPA, HIPAA/HITECH, PCI DSS, NIST Cybersecurity Framework, and other industry-specific standards. It’s essential to understand the requirements of each regulation and how they impact the collection, storage, and processing of personal data.

To identify applicable regulations, organizations should consider the following factors:

• The location of the organization and the personal data it collects, stores, and processes
• The type of personal data being collected, stored, and processed
• The industries or sectors involved in the collection, storage, and processing of personal data (e.g., healthcare, finance, retail)
• Any relevant regulatory bodies or authorities that oversee the organization’s handling of personal data

Once applicable regulations are identified, organizations can conduct a Data Protection Impact Assessment (DPIA) to assess privacy risks and evaluate the effectiveness of existing controls.

Conduct a data protection impact assessment (DPIA)

Conducting a data protection impact assessment (DPIA) helps organizations identify and mitigate potential privacy risks associated with the system of record. A DPIA involves assessing the likelihood and severity of potential privacy breaches, evaluating the effectiveness of existing controls, and recommending additional measures to minimize risk. The DPIA should be documented and updated regularly to ensure that the system of record remains compliant with evolving privacy regulations.

To conduct a DPIA, organizations should follow these steps:

• Identify the personal data processing activities that pose high privacy risks (e.g., large-scale processing of sensitive data, processing of data from vulnerable populations)
• Assess the likelihood and severity of potential privacy breaches resulting from these activities
• Evaluate the effectiveness of existing controls and procedures for protecting personal data
• Recommend additional measures to minimize privacy risks, such as implementing encryption, access controls, or anonymization techniques
• Document the findings and recommendations of the DPIA and update them regularly to reflect changes in the system of record or applicable regulations

After completing the DPIA, organizations can design and implement privacy controls to address identified risks.

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Design and implement privacy controls

Based on the findings from the DPIA, design and implement privacy controls to address identified risks. These controls may include technical measures such as encryption, access controls, and pseudonymization, as well as organizational measures such as data protection policies, procedures, and training programs. It’s important to involve stakeholders from various departments, including IT, legal, and compliance, to ensure that the controls are effective and practical to implement.

When designing and implementing privacy controls, organizations should consider the following factors:

• The specific privacy risks identified in the DPIA
• The type of personal data being collected, stored, and processed
• The sources of personal data (e.g., customer databases, employee records)
• The parties who will have access to the personal data (e.g., employees, contractors, third-party vendors)
• Any applicable industry standards or best practices for protecting personal data

Privacy controls should be designed to meet the requirements of applicable regulations while also being practical to implement and maintain. Organizations should test their controls regularly to ensure they remain effective in mitigating privacy risks.

Develop a data management plan

A data management plan outlines how personal data will be collected, stored, processed, and deleted within the system of record. It should include details about data retention periods, data backup and recovery processes, incident response plans, and data subject rights. The plan should also address how third-party processors or sub-processors will handle personal data and how they will comply with applicable regulations.

To develop a data management plan, organizations should consider the following factors:

• The types of personal data being collected, stored, and processed
• The sources of personal data (e.g., customer databases, employee records)
• The purposes of collecting personal data (e.g., marketing, sales, customer service, HR management)
• The parties who will have access to the personal data (e.g., employees, contractors, third-party vendors)
• Any applicable regulations or industry standards for managing personal data
• Data retention periods and schedules for deleting personal data
• Procedures for backing up and restoring personal data
• Incident response plans for responding to data breaches or other security incidents
• Processes for handling data subject requests (e.g., requests for access, correction, deletion)

The data management plan should be regularly reviewed and updated to reflect changes in the system of record or applicable regulations.

Establish accountability and governance structure

Establishing an accountability and governance structure ensures that the system of record is managed in accordance with applicable regulations and industry best practices. This includes appointing a data protection officer (DPO) or equivalent, establishing a data governance committee, defining roles and responsibilities for data handling and processing, and developing policies and procedures for data management and security. Regular audits and assessments should be conducted to ensure that the governance structure remains effective and compliant.

To establish an accountability and governance structure, organizations should consider the following factors:

• Applicable regulations and industry standards for data privacy and security
• The size and complexity of the organization’s data processing activities
• The types of personal data being collected, stored, and processed
• The parties who will have access to the personal data (e.g., employees, contractors, third-party vendors)
• Roles and responsibilities for managing personal data and ensuring compliance
• Policies and procedures for data management and security
• Training programs for educating personnel about data privacy and security
• Incident response plans for responding to data breaches or other security incidents
• Regular audits and assessments to evaluate the effectiveness of the governance structure

By establishing a robust accountability and governance structure, organizations can ensure that their system of record remains compliant with evolving privacy regulations and industry best practices.

Train personnel and communicate with stakeholders

Training personnel and communicating with stakeholders helps ensure that everyone involved in the system of record understands their roles and responsibilities regarding privacy and compliance. Training programs should cover topics such as data protection principles, regulations, security measures, and incident response procedures. Stakeholders should include employees, contractors, third-party vendors, and any other parties who will have access to personal data.

To train personnel and communicate with stakeholders, organizations should consider the following factors:

• The types of personal data being collected, stored, and processed
• Applicable regulations and industry standards for data privacy and security
• Roles and responsibilities for managing personal data and ensuring compliance
• Policies and procedures for data management and security
• Training programs for educating personnel about data privacy and security
• Incident response plans for responding to data breaches or other security incidents
• Regular evaluations of the effectiveness of training programs and communication strategies

By training personnel and communicating with stakeholders, organizations can ensure that everyone involved in the system of record is aware of their responsibilities regarding privacy and compliance. This helps minimize the risk of non-compliance and protects the organization from potential legal and reputational harm.

Building a system of record for privacy and compliance is a complex task, but it is essential for businesses that collect and process personal data. By following the steps outlined in this article, organizations can create a SOR that meets their specific needs and helps them to protect their customers’ privacy.

Featured image credit:  kjpargeter/Freepik.

A seismic shift is underway in the halls of European power. The European Commission has unfurled a regulatory juggernaut poised to transform the landscape of Big Tech as we know it: the Digital Markets Act (DMA). This groundbreaking legislation, a triumph of determination in the face of digital dominance, marks Europe’s resolute bid to level the playing field in the tech arena.

But why does this matter? The DMA is not just another set of bureaucratic guidelines; it’s a resounding declaration that the era of unchecked tech supremacy is drawing to a close. In this article, we’ll delve into the DMA’s core provisions, identify the ‘gatekeepers’ it seeks to rein in and explore the profound implications for both the tech giants and the digital realm itself.

Defining the DMA’s gatekeepers

The term “gatekeeper” is central to the DMA’s mission. These gatekeepers are tech companies that wield substantial market influence, and they are now bound by a set of stringent obligations aimed at leveling the digital playing field. The list of gatekeepers reads like a who’s who of the tech world, with Alphabet, Amazon, Apple, Meta, and Microsoft hailing from the United States and ByteDance representing China.

It’s D-Day for #DMA!

The most impactful online companies will now have to play by our EU rules.#Gatekeepers are:

Alphabet
Amazon
Apple
ByteDance
Meta
Microsoft

DMA means more choice for consumers.

Fewer obstacles for smaller competitors.

Opening the gates to the Internet🇪🇺 pic.twitter.com/xaTluUfBax

— Thierry Breton (@ThierryBreton) September 6, 2023

The DMA identifies 22 core platform services that these gatekeepers must bring into compliance by March 2024. These services span various domains, including social networks (such as TikTok, Facebook, Instagram, and LinkedIn), messaging services (WhatsApp and Messenger), intermediation (Google Maps, Google Play, Google Shopping, Amazon Marketplace, Apple’s App Store, and Meta Marketplace), video sharing (YouTube), advertising services (Google, Amazon, and Meta), web browsers (Chrome and Safari), search engines (Google Search), and operating systems (Android, iOS, and Windows).

Rules of engagement

The DMA introduces a set of rules tailored to each core platform service, ensuring that gatekeepers operate fairly and transparently. For example, major messaging apps will need to ensure interoperability with competitors. Operating systems will be required to support third-party app stores and alternative in-app payment options.

Additionally, search engines like Google and potential additions like Microsoft’s Bing will have to offer users a choice of other search engines. Operating system providers must allow users to uninstall pre-installed apps and customize system defaults, such as virtual assistants and web browsers. Gatekeepers will also be prohibited from favoring their products and services over those of competitors on their platforms.

The gatekeeper criteria

The DMA employs specific criteria to designate companies and their services as gatekeepers. Among these criteria are annual turnover thresholds of over €7.5 billion and market capitalization exceeding €75 billion. Services must also boast more than 45 million monthly active users within the European Union.

Industry responses

Unsurprisingly, tech giants have reacted with mixed sentiments to their gatekeeper designations. Apple expressed concerns about the DMA’s potential impact on user privacy and security while committing to delivering exceptional products and services to Reuters. Meta, the parent company of Facebook and Instagram, and Microsoft welcomed the investigations into their services’ potential inclusion under the DMA.

Google is in the process of reviewing its designation and assessing the implications, with a focus on meeting the new requirements while preserving the user experience. Amazon is collaborating with the European Commission to finalize its implementation plans.

ByteDance, the company behind TikTok, stands out as a vocal critic of its gatekeeper designation. TikTok’s Brussels public policy head, Caroline Greer, expressed strong disagreement with the decision, emphasizing how TikTok has introduced choice into a market traditionally dominated by incumbents.

The road ahead

For gatekeepers that fail to comply with the DMA’s regulations, the European Commission wields a formidable arsenal of penalties. These include fines of up to 10 percent of a company’s global turnover, which can escalate to 20 percent for repeat offenders. Structural remedies, such as forcing a gatekeeper to divest part of its business, are also on the table.

While the DMA represents a significant milestone in regulating Big Tech, it is far from the end of the story. Legal challenges are expected, echoing previous battles between tech giants and regulators. As the European Commission forges ahead with its ambitious digital agenda, the world watches closely, aware that the outcome will have far-reaching implications for the future of the digital economy.

The DMA signifies Europe’s resolve to rebalance the power dynamics in the tech industry, aiming to foster innovation, protect consumers, and ensure fair competition in the digital age. As tech giants brace for compliance, the regulatory landscape continues to evolve, with profound consequences for the tech industry and society as a whole.

For more detailed information, click here.

The modern corporate world is more data-driven, and companies are always looking for new methods to make use of the vast data at their disposal. Cloud analytics is one example of a new technology that has changed the game. It’s not simply a trend; it’s a game-changer.

Let’s delve into what cloud analytics is, how it differs from on-premises solutions, and, most importantly, the eight remarkable ways it can propel your business forward – while keeping a keen eye on the potential pitfalls.

What is cloud analytics?

Cloud analytics is the art and science of mining insights from data stored in cloud-based platforms. By tapping into the power of cloud technology, organizations can efficiently analyze large datasets, uncover hidden patterns, predict future trends, and make informed decisions to drive their businesses forward.

While the essence of analytics remains the same, cloud analytics offers distinct advantages over traditional on-premises solutions. One of the most prominent differences is the elimination of the need for costly data centers. Cloud analytics provides a more efficient and scalable approach in today’s data-rich world, where information flows from diverse sources.

How does cloud analytics work?

Cloud analytics systems are hosted in secure cloud environments, providing a centralized hub for data storage and analysis. Unlike on-premises solutions, cloud analytics processes data within the cloud itself, eliminating the need to move or duplicate data. This ensures that insights are always up-to-date and readily accessible from any internet-connected device.

Key features of cloud analytics solutions include:

• Data models,
• Processing applications, and
• Analytics models.

Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

Cloud analytics types

Cloud analytics encompasses various types, each tailored to specific business needs and use cases. Here are some of the key types of cloud analytics:

• Descriptive analytics: This type focuses on summarizing historical data to provide insights into what has happened in the past. It helps organizations understand trends, patterns, and anomalies in their data. Descriptive analytics often involves data visualization techniques to present information in a more accessible format.
• Diagnostic analytics: Diagnostic analytics goes a step further by analyzing historical data to determine why certain events occurred. It seeks to identify the root causes of specific outcomes or issues. By understanding the “why” behind past events, organizations can make informed decisions to prevent or replicate them.
• Predictive analytics: Predictive analytics leverages historical data and statistical algorithms to make predictions about future events or trends. It’s particularly valuable for forecasting demand, identifying potential risks, and optimizing processes. For example, predictive analytics can be used in financial institutions to predict customer default rates or in e-commerce to forecast product demand.
• Prescriptive analytics: Prescriptive analytics takes predictive analytics a step further by not only predicting future outcomes but also recommending actions to optimize those outcomes. It provides actionable insights, suggesting what actions should be taken to achieve desired results. For instance, in healthcare, prescriptive analytics can recommend personalized treatment plans based on a patient’s medical history and current condition.
• Diagnostic analytics: Diagnostic analytics focuses on examining data to understand why certain events or trends occurred. It involves drilling down into data to identify the root causes of specific outcomes. This type of analytics is valuable for troubleshooting and problem-solving.
• Text analytics: Text analytics, also known as text mining, deals with unstructured text data, such as customer reviews, social media comments, or documents. It uses natural language processing (NLP) techniques to extract valuable insights from textual data. Text analytics is crucial for sentiment analysis, content categorization, and identifying emerging trends.
• Big data analytics: Big data analytics is designed to handle massive volumes of data from various sources, including structured and unstructured data. It involves the use of specialized tools and technologies to process, store, and analyze vast datasets. Big data analytics is essential for organizations dealing with large-scale data, such as social media platforms, e-commerce giants, and scientific research.
• Real-time analytics: Real-time analytics focuses on processing and analyzing data as it is generated, providing immediate insights. It’s crucial for applications that require instant decision-making, such as fraud detection in financial transactions, monitoring network performance, or optimizing supply chain operations.
• Cloud-based business intelligence (BI): Cloud-based BI tools enable organizations to access and analyze data from cloud-based sources and on-premises databases. These tools offer the flexibility of accessing insights from anywhere, and they often integrate with other cloud analytics solutions.
• Machine learning and AI analytics: Machine learning and AI analytics leverage advanced algorithms to automate the analysis of data, discover hidden patterns, and make predictions. These technologies are used for recommendation systems, image recognition, and anomaly detection, among other applications.
• IoT analytics: IoT (Internet of Things) analytics deals with data generated by IoT devices, such as sensors, connected appliances, and industrial equipment. It involves analyzing large streams of real-time data to derive insights, optimize processes, and monitor device performance.
• Spatial analytics: Spatial analytics focuses on geographical data, such as maps and location-based data. It’s used in fields like urban planning, logistics, and geospatial analysis to understand spatial relationships, optimize routes, and make location-based decisions.

These types of cloud analytics can be used individually or in combination to address specific business challenges and objectives. The choice of which type to use depends on the data’s nature, the analysis’s goals, and the desired outcomes.

Cloud analytics’ advantages

Here are the benefits of cloud analytics that can elevate your work:

• Scalability and flexibility: Cloud analytics technologies are scalable, accommodating your business’s changing computing and storage needs. Pay-as-you-go models mean you only pay for what you use, allowing for cost-effective growth.
• Enhanced collaboration: Cloud analytics breaks down departmental silos by providing a unified view of data, fostering transparency and informed decision-making. Everyone shares the same version of the truth, eliminating discrepancies and confusion.
• Leveraging third-party data: Incorporating external data sources, such as weather, social media trends, and market reports, enriches your analysis, providing a more comprehensive understanding of customer behavior and market dynamics.
• Opportunity identification: Cloud analytics empowers organizations to pinpoint successes, detect problems, and identify opportunities swiftly. AI and augmented analytics assist users in navigating complex data sets, offering valuable insights.
• Cost reduction: Uncover and eliminate inefficiencies within your operations using cloud analytics. Identify areas for improvement, such as sales strategies or HR processes, to reduce costs and enhance profitability.
• Product and service enhancement: Test and measure the success of new products or services quickly and efficiently. Embed data into your products to create better user experiences and increase customer satisfaction.
• Improved customer experience: Monitor and optimize the customer experience in real-time, making data-driven improvements at every stage of the buyer’s journey. Personalize engagement to meet and exceed customer expectations.
• Optimized sales and pricing strategies: Understand customer behavior to fine-tune pricing and packaging strategies. Cloud analytics helps identify buying patterns and behaviors, enabling more effective marketing campaigns and revenue growth.

Cloud analytics’ disadvantages

As with any technology, cloud analytics comes with its own set of challenges and pitfalls. It’s crucial to be aware of these potential downsides to make the most of your cloud analytics journey:

• Security concerns: While cloud providers invest heavily in security, breaches can still occur. Organizations must diligently manage access controls, encryption, and data protection to mitigate risks. For example, the 2019 Capital One breach exposed over 100 million customer records, highlighting the need for robust security measures.
• Data privacy and compliance: With data stored in the cloud, navigating complex data privacy regulations like GDPR and CCPA becomes essential. Non-compliance can result in hefty fines. For instance, British Airways faced a fine of £183 million ($230 million) for a GDPR breach in 2018.
• Data integration challenges: Merging data from various sources into a cohesive analytics platform can be complex and time-consuming. Poor data integration can lead to inaccurate insights. A well-documented case is the UK government’s failed attempt to create a unified healthcare records system, which wasted billions of taxpayer dollars.
• Dependency on service providers: Relying on third-party cloud service providers means your operations are dependent on their uptime and reliability. Downtime, like the AWS outage in 2017 that affected several high-profile websites, can disrupt business operations.
• Cost overruns: While the pay-as-you-go model is cost-effective, it can lead to unexpected costs if not managed carefully. Without proper monitoring, cloud expenses can spiral out of control.

Best cloud analytics practices

Implementing best practices in cloud analytics is essential for organizations to maximize the value of their data and make data-driven decisions effectively. Here are some of the best cloud analytics practices:

• Define clear objectives: Start by clearly defining your business objectives and the specific goals you want to achieve with cloud analytics. Understand what insights you need to gain from your data to drive business growth and strategy.

• Data governance: Establish robust data governance practices to ensure data quality, security, and compliance. Define data ownership, access controls, and data management processes to maintain the integrity and confidentiality of your data.
• Data integration: Integrate data from various sources into a centralized cloud data warehouse or data lake. Ensure that data is clean, consistent, and up-to-date. Use ETL (Extract, Transform, Load) processes or data integration tools to streamline data ingestion.
• Scalable architecture: Design a scalable cloud architecture that can handle growing data volumes and user demands. Cloud platforms like AWS, Azure, and Google Cloud offer scalable resources that can be provisioned on-demand.
• Data catalog: Implement a data catalog to organize and catalog your data assets. A data catalog makes it easier for users to discover and access relevant data, improving data collaboration and reuse.
• Data visualization: Use data visualization tools to create meaningful dashboards and reports. Visualizations make complex data more understandable and help stakeholders make informed decisions quickly.
• Self-service analytics: Empower business users with self-service analytics tools that enable them to explore and analyze data independently. Provide training and support to ensure users can effectively utilize these tools.
• Advanced analytics: Embrace advanced analytics techniques such as machine learning and predictive modeling to uncover hidden insights and make data-driven predictions. Cloud platforms often provide pre-built machine learning models and services.
• Data security: Prioritize data security by implementing encryption, access controls, and auditing. Regularly monitor data access and usage to detect and respond to security threats promptly.
• Cost management: Monitor and optimize cloud analytics costs. Leverage cost management tools provided by cloud providers to track spending and identify cost-saving opportunities. Ensure that resources are scaled appropriately to avoid over-provisioning.
• Performance monitoring: Continuously monitor the performance of your cloud analytics solutions. Use performance analytics and monitoring tools to identify bottlenecks, optimize queries, and ensure responsive performance for end-users.
• Data backup and recovery: Implement data backup and recovery strategies to safeguard against data loss or system failures. Cloud providers offer data redundancy and backup solutions to ensure data durability.
• Collaboration: Foster collaboration among data analysts, data scientists, and business users. Encourage cross-functional teams to work together to derive insights and drive business value.
• Regular training: Keep your team updated with the latest cloud analytics technologies and best practices through regular training and skill development programs.
• Compliance and regulation: Stay informed about data privacy regulations and compliance requirements relevant to your industry and geographic location. Ensure that your cloud analytics practices align with these regulations, such as GDPR, HIPAA, or CCPA.
• Feedback loop: Establish a feedback loop with users to gather input on analytics solutions and continuously improve them based on user needs and feedback.
• Documentation: Maintain comprehensive documentation for data sources, analytics processes, and data transformations. Well-documented processes ensure consistency and ease of maintenance.

By implementing these best practices in cloud analytics, organizations can effectively harness the power of their data, drive informed decision-making, and gain a competitive edge in today’s data-driven business landscape.

In conclusion, cloud analytics isn’t just a tool; it’s a transformational force that can reshape the way businesses operate. By leveraging its power and addressing potential pitfalls, organizations can unlock new growth opportunities, streamline operations, enhance customer experiences, and stay ahead in an ever-evolving market. Embrace cloud analytics wisely, and watch your business soar to new heights in the digital era, while guarding against the challenges that may arise along the way.

Featured image credit: ThisIsEngineering/Pexels

This article aims to demystify the concept of data storytelling, explaining why it’s more than just charts and graphs because understanding data is essential, but making it relatable and actionable is often a greater challenge.

What is a data storytelling?

Unpacking the term data storytelling reveals it as an art form that marries quantitative information with narrative context to engage audiences in a compelling way. It’s more than mere numbers and charts; it encompasses a rich blend of data analysis, domain knowledge, and effective communication.

Understanding data storytelling and visualization

While data visualizations serve as valuable aids in the storytelling process, they shouldn’t be mistaken for the story itself. These visual aids enhance the narrative but are not a substitute for the analytical depth and context that complete a data story. Data storytelling synthesizes visual elements with sector-specific insights and expert communication to offer a holistic understanding of the subject at hand.

Consider the example of tracking sales fluctuations for a particular product. While data visualizations can clearly show upward or downward trends, a well-crafted data story would dig deeper. It might illuminate how a recent marketing blitz boosted sales, or how supply chain issues have acted as a bottleneck, restricting product availability. This broader narrative turns a mere data point into actionable intelligence, answering not just the ‘what’ but also the ‘why’ and ‘how,’ making it invaluable for decision-making.

How does data storytelling work?

Data storytelling is a trifecta of key components: raw data, visual representations, and the overarching narrative:

• Data: Let’s say your analysis reveals that a specific type of renewable energy source, such as solar power, is most efficiently generated in coastal areas. Another finding could be that peak production coincides with the tourism season.
• Visualizations: After collecting the data, visual tools step into the spotlight. These could be heat maps showing solar power hotspots or seasonal trend lines that plot energy production against tourist numbers. These visualizations act as a bridge between complex data and audience understanding.
• Narrative: The narrative is the soul of your data story. This is where you introduce the issue at hand—for example, the importance of locating renewable energy sources efficiently—and then lay out your data-supported findings. The narrative should climax in a specific call to action, perhaps urging local governments to consider these factors in their renewable energy planning.

Each of these elements is a vital chapter in the larger book that is your data story, working in harmony to create a resonant, impactful message for your audience.

How to do data storytelling?

Creating a compelling data story goes beyond merely throwing some charts and graphs into a presentation. It’s a calculated process that requires a synthesis of raw data, analytical insight, and narrative flair. So, how does one embark on this journey to create a narrative that’s not only engaging but also informative and actionable?

Data storytelling techniques step by step

Data storytelling is not a skill developed overnight. Like any other form of storytelling, it involves multiple components and steps that contribute to the final masterpiece. Here is a more granular look at some steps to create a compelling data story:

Step 1: Know your audience inside out

First and foremost, you need to be intimately familiar with your target audience. Why? Because effective data storytelling isn’t one-size-fits-all. What are the pain points or challenges that your audience faces? Why would your findings resonate with them? By addressing these questions, you can pinpoint the aspects of your data that will truly captivate your listeners or readers.

Step 2: Weave an intriguing narrative

The narrative is the backbone of your data storytelling venture. You’re not just spewing out numbers, but building a plot that guides your audience to a well-defined outcome. So, how to go about this?

Kick off by establishing the backdrop: Explain why you decided to dive into this specific data set and what pressing issue or curiosity you aimed to resolve.

Transition to your discoveries: What did your deep dive reveal? Highlight the key insights that have the most direct impact on the problem or question you began with. It’s about sifting through your data haystack to reveal the ‘golden needles.’

Close with action steps: Armed with these revelations, what should your audience do next? Offer clear, data-backed recommendations that lead to measurable results.

Step 3: Fine-tune your data visualizations

Chances are, you’ve already developed some form of data visuals during your analysis. Now’s the time to refine them. Do they successfully spotlight the most critical data points? If yes, focus on arranging them in a sequence that enhances your narrative flow. If not, go back to the drawing board and conjure new visuals that can do your ‘golden needles’ justice.

By following these steps, you not only make your data understandable but also imbue it with meaning and actionable insights, making your data storytelling endeavors not just digestible, but also indispensable.

Step 4: Lay out a familiar story arc

To make your data story resonate, consider employing a storytelling structure that your audience is already comfortable with. This includes an introduction to set the stage, a build-up that incrementally raises the stakes or complexities, a climax that delivers the pivotal data insight, followed by a resolution that ties up loose ends. Utilizing a well-known narrative framework helps your audience navigate through the data points effortlessly, and fully grasp the significance and implications of what the data reveals.

Step 5: Take your story public

Once you’ve hammered out a compelling narrative backed by solid data and visuals, it’s time to get it out into the world. A presentation deck is often the go-to medium for sharing your data story. It allows you to encapsulate each aspect of your narrative, from initial context to final conclusions, in a way that’s both visually appealing and easily digestible.

Step 6: Refine for precision and clarity

The last step in data storytelling is often the most overlooked: editing for conciseness and lucidity. Your data story needs to be both captivating and straightforward. This means cutting away any extraneous information or decorative language that doesn’t serve the central narrative. Blaise Pascal once said, “If I had more time, I would have written a shorter letter.” This sentiment rings true for data stories as well; refining your narrative to its most essential elements will ensure your audience remains engaged and walks away with the key takeaways.

Why is data storytelling important?

The significance of data storytelling lies in its ability to contextualize and simplify complex data, making it accessible and understandable to a wide-ranging audience. Unlike dry statistics or raw data, storytelling weaves these elements into a narrative that not only illustrates what the data is, but also why it matters. This creates a deeper emotional connection and engagement, driving home the implications of the data in a more impactful way.

Data storytelling accommodates various learning preferences, from auditory to visual to kinesthetic, enhancing its reach and effectiveness. Whether through a narrated presentation for those who learn best through listening, or through charts and graphs for visual learners, a well-crafted data story can adapt its medium to best engage its audience.

By employing a mix of these elements, data storytelling ensures that its message resonates across a diverse set of listeners or viewers, making the data not just informative, but also persuasive and memorable.

5 data storytelling examples you should check out

Let us explore a curated selection of some of the best data storytelling examples.

Chris Williams: Fry Universe

The perpetual debate surrounding the optimum type of fried potato is humorously explored here. The project uses visuals to demonstrate how different ratios of fried-to-unfried surface areas significantly influence the gastronomic experience.

Check it out!

Periscopic: US Gun Deaths

The focus of this visualization is the “stolen years” attributable to fatalities from firearms. It excels in evoking an emotional response, masterfully unfolding the data in stages to engage the viewer deeply.

Check it out!

Krisztina Szucs: Animated Sport Results

Rather than traditional narratives, these are effervescent, animated data vignettes that depict the dynamics of various sports competitions. Szucs employs a variety of visualization styles to match different scoring methods, all while capturing the essence and excitement of each event better than any conventional box score could.

Check it out!

Kayla Brewer: Cicadas, A Data Story

This data story offers an educational adventure about the appearance of Cicada ‘Brood X,’ colloquially described as “small fly bois bring big noise.” The project showcases how a public dataset can become a compelling data exploration using the Juicebox platform.

Check it out!

Jonathan Harris: We Feel Fine

This early pioneer in data storytelling is an interactive platform that scans the web at 10-minute intervals to collect expressions of human feelings from blogs. It then presents the data in several visually striking formats. It has been a source of inspiration for many in the data visualization field.

Check it out!

Databases are the unsung heroes of AI

Why it matters

Data storytelling is far from a nice-to-have skill; it’s a necessity in today’s data-driven world. It’s about making complex data easy to understand, relevant, and actionable. As we’ve shown, the impact of a well-crafted data story extends beyond mere understanding—it influences decisions. The examples provided underscore the wide range of applications and the potential to make your data not just understandable but also impactful.

Accurate detection and recognition of human emotions are significant challenges in various fields, including psychology, human-computer interaction, and mental health. The advancement of artificial intelligence provides new opportunities to automate these processes by leveraging multimedia data, such as voice, body language, and facial expressions. This publication presents an in-depth analysis of the latest artificial intelligence techniques used for emotion detection, providing detailed technical explanations, discussing their advantages and limitations, and identifying future perspectives for a better understanding and utilization of these methods.

Accurately detecting human emotions is a complex and multidimensional challenge that has garnered increasing interest in the field of artificial intelligence. Machine learning, computer vision, and signal processing techniques have been extensively explored to address this problem by leveraging information from various multimedia data sources. This publication aims to provide an in-depth analysis of the most relevant artificial intelligence techniques, delving into their technical foundations, examining their strengths and limitations, and identifying future prospects for enhanced comprehension and application of these methods.

In-depth analysis of artificial intelligence techniques for emotion detection

Voice analysis

Voice analysis is a commonly used method for emotion detection. Emotions can be expressed through various acoustic and prosodic features present in the vocal signal. Machine learning techniques, including deep neural networks and acoustic models, are often used to extract these features and predict emotional states.

• Acoustic features: Acoustic features include parameters such as fundamental frequency, energy, spectral content, and formants. Fundamental frequency is related to voice pitch and can provide information about emotional state. Energy reflects the intensity of the vocal signal and can be used to detect expressiveness variations. Spectral content represents the frequency energy distribution in the vocal signal, while formants are resonance peaks in the vocal tract and can be used to differentiate emotions.
• Prosodic features: Prosodic features are related to the melodic and rhythmic aspects of speech. They include parameters such as duration, intensity, and frequency variations. Emotions can modify these prosodic features, for example, by increasing speech rate during emotional excitement or elongating pauses during sadness.
• Machine learning models: Machine learning models, such as support vector machines, recurrent neural networks, and convolutional neural networks, are used to predict emotional states from the acoustic and prosodic features extracted from the voice. These models can be trained on annotated datasets, where each vocal recording is associated with a specific emotion. Deep learning techniques have particularly excelled in emotion detection from voice.

Body language analysis

Body language analysis is a crucial approach in emotion detection as it captures emotional signals expressed through body movements, gestures, and postures. The use of artificial intelligence techniques for body language analysis opens up new possibilities for accurate emotion detection and enhancing human-machine interactions.

• Extraction of body language features: The fundamental step in body language analysis is to extract meaningful features from motion data. This can be achieved using various techniques such as motion analysis, joint detection, and temporal segmentation of gestures. Motion data can come from various sources, including videos, motion sensors, and virtual reality technologies.
• Modeling body language with machine learning: Once the body language features have been extracted, machine learning models can be used to learn and predict emotions from this data. Recurrent Neural Networks (RNNs) are commonly used to capture temporal dependencies in motion sequences. Deep learning models, such as Convolutional Neural Networks (CNNs), can also be employed to extract discriminative features from motion data.
• Emotion detection from body language: Once the model has been trained, it can be used to detect emotions from body language signals. This may involve the classification of discrete emotions such as joy, sadness, anger, etc., or the prediction of continuous emotional dimensions such as emotional intensity. Training emotion detection models from body language typically requires annotated datasets where gestures are associated with specific emotional states.
• Integration of body language with other modalities: To achieve more accurate emotion detection, it is common to integrate body language with other modalities such as voice and facial expressions. By combining information from multiple multimedia sources, it is possible to enhance the robustness and reliability of emotion detection. This can be achieved using data fusion approaches, such as decision fusion or feature fusion, which combine information from different sources.
• Applications of body language analysis: Body language analysis finds applications in various domains, including psychology, mental health, human-machine interactions, and virtual reality. For example, in the field of psychology, body language analysis can be used to study emotional responses during specific social situations. In human-machine interactions, it can enable the development of more intuitive and empathetic interfaces by adapting responses based on the emotions expressed by users.

Body language analysis is a promising approach in emotion detection, capturing emotional signals expressed through body movements and gestures. Artificial intelligence techniques, including machine learning and neural network modeling, enable the extraction of meaningful features and prediction of emotions from body language. By integrating body language with other modalities, the accuracy and reliability of emotion detection can be improved. The applications of body language analysis are vast, ranging from psychology to human-machine interaction.

Facial expression analysis

Facial expression analysis is a commonly used approach for emotion detection. It relies on understanding the visual information present in human facial expressions, such as facial muscle movements, shape changes, and texture variations. Artificial intelligence techniques, particularly computer vision and machine learning, have led to significant advancements in this field.

• Face detection: The first step in facial expression analysis is to detect and locate faces in an image or video sequence. Face detection algorithms based on geometric models, such as the Haar cascades model, or machine learning-based approaches, such as convolutional neural networks (CNNs), have been used to perform this task. CNNs, in particular, have shown superior performance due to their ability to automatically extract discriminative features from images.
• Facial feature extraction: Once faces are detected, it is essential to extract relevant features from facial expressions. Various approaches have been used to represent these features, including:
  • Geometric descriptors: These descriptors capture the relative positions of facial landmarks, such as the eyes, eyebrows, nose, and mouth. Algorithms such as fiducial landmark detection and shape vector representation have been employed to extract these descriptors.
  • Motion-based descriptors: These descriptors capture the temporal variations in facial expressions, focusing on changes in the position and intensity of facial landmarks over time. Techniques such as optical flow and landmark tracking have been used to extract these descriptors.
  • Machine learning-based descriptors: Convolutional neural networks (CNNs) have been widely used to automatically extract discriminative features from facial expressions. Pre-trained models such as VGGFace, Inception-ResNet, or architectures specifically designed for emotion recognition have enabled obtaining rich and informative representations of facial expressions
• Emotion recognition: Once the features are extracted, various machine learning approaches can be used for emotion recognition from facial expressions. These approaches include:
  • Traditional classifiers: Traditional classification algorithms, such as Support Vector Machines (SVMs) and linear classifiers, have been used to predict emotional states from the extracted features.
  • Deep Neural Networks: Deep neural networks, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs), have shown remarkable performance in emotion recognition from facial expressions. These networks can learn highly discriminative representations of facial expressions by exploiting the spatial-temporal structure and patterns in the data.
• Datasets: Several datasets have been developed and used by the research community to train and evaluate facial expression detection models. Some commonly used datasets include CK+ (Extended Cohn-Kanade dataset), MMI (Multimedia Understanding Group database), AffectNet, and FER2013 (Facial Expression Recognition 2013).

Perspectives and future challenges: While significant progress has been made in facial expression analysis for emotion detection, challenges persist. Major challenges include:

• Interindividual variability: Facial expressions can vary significantly from person to person, making the task of emotion detection and recognition more complex. Robust strategies need to be developed to account for this variability.
• Biased training data: Machine learning models can be influenced by biases present in the training data, which can lead to biased or non-generalizable results. Approaches for collecting more balanced training data and bias correction techniques are needed.
• Micro-expression detection: Micro-expressions are very brief facial expressions that can provide important insights into felt emotions. Accurate detection and recognition of these micro-expressions pose a major challenge and require advanced techniques.
• Model interpretability: AI models used for emotion detection need to be interpretable to understand the patterns and features influencing predictions. This is particularly important in fields such as clinical psychology, where precise interpretation of results is essential.

In conclusion, facial expression analysis is a commonly used approach for emotion detection from multimedia data. Artificial intelligence techniques, particularly computer vision and machine learning, have shown promising results in this field. However, there are still technical and methodological challenges, such as interindividual variability, biases in training data, and micro-expression detection. Further research is needed to develop more robust and high-performance methods.

Perspectives and future challenges

Despite significant progress in emotion detection using artificial intelligence, there are still several technical and methodological challenges to address. These challenges include interindividual variability in emotional expression, the need for well-annotated and balanced datasets, and the robustness of models against biases introduced by training data. Additionally, generalizing emotion detection models to new cultures, genders, and age groups remains a major challenge.

To tackle these challenges, hybrid approaches that combine multiple sources of multimedia data, such as voice, body language, and facial expressions, could be explored. Furthermore, it is crucial to develop techniques for explainability and transparency to better understand the underlying processes in emotion detection, promoting responsible and ethical use of these artificial intelligence models.

Conclusion

This publication has provided an in-depth analysis of artificial intelligence techniques used for emotion detection from multimedia data. The results demonstrate that approaches based on machine learning, computer vision, and signal processing have the potential to improve emotion detection, but technical and methodological challenges persist. Further research is needed to develop more robust methods, address specific challenges in real-world emotion detection scenarios, and ensure the ethical and responsible use of these technologies. By leveraging the opportunities offered by artificial intelligence, practical applications can be developed in various fields, ranging from clinical psychology to the design of emotionally intelligent user interfaces.

Featured image credit: Andrea Piacquadio/Pexels

Today’s question is, “What does a data scientist do.” Step into the realm of data science, where numbers dance like fireflies and patterns emerge from the chaos of information. In this blog post, we’re embarking on a thrilling expedition to demystify the enigmatic role of data scientists. Think of them as modern-day detectives, archeologists, and alchemists combined, all working their magic to decipher the language of data and unearth the gems hidden within.

Imagine a locked door behind which lies a wealth of secrets waiting to be discovered. Data scientists are the master keyholders, unlocking this portal to reveal the mysteries within. They wield algorithms like ancient incantations, summoning patterns from the chaos and crafting narratives from raw numbers. With a blend of technical prowess and analytical acumen, they unravel the most intricate puzzles hidden within the data landscape.

But make no mistake; data science is not a solitary endeavor; it’s a ballet of complexities and creativity. Data scientists waltz through intricate datasets, twirling with statistical tools and machine learning techniques. They craft models that predict the future, using their intuition as partners in this elegant dance of prediction and possibility.

Prepare to be amazed as we unravel the mysteries and unveil the fascinating world of data science, where data isn’t just numbers; it’s a portal to a universe of insights and possibilities.? Keep reading and learn everything you need to answer the million-dollar question, what does a data scientist do?

What is a data scientist?

At its core, a data scientist is a skilled professional who extracts meaningful insights and knowledge from complex and often large datasets. They bridge the gap between raw data and valuable insights, using a blend of technical skills, domain knowledge, and analytical expertise. Imagine data scientists as modern-day detectives who sift through a sea of information to uncover hidden patterns, trends, and correlations that can inform decision-making and drive innovation.

Data scientists utilize a diverse toolbox of techniques, including statistical analysis, machine learning, data visualization, and programming, to tackle a wide range of challenges across various industries. They possess a unique ability to transform data into actionable insights, helping organizations make informed choices, solve complex problems, and predict future outcomes.

In a nutshell, a data scientist is:

• A problem solver: Data scientists tackle real-world problems by designing and implementing data-driven solutions. Whether it’s predicting customer behavior, optimizing supply chains, or improving healthcare outcomes, they apply their expertise to solve diverse challenges.
• A data explorer: Much like explorers of old, data scientists venture into the unknown territories of data. They dive deep into datasets, discovering hidden treasures of information that might not be apparent to the untrained eye.
• A model builder: Data scientists create models that simulate real-world processes. These models can predict future events, classify data into categories, or uncover relationships between variables, enabling better decision-making.
• An analyst: Data scientists meticulously analyze data to extract meaningful insights. They identify trends, anomalies, and outliers that can provide valuable information to guide business strategies.
• A storyteller: Data scientists don’t just crunch numbers; they are skilled storytellers. They convey their findings through compelling visualizations, reports, and presentations that resonate with both technical and non-technical audiences.
• An innovator: In a rapidly evolving technological landscape, data scientists continuously seek new ways to harness data for innovation. They keep up with the latest advancements in their field and adapt their skills to suit the ever-changing data landscape.

Data scientists play a pivotal role in transforming raw data into actionable knowledge, shaping industries, and guiding organizations toward data-driven success. As the digital world continues to expand, the demand for data scientists is only expected to grow, making them a crucial driving force behind the future of innovation and decision-making.

Wondering, “What does a data scientist do?” Look no further – they manipulate data, build models, and drive informed decisions.

What does a data scientist do: Responsibilities and duties

“What does a data scientist do?” The answer encompasses data exploration, feature engineering, and model refinement. In the grand performance of data science, data scientists don multiple hats, each with a unique flair that contributes to the harmonious masterpiece.

• Data collection and cleaning: Data scientists kick off their journey by embarking on a digital excavation, unearthing raw data from the digital landscape. Just like sifting through ancient artifacts, they meticulously clean and refine the data, preparing it for the grand unveiling.
• Exploratory Data Analysis (EDA): Like intrepid explorers wandering through an uncharted forest, data scientists traverse the terrain of data with curiosity. They create visualizations that resemble vibrant treasure maps, unveiling trends, anomalies, and secrets hidden within the data’s labyrinth.
• Model development: Crafting magic from algorithms! Picture data scientists as wizards conjuring spells from algorithms. They build models that can predict the future, classify the unknown, and even find patterns in the seemingly chaotic.
• Feature engineering: In the alchemical process of data science, data scientists are the masters of distillation. They transform raw ingredients (data) into refined essences (features) that fuel their predictive concoctions.
• Machine learning and AI: Are you ready to casting predictive spells? Enter the realm of enchantment where data scientists train machine learning models. It’s a bit like teaching a dragon to dance – a careful choreography of parameters and data to breathe life into these models.
• Evaluation and optimization: Data scientists embark on a quest to fine-tune their creations. It’s a journey of trial and error, with the goal of crafting models that are as accurate as a marksman’s arrow.
• Communication and visualization: Data scientists don’t just crunch numbers; they weave tales. Like master storytellers, they craft visualizations and reports that captivate the minds of decision-makers and stakeholders.

At the nexus of technology and analysis, the solution to “What does a data scientist do?” becomes clear: they wield data as a compass.

Is data science a good career?

What does a data scientist do: The impact on industries

The impact of data scientists extends far and wide, like ripples from a stone cast into a pond.

Let’s explore the realms they conquer:

• Healthcare: Data scientists are like healers armed with foresight in healthcare. They predict disease outbreaks, patient outcomes, and medical trends, aiding doctors in delivering timely interventions.
• Finance: Imagine data scientists as financial wizards, foreseeing market trends and curating investment strategies that seem almost magical in their precision.
• Retail and e-commerce: In the world of retail, data scientists craft potions of customer satisfaction. They analyze buying behaviors and concoct personalized recommendations that leave shoppers spellbound.
• Manufacturing: In manufacturing, data scientists work like production sorcerers, optimizing processes, reducing defects, and ensuring every cog in the machinery dances to the tune of efficiency.
• Social Sciences: Data scientists are also modern-day Sherlock Holmes, helping social scientists unravel the mysteries of human behavior, from sentiment analysis to demographic shifts.

Exploring the multifaceted answer to “What does a data scientist do?” reveals their pivotal role in turning data into informed decisions.

What is a data scientist salary?

The salary of a data scientist varies depending on their experience, skills, and location. In the United States, the average salary for a data scientist is $152,260 per year. However, salaries can range from $99,455 to $237,702 per year.

Glimpsing into their world, the response to “What does a data scientist do?” unfolds as a blend of data exploration and storytelling. Here is a breakdown of the average salary for data scientists in different industries:

• Technology: $157,970 per year
• Finance: $156,390 per year
• Healthcare: $147,460 per year
• Retail: $139,170 per year
• Government: $136,020 per year

Data scientists in large cities tend to earn higher salaries than those in smaller cities. For example, the average salary for a data scientist in San Francisco is $165,991 per year, while the average salary for a data scientist in Austin, Texas, is $129,617 per year.

When pondering, “What does a data scientist do?” remember their art of turning data chaos into strategic clarity.

Where do data scientists work?

Data scientists work in a variety of industries, including:

• Technology: Technology companies are always looking for data scientists to help them develop new products and services. Some of the biggest tech companies that hire data scientists include Google, Facebook, Amazon, and Microsoft.
• Finance: Financial institutions use data scientists to analyze market data, predict trends, and make investment decisions. Some of the biggest financial institutions that hire data scientists include Goldman Sachs, Morgan Stanley, and JP Morgan Chase.
• Healthcare: Healthcare organizations use data scientists to improve patient care, develop new treatments, and reduce costs. Some of the biggest healthcare organizations that hire data scientists include Kaiser Permanente, Mayo Clinic, and Johns Hopkins Hospital.
• Retail: Retail companies use data scientists to understand customer behavior, optimize inventory, and personalize marketing campaigns. Some of the biggest retail companies that hire data scientists include Walmart, Amazon, and Target.
• Government: Government agencies use data scientists to analyze data, make policy decisions, and fight crime. Some of the biggest government agencies that hire data scientists include the Department of Defense, the Department of Homeland Security, and the National Security Agency.

In addition to these industries, data scientists can also work in a variety of other sectors, such as education, manufacturing, and transportation. The demand for data scientists is growing rapidly, so there are many opportunities to find a job in this field.

Here are some specific examples of companies that hire data scientists:

• Google: Google is one of the biggest tech companies in the world, and they hire data scientists to work on a variety of projects, such as developing new search algorithms, improving the accuracy of Google Maps, and creating personalized advertising campaigns.
• Facebook: Facebook is another big tech company that hires data scientists. Data scientists at Facebook work on projects such as developing new ways to recommend friends, predicting what content users will like, and preventing the spread of misinformation.
• Amazon: Amazon is a major e-commerce company that hires data scientists to work on projects such as improving the accuracy of product recommendations, optimizing the shipping process, and predicting customer demand.
• Microsoft: Microsoft is a software company that hires data scientists to work on projects such as developing new artificial intelligence (AI) technologies, improving the security of Microsoft products, and analyzing customer data.
• Walmart: Walmart is a major retailer that hires data scientists to work on projects such as optimizing inventory, reducing food waste, and personalizing marketing campaigns.

These are just a few examples of companies that hire data scientists. As the demand for data scientists continues to grow, there will be even more opportunities to find a job in this field.

At the heart of the question, “What does a data scientist do?” lies their ability to craft algorithms that illuminate trends.

Data scientist vs data analyst: A needed comparison

The differences between these two terms, which are often confused, are as follows:

How long does it take to become a data scientist?

The amount of time it takes to become a data scientist varies depending on your educational background, prior experience, and the skills you want to learn. Suppose you have a bachelor’s degree in a related field, such as computer science, mathematics, or statistics. In that case, you can become a data scientist in about 2 years by completing a master’s degree in data science or a related field.

If you don’t have a bachelor’s degree in a related field, you can still become a data scientist by completing a boot camp or an online course. However, you will need to be self-motivated and have a strong foundation in mathematics and statistics.

No matter what path you choose, gaining experience in data science by working on projects, participating in hackathons, and volunteering is important.

Here is a general timeline for becoming a data scientist:

• 0-2 years: Complete a bachelor’s degree in a related field.
• 2-3 years: Complete a master’s degree in data science or a related field.
• 3-5 years: Gain experience in data science by working on projects, participating in hackathons, and volunteering.
• 5+ years: Build your portfolio and apply for data science jobs.

Of course, this is just a general timeline. The time it takes to become a data scientist will vary depending on your circumstances. However, if you are passionate about data science and willing to work hard, you can become a data scientist in 2-5 years.

If you want to learn how to become a data scientist, visit the related article and explore! The magic of “What does a data scientist do?” is in their ability to transform raw data into strategic wisdom.

Shaping tomorrow’s horizons

At its core, the answer to “What does a data scientist do?” revolves around transforming data into a strategic asset.

As we conclude our journey through the captivating landscape of data science, remember that data scientists are the architects of insights, the conjurers of predictions, and the artists of transformation. They wield algorithms like wands, uncovering the extraordinary within the ordinary. The future lies in the hands of these modern explorers, charting uncharted territories and sculpting a world where data illuminates the path ahead.

So, the next time you encounter a data scientist, remember they are not just crunching numbers – they are painting the canvas of our data-driven future with strokes of innovation and brilliance!

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In the world of science, one of the most sought-after materials has been the elusive ambient-pressure superconductor that operates at room temperature. Such a discovery could revolutionize the electricity and electronics industries by enabling the transmission of electricity without any resistance, leading to unprecedented efficiency and technological advancements.

Recently, a team of physicists from South Korea made headlines with their claim to have created the first room-temperature, ambient-pressure superconductor, LK-99. To understand the significance of room-temperature ambient-pressure superconductors, we must first grasp the concept of superconductivity. When electrons flow through a typical conductive material, they encounter obstacles in the form of atoms, leading to resistance, which results in heat dissipation and energy loss. Superconductivity, however, offers a fascinating phenomenon. At extremely low temperatures, close to absolute zero, electrons can pair up and move effortlessly through the material, defying resistance and conducting electricity without any loss. This lack of resistance leads to near-perfect energy transmission.

Check out the LK-99 APS evidence

Traditionally, superconductors required ultra-cold temperatures to exhibit their remarkable properties, making their practical applications limited to specialized industries. The discovery of “high-temperature” superconductors in the late 1980s brought renewed hope, as they could operate at temperatures achievable using relatively inexpensive liquid nitrogen. Nonetheless, these high-temperature superconductors remained impractically brittle and challenging to work with, hindering widespread adoption.

Ambient-pressure superconductor discovered by a Korean research team

The holy grail of superconductivity has been the quest for a material that can achieve superconductivity at room temperature and under normal atmospheric pressure. The recent claim by the Korean team, stating they have created the first room-temperature, ambient-pressure superconductor, opens up unprecedented possibilities for technology and physics.

What is LK-99: Visit the related article and learn possible room temperature superconductor use cases

The research team from South Korea introduced their breakthrough material, LK-99, synthesized through a solid-state reaction between lanarkite (Pb2SO5) and copper phosphide (Cu3P). LK-99 possesses a unique structure with a modified lead-apatite structure that allows it to maintain and exhibit superconductivity at room temperature and ambient pressure. Notably, LK-99’s superconductivity arises from minute structural distortion due to slight volume shrinkage caused by Cu2+ substitution of Pb2+ ions in the insulating network of Pb(2)-phosphate. This structural distortion creates superconducting quantum wells (SQWs) in the cylindrical column interface of LK-99.

In their preprint papers, the researchers demonstrated various characteristics of superconductivity in LK-99. The critical temperature (Tc) of LK-99 was reported to be above 400 K (127°C), marking its ability to achieve superconductivity at room temperature. The team observed a sharp drop in electrical resistivity around 378 K (220°C) and near-zero resistivity at 333 K (140°C), further supporting the claim of superconductivity. Additionally, the researchers presented evidence of the Meissner effect, a hallmark of superconductivity, where LK-99 exhibited levitation when placed on a magnet.

LK-99 left the scientific community in excitement and skepticism

The announcement of room-temperature ambient-pressure superconductors generated widespread excitement and anticipation. The potential applications of such materials are vast and could bring about revolutionary changes in multiple industries.

Among the possibilities are:

• Much more efficient batteries
• Quantum computers
• Storage of renewable energy sources
• Power and range leap in air, sea, and land vehicles
• Super-fast magnetic trains
• Increased efficiency in energy distribution

Much more efficient batteries

LK99, the room-temperature superconductor, could revolutionize battery technology. Its use in batteries could lead to significantly higher energy storage capacities and faster charging times for various devices, such as smartphones, laptops, and electric vehicles. This would enhance daily usage by providing longer-lasting and more reliable power sources.

Quantum computers

The development of LK99 could be a major breakthrough for quantum computing. Superconducting materials are crucial for creating and maintaining the delicate quantum states required for processing complex computations. If LK99 proves to be a viable room-temperature superconductor, it could pave the way for more accessible and practical quantum computers, enabling faster and more powerful data processing for various industries.

Storage of renewable energy sources

Renewable energy sources, such as solar and wind, often generate power intermittently. With LK99’s potential as a room-temperature superconductor, it could be used to efficiently store surplus energy during peak production times. This stored energy could then be released during periods of low energy generation, ensuring a consistent and stable supply of renewable energy, making it more feasible to rely on clean energy sources for daily power needs.

Power and range leap in air, sea, and land vehicles

The application of LK99 in electrical motors and propulsion systems could lead to significant advancements in transportation. Electric vehicles (EVs), airplanes, ships, and trains could benefit from improved energy efficiency and performance. With LK99, EVs could have longer ranges and faster charging capabilities, making them more practical for daily commuting and reducing carbon emissions.

Super-fast magnetic trains

Magnetic levitation (maglev) trains, which already achieve impressive speeds, could experience even greater advancements with LK99. By reducing energy loss during propulsion, the superconductor could enable maglev trains to achieve higher speeds and improve daily commuting for passengers in urban areas.

Increased efficiency in energy distribution

The implementation of LK99 in electrical power transmission systems could significantly minimize energy losses during long-distance distribution. This enhanced efficiency would result in reduced electricity costs and a more reliable power grid, benefiting households and industries alike in their daily use of electricity.

It is of utmost importance to underscore that the aforementioned application domains are purely conjectural and yet to be sanctioned by the scientific fraternity. As of the present moment, the conception and realization of a room-temperature superconductor akin to LK99 remain unattested, and its veritable potentials and pragmatic utilities stand shrouded in ambiguity.

However, amid the excitement, there is also skepticism. The field of superconductivity has witnessed numerous past claims of room-temperature superconductors that failed to withstand rigorous scrutiny. Therefore, the scientific community remains cautious and urges further validation of the Korean team’s findings. Peer-reviewed studies and independent replication of results are essential to establish the validity of their discovery.

Step by step to the future we dream of

The future we dream of is fast approaching, driven by a wave of groundbreaking innovations that promise to revolutionize the way we live, work, and interact with the world around us.

Artificial Intelligence, once confined to science fiction, has now become an integral part of our daily lives. AI’s ability to process vast amounts of data, learn from patterns, and make autonomous decisions has led to transformative applications in various domains. In industries like finance, healthcare, and logistics, AI-driven algorithms optimize operations, enhance decision-making, and improve efficiency.

VR and AR technologies are redefining the way we perceive and interact with our surroundings. VR immerses users in computer-generated environments, opening new possibilities in gaming, education, training, and therapy. On the other hand, AR overlays digital elements onto the real world, enriching experiences ranging from navigation to industrial maintenance. The merging of AI and VR/AR is leading to powerful applications, such as AI-powered AR assistants and virtual training simulations. As these technologies advance, they have the potential to reshape education, entertainment, and communication, bringing us closer to a seamlessly blended physical and digital reality.

It’s time for a leap forward in education

Generative tools, empowered by AI and machine learning, are unlocking unparalleled creativity. From generative art and music to content creation and design, these tools offer novel ways to explore and express ideas. Designers, artists, and content creators can harness generative algorithms to produce unique and inspiring works. Moreover, generative adversarial networks (GANs) have demonstrated the ability to create realistic images and even aid in drug discovery. As these technologies mature, they hold the potential to revolutionize creative industries and open doors to unexplored artistic territories.

The shift towards electric vehicles (EVs) marks a pivotal moment in the quest for sustainable transportation. EVs significantly reduce greenhouse gas emissions and dependency on fossil fuels. As battery technology advances, EVs offer longer ranges and faster charging times, making them increasingly practical for daily use. With governments and industries committing to the electrification of transportation, we are witnessing the emergence of EV ecosystems, encompassing charging infrastructure, renewable energy integration, and smart grid technologies. The transition to EVs is not only transforming the automotive industry but also contributing to a greener and cleaner future.

The Internet of Things has interconnected everyday objects, creating a vast network of devices capable of exchanging data and information. IoT-enabled smart homes, wearables, and industrial devices enhance efficiency, convenience, and safety. Smart cities are using IoT to optimize traffic management, waste disposal, and energy consumption. With 5G networks becoming more prevalent, the IoT landscape is poised to grow even further, enabling real-time communication, edge computing, and AI-driven automation. IoT devices are fostering a more connected and data-driven world, facilitating seamless integration and enhancing overall quality of life.

The prospect of room-temperature ambient-pressure superconductors has captured the imagination of scientists and the public alike. The potential impact of such a breakthrough on electricity transmission, electronics, transportation, and medical applications is enormous. However, while the discovery by the Korean team is promising, it is vital to approach it with scientific rigor and skepticism until further peer-reviewed research validates the claim. If confirmed, the era of room-temperature superconductors could usher in a new era of technological advancement, pushing the boundaries of what we once thought was possible.

Featured image credit: Image by vecstockon on Freepik.

Data preprocessing is a fundamental and essential step in the field of sentiment analysis, a prominent branch of natural language processing (NLP). Sentiment analysis focuses on discerning the emotions and attitudes expressed in textual data, such as social media posts, product reviews, customer feedback, and online comments. By analyzing the sentiment of users towards certain products, services, or topics, sentiment analysis provides valuable insights that empower businesses and organizations to make informed decisions, gauge public opinion, and improve customer experiences.

In the digital age, the abundance of textual information available on the internet, particularly on platforms like Twitter, blogs, and e-commerce websites, has led to an exponential growth in unstructured data. This unstructured nature poses challenges for direct analysis, as sentiments cannot be easily interpreted by traditional machine learning algorithms without proper preprocessing.

The goal of data preprocessing in sentiment analysis is to convert raw, unstructured text data into a structured and clean format that can be readily fed into sentiment classification models. Various techniques are employed during this preprocessing phase to extract meaningful features from the text while eliminating noise and irrelevant information. The ultimate objective is to enhance the performance and accuracy of the sentiment analysis model.

Role of data preprocessing in sentiment analysis

Data preprocessing in the context of sentiment analysis refers to the set of techniques and steps applied to raw text data to transform it into a suitable format for sentiment classification tasks. Text data is often unstructured, making it challenging to directly apply machine learning algorithms for sentiment analysis. Preprocessing helps extract relevant features and eliminate noise, improving the accuracy and effectiveness of sentiment analysis models.

The process of data preprocessing in sentiment analysis typically involves the following steps:

• Lowercasing: Converting all text to lowercase ensures uniformity and prevents duplication of words with different cases. For example, “Good” and “good” will be treated as the same word
• Tokenization: Breaking down the text into individual words or tokens is crucial for feature extraction. Tokenization divides the text into smaller units, making it easier for further analysis
• Removing punctuation: Punctuation marks like commas, periods, and exclamation marks do not contribute significantly to sentiment analysis and can be removed to reduce noise
• Stopword removal: Commonly occurring words like “the,” “and,” “is,” etc., known as stopwords, are removed as they add little value in determining the sentiment and can negatively affect accuracy
• Lemmatization or Stemming: Lemmatization reduces words to their base or root form, while stemming trims words to their base form by removing prefixes and suffixes. These techniques help to reduce the dimensionality of the feature space and improve classification efficiency
• Handling negations: Negations in text, like “not good” or “didn’t like,” can change the sentiment of the sentence. Properly handling negations is essential to ensure accurate sentiment analysis
• Handling intensifiers: Intensifiers, like “very,” “extremely,” or “highly,” modify the sentiment of a word. Handling these intensifiers appropriately can help in capturing the right sentiment
• Handling emojis and special characters: Emojis and special characters are common in text data, especially in social media. Processing these elements correctly is crucial for accurate sentiment analysis
• Handling rare or low-frequency words: Rare or low-frequency words may not contribute significantly to sentiment analysis and can be removed to simplify the model
• Vectorization: Converting processed text data into numerical vectors is necessary for machine learning algorithms to work. Techniques like Bag-of-Words (BoW) or TF-IDF are commonly used for this purpose

Data preprocessing is a critical step in sentiment analysis as it lays the foundation for building effective sentiment classification models. By transforming raw text data into a clean, structured format, preprocessing helps in extracting meaningful features that reflect the sentiment expressed in the text.

For instance, sentiment analysis on movie reviews, product feedback, or social media comments can benefit greatly from data preprocessing techniques. The cleaning of text data, removal of stopwords, and handling of negations and intensifiers can significantly enhance the accuracy and reliability of sentiment classification models. Applying preprocessing techniques ensures that the sentiment analysis model can focus on the relevant information in the text and make better predictions about the sentiment expressed by users.

Influence of data preprocessing on text classification

Text classification is a significant research area that involves assigning natural language text documents to predefined categories. This task finds applications in various domains, such as topic detection, spam e-mail filtering, SMS spam filtering, author identification, web page classification, and sentiment analysis.

The process of text classification typically consists of several stages, including preprocessing, feature extraction, feature selection, and classification.

Different languages, different results

Numerous studies have delved into the impact of data preprocessing methods on text classification accuracy. One aspect explored in these studies is whether the effectiveness of preprocessing methods varies between languages.

For instance, a study compared the performance of preprocessing methods for English and Turkish reviews. The findings revealed that English reviews generally achieved higher accuracy due to differences in vocabulary, writing styles, and the agglutinative nature of the Turkish language.

This suggests that language-specific characteristics play a crucial role in determining the effectiveness of different data preprocessing techniques for sentiment analysis.

A systematic approach is the key

To enhance text classification accuracy, researchers recommend performing a diverse range of preprocessing techniques systematically. The combination of different preprocessing methods has proven beneficial in improving sentiment analysis results.

For example, stopword removal was found to significantly enhance classification accuracy in some datasets. At the same time, in other datasets, improvements were observed with the conversion of uppercase letters into lowercase letters or spelling correction. This emphasizes the need to experiment with various preprocessing methods to identify the most effective combinations for a given dataset.

Bag-of-Words representation

The bag-of-words (BOW) representation is a widely used technique in sentiment analysis, where each document is represented as a set of words. Data preprocessing significantly influences the effectiveness of the BOW representation for text classification.

Researchers have performed extensive and systematic experiments to explore the impact of different combinations of preprocessing methods on benchmark text corpora. The results suggest that a thoughtful selection of preprocessing techniques can lead to improved accuracy in sentiment analysis tasks.

Requirements for data preprocessing

To ensure the accuracy, efficiency, and effectiveness of these processes, several requirements must be met during data preprocessing. These requirements are essential for transforming unstructured or raw data into a clean, usable format that can be used for various data-driven tasks.

Data completeness

One of the primary requirements for data preprocessing is ensuring that the dataset is complete, with minimal missing values. Missing data can lead to inaccurate results and biased analyses. Data scientists must decide on appropriate strategies to handle missing values, such as imputation with mean or median values or removing instances with missing data. The choice of approach depends on the impact of missing data on the overall dataset and the specific analysis or model being used.

Data cleaning

Data cleaning is the process of identifying and correcting errors, inconsistencies, and inaccuracies in the dataset. It involves removing duplicate records, correcting spelling errors, and handling noisy data. Noise in data can arise due to data collection errors, system glitches, or human errors.

By addressing these issues, data cleaning ensures the dataset is free from irrelevant or misleading information, leading to improved model performance and reliable insights.

Data transformation

Data transformation involves converting data into a suitable format for analysis and modeling. This step includes scaling numerical features, encoding categorical variables, and transforming skewed distributions to achieve better model convergence and performance.

How to become a data scientist

Data transformation also plays a crucial role in dealing with varying scales of features, enabling algorithms to treat each feature equally during analysis

Noise reduction

As part of data preprocessing, reducing noise is vital for enhancing data quality. Noise refers to random errors or irrelevant data points that can adversely affect the modeling process.

Techniques like binning, regression, and clustering are employed to smooth and filter the data, reducing noise and improving the overall quality of the dataset.

Feature engineering

Feature engineering involves creating new features or selecting relevant features from the dataset to improve the model’s predictive power. Selecting the right set of features is crucial for model accuracy and efficiency.

Feature engineering helps eliminate irrelevant or redundant features, ensuring that the model focuses on the most significant aspects of the data.

Handling imbalanced data

In some datasets, there may be an imbalance in the distribution of classes, leading to biased model predictions. Data preprocessing should include techniques like oversampling and undersampling to balance the classes and prevent model bias.

This is particularly important in classification algorithms to ensure fair and accurate results.

Data integration

Data integration involves combining data from various sources and formats into a unified and consistent dataset. It ensures that the data used in analysis or modeling is comprehensive and comprehensive.

Integration also helps avoid duplication and redundancy of data, providing a comprehensive view of the information.

Exploratory data analysis (EDA)

Before preprocessing data, conducting exploratory data analysis is crucial to understand the dataset’s characteristics, identify patterns, detect outliers, and validate missing values.

EDA provides insights into the data distribution and informs the selection of appropriate preprocessing techniques.

By meeting these requirements during data preprocessing, organizations can ensure the accuracy and reliability of their data-driven analyses, machine learning models, and data mining efforts. Proper data preprocessing lays the foundation for successful data-driven decision-making and empowers businesses to extract valuable insights from their data.

What are the best data preprocessing tools of 2023?

In 2023, several data preprocessing tools have emerged as top choices for data scientists and analysts. These tools offer a wide range of functionalities to handle complex data preparation tasks efficiently.

Here are some of the best data preprocessing tools of 2023:

• Microsoft Power BI
• Tableau
• Trifacta
• Talend
• Toad Data Point
• Power Query

Microsoft Power BI

Microsoft Power BI is a comprehensive data preparation tool that allows users to create reports with multiple complex data sources. It offers integration with various sources securely and features a user-friendly drag-and-drop interface for creating reports.

The tool also employs AI capabilities for automatically providing attribute names and short descriptions for reports, making it easy to use and efficient for data preparation.

In recent weeks, Microsoft has included Power BI in Microsoft Fabric, which it markets as the absolute solution for your data problems.

Tableau

Tableau is a powerful data preparation tool that serves as a solid foundation for data analytics. It is known for its ability to connect to almost any database and offers features like reusable data flows, automating repetitive work.

With its user-friendly interface and drag-and-drop functionalities, Tableau enables the creation of interactive data visualizations and dashboards, making it accessible to both technical and non-technical users.

Trifacta

Trifacta is a data profiling and wrangling tool that stands out with its rich features and ease of use. It offers data engineers and analysts various functionalities for data cleansing and preparation.

The platform provides machine learning models, enabling users to interact with predefined codes and select options as per business requirements.

Talend

Talend Data Preparation tool is known for its exhaustive set of tools for data cleansing and transformation. It facilitates data engineers in performing tasks like handling missing values, outliers, redundant data, scaling, imbalanced data, and more.

Additionally, it provides machine learning models for data preparation purposes.

Toad Data Point

Toad Data Point is a user-friendly tool that makes querying and updating data with SQL simple and efficient. Its click-of-a-button functionality empowers users to write and update queries easily, making it a valuable asset in the data toolbox for data preparation and transformation.

Power Query (part of Microsoft Power BI and Excel)

Power Query is a component of Microsoft Power BI, Excel, and other data analytics applications, designed for data extraction, conversion, and loading (ETL) from diverse sources into a structured format suitable for analysis and reporting.

It facilitates preparing and transforming data through its easy-to-use interface and offers a wide range of data transformation capabilities.

Featured image credit: Image by rawpixel.com on Freepik.

Is data science a good career? Long story short, the answer is yes. We understand how career-building steps are stressful and time-consuming. In the corporate world, fast wins. So, if a simple yes has convinced you, you can go straight to learning how to become a data scientist. But if you want to learn more about data science, today’s emerging profession that will shape your future, just a few minutes of reading can answer all your questions. Like your career, it all depends on your choices.

In the digital age, we find ourselves immersed in an ocean of data generated by every online action, device interaction, and business transaction. To navigate this vast sea of information, we need skilled professionals who can extract meaningful insights, identify patterns, and make data-driven decisions. That’s where data science comes into our lives, the interdisciplinary field that has emerged as the backbone of the modern information era. That’s why, in this article, we’ll explore why data science is not only a good career choice but also a thriving and promising one.

Is data science a good career? First, understand the fundamentals of data science

What is data science? Data science can be understood as a multidisciplinary approach to extracting knowledge and actionable insights from structured and unstructured data. It combines techniques from mathematics, statistics, computer science, and domain expertise to analyze data, draw conclusions, and forecast future trends. Data scientists use a combination of programming languages (Python, R, etc.), data visualization tools, machine learning algorithms, and statistical models to uncover valuable information hidden within data.

In recent years, data science has emerged as one of the most promising and sought-after careers in the tech industry. With the exponential growth in data generation and the rapid advancement of technology, the demand for skilled data scientists has skyrocketed.

The growing demand for data scientists

Is data science a good career? The need for skilled data scientists has increased rapidly in recent years. This surge in demand can be attributed to several factors. Firstly, the rapid growth of technology has led to an exponential increase in data generation. Companies now realize that data is their most valuable asset and are eager to harness its power to gain a competitive edge.

Secondly, data-driven decision-making has become necessary for businesses aiming to thrive in the digital landscape. Data science enables organizations to optimize processes, improve customer experiences, personalize marketing strategies, and reduce costs.

The third factor contributing to the rise in demand for data scientists is the development of AI and machine learning. Data scientists play a crucial part in the development and upkeep of these models, which in turn rely largely on vast datasets for training and improvement.

Versatility and industry applications

Is data science a good career? One of the most enticing aspects of a data science career is its versatility. Data scientists are not restricted to a particular industry or sector. In fact, they are in demand across an array of fields, such as:

• E-commerce and retail: Data science is used to understand customer behavior, recommend products, optimize pricing strategies, and forecast demand.
• Healthcare: Data scientists analyze patient data to identify patterns, diagnose diseases, and improve treatment outcomes.
• Finance: In the financial sector, data science is used for fraud detection, risk assessment, algorithmic trading, and personalized financial advice.
• Marketing and Advertising: Data-driven marketing campaigns are more effective, and data science helps in targeted advertising, customer segmentation, and campaign evaluation.
• Technology: Data science is at the core of technology companies, aiding in product development, user analytics, and cybersecurity.
• Transportation and logistics: Data science optimizes supply chains, reduces delivery times, and enhances fleet management.

These are just a few examples, and the list goes on. From agriculture to entertainment, data science finds applications in almost every domain.

Is data science a good career? Here are its advantages

What awaits you if you take part in the data science sector? Let’s start with the positives first:

• High demand and competitive salaries: The growing need for data-driven decision-making across industries has created a tremendous demand for data scientists. Organizations are willing to pay top dollar for skilled professionals who can turn data into actionable insights. As a result, data scientists often enjoy attractive remuneration packages and numerous job opportunities.
• Diverse job roles: Data science offers a wide array of job roles catering to various interests and skill sets. Some common positions include data analyst, machine learning engineer, data engineer, and business intelligence analyst. This diversity allows individuals to find a niche that aligns with their passions and expertise.
• Impactful work: Data scientists are crucial in shaping business strategies, driving innovation, and solving complex problems. Their work directly influences crucial decisions, leading to improved products and services, increased efficiency, and enhanced customer experiences.
• Constant learning and growth: Data science is a rapidly evolving field with new tools, techniques, and algorithms emerging regularly. This constant evolution keeps data scientists on their toes and provides ample opportunities for continuous learning and skill development.
• Cross-industry applicability: Data science skills are highly transferable across industries, allowing professionals to explore diverse sectors, from healthcare and finance to marketing and e-commerce. This versatility provides added job security and flexibility in career choices.
• Big data revolution: The advent of big data has revolutionized the business landscape, enabling data scientists to analyze and interpret massive datasets that were previously inaccessible. This has opened up unprecedented opportunities for valuable insights and discoveries.

Disadvantages and challenges in data science

Is data science a good career? It depends on your reaction to the following. Like every lucrative career option, data science is not easy to handle. Here is why:

• Skill and knowledge requirements: Data science is a multidisciplinary field that demands proficiency in statistics, programming languages (such as Python or R), machine learning algorithms, data visualization, and domain expertise. Acquiring and maintaining this breadth of knowledge can be challenging and time-consuming.
• Data quality and accessibility: The success of data analysis heavily relies on the quality and availability of data. Data scientists often face the challenge of dealing with messy, incomplete, or unstructured data, which can significantly impact the accuracy and reliability of their findings.
• Ethical considerations: Data scientists must be mindful of the ethical implications of their work. Dealing with sensitive data or building algorithms with potential biases can lead to adverse consequences if not carefully addressed.
• Intense competition: As data science gains popularity, the competition for job positions has become fierce. To stand out in the job market, aspiring data scientists need to possess a unique skill set and showcase their abilities through projects and contributions to the community.
• Demanding workload and deadlines: Data science projects can be time-sensitive and require intense focus and dedication. Meeting tight deadlines and managing multiple projects simultaneously can lead to high levels of stress.
• Continuous learning: While continuous learning is advantageous, it can also be challenging. Staying updated with the latest tools, technologies, and research papers can be overwhelming, especially for professionals with limited time and resources.

Are you still into becoming a data scientist? If so, let’s briefly explore the skill and knowledge requirements we mentioned before.

Prerequisites and skills

Embarking on a career in data science requires a solid educational foundation and a diverse skill set. While a degree in data science or a related field is beneficial, it is not the only pathway. Many successful data scientists come from backgrounds in mathematics, computer science, engineering, economics, or natural sciences.

Is data science a good career? If you have the following, especially for you, it can be excellent! Apart from formal education, some key skills are crucial for a data scientist:

• Programming: Proficiency in programming languages like Python, R, SQL, and Java is essential for data manipulation and analysis.
• Statistics and mathematics: A solid understanding of statistics and mathematics is crucial for developing and validating models.
• Data visualization: The ability to create compelling visualizations to communicate insights effectively is highly valued.
• Machine learning: Knowledge of machine learning algorithms and techniques is fundamental for building predictive models.
• Big data tools: Familiarity with big data tools like Hadoop, Spark, and NoSQL databases is advantageous for handling large-scale datasets.
• Domain knowledge: Understanding the specific domain or industry you work in will enhance the relevance and accuracy of your analyses.

If you want to work in the data science industry, you will need to learn a lot! Data science is a rapidly evolving field, and staying up-to-date with the latest technologies and techniques is essential for success. Data scientists must be lifelong learners, always eager to explore new methodologies, libraries, and frameworks. Continuous learning can be facilitated through online courses, workshops, conferences, and participation in data science competitions.

How to build a successful data science career

Do you have all the skills and think you can overcome the challenges? Here is a brief road map to becoming a data scientist:

• Education and skill development: A solid educational foundation in computer science, mathematics, or statistics is essential for aspiring data scientists. Additionally, gaining proficiency in programming languages (Python or R), data manipulation, and machine learning is crucial.
• Hands-on projects and experience: Practical experience is invaluable in data science. Working on real-world projects, contributing to open-source initiatives, and participating in Kaggle competitions can showcase your skills and attract potential employers.
• Domain knowledge: Data scientists who possess domain-specific knowledge can offer unique insights into their respective industries. Developing expertise in a particular domain can give you a competitive edge in the job market.
• Networking and collaboration: Building a strong professional network can open doors to job opportunities and collaborations. Attending data science conferences, meetups, and networking events can help you connect with like-minded professionals and industry experts.
• Continuous learning and adaptation: Stay updated with the latest trends and advancements in data science. Participate in online courses, webinars, and workshops to keep your skills relevant and in demand.

Then repeat the process endlessly.

Conclusion: Is data science a good career?

Yes, data science presents an exciting and rewarding career path for individuals with a passion for data analysis, problem-solving, and innovation. While it offers numerous advantages, such as high demand, competitive salaries, and impactful work, it also comes with its share of challenges, including intense competition and continuous learning requirements.

By focusing on education, practical experience, and staying adaptable to changes in the field, aspiring data scientists can pave the way for a successful and fulfilling career in this dynamic and ever-evolving domain.

Is data science a good career? While the journey to becoming a data scientist may require dedication and continuous learning, the rewards are well worth the effort. Whether you’re a recent graduate or a seasoned professional considering a career transition, data science offers a bright and promising future filled with endless possibilities. So, dive into the world of data science and embark on a journey of exploration, discovery, and innovation. Your data-driven adventure awaits!

Featured image credit: Pexels

If you’ve found yourself asking, “How to become a data scientist?” you’re in the right place.

In this detailed guide, we’re going to navigate the exciting realm of data science, a field that blends statistics, technology, and strategic thinking into a powerhouse of innovation and insights.

From the infinite realm of raw data, a unique professional emerges: the data scientist. Their mission? To sift through the noise, uncover patterns, predict trends, and essentially turn data into a veritable treasure trove of business solutions. And guess what? You could be one of them.

In the forthcoming sections, we’ll illuminate the contours of the data scientist’s world. We’ll dissect their role, delve into their day-to-day responsibilities, and explore the unique skill set that sets them apart in the tech universe. But more than that, we’re here to help you paint a roadmap, a personalized pathway that you can follow to answer your burning question: “How to become a data scientist?”

So, buckle up and prepare for a deep dive into the data universe. Whether you’re a seasoned tech professional looking to switch lanes, a fresh graduate planning your career trajectory, or simply someone with a keen interest in the field, this blog post will walk you through the exciting journey towards becoming a data scientist. It’s time to turn your question into a quest. Let’s get started!

What is a data scientist?

​​Before we answer the question, “how to become a data scientist?” it’s crucial to define who a data scientist is. In simplest terms, a data scientist is a professional who uses statistical methods, programming skills, and industry knowledge to interpret complex digital data. They are detectives of the digital age, unearthing insights that drive strategic business decisions. To put it another way, a data scientist turns raw data into meaningful information using various techniques and theories drawn from many fields within the broad areas of mathematics, statistics, information science, and computer science.

What does a data scientist do?

In the heart of their role, data scientists formulate and solve complex problems to aid a business’s strategy. This involves collecting, cleaning, and analyzing large data sets to identify patterns, trends, and relationships that might otherwise be hidden. They use these insights to predict future trends, optimize operations, and influence strategic decisions.

Life of modern-day alchemists: What does a data scientist do?

Beyond these tasks, data scientists are also communicators, translating their data-driven findings into language that business leaders, IT professionals, engineers, and other stakeholders can understand. They play a pivotal role in bridging the technical and business sides of an organization, ensuring that data insights lead to tangible actions and results.

Essential data scientist skills

If you’re eager to answer the question “how to become a data scientist?”, it’s important to understand the essential skills required in this field. Data science is multidisciplinary, and as such, calls for a diverse skill set. Here, we’ve highlighted a few of the most important ones:

Mathematics and statistics

At the core of data science is a strong foundation in mathematics and statistics. Concepts such as linear algebra, calculus, probability, and statistical theory are the backbone of many data science algorithms and techniques.

Is data science a good career?

Programming skills

A proficient data scientist should have strong programming skills, typically in Python or R, which are the most commonly used languages in the field. Coding skills are essential for tasks such as data cleaning, analysis, visualization, and implementing machine learning algorithms.

Data management and manipulation

Data scientists often deal with vast amounts of data, so it’s crucial to understand databases, data architecture, and query languages like SQL. Skills in manipulating and managing data are also necessary to prepare the data for analysis.

Machine learning

Machine learning is a key part of data science. It involves developing algorithms that can learn from and make predictions or decisions based on data. Familiarity with regression techniques, decision trees, clustering, neural networks, and other data-driven problem-solving methods is vital.

Data visualization and communication

It’s not enough to uncover insights from data; a data scientist must also communicate these insights effectively. This is where data visualization comes in. Tools like Tableau, Matplotlib, Seaborn, or Power BI can be incredibly helpful. Good communication skills ensure you can translate complex findings into understandable insights for business stakeholders.

Your data can have a digital fingerprint

Domain knowledge

Lastly, domain knowledge helps data scientists to formulate the right questions and apply their skills effectively to solve industry-specific problems.

How to become a data scientist?

Data science is a discipline focused on extracting valuable insights from copious amounts of data. As such, professionals skilled in interpreting and leveraging data for their organizations’ advantage are in high demand. As a data scientist, you will be instrumental in crafting data-driven business strategies and analytics. Here’s a newly paraphrased guide to help you get started:

Phase 1: Bachelor’s degree

An excellent entry point into the world of data science is obtaining a bachelor’s degree in a related discipline such as data science itself, statistics, or computer science. This degree is often a primary requirement by organizations when considering candidates for data scientist roles.

Phase 2: Mastering appropriate programming languages

While an undergraduate degree provides theoretical knowledge, practical command of specific programming languages like Python, R, SQL, and SAS is crucial. These languages are particularly pivotal when dealing with voluminous datasets.

Phase 3: Acquiring ancillary skills

Apart from programming languages, data scientists should also familiarize themselves with tools and techniques for data visualization, machine learning, and handling big data. When faced with large datasets, understanding how to manage, cleanse, organize, and analyze them is critical.

Phase 4: Securing recognized certifications

Obtaining certifications related to specific tools and skills is a solid way to demonstrate your proficiency and expertise. These certifications often carry weight in the eyes of potential employers.

Phase 5: Gaining experience through internships

Internships provide a valuable platform to kickstart your career in data science. They offer hands-on experience and exposure to real-world applications of data science. Look for internships in roles like data analyst, business intelligence analyst, statistician, or data engineer.

Phase 6: Embarking on a data science career

After your internship, you may have the opportunity to continue with the same company or start seeking entry-level positions elsewhere. Job titles to look out for include data scientist, data analyst, and data engineer. As you gain more experience and broaden your skill set, you can progress through the ranks and take on more complex challenges.

Journeying into the realms of ML engineers and data scientists

How long does it take to become a data scientist?

“How to become a data scientist?” is a question many aspiring professionals ask, and an equally important question is “How long does it take to become a data scientist?” The answer can vary depending on several factors, including your educational path, the depth of knowledge you need to acquire in relevant skills, and the level of practical experience you need to gain.

Typically, earning a bachelor’s degree takes around four years. Following that, many data scientists choose to deepen their expertise with a master’s degree, which can take an additional two years. Beyond formal education, acquiring proficiency in essential data science skills like programming, data management, and machine learning can vary greatly in time, ranging from a few months to a couple of years. Gaining practical experience through internships and entry-level jobs is also a significant part of the journey, which can span a few months to several years.

Therefore, on average, it could take anywhere from six to ten years to become a fully-fledged data scientist, but it’s important to note that learning in this field is a continuous process and varies greatly from individual to individual.

How to become a data scientist without a degree?

Now that we’ve discussed the traditional route of “how to become a data scientist?” let’s consider an alternate path. While having a degree in a relevant field is beneficial and often preferred by employers, it is possible to become a data scientist without one. Here are some steps you can take to pave your way into a data science career without a degree:

Self-learning

Start by learning the basics of data science online. There are numerous online platforms offering free or low-cost courses in mathematics, statistics, and relevant programming languages such as Python, R, and SQL. Websites like Coursera, edX, and Khan Academy offer a range of courses from beginner to advanced levels.

Specialize in a specific skill

While a data scientist must wear many hats, it can be advantageous to become an expert in a particular area, such as machine learning, data visualization, or big data. Specializing can make you stand out from other candidates.

Learn relevant tools

Familiarize yourself with data science tools and platforms, such as Tableau for data visualization, or Hadoop for big data processing. Having hands-on experience with these tools can be a strong point in your favor.

Build a portfolio

Showcase your knowledge and skills through practical projects. You could participate in data science competitions on platforms like Kaggle, or work on personal projects that you’re passionate about. A strong portfolio can often make up for a lack of formal education.

Networking

Join online communities and attend meetups or conferences. Networking can help you learn from others, stay updated with the latest trends, and even find job opportunities.

Gain experience

While it might be hard to land a data scientist role without a degree initially, you can start in a related role like data analyst or business intelligence analyst. From there, you can learn on the job, gain experience, and gradually transition into a data scientist role.

Remember, the field of data science values skills and practical experience highly. While it’s a challenging journey, especially without a degree, it’s certainly possible with dedication, continual learning, and hands-on experience.

Data scientist salary

According to Glassdoor’s estimates, in the United States, the overall compensation for a data scientist is projected to be around $152,182 annually, with the median salary standing at approximately $117,595 per year. These figures are generated from our unique Total Pay Estimate model and are drawn from salary data collected from users. The additional estimated compensation, which could encompass cash bonuses, commissions, tips, and profit sharing, is around $34,587 per year. The “Most Likely Range” includes salary data that falls within the 25th and 75th percentile for this profession.

In Germany, a data scientist’s estimated annual total compensation is around €69,000, with a median salary of about €64,000 per year. These numbers originate from our unique Total Pay Estimate model and are based on salary figures given by our users. The additional estimated pay, which might consist of cash bonuses, commissions, tips, and profit sharing, stands at approximately €5,000 per year. The “Most Likely Range” here depicts salary data falling within the 25th and 75th percentile for this occupation.

Data scientist vs data analyst

To round out our exploration of “how to become a data scientist?” let’s compare the role of a data scientist to that of a data analyst, as these terms are often used interchangeably, although they represent different roles within the field of data.

In simplest terms, a data analyst is focused on interpreting data and uncovering actionable insights to help guide business decisions. They often use tools like SQL and Excel to manipulate data and create reports.

Data is the new gold and the industry demands goldsmiths

On the other hand, a data scientist, while also interpreting data, typically deals with larger and more complex data sets. They leverage advanced statistical techniques, machine learning, and predictive modeling to forecast future trends and behaviors. In addition to tools used by data analysts, they often require a broader set of programming skills, including Python and R.

While there’s overlap between the two roles, a data scientist typically operates at a higher level of complexity and has a broader skill set than a data analyst. Each role has its unique set of responsibilities and requirements, making them both integral parts of a data-driven organization.

Final words

Back to our original question: How to become a data scientist? The journey is as exciting as it is challenging. It involves gaining a solid educational background, acquiring a broad skill set, and constantly adapting to the evolving landscape of data science.

Despite the effort required, the reward is a career at the forefront of innovation and an opportunity to influence strategic business decisions with data-driven insights. So whether you’re just starting out or looking to transition from a related field, there’s never been a better time to dive into data science. We hope this guide offers you a clear path and inspires you to embark on this exciting journey. Happy data diving!

All images in this post, including the featured image, are generated by Kerem Gülen using Midjouney.

In our increasingly connected world, where data is generated at an astonishing rate, edge processing has emerged as a transformative technology. Edge processing is a cutting-edge paradigm that brings data processing closer to the sources, enabling faster and more efficient analysis. But what exactly is edge processing, and how does it revolutionize the way we harness the power of data?

Simply put, edge processing refers to the practice of moving data processing and storage closer to where it is generated, rather than relying on centralized systems located far away. By placing computational power at the edge, edge processing reduces the distance data needs to travel, resulting in quicker response times and improved efficiency. This technology holds the potential to reshape industries and open up new possibilities for businesses across the globe.

Imagine a world where data is processed right where it is generated, at the edge of the network. This means that the massive volumes of data produced by our devices, sensors, and machines can be analyzed and acted upon in real-time, without the need to transmit it to distant data centers. It’s like having a supercharged brain at the edge, capable of making split-second decisions and unlocking insights that were previously out of reach.

Edge processing introduces a fascinating concept that challenges the traditional approach to data processing. By distributing computational power to the edge of the network, closer to the devices and sensors that collect the data, edge processing offers exciting possibilities. It promises reduced latency, enhanced security, improved bandwidth utilization, and a whole new level of flexibility for businesses and industries seeking to leverage the full potential of their data.

What is edge processing?

Edge processing is a computing paradigm that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth. Edge computing is an architecture rather than a specific technology, and a topology- and location-sensitive form of distributed computing.

In the context of sensors, edge processing refers to the ability of sensors to perform some level of processing on the data they collect before sending it to a central location. This can be done for a variety of reasons, such as to reduce the amount of data that needs to be sent, to improve the performance of the sensor, or to enable real-time decision-making.

How does edge processing work?

Edge processing works by distributing computing and data storage resources closer to the sources of data. This can be done by deploying edge devices, such as gateways, routers, and smart sensors, at the edge of the network. Edge devices are typically equipped with more powerful processors and storage than traditional sensors, which allows them to perform more complex processing tasks.

When data is collected by a sensor, it is first sent to an edge device. The edge device then performs some level of processing on the data, such as filtering, aggregating, or analyzing. The processed data is then either stored on the edge device or sent to a central location for further processing.

Edge computing systems cannot work without these components

An edge computing system comprises several vital components that work together seamlessly to enable efficient data processing and analysis. These components include:

• Edge devices
• Edge software
• Network
• Cloud

Edge devices play a crucial role in an edge computing system. These physical devices are strategically positioned at the network’s edge, near the sources of data. They act as frontline processors, responsible for executing tasks related to data collection, analysis, and transmission. Examples of edge devices include sensors, gateways, and small–scale computing devices.

To effectively manage and control the operations of edge devices, edge software comes into play. Edge software refers to the specialized programs and applications that run on these devices. Its primary purpose is to facilitate data collection from sensors, carry out processing tasks at the edge, and subsequently transmit the processed data to a centralized location or other connected devices. Edge software essentially bridges the gap between the physical world and the digital realm.

The network forms the backbone of an edge computing system, linking the various edge devices together as well as connecting them to a central location. This network can be established through wired or wireless means, depending on the specific requirements and constraints of the system. It ensures seamless communication and data transfer between edge devices, enabling them to collaborate efficiently and share information.

A fundamental component of the overall edge computing infrastructure is the cloud. The cloud serves as a centralized location where data can be securely stored and processed. It provides the necessary computational resources and storage capacity to handle the vast amounts of data generated by edge devices. By utilizing the cloud, an edge computing system can leverage its scalability and flexibility to analyze data, extract valuable insights, and support decision-making processes.

Cloud vs edge computing

Cloud computing and edge computing are two different computing paradigms that have different strengths and weaknesses. Cloud computing is a centralized computing model where data and applications are stored and processed in remote data centers. Edge computing is a decentralized computing model where data and applications are stored and processed closer to the end users.

Here is a table that summarizes the key differences between cloud computing and edge computing:

Edge processing applications are limitless

The applications of edge processing are vast and diverse, extending to numerous domains. One prominent application is industrial automation, where edge processing plays a pivotal role in enhancing manufacturing processes. By collecting data from sensors deployed across the factory floor, edge devices can perform real-time control and monitoring. This empowers manufacturers to optimize efficiency, detect anomalies, and prevent equipment failures, ultimately leading to increased productivity and cost savings.

As for smart cities, edge processing is instrumental in harnessing the power of data to improve urban living conditions. By collecting data from various sensors dispersed throughout the city, edge devices can perform real-time analytics. This enables efficient traffic management, as the system can monitor traffic patterns and implement intelligent strategies to alleviate congestion. Furthermore, edge processing in smart cities facilitates energy efficiency by monitoring and optimizing the usage of utilities, while also enhancing public safety through real-time monitoring of public spaces.

10 edge computing innovators to keep an eye on in 2023

The healthcare industry greatly benefits from edge processing capabilities as well. By collecting data from medical devices and leveraging real-time analytics, healthcare providers can improve patient care and prevent medical errors. For instance, edge devices can continuously monitor patients’ vital signs, alerting medical professionals to any abnormalities or emergencies. This proactive approach ensures timely interventions and enhances patient outcomes.

Edge processing also finds application in the transportation sector. By collecting data from vehicles, such as GPS information, traffic patterns, and vehicle diagnostics, edge devices can perform real-time analytics. This empowers transportation authorities to enhance traffic safety measures, optimize routes, and reduce congestion on roadways. Furthermore, edge processing can facilitate the development of intelligent transportation systems that incorporate real-time data to support efficient and sustainable mobility solutions.

How to implement edge processing in 6 simple steps

To understand how edge computing will affect small businesses, it’s crucial to recognize the potential benefits it brings. By implementing edge computing, small businesses can leverage its capabilities to transform their operations, enhance efficiency, and gain a competitive edge in the market.

Step 1: Define your needs

To begin the implementation of edge computing in your application, the first crucial step is to define your specific edge computing requirements. This involves gaining a clear understanding of the data you need to collect, where it needs to be collected from, and how it should be processed.

By comprehending these aspects, you can effectively design your edge computing system to cater to your unique needs and objectives.

Step 2: Choose an MCU or MPU solution

Once you have defined your requirements, the next step is to choose the appropriate MCU (Microcontroller Unit) or MPU (Microprocessor Unit) solution for your edge devices. MCUs and MPUs are the types of processors commonly utilized in edge devices.

With a variety of options available, it is important to select the one that aligns with your specific needs and technical considerations.

Step 3: Design your core application stack

Designing your core application stack comes next in the implementation process. The core application stack refers to the software that runs on your edge devices, responsible for tasks such as data collection from sensors, edge processing, and transmission of data to a central location.

It is essential to design this application stack in a manner that meets your precise requirements, ensuring seamless functionality and efficient data processing.

Step 4: Implement the application logic in the stack

After designing the core application stack, the subsequent step involves implementing the application logic within the stack. This entails writing the necessary code that enables your edge devices to effectively collect data from sensors, perform edge processing operations, and transmit the processed data to a central location.

By implementing the application logic correctly, you ensure the proper functioning and execution of your edge computing system.

Step 5: Secure the system and monitor usage characteristics

To ensure the security and integrity of your edge computing system, it is crucial to focus on securing the system and monitoring its usage characteristics. This involves implementing robust security measures to protect edge devices from potential cyber threats or unauthorized access.

Additionally, monitoring the system’s usage characteristics allows you to assess its performance, detect any anomalies, and ensure that it operates as expected, delivering the desired outcomes.

Step 6: Monitor usage metrics to ensure optimal performance has been achieved

Lastly, it is important to monitor usage metrics to evaluate the system’s performance and achieve optimal efficiency. This includes monitoring factors such as system latency, bandwidth usage, and energy consumption.

By closely monitoring these metrics, you can identify areas for improvement, make necessary adjustments, and ensure that your edge computing system operates at its highest potential.

The bottom line is, edge computing is a game-changing technology that holds immense promise for businesses and industries worldwide. By bringing data processing closer to the edge, this innovative paradigm opens up a realm of possibilities, empowering organizations to harness the full potential of their data in real time. From faster response times to enhanced efficiency and improved security, edge computing offers a multitude of benefits that can revolutionize how we leverage information.

Throughout this article, we have explored the concept of edge computing, unraveling its potential applications in diverse sectors and shedding light on the exciting opportunities it presents. We have witnessed how edge computing can enable manufacturing processes to become more efficient, how it can transform transportation systems, and how it can revolutionize healthcare, among many other industries.

The era of edge computing is upon us, and it is a thrilling time to witness the convergence of cutting-edge technology and data-driven insights. As businesses embrace the power of edge computing, they gain the ability to make real-time, data-informed decisions, enabling them to stay ahead in today’s fast-paced digital landscape.

Featured image credit: Photo by Mike Kononov on Unsplash.

Unveiling one of the best large language models, OpenAI’s ChatGPT, has provoked a competitive surge in the AI field. A diverse tapestry of participants, ranging from imposing corporate giants to ambitious startups, and extending to the altruistic open-source community, is deeply engrossed in the exciting endeavor to innovate the most advanced large language models.

In the bustling realm of technology in 2023, it’s an inescapable truth: one cannot neglect the revolutionary influence of trending phenomena such as Generative AI and the mighty large language models (LLMs) that fuel the intellect of AI chatbots.

In a whirlwind of such competition, there have already been a plethora of LLMs unveiled – hundreds, in fact. Amid this dizzying array, the key question persists: which models truly stand out as the most proficient? Which are worthy of being crowned among the best large language models? To offer some clarity, we embark on a revealing journey through the finest proprietary and open-source large language models in 2023.

Best large language models (LLMs)

Now, we delve into an eclectic collection of some of the best large language models that are leading the charge in 2023. Rather than offering a strict ranking from the best to the least effective, we present an unbiased compilation of LLMs, each uniquely tailored to serve distinct purposes. This list celebrates the diversity and broad range of capabilities housed within the domain of large language models, opening a window into the intricate world of AI.

GPT-4

The vanguard of AI large language models in 2023 is without a doubt, OpenAI’s GPT-4. Unveiled in March of that year, this model has demonstrated astonishing capabilities: it possesses a deep comprehension of complex reasoning, advanced coding abilities, excels in a multitude of academic evaluations, and demonstrates many other competencies that echo human-level performance. Remarkably, GPT-4 is the first model to incorporate a multimodal capability, accepting both text and image inputs. Although ChatGPT hasn’t yet inherited this multimodal ability, some fortunate users have experienced it via Bing Chat, which leverages the power of the GPT-4 model.

GPT-4 has substantially addressed and improved upon the issue of hallucination, a considerable leap in maintaining factuality. When pitted against its predecessor, ChatGPT-3.5, the GPT-4 model achieves a score nearing 80% in factual evaluations across numerous categories. OpenAI has invested significant effort to align the GPT-4 model more closely with human values, employing Reinforcement Learning from Human Feedback (RLHF) and domain-expert adversarial testing.

GPT-4 API is now generally available

This titan, trained on a colossal 1+ trillion parameters, boasts a maximum context length of 32,768 tokens. The internal architecture of GPT-4, once a mystery, was unveiled by George Hotz of The Tiny Corp. GPT-4 is a unique blend of eight distinct models, each comprising 220 billion parameters. Consequently, it deviates from the traditional single, dense model we initially believed it to be.

Engaging with GPT-4 is achievable through ChatGPT plugins or web browsing via Bing. Despite its few drawbacks, such as a slower response and higher inference time leading some developers to opt for the GPT-3.5 model, the GPT-4 model stands unchallenged as the best large language model available in 2023. For serious applications, it’s highly recommended to subscribe to ChatGPT Plus, available for $20. Alternatively, for those preferring not to pay, third-party portals offer access to ChatGPT 4 for free.

GPT-3.5

Hot on the heels of GPT-4, OpenAI holds its ground with the GPT-3.5 model, taking a respectable second place. GPT-3.5 is a general-purpose LLM, akin to GPT-4, albeit lacking in specialized domain expertise. Its key advantage lies in its remarkable speed; it formulates complete responses within mere seconds.

From creative tasks like crafting essays with ChatGPT to devising business plans, GPT-3.5 performs admirably. OpenAI has also extended the context length to a generous 16K for the GPT-3.5-turbo model. Adding to its appeal, it’s free to use without any hourly or daily restrictions.

ChatGPT down: What to do if ChatGPT is not working

However, GPT-3.5 does exhibit some shortcomings. Its tendency to hallucinate results in the frequent propagation of incorrect information, making it less suitable for serious research work. Despite this, for basic coding queries, translation, comprehension of scientific concepts, and creative endeavors, GPT-3.5 holds its own.

GPT-3.5’s performance on the HumanEval benchmark yielded a score of 48.1%, while its more advanced sibling, GPT-4, secured a higher score of 67%. This distinction stems from the fact that while GPT-3.5 was trained on 175 billion parameters, GPT-4 had the advantage of being trained on over 1 trillion parameters.

PaLM 2 (Bison-001)

Carving its own niche among the best large language models of 2023, we find Google’s PaLM 2. Google has enriched this model by concentrating on aspects such as commonsense reasoning, formal logic, mathematics, and advanced coding across a diverse set of over 20 languages. The most expansive iteration of PaLM 2 is reportedly trained on 540 billion parameters, boasting a maximum context length of 4096 tokens.

Google has introduced a quartet of models based on the PaLM 2 framework, in varying sizes (Gecko, Otter, Bison, and Unicorn). Currently, Bison is the available offering. In the MT-Bench test, Bison secured a score of 6.40, somewhat overshadowed by GPT-4’s impressive 8.99 points. However, in reasoning evaluations, such as WinoGrande, StrategyQA, XCOPA, and similar tests, PaLM 2 exhibits a stellar performance, even surpassing GPT-4. Its multilingual capabilities enable it to understand idioms, riddles, and nuanced texts from various languages – a feat other LLMs find challenging.

PaLM 2 also offers the advantage of quick responses, providing three at a time. Users can test the PaLM 2 (Bison-001) model on Google’s Vertex AI platform, as detailed in our article. For consumer usage, Google Bard, powered by PaLM 2, is the way to go.

Codex

OpenAI Codex, an offspring of GPT-3, shines in the realms of programming, writing, and data analysis. Launched in conjunction with GitHub for GitHub Copilot, Codex displays proficiency in over a dozen programming languages. This model can interpret straightforward commands in natural language and execute them, paving the way for natural language interfaces for existing applications. Codex shows exceptional aptitude in Python, extending its capabilities to languages such as JavaScript, Go, Perl, PHP, Ruby, Swift, TypeScript, and Shell. With an expanded memory of 14KB for Python code, Codex vastly outperforms GPT-3 by factoring in over three times the contextual information during task execution.

Text-ada-001

Also known as Text-ada-001, Ada represents a fast and cost-effective model in the GPT-3 series, crafted for simpler tasks. As the quickest and most affordable option, Ada lands on the less complex end of the capabilities spectrum. Other models like Curie (text-curie-001) and Babbage (text-babbage-001) provide intermediate capabilities. Variations of Ada text modules, such as Text-similarity-ada-001, Text-search-ada-doc-001, and Code-search-ada-text-001, each carry unique strengths and limitations concerning quality, speed, and availability. This article delves into a comprehensive understanding of these modules and their relevance to specific requirements, positioning Text-ada-001 as well-suited for tasks like text parsing, address correction, and simple classification.

Claude v1

Emerging from the stables of Anthropic, a company receiving support from Google and co-founded by former OpenAI employees, is Claude – an impressive contender among the best large language models of 2023. The company’s mission is to create AI assistants that embody helpfulness, honesty, and harmlessness. Anthropic’s Claude v1 and Claude Instant models have shown tremendous potential in various benchmark tests, even outperforming PaLM 2 in the MMLU and MT-Bench examinations.

Claude v1 delivers an impressive performance, not far from GPT-4, scoring 7.94 in the MT-Bench test (compared to GPT-4’s 8.99). It secures 75.6 points in the MMLU benchmark, slightly behind GPT-4’s 86.4. Anthropic made a pioneering move by offering a 100k token as the largest context window in its Claude-instant-100k model. This allows users to load close to 75,000 words in a single window – a feat that is truly mind-boggling. Interested readers can learn how to use Anthropic’s Claude via our detailed tutorial.

Text-babbage-001

Best suited for moderate classification and semantic search classification tasks, Text-babbage-001, a GPT-3 language model, is known for its nimble response time and lower costs in comparison to other models. If you want to link your repository with the topic of text-babbage-001, you can easily do so by visiting your repository’s landing page and selecting the “manage topics” option.

Cohere

Founded by former Google Brain team members, including Aidan Gomez, a co-author of the influential “Attention is all you Need” paper that introduced the Transformer architecture, Cohere is an AI startup targeting enterprise customers. Unlike other AI companies, Cohere focuses on resolving generative AI use cases for corporations. Its range of models varies from small ones, with just 6B parameters, to large models trained on 52B parameters.

The recent Cohere Command model is gaining acclaim for its accuracy and robustness. According to Stanford HELM, the Cohere Command model holds the highest accuracy score among its peers. Corporations like Spotify, Jasper, and HyperWrite employ Cohere’s model to deliver their AI experience.

In terms of pricing, Cohere charges $15 to generate 1 million tokens, while OpenAI’s turbo model charges $4 for the same quantity. However, Cohere offers superior accuracy compared to other LLMs. Therefore, if you are a business seeking the best large language model to integrate into your product, Cohere’s models deserve your attention.

Text-curie-001

Best suited for tasks like language translation, complex classification, text sentiment analysis, and summarization, Text-curie-001 is a competent language model that falls under the GPT-3 series. Introduced in June 2020, this model excels in speed and cost-effectiveness compared to Davinci. With 6.7 billion parameters, Text-curie-001 is built for efficiency while maintaining a robust set of capabilities. It stands out in various natural language processing tasks and serves as a versatile choice for processing text-based data.

Text-davinci-003

Designed for tasks such as complex intent recognition, cause and effect understanding, and audience-specific summarization, Text-davinci-003 is a language model with capabilities parallel to text-davinci-003 but utilizes a different training approach. This model adopts supervised fine-tuning instead of reinforcement learning. As a result, it surpasses the curie, babbage, and ada models in terms of quality, output length, and consistent adherence to instructions. It also offers extra features like the ability to insert text.

Alpaca-7b

Primarily useful for conversing, writing and analyzing code, generating text and content, and querying specific information, Stanford’s Alpaca and LLaMA models aim to overcome the limitations of ChatGPT by facilitating the creation of custom AI chatbots that function locally and are consistently available offline. These models empower users to construct AI chatbots tailored to their individual requirements, free from dependencies on external servers or connectivity concerns.

Alpaca exhibits behavior similar to text-davinci-003, while being smaller, more cost-effective, and easy to replicate. The training recipe for this model involves using strong pre-trained language models and high-quality instruction data generated from OpenAI’s text-davinci-003. Although the model is released for academic research purposes, it highlights the necessity of further evaluation and reporting on any troubling behaviors.

StableLM-Tuned-Alpha-7B

Ideal for conversational tasks like chatbots, question-answering systems, and dialogue generation, StableLM-Tuned-Alpha-7B is a decoder-only language model with 7 billion parameters. It builds upon the StableLM-Base-Alpha models and is fine-tuned further on chat and instruction-following datasets. Utilizing a new dataset derived from The Pile, it has an enormous size, containing approximately 1.5 trillion tokens. This model has also been fine-tuned using datasets from multiple AI research entities and will be released as StableLM-Tuned-Alpha.

30B-Lazarus

The 30B-Lazarus model by CalderaAI, grounded on the LLaMA model, has been trained using LoRA-tuned datasets from a diverse array of models. Due to this, it performs exceptionally well on many LLM benchmarks. If your use case primarily involves text generation and not conversational chat, the 30B Lazarus model may be a sound choice.

Open-Assistant SFT-4 12B

Intended for functioning as an assistant, responding to user queries with helpful answers, the Open-Assistant SFT-4 12B is the fourth iteration of the Open-Assistant project. Derived from a Pythia 12B model, it has been fine-tuned on human demonstrations of assistant conversations collected through an application. This open-source chatbot, an alternative to ChatGPT, is now accessible free of charge.

WizardLM

Built to follow complex instructions, WizardLM is a promising open-source large language model. Developed by a team of AI researchers using an Evol-instruct approach, this model can rewrite initial sets of instructions into more complex ones. The generated instruction data is then used to fine-tune the LLaMA model.

FLAN-UL2

Created to provide a reliable and scalable method for pre-training models that excel across a variety of tasks and datasets, FLAN-UL2 is an encoder-decoder model grounded on the T5 architecture. This model, a fine-tuned version of the UL2 model, shows significant improvements. It has an extended receptive field of 2048, simplifying inference and fine-tuning processes, making it more suited for few-shot in-context learning. The FLAN datasets and methods have been open-sourced, promoting effective instruction tuning.

GPT-NeoX-20b

Best used for a vast array of natural language processing tasks, GPT-NeoX-20B is a dense autoregressive language model with 20 billion parameters. This model, trained on the Pile dataset, is currently the largest autoregressive model with publicly accessible weights. With the ability to compete in language-understanding, mathematics, and knowledge-based tasks, the GPT-NeoX-20B model utilizes a different tokenizer than GPT-J-6B and GPT-Neo. Its enhanced suitability for tasks like code generation stems from the allocation of extra tokens for whitespace characters.

BLOOM

Optimized for text generation and exploring characteristics of language generated by a language model, BLOOM is a BigScience Large Open-science Open-access Multilingual Language Model funded by the French government. This autoregressive model can generate coherent text in 46 natural languages and 13 programming languages and can perform text tasks that it wasn’t explicitly trained for. Despite its potential risks and limitations, BLOOM opens avenues for public research on large language models and can be utilized by a diverse range of users including researchers, students, educators, engineers/developers, and non-commercial entities.

BLOOMZ

Ideal for performing tasks expressed in natural language, BLOOMZ and mT0 are Bigscience-developed models that can follow human instructions in multiple languages without prior training. These models, fine-tuned on a cross-lingual task mixture known as xP3, can generalize across different tasks and languages. However, performance may vary depending on the prompt provided. To ensure accurate results, it’s advised to clearly indicate the end of the input and to provide sufficient context. These measures can significantly improve the models’ accuracy and effectiveness in generating appropriate responses to user instructions.

FLAN-T5-XXL

Best utilized for advancing research on language models, FLAN-T5-XXL is a powerful tool in the field of zero-shot and few-shot learning, reasoning, and question-answering. This language model surpasses T5 by being fine-tuned on over 1000 additional tasks and encompassing more languages. It’s dedicated to promoting fairness and safety research, as well as mitigating the limitations of current large language models. However, potential harmful usage of language models like FLAN-T5-XXL necessitates careful safety and fairness evaluations before application.

Command-medium-nightly

Ideal for developers who require rapid response times, such as those building chatbots, Cohere’s Command-medium-nightly is the regularly updated version of the command model. These nightly versions assure continuous performance enhancements and optimizations, making them a valuable tool for developers.

Falcon

Falcon, open-sourced under an Apache 2.0 license, is available for commercial use without any royalties or restrictions. The Falcon-40B-Instruct model, fine-tuned for most use cases, is particularly useful for chatting applications.

Gopher – Deepmind

Deepmind’s Gopher is a 280 billion parameter model exhibiting extraordinary language understanding and generation capabilities. Gopher excels in various fields, including math, science, technology, humanities, and medicine, and is adept at simplifying complex subjects during dialogue-based interactions. It’s a valuable tool for reading comprehension, fact-checking, and understanding toxic language and logical/common sense tasks.

Vicuna 33B

Vicuna 33B, derived from LLaMA and fine-tuned using supervised instruction, is ideal for chatbot development, research, and hobby use. This auto-regressive large language model has been trained on 33 billion parameters, using data collected from sharegpt.com.

Jurassic-2

The Jurassic-2 family, including the Large, Grande, and Jumbo base language models, excels at reading and writing-related use cases. With the introduction of zero-shot instruction capabilities, the Jurassic-2 models can be guided with natural language without the use of examples. They have demonstrated promising results on Stanford’s Holistic Evaluation of Language Models (HELM), the leading benchmark for language models.

LLM cosmos and wordsmith bots

In the rich tapestry of the artificial intelligence and natural language processing world, Large Language Models (LLMs) emerge as vibrant threads weaving an intricate pattern of advancements. The number of these models is not static; it’s an ever-expanding cosmos with new stars born daily, each embodying their unique properties and distinctive functionalities.

Each LLM acts as a prism, diffracting the raw light of data into a spectrum of insightful information. They boast specific abilities, designed and honed for niche applications. Whether it’s the intricate art of decoding labyrinthine instructions, scouring vast data galaxies to extract relevant patterns, or translating the cryptic languages of code into human-readable narratives, each model holds a unique key to unlock these capabilities.

Not all models are created equal. Some are swift as hares, designed to offer rapid response times, meeting the demands of real-time applications, such as the vibrant, chatty world of chatbot development. Others are more like patient, meticulous scholars, dedicated to unraveling complex topics into digestible knowledge nuggets, aiding the pursuit of academic research or providing intuitive explanations for complex theories.

All images in this post, including the featured image, is created by Kerem Gülen using Midjourney

Decision intelligence plays a crucial role in modern organizations, enabling them to navigate the intricate and dynamic business landscape of today. By harnessing the power of data and analytics, companies can gain a competitive edge, enhance customer satisfaction, and mitigate risks effectively.

Leveraging a combination of data, analytics, and machine learning, it emerges as a multidisciplinary field that empowers organizations to optimize their decision-making processes. Its applications span across various facets of business, encompassing customer service enhancement, product development streamlining, and robust risk management strategies.

What is decision intelligence?

Decision intelligence is a relatively new field, but it is rapidly gaining popularity. Gartner, a leading research and advisory firm, predicts that by 2023, more than a third of large organizations will have analysts practicing decision intelligence, including decision modeling.

This business model is a combination of several different disciplines, including:

Data science: The process of collecting, cleaning, and analyzing data

Analytics: The process of using data to identify patterns and trends

Machine learning: The process of teaching computers to learn from data and make predictions

These platforms use these disciplines to help organizations make better decisions. These platforms typically provide users with a centralized repository for data, as well as tools for analyzing and visualizing data. They also typically include features for creating and managing decision models.

There are many benefits of having decision intelligence

Decision intelligence can offer a number of benefits to organizations.

Decision intelligence platforms can help organizations make decisions more quickly and accurately by providing them with access to real-time data and insights. This is especially important in today’s fast-paced business world, where organizations need to be able to react to changes in the market or customer behavior quickly.

For example, a retailer might use decision intelligence to track customer behavior in real-time and make adjustments to its inventory levels accordingly. This can help the retailer avoid running out of stock or overstocking products, which can both lead to lost sales.

Artificial intelligence is both Yin and Yang

It also can help organizations make better decisions by providing them with a more holistic view of the data. This is because decision intelligence platforms can analyze large amounts of data from multiple sources, including internal data, external data, and social media data. This allows organizations to see the big picture and make decisions that are more informed and less likely to lead to problems.

A financial services company might use decision intelligence to analyze data on customer demographics, spending habits, and credit history. This information can then be used to make more informed decisions about who to approve for loans and what interest rates to charge.

Utilizing it can help organizations reduce risk by identifying potential problems before they occur. This is because decision intelligence platforms can use machine learning algorithms to identify patterns and trends in data.

Let’s imagine that, a manufacturing company uses decision intelligence to track data on machine performance. If the platform detects a patern of increasing machine failures, the company can take steps to prevent a major breakdown. This can save the company time and money in the long run.

It may help organizations become more efficient by automating decision-making processes. This can free up human resources to focus on more strategic tasks.

For example, a customer service company might use decision intelligence to automate the process of routing customer calls to the appropriate department. This can save the company time and money, and it can also improve the customer experience by ensuring that customers are routed to the right person the first time.

And last but not least, Decision intelligence can help organizations improve customer satisfaction by providing them with a more personalized and relevant customer experience. This is because decision intelligence platforms can use data to track customer preferences and behaviors.

For example, an online retailer might use decision intelligence to recommend products to customers based on their past purchases and browsing history. This can help customers find the products they’re looking for more quickly and easily, which can lead to increased satisfaction.

How to develop decision intelligence?

There are a number of steps that organizations can take to develop decision intelligence capabilities. These steps include:

• Investing in data and analytics: Organizations need to invest in the data and analytics infrastructure that will support decision intelligence. This includes collecting and storing data, cleaning and preparing data, and analyzing data.
• Developing decision models: Organizations need to develop decision models that can be used to make predictions and recommendations. These models can be developed using machine learning algorithms or by using expert knowledge.
• Deploying decision intelligence platforms: Organizations need to deploy these platforms that can be used to manage and execute decision models. These platforms should provide users with a user-friendly interface for interacting with decision models and for making decisions.
• Training employees: Organizations need to train employees on how to use decision intelligence platforms and how to make decisions based on the output of those platforms. This training should cover the basics of data science, analytics, and machine learning.

Automation’s role is vital in decision intelligence

Automation is playing an increasingly important role in decision intelligence. Automation can be used to automate a number of tasks involved in decision-making, such as data collection, data preparation, and model deployment. This can free up human resources to focus on more strategic tasks, such as developing new decision models and managing decision intelligence platforms.

In addition, automation can help to improve the accuracy and consistency of decision-making. By automating tasks that are prone to human error, such as data entry and model validation, automation can help to ensure that decisions are made based on the most accurate and up-to-date data.

Big tech is already familiar with this concept

Decision intelligence is a powerful tool that can be used by organizations of all sizes and in all industries. By providing organizations with access to real-time data, insights, and automation, it can help organizations make faster, more accurate, and more efficient decisions.

Amazon

Amazon uses it to make decisions about product recommendations, pricing, and logistics. For example, Amazon’s recommendation engine uses it to recommend products to customers based on their past purchases and browsing history.

Google

Google uses decision intelligence to make decisions about search results, advertising, and product development. For example, Google’s search algorithm uses decision intelligence to rank search results based on a variety of factors, including the relevance of the results to the query and the quality of the results.

Facebook

Facebook uses it to make decisions about newsfeed ranking, ad targeting, and user safety. For example, Facebook’s newsfeed ranking algorithm uses decision intelligence to show users the most relevant and interesting content in their newsfeed.

Microsoft

Microsoft utilizes this technology to make decisions about product recommendations, customer support, and fraud detection. For example, Microsoft’s product recommendations engine uses it to recommend products to customers based on their past purchases and browsing history.

Apple

Apple uses this business model to make decisions about product recommendations, app store curation, and fraud detection. For example, Apple’s app store curation team uses it to identify and remove apps that violate the app store guidelines.

Data science and decision intelligence are not related concepts

Data science and decision intelligence are both fields that use data to make better decisions. However, there are some key differences between the two fields.

Data science is a broader field that encompasses the collection, cleaning, analysis, and visualization of data. Data scientists use a variety of tools and techniques to extract insights from data, such as statistical analysis, machine learning, and natural language processing.

Decision intelligence is a more specialized field that focuses on using data to make decisions. Professionals use data science techniques to develop decision models, which are mathematical or statistical models that can be used to make predictions or recommendations. Professionals also work with business stakeholders to understand their decision-making needs and to ensure that decision models are used effectively.

In other words, data science is about understanding data, while decision intelligence is about using data to make decisions.

Here is a table that summarizes the key differences between data science and decision intelligence:

As you can see, data science and decision intelligence are complementary fields. Data science provides the foundation for decision intelligence, but decision intelligence requires an understanding of business needs and the ability to communicate with decision-makers.

In practice, many data scientists also work in decision intelligence roles. This is because data scientists have the skills and experience necessary to develop and use decision models. As the field of decision intelligence continues to grow, we can expect to see even more data scientists working in this area.

Featured image credit: Photo by Google DeepMind on Unsplash.

At a time when business models are becoming more and more virtual, reliable data has become a cornerstone of successful organizations. Reliable data serves as the bedrock of informed decision-making, enabling companies to gain valuable insights, identify emerging trends, and make strategic choices that drive growth and success. But what exactly is reliable data, and why is it so crucial in today’s business landscape?

Reliable data refers to information that is accurate, consistent, and trustworthy. It encompasses data that has been collected, verified, and validated using robust methodologies, ensuring its integrity and usability. Reliable data empowers businesses to go beyond assumptions and gut feelings, providing a solid foundation for decision-making processes.

Understanding the significance of reliable data and its implications can be a game-changer for businesses of all sizes and industries. It can unlock a wealth of opportunities, such as optimizing operations, improving customer experiences, mitigating risks, and identifying new avenues for growth. With reliable data at their disposal, organizations can navigate the complexities of the modern business landscape with confidence and precision.

What is reliable data?

Reliable data is information that can be trusted and depended upon to accurately represent the real world. It is obtained through reliable sources and rigorous data collection processes. When data is considered reliable, it means that it is credible, accurate, consistent, and free from bias or errors.

One major advantage of reliable data is its ability to inform decision-making. When we have accurate and trustworthy information at our fingertips, we can make better choices. It allows us to understand our circumstances, spot patterns, and evaluate potential outcomes. With reliable data, we can move from guesswork to informed decisions that align with our goals.

Planning and strategy also benefit greatly from reliable data. By analyzing trustworthy information, we gain insights into market trends, customer preferences, and industry dynamics. This knowledge helps us develop effective plans and strategies. We can anticipate challenges, seize opportunities, and position ourselves for success.

Efficiency and performance receive a boost when we work with reliable data. With accurate and consistent information, we can optimize processes, identify areas for improvement, and streamline operations. This leads to increased productivity, reduced costs, and improved overall performance.

Risk management becomes more effective with reliable data. By relying on accurate information, we can assess potential risks, evaluate their impact, and devise strategies to mitigate them. This proactive approach allows us to navigate uncertainties with confidence and minimize negative consequences.

Reliable data also fosters trust and credibility in our professional relationships. When we base our actions and presentations on reliable data, we establish ourselves as trustworthy partners. Clients, stakeholders, and colleagues have confidence in our expertise and the quality of our work.

How do you measure data reliability?

We emphasized the importance of data reliability for your business, but how much can you trust the data you have?

You need to ask yourself this question in any business. Almost 90% of today’s business depends on examining certain data well enough and starting with wrong information will cause your long-planned enterprise to fail. Therefore, to measure reliable data, you need to make sure that the data you have meets certain standards.

Accuracy

At the heart of data reliability lies accuracy—the degree to which information aligns with the truth. To gauge accuracy, several approaches can be employed. One method involves comparing the data against a known standard, while statistical techniques can provide valuable insights.

By striving for accuracy, we ensure that the data faithfully represents the real world, enabling confident decision-making.

Completeness

A reliable dataset should encompass all the pertinent information required for its intended purpose. This attribute, known as completeness, ensures that no crucial aspects are missing. Evaluating completeness may involve referencing a checklist or employing statistical techniques to gauge the extent to which the dataset covers relevant dimensions.

By embracing completeness, we avoid making decisions based on incomplete or partial information.

Consistency

Consistency examines the uniformity of data across various sources or datasets. A reliable dataset should exhibit coherence and avoid contradictory information. By comparing data to other datasets or applying statistical techniques, we can assess its consistency.

Striving for consistency enables us to build a comprehensive and cohesive understanding of the subject matter.

Bias

Guarding against bias is another critical aspect of measuring data reliability. Bias refers to the influence of personal opinions or prejudices on the data. A reliable dataset should be free from skewed perspectives and impartially represent the facts. Detecting bias can be achieved through statistical techniques or by comparing the data to other trustworthy datasets.

By recognizing and addressing bias, we ensure a fair and objective portrayal of information.

Error rate

Even the most carefully curated datasets can contain errors. Evaluating the error rate allows us to identify and quantify these inaccuracies. It involves counting the number of errors present or applying statistical techniques to uncover discrepancies.

Understanding the error rate helps us appreciate the potential limitations of the data and make informed judgments accordingly.

Considerations beyond the methods

While the aforementioned methods form the foundation of measuring data reliability, there are additional factors to consider:

• Source of the data: The credibility and reliability of data are influenced by its source. Data obtained from reputable and authoritative sources is inherently more trustworthy than data from less reputable sources. Being mindful of the data’s origin enhances our confidence in its reliability
• Method of data collection: The method employed to collect data impacts its reliability. Data collected using rigorous and scientifically sound methodologies carries greater credibility compared to data collected through less meticulous approaches. Awareness of the data collection method allows us to evaluate its reliability accurately
• Quality of data entry: Accurate and careful data entry is vital to maintain reliability. Data that undergoes meticulous and precise entry procedures is more likely to be reliable than data that is carelessly recorded or contains errors. Recognizing the importance of accurate data entry safeguards the overall reliability of the dataset
• Storage and retrieval of data: The way data is stored and retrieved can influence its reliability. Secure and consistent storage procedures, coupled with reliable retrieval methods, enhance the integrity of the data. Understanding the importance of proper data management ensures the long-term reliability of the dataset

What are the common data reliability issues?

Various common issues can compromise the reliability of data, affecting the accuracy and trustworthiness of the information being analyzed. Let’s delve into these challenges and explore how they can impact the usability of reliable data.

One prevalent issue is the presence of inconsistencies in reliable data, which can arise when there are variations or contradictions in data values within a dataset or across different sources. These inconsistencies can occur due to human errors during data entry, differences in data collection methods, or challenges in integrating data from multiple systems. When reliable data exhibits inconsistencies, it becomes difficult to obtain accurate insights and make informed decisions.

Reliable data may also be susceptible to errors during the data entry process. These errors occur when incorrect or inaccurate information is entered into a dataset. Human mistakes, such as typographical errors, misinterpretation of data, or incorrect recording, can lead to unreliable data. These errors can propagate throughout the analysis, potentially resulting in flawed conclusions and unreliable outcomes.

The absence of information or values in reliable data, known as missing data, is another significant challenge. Missing data can occur due to various reasons, such as non-response from survey participants, technical issues during data collection, or intentional exclusion of certain data points. When reliable data contains missing values, it introduces biases, limits the representativeness of the dataset, and can impact the validity of any findings or conclusions drawn from the data.

Another issue that affects reliable data is sampling bias, which arises when the selection of participants or data points is not representative of the population or phenomenon being studied. Sampling bias can occur due to non-random sampling methods, self-selection biases, or under or over-representation of certain groups. When reliable data exhibits sampling bias, it may not accurately reflect the larger population, leading to skewed analyses and limited generalizability of the findings.

Measurement errors can also undermine the reliability of data. These errors occur when there are inaccuracies or inconsistencies in the instruments or methods used to collect data. Measurement errors can stem from faulty measurement tools, subjective interpretation of data, or inconsistencies in data recording procedures. Such errors can introduce distortions in reliable data and undermine the accuracy and reliability of the analysis.

Ensuring the security and privacy of reliable data is another critical concern. Unauthorized access, data breaches, or mishandling of sensitive data can compromise the integrity and trustworthiness of the dataset. Implementing robust data security measures, and privacy safeguards, and complying with relevant regulations are essential for maintaining the reliability of data and safeguarding its confidentiality and integrity.

Lastly, bias and prejudice can significantly impact the reliability of data. Bias refers to systematic deviations of data from the true value due to personal opinions, prejudices, or preferences. Various types of biases can emerge, including confirmation bias, selection bias, or cultural biases. These biases can influence data collection, interpretation, and analysis, leading to skewed results and unreliable conclusions.

Addressing these common challenges and ensuring the reliability of data requires implementing robust data collection protocols, conducting thorough data validation and verification, ensuring quality control measures, and adopting secure data management practices. By mitigating these issues, we can enhance the reliability and integrity of data, enabling more accurate analysis and informed decision-making.

How to create business impact with reliable data

Leveraging reliable data to create a significant impact on your business is essential for informed decision-making and driving success. Here are some valuable tips on how to harness the power of reliable data and make a positive difference in your organization:

Instead of relying solely on intuition or assumptions, base your business decisions on reliable data insights. For example, analyze sales data to identify trends, patterns, and opportunities, enabling you to make informed choices that can lead to better outcomes.

Determine the critical metrics and key performance indicators (KPIs) that align with your business goals and objectives. For instance, track customer acquisition rates, conversion rates, or customer satisfaction scores using reliable data. By measuring performance accurately, you can make data-driven adjustments to optimize your business operations.

Utilize reliable data to uncover inefficiencies, bottlenecks, or areas for improvement within your business processes. For example, analyze production data to identify areas where productivity can be enhanced or costs can be reduced. By streamlining operations based on reliable data insights, you can ultimately improve the overall efficiency of your business.

Elevating business decisions from gut feelings to data-driven excellence

Reliable data provides valuable insights into customer behavior, preferences, and satisfaction levels. Analyze customer data, such as purchase history or feedback, to personalize experiences and tailor marketing efforts accordingly. By understanding your customers better, you can improve customer service, leading to enhanced satisfaction and increased customer loyalty.

Analyzing reliable data allows you to stay ahead of the competition by identifying market trends and anticipating shifts in customer demands. For instance, analyze market data to identify emerging trends or changing customer preferences. By leveraging this information, you can make strategic business decisions and adapt your offerings to meet the evolving needs of the market.

Reliable data is instrumental in identifying and assessing potential risks and vulnerabilities within your business. For example, analyze historical data and monitor real-time information to detect patterns or indicators of potential risks. By proactively addressing these risks and making informed decisions, you can implement risk management strategies to safeguard your business.

Utilize reliable data to target your marketing and sales efforts more effectively. For instance, analyze customer demographics, preferences, and buying patterns to develop targeted marketing campaigns. By personalizing communications and optimizing your sales strategies based on reliable data insights, you can improve conversion rates and generate higher revenue.

Reliable data offers valuable insights into customer feedback, market demand, and emerging trends. For example, analyze customer surveys, reviews, or market research data to gain insights into customer needs and preferences. By incorporating these insights into your product development processes, you can create products or services that better meet customer expectations and gain a competitive edge.

Cultivate a culture within your organization that values data-driven decision-making. Encourage employees to utilize reliable data in their day-to-day operations, provide training on data analysis tools and techniques, and promote a mindset that embraces data-driven insights as a critical factor for success. By fostering a data-driven culture, you can harness the full potential of reliable data within your organization.

Regularly monitor and evaluate the impact of your data-driven initiatives. Track key metrics, analyze results, and iterate your strategies based on the insights gained from reliable data. By continuously improving and refining your data-driven approach, you can ensure ongoing business impact and success.

By effectively leveraging reliable data, businesses can unlock valuable insights, make informed decisions, and drive positive impacts across various aspects of their operations. Embracing a data-driven mindset and implementing data-driven strategies will ultimately lead to improved performance, increased competitiveness, and sustainable growth.

Featured image credit: Photo by Dan Gold on Unsplash.

YouTube, the widely-used video-sharing platform, is actively refining its Studio Analytics interface, according to a video published by Creator Insider. It’s a pivotal shift aimed at mollifying the discomfort linked with comparative data interpretation. Some content creators felt that the previous design, which included an immediate comparative analysis of video performance against average response rates, was not always beneficial or motivating. This feature is currently under a transformation, as you can see in the images below.

YouTube Studio Analytics has a new UI

In the original design, the comparative data presentation offered an immediate juxtaposition of a new video’s performance with the creator’s usual response rates. For some creators, this feature was a valuable asset, illuminating their improvement over time. However, for others, the comparative data feature was less encouraging. It was particularly disheartening for those who discovered their recent content was not resonating as strongly with their audience as they had hoped.

In response to such feedback, YouTube’s current alteration grants users the ability to minimize the comparative data field as they see fit. If a user decides to condense the information, the analytics card retains its condensed state in subsequent logins. This feature offers a creator the liberty to opt-out completely from this comparative aspect, if desired. Crucially, this user-selected setting is consistent across channel switches, allowing a more personalized analytics experience.

The strategic value of IoT development and data analytics

On a parallel front, YouTube is unveiling new weekly and monthly digests of channel performance. These digests are designed to foster sustained engagement with analytics, eliminating the pressure of continuously scrutinizing channel statistics. Traditionally, analytics required creators to delve deep into their performance numbers, often necessitating a detailed, manual analysis. However, these revamped performance reports offer a more generalized overview of key metrics, circumventing the necessity for creators to wade through the data themselves.

These upcoming performance recaps are set to feature an array of gamified elements as well, designed to alleviate the potential stress of performance evaluation. Gamification in analytics brings a playful element to data interpretation, making the process more interactive and less daunting. With this approach, YouTube aims to make the analytics experience more enjoyable and less pressurizing.

Both of these updates are tailored to alleviate the stress associated with raw data interpretation. Raw numbers can be a powerful tool, providing valuable insights into a channel’s performance. However, they can also create pressure, particularly when a creator’s content isn’t performing as well as anticipated. By reshaping how these analytics are presented, YouTube aims to change the perception around data interpretation, minimize stress, and prevent discouragement among creators in their journey.

YouTube makes monetization program more accessible than ever

These innovative alterations aren’t just limited to a single platform. YouTube announced that these updates would be rolled out progressively to Studio users on both web and mobile platforms in the forthcoming days.

The power of data

In the broader landscape of social media management, data analytics prove to be an invaluable tool. Comprehensive data analysis allows for a more profound understanding of content performance, audience engagement, and growth trends. This understanding can inform decisions around content strategy, audience targeting, and engagement initiatives.

Precise analytics can help identify successful content, highlighting what resonates with viewers and encourages interaction. By understanding what works, creators can tailor future content to maximize audience engagement and growth. Conversely, analytics can also identify less successful content, offering insights into what isn’t working and providing an opportunity for course correction.

Additionally, tracking engagement over time can identify trends, showing when a channel is gaining momentum or when it’s slowing down. This knowledge can guide strategic planning, helping creators adapt their content strategy based on these insights.

The success of a social media account is closely linked to its analytics. As YouTube continues to refine its Studio Analytics interface, it’s clear that the platform recognizes the importance of data analytics in content creation and social media management.

Featured image credit: CardMapr.nl/Unsplash

Data loss can often be a critical predicament, especially if a backup has not been maintained regularly. Such situations, while precarious, do not necessarily spell absolute doom. The initial response in these instances should be to deploy a reliable RAID data recovery software to retrieve as much of the lost data as possible. One software that can potentially rise to this challenge is the Stellar Data Recovery Technician, designed to provide a robust solution in such cases.

This software suite is compatible with both Windows and macOS operating systems, and offers a spectrum of six different editions to cater to diverse user needs. This includes a complimentary edition that allows for the recovery of up to 1 GB of data, a testament to the company’s commitment to accessibility.

They also provide users with the ability to scrutinize various types of storage media, spanning a broad range of file systems such as NTFS, FAT16, FAT32, exFAT, Ext4, Ext3 and Btrfs file system. It is equipped to restore an array of file types, encompassing documents, photographs, videos, and more. Intriguingly, it also extends its capabilities to the retrieval of files from a system that has previously undergone formatting.

When would you need Stellar Data Recovery Technician?

There are a myriad of situations where Stellar Data Recovery Technician could come to your rescue. From data loss due to RAID array failures, to inadvertent data deletion, or even system corruption leading to inaccessible data, the Stellar Data Recovery Technician can prove indispensable.

Lost RAID data

Stellar Data Recovery Technician excels when employed to recover data from logically compromised, inaccessible, or non-functional RAID arrays. The software is adept at navigating issues such as accidental deletions, file system corruption, malicious software, logical errors, or power outages, making it an essential tool in the recovery of lost RAID data.

RAID rebuild mishaps

In situations where a RAID rebuild is unsuccessful due to improper configuration, logical corruption, incorrect stripe size, or misplaced disk orders, the software proves its worth. With Stellar Data Recovery Technician, you can restore RAID data that has been erased due to an incorrect RAID array rebuild.

Dealing with RAID errors

RAID errors, such as read/write errors or “Can’t read data from RAID disk,” can render volumes unreadable, leading to data inaccessibility. In such scenarios, Stellar Data Recovery Technician can be a reliable ally, recovering data from volumes and disks that present RAID errors.

Key capabilities of Stellar Data Recovery Technician

Stellar Data Recovery Technician boasts a host of sophisticated features designed to tackle diverse data loss scenarios. Its capacities extend beyond mere data recovery. Let’s explore them in detail without further spoilers!

Restoring data from inaccessible RAID volumes

The Stellar Data Recovery Technician excels at extracting data from logically impaired and inaccessible RAID 0, RAID 5 ,RAID 6 and Hybrid RAID volumes and partitions. The software skillfully scans for lost or deleted RAID volumes, recovering RAID data from RAW and missing RAID volumes, even without the presence of a RAID controller card.

SSD RAID data recovery

RAID systems utilizing solid-state drives can sometimes falter due to RAID controller failure, software glitch, sudden power outage, RAID errors, or other hardware issues. In these instances, Stellar Data Recovery Technician springs into action, recovering data from SSDs configured with RAID 0, RAID 5, or RAID 6 arrays. The software supports recovery from formatted, deleted, or logically corrupted SSD RAID drives.

Formatted RAID array data restoration

For formatted RAID 0, RAID 5, and RAID 6 volumes and partitions, Stellar Data Recovery Technician offers reliable data recovery. The software capably rebuilds a virtual RAID array, enabling you to save the recovered data to an internal or external disk, even without knowledge of the RAID parameters for reconstruction.

Recovery from deleted RAID partitions

The software proves its prowess by recovering crucial data from deleted, lost, failed, or corrupted RAID partitions. It can scan and retrieve data lost due to accidental deletion, failed RAID stripping, sudden power failures, malware intrusion, bad sectors, software errors, and more.

Data recovery from RAID configured NAS

Stellar Data Recovery Technician efficiently recovers lost data from NAS devices configured with RAID 0, 1, 5, 6 and SHR drives. The software is equipped to restore data from corrupted and inaccessible RAID-based NAS servers of various brands.

Virtual RAID construction for recovery

In cases where RAID parameters are unknown, Stellar Data Recovery can rebuild a likely RAID configuration. The software automatically matches patterns and identifies the RAID parameters, enabling data recovery.

Hardware and software RAID recovery

The software is versatile, capable of recovering data from both hardware and software-based RAID 0, RAID 5, and RAID 6 arrays, even without the presence of controller cards or additional hardware and software requirements.

Recovery from non-booting Windows systems

For Windows systems that fail to boot, Stellar Data Recovery Technician can still restore RAID data. The software creates a bootable USB media that can be used to boot the Windows system and initiate RAID data recovery.

How to use Stellar Data Recovery Technician?

Just follow the instructions below to start using the software easily:

• Choose the type of data you’re interested in recovering and press the “Next” button.

• Opt for “Raid Recovery” for restoring the Raid Arrays.

• Identify the Hard Drives that are included in the RAID array using the arrow keys to build the probable RAID.

• The software will display the files that can be recovered, providing the option to retrieve an entire folder or an individual file.

• You will be prompted to specify the destination where you’d like to store your data. Once the path is provided, your data will be saved in your chosen location.

It is that easy!

System requirements

• Processor: Intel compatible (x86, x64)
• Memory: 4 GB minimum (8 GB recommended)
• HDD: 250 MB for installation files
• OS: Windows 11, 10, 8.1, 8 & 7 (Service Pack 1)

A burden shared is a burden halved

In the face of data loss, remember the timeless wisdom of T.A. Webb: “A burden shared is a burden halved.” Just as a good companion eases the weight of hardship, Stellar Data Recovery Technician can be your trusted ally in recovering what seems lost. With its powerful capabilities and comprehensive approach, this software offers a ray of hope in the midst of despair. Let Stellar Data Recovery Technician be your companion on the path to restoring your valuable data and alleviating the burden of loss.

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.

Hello! My name is Alexander, and I have been a programmer for over 14 years, with the last six years spent leading multiple diverse product teams at VK. During this time, I’ve taken on roles as both a team lead and technical lead, while also making all key product decisions. These responsibilities ranged from hiring and training developers, to conceptualizing and launching features, forecasting, conducting research, and analyzing data.

In this article, I’d like to share my experiences on how product analytics intersects with development, drawing on real-world challenges I’ve encountered while working on various web projects. My hope is that these insights will help you avoid some common pitfalls and navigate the complexities of product analytics.

Let’s explore this process by walking through the major stages of product analytics.

The product exists, but there are no metrics

The first and most obvious point: product analytics is essential. Without it, you have no visibility into what is happening with your project. It’s like flying a plane without any instruments — extremely risky and prone to errors that could have been avoided with proper visibility.

I approach product work through the Jobs to Be Done (JTBD) framework, believing that a successful product solves a specific user problem, which defines its value. In other words, a product’s success depends on how well it addresses a user’s need. Metrics, then, serve as the tool to measure how well the product solves this problem and how effectively it meets user expectations.

Types of metrics

From a developer’s perspective, metrics can be divided into two key categories:

1. Quantitative Metrics: These provide numerical insight into user actions over a specific period. Examples include Monthly Active Users (MAU), clicks on certain screens during the customer journey, the amount of money users spend, and how often the app crashes. These metrics typically originate from the product’s code and give a real-time view of user behavior.
2. Qualitative Metrics: These assess the quality of the product and its audience, allowing for comparison with other products. Examples include Retention Rate (RR), Lifetime Value (LTV), Customer Acquisition Cost (CAC), and Average Revenue Per User (ARPU). Qualitative metrics are derived from quantitative data and are essential for evaluating a product’s long-term value and growth potential.

One common mistake at this stage is failing to gather enough quantitative data to build meaningful qualitative metrics. If you miss tracking user actions at certain points in the customer journey, it can lead to inaccurate conclusions about how well your product is solving user problems. Worse, if you delay fixing this problem, you’ll lose valuable historical data that could have helped fine-tune your product’s strategy.

There are metrics, but no data

Once you have identified the right metrics, the next challenge is data collection. Gathering and storing the correct data is critical to ensure that your metrics are reliable and actionable. At this stage, product managers and developers must work closely to implement the necessary changes in the project’s code to track the required data.

Common pitfalls in data collection

Several potential issues can arise during the data collection phase:

• Misunderstanding Data Requirements: Even the most skilled developers might not fully grasp what data needs to be collected. This is where you must invest time in creating detailed technical specifications (TS) and personally reviewing the resulting analytics. It’s vital to verify that the data being collected aligns with the business goals and the hypotheses you aim to test.
• Broken Metrics: As the product evolves, metrics can break. For instance, adding new features or redesigning parts of the product can inadvertently disrupt data collection. To mitigate this, set up anomaly monitoring, which helps detect when something goes wrong — whether it’s a fault in data collection or the product itself.
• Lack of Diagnostic Analytics: Sometimes, metrics such as time spent on specific screens, the number of exits from a screen, or the number of times users return to a previous screen are crucial for diagnosing problems in the customer journey. These diagnostic metrics don’t need to be stored long-term but can help uncover issues in key metrics or highlight areas of the product that need improvement.

For maximum flexibility and accuracy, always aim to collect raw data instead of pre-processed data. Processing data within the code increases the likelihood of errors and limits your ability to adjust calculations in the future. Raw data allows you to recalculate metrics if you discover an error or if data quality changes, such as when additional data becomes available or filters are applied retroactively.

To streamline analysis without sacrificing flexibility, it can be useful to implement materialized views — precomputed tables that aggregate raw data. These views allow faster access to key metrics while maintaining the ability to recalculate metrics over time. Many analytical systems, including columnar databases like ClickHouse, which we used at VK, support materialized views, making them well-suited for handling large datasets. Additionally, you can reduce storage requirements by isolating frequently accessed data, such as user information, into daily aggregates and joining them back into calculations when needed.

There is data, but no hypotheses

Once you have collected sufficient data, the next challenge is forming hypotheses based on the information at hand. This is often more challenging than it seems, especially when dealing with large datasets. Identifying patterns and actionable insights from the data can be difficult, especially if you’re looking at overwhelming amounts of information without a clear focus.

Strategies for generating hypotheses

To overcome this challenge, here are some strategies I’ve found useful for generating hypotheses:

• Look at the Big Picture: Historical data provides essential context. Data collected over a longer period — preferably several years — gives a clearer understanding of long-term trends and eliminates the noise caused by short-term fluctuations. This broader view helps in forming more accurate conclusions about your product’s health and trajectory.
• User Segmentation: Users behave differently based on various factors such as demographics, usage frequency, and preferences. Segmenting users based on behavioral data can significantly improve your ability to forecast trends and understand different user groups. For example, using clustering algorithms like k-means to segment users into behavioral groups allows you to track how each segment interacts with the product, leading to more targeted product improvements.
• Identify Key Actions: Not all user actions carry the same weight. Some actions are more critical to your product’s success than others. For instance, determining which actions lead to higher retention or user satisfaction can be key to unlocking growth. Using tools like decision trees with retention as the target metric can help pinpoint which actions matter most within the customer journey, allowing you to optimize the most impactful areas.

There are hypotheses, now test them

Once you have formed hypotheses, the next step is to test them. Among the various methods available for hypothesis testing, A/B testing is one of the most effective in web projects. It allows you to test different variations of your product to see which performs better, helping you make informed decisions about product changes.

Benefits of A/B testing

• Isolation from External Factors: A/B tests allow you to conduct experiments in a controlled environment, minimizing the influence of external variables. This means that you can focus on the direct impact of your changes without worrying about audience variability or other uncontrollable factors.
• Small Incremental Improvements: A/B testing makes it possible to test even minor product improvements that might not show up in broader user surveys or focus groups. These small changes can accumulate over time, resulting in significant overall product enhancements.
• Long-term Impact: A/B tests are particularly useful for tracking the influence of features on complex metrics like retention. By using long-term control groups, you can see how a feature affects user behavior over time, not just immediately after launch.

Challenges of A/B testing

Despite its advantages, A/B testing comes with its own set of challenges. Conducting these tests is not always straightforward, and issues such as uneven user distribution, user fatigue in test groups, and misinterpreted results often lead to the need for repeated tests.

In my experience conducting hundreds of A/B tests, I’ve encountered more errors due to test execution than from faulty analytics. Mistakes in how tests are set up or analyzed often lead to costly re-runs and delayed decision-making. Here are the most common issues that lead to recalculations or test restarts:

• Uneven user distribution in test groups. Even with a well-established testing infrastructure, problems can arise when introducing a new feature.
  • The timing of when users are added to a test can be incorrect. For a product manager, a feature starts where the user sees it. For a developer, it starts where the code starts. Because of this, developers may insert users too early (before they’ve interacted with the feature) or too late (and you’ll miss out on some of your feature audience). This leads to noise in the test results. In the worst case, you’ll have to redo the test; in the best case, an analyst can attempt to correct the bias, but you still won’t have an accurate forecast of the feature’s overall impact.
  • Audience attrition in one of the groups can skew results. The probable reason for this is that the feature in the test group has a usage limit in frequency or time. For instance, in the control group, users do not receive push notifications, while in the test group they do, but not more than once a week. As a result, the test group audience will begin to shrink if the inclusion in the test occurs after checking the ability to send a push.
    Another similar reason for the same result is caching. At the first interaction, we include the user in the test, at subsequent ones – not.

In most cases, fixing these issues requires code changes and restarting the test.

• Errors during result analysis.
  • Insufficient sample size can prevent reaching statistical significance, wasting both time and developer resources.
  • Ending a test too early after seeing a noticeable effect can result in false positives or false negatives, leading to incorrect conclusions and poor product decisions.

In addition, conflicts with parallel tests can make it impossible to properly assess a feature’s impact. If your testing system doesn’t handle mixing user groups across tests, you’ll need to restart. Other complications, like viral effects (e.g., content sharing) influencing both test and control groups, can also distort results. Finally, if the analytics are broken or incorrect, it can disrupt everything — something I’ve covered in detail above.

Best practices for A/B testing

To address these issues in my teams, I’ve taken several steps:

• During test implementation:
  • I helped developers better understand the product and testing process, providing training and writing articles to clarify common issues. We also worked together to resolve each problem we found.
  • I worked with tech leads to ensure careful integration of A/B tests during code reviews and personally reviewed critical tests.
  • I included detailed analytics descriptions in technical specifications and checklists, ensuring analysts defined required metrics beforehand.
  • My team developed standard code wrappers for common A/B tests to reduce human error.
• During result analysis:
  • I collaborated with analysts to calculate the required sample size and test duration, considering the test’s desired power and expected effects.
  • I monitored group sizes and results, catching issues early and ensuring that tests weren’t concluded before P-values and audience sizes had stabilized.
  • I pre-calculated the feature’s potential impact on the entire audience, helping to identify discrepancies when comparing test results with post-launch performance.

By refining how tests are implemented and analyzed, I hope this guidance will make your work with product analytics more reliable, ensuring that the process leads to profitable decisions.

Conclusion

In today’s competitive market, leveraging product analytics is no longer optional but essential for product teams. Adopting a data-driven mindset enables companies to gain valuable insights, enhance product performance, and make more informed decisions.

A focus on data throughout the development cycle helps companies not only address immediate challenges but also achieve long-term success. In other words, a data-driven approach unlocks the true potential for product innovation and sustainable growth. I genuinely hope that the information provided here will help you make your work with product analytics more effective and profitable!

Featured image credit: Scott Graham/Unsplash

Certified Business Analysis Professionals, equipped with the necessary skills and expertise, play a pivotal role in the ever-changing world of business. In order to remain relevant and seize opportunities, organizations must make well-timed, informed decisions. This is precisely where the proficiency of business examiners, commonly known as business analysts, becomes invaluable. Certified Business Analysis Professionals specialize in evaluating multiple factors within a company, thereby fostering its growth.

To thrive in the role of a business analyst, it is imperative to stay updated with the latest industry developments. And what better way to achieve this than by obtaining the prestigious CBAP certification? Offered by the International Institute of Business Analysis, headquartered in Canada, this certification carries immense value.

It signifies a level 3 certification, serving as a testament to the individual’s experience and prowess in the field. Armed with this distinguished certificate, professionals can anticipate securing positions at the intermediate or senior level, aligning with their exceptional abilities.

Who is a Certified Business Analysis Professional?

A Certified Business Analysis Professional (CBAP) refers to an individual who has successfully acquired the CBAP certification, a prestigious credential bestowed by the International Institute of Business Analysis (IIBA). These professionals specialize in the field of business analysis and showcase a remarkable level of knowledge, expertise, and experience in this particular domain.

The CBAP certification serves as a testament to the extensive expertise and comprehensive understanding of business analysis possessed by individuals who have earned this prestigious designation. It is specifically designed for seasoned business analysts who have demonstrated proficiency across various facets of the discipline. These include requirements planning and management, enterprise analysis, elicitation and collaboration, requirements analysis, solution assessment, and validation, as well as business analysis planning and monitoring.

By attaining the CBAP certification, professionals validate their proficiency and commitment to excellence in the field of business analysis, thereby distinguishing themselves as highly skilled practitioners. This prestigious credential enhances their credibility, opens up new career opportunities, and sets them apart as recognized leaders in the realm of business analysis.

Working areas of Certified Business Analysis Professional

Certified Business Analysis Professionals (CBAPs) are highly versatile and can be found working in various areas within the field of business analysis. Their expertise and knowledge equip them to handle diverse roles and responsibilities. Here are some common working areas where CBAPs make a significant impact:

Elicitation and analysis: Certified Business Analysis Professionals excel in gathering and comprehending business requirements from stakeholders. They employ techniques such as interviews, workshops, and surveys to extract requirements and analyze them to ensure alignment with organizational objectives.

Planning and management: CBAPs possess the skills to develop strategies and plans for effectively managing requirements throughout the project lifecycle. They establish processes for change management, prioritize requirements, and create requirements traceability matrices.

Business process analysis: CBAPs evaluate existing business processes to identify areas for improvement. They collaborate with stakeholders to streamline workflows, enhance operational efficiency, and boost productivity.

Solution assessment and validation: CBAPs play a crucial role in evaluating and validating proposed solutions to ensure they meet desired business objectives. They conduct impact analyses, assess risks, and perform user acceptance testing to verify solution effectiveness.

Business analysis planning and monitoring: CBAPs contribute to defining the scope and objectives of business analysis initiatives. They develop comprehensive plans, set realistic timelines, allocate resources, and monitor progress to ensure successful project delivery.

Stakeholder engagement and communication: CBAPs possess excellent communication and interpersonal skills, enabling them to engage with stakeholders effectively. They facilitate workshops, conduct presentations, and foster clear communication between business units and project teams.

Enterprise analysis: CBAPs possess a holistic understanding of the organization and conduct enterprise analysis. They assess strategic goals, perform feasibility studies, and identify opportunities for business improvement and innovation.

Data analysis and modeling: Certified Business Analysis Professionals have a solid grasp of data analysis techniques and can create data models to support business analysis activities. They identify data requirements, develop data dictionaries, and collaborate with data management teams.

Business case development: CBAPs contribute to the development of business cases by evaluating costs, benefits, and risks associated with proposed projects. They provide recommendations for investment decisions and assist in justifying initiatives.

Continuous improvement: Certified Business Analysis Professionals actively contribute to the continuous improvement of business analysis practices within organizations. They identify areas for process enhancement, propose new methodologies, and mentor other business analysts.

These examples illustrate the wide range of working areas where Certified Business Analysis Professionals thrive, leveraging their versatile skill set to drive effective analysis and strategic decision-making. Their contributions are instrumental in helping organizations achieve their business goals.

Is CBAP certification recognized?

The CBAP certification is widely recognized and highly regarded in the business analysis industry. It holds international recognition and carries significant value among employers, industry professionals, and organizations.

Employers often prioritize candidates with CBAP certification when hiring for business analysis positions. This certification serves as tangible evidence of a candidate’s proficiency and commitment to the field. It validates their expertise in business analysis principles, techniques, and methodologies.

How to get certified as a business analyst

The CBAP certification is acknowledged as a significant milestone in professional development within the business analysis domain. It can substantially broaden career prospects, open doors to new opportunities, and distinguish individuals in a competitive job market.

The International Institute of Business Analysis (IIBA), the governing body responsible for granting the CBAP certification, is globally acknowledged as a leading authority in the field of business analysis. The IIBA upholds rigorous standards for the certification process, ensuring that Certified Business Analysis Professionals meet the necessary requirements and possess the skills and knowledge essential for success in their roles.

Benefits of becoming a Certified Business Analysis Professional

CBAP certification offers several compelling benefits that can significantly impact your career trajectory. Let’s explore some of the key advantages that can propel your professional growth to new heights.

Undergoing CBAP certification training can greatly enhance your chances of passing the certification exam on your first attempt. Additionally, you can find more detailed information about CBAP benefits here.

Credibility: The CBAP certification holds wide acceptance, which translates to increased credibility in the eyes of employers. It serves as tangible proof of your expertise and competence as a skilled business analyst, making you a desirable candidate for job opportunities.

Job satisfaction: Attaining CBAP certification grants you access to a wealth of valuable tools and resources that streamline your job responsibilities. This means you can work on critical and impactful projects, instilling a sense of importance and confidence in your role. In reputable organizations, knowledge is highly valued, enabling you to apply your expertise and derive job satisfaction from making a meaningful impact.

Skill development: Becoming a Certified Business Analysis Professional equips you with a diverse range of techniques that further develop your skills and enhance your problem-solving abilities. The comprehensive curriculum provides valuable insights and practical knowledge to excel in your business analysis endeavors.

Salary advancement: CBAP certification opens doors to potential salary increases and career advancement opportunities. With this prestigious certification, you demonstrate your proficiency in handling complex programs and projects, positioning yourself for higher-paying roles within the industry.

Industry recognition: Certified Business Analysis Professionals are held in high regard within the business analysis domain. Their commitment to continuous learning and professional development makes them highly sought after by top industries and organizations.

Networking opportunities: Effective networking plays a pivotal role in the business analysis field. CBAP certification provides you with the opportunity to tap into the untapped potential of the industry and connect with like-minded peers, expanding your professional network and fostering valuable collaborations.

By utilizing the benefits of CBAP certification, you can elevate your career prospects, gain industry recognition, and unlock new opportunities for growth and success.

How much does a Certified Business Analysis Professional earn?

In recent years, companies have increasingly sought out professionals with CBAP certifications due to their specialized skill sets and expertise in business analysis. As a result, individuals holding the CBAP designation enjoy several advantages, including better job opportunities, higher income potential, and global recognition.

One of the key benefits of CBAP certification is the improved job prospects it offers. Companies value the comprehensive knowledge and advanced competencies that CBAP recipients possess, making Certified Business Analysis Professionals highly desirable candidates for business analysis roles. With a CBAP certification, you have a competitive edge in the job market, opening doors to a wider range of career opportunities.

The global recognition of CBAP certification further enhances its value. Certified Business Analysis Professionals are acknowledged internationally for their proficiency in business analysis and their adherence to globally recognized standards. This recognition not only adds prestige to your professional profile but also facilitates career advancement on a global scale.

According to data from Indeed, a Certified Business Analysis Professional earn an average salary of $83,000 per year. This figure showcases the financial benefits that come with attaining the CBAP certification, solidifying its reputation as a valuable credential in the field of business analysis.

Alternative certifications to CBAP certification

The CBAP is not the only certification available for professionals who want to demonstrate their skills as business analysts. The Professional in Business Analysis (PBA) certification offered by the Project Management Institute (PMI) is also popular among industry professionals.

Here is a quick overview of the key differences between the two certifications to help you determine which one aligns better with your goals:

CBAP certification

• Requirements: Complete a minimum of 7,500 hours of business analysis work experience within the past ten years, with at least 3,600 hours dedicated to combined areas outlined in the BABOK (Business Analysis Body of Knowledge); 35 hours of professional development within the last four years
• Exam: Consists of 120 multiple-choice questions to be answered within a time frame of 3.5 hours

PBA certification

• Requirements: With a secondary degree, complete 7,500 hours of work experience as a business analysis practitioner, earned within the last eight years, with at least 2,000 hours focused on working on project teams. With a Bachelor’s degree or higher, complete 4,500 hours of work experience as a business analysis practitioner, with at least 2,000 hours dedicated to working on project teams. Both secondary degree and Bachelor’s degree holders require 35 hours of training in business analysis
• Exam: Consists of 200 multiple-choice questions to be answered within a time frame of over 4 hours

The choice of certification will depend on personal preferences. PMI has been established for a longer time than IIBA, but CBAP has been around longer than PBA. Consequently, some employers may be more familiar with one organization or certification than the other. Nevertheless, both certifications are highly regarded. In 2020, for instance, CIO, a notable tech publication, listed CBAP and PBA among the top ten business analyst certifications.

The role of Certified Business Analysis Professionals (CBAPs) in the field of business analysis cannot be overstated. These highly skilled individuals have demonstrated their expertise, knowledge, and commitment to the profession through the rigorous CBAP certification process. The CBAP designation not only signifies credibility and recognition on a global scale but also opens doors to better job opportunities and higher income potential.

In a world of evolving business landscapes and increasing demand for effective decision-making, CBAPs play a crucial role in driving organizational success. Their expertise, coupled with their commitment to excellence, makes them instrumental in delivering impactful business solutions and fostering innovation. With their invaluable contributions to the field of business analysis, CBAPs shape the future of organizations and drive success in an ever-changing business world.

Regression in machine learning involves understanding the relationship between independent variables or features and a dependent variable or outcome. Regression’s primary objective is to predict continuous outcomes based on the established relationship between variables.

Machine learning has revolutionized the way we extract insights and make predictions from data. Among the various techniques employed in this field, regression stands as a fundamental approach.

Regression models play a vital role in predictive analytics, enabling us to forecast trends and predict outcomes with remarkable accuracy. By leveraging labeled training data, these models learn the underlying patterns and associations between input features and the desired outcome. This knowledge empowers the models to make informed predictions for new and unseen data, opening up a world of possibilities in diverse domains such as finance, healthcare, retail, and more.

What is regression in machine learning?

Regression, a statistical method, plays a crucial role in comprehending the relationship between independent variables or features and a dependent variable or outcome. Once this relationship is estimated, predictions of outcomes become possible. Within the area of machine learning, regression constitutes a significant field of study and forms an essential component of forecast models.

By utilizing regression as an approach, continuous outcomes can be predicted, providing valuable insights for forecasting and outcome prediction from data.

Regression in machine learning typically involves plotting a line of best fit through the data points, aiming to minimize the distance between each point and the line to achieve the optimal fit. This technique enables the accurate estimation of relationships between variables, facilitating precise predictions and informed decision-making.

In conjunction with classification, regression represents one of the primary applications of supervised machine learning. While classification involves the categorization of objects based on learned features, regression focuses on forecasting continuous outcomes. Both classification and regression are predictive modeling problems that rely on labeled input and output training data. Accurate labeling is crucial as it allows the model to understand the relationship between features and outcomes.

Regression analysis is extensively used to comprehend the relationship between different independent variables and a dependent variable or outcome. Models trained with regression techniques are employed for forecasting and predicting trends and outcomes. These models acquire knowledge of the relationship between input and output data through labeled training data, enabling them to forecast future trends, predict outcomes from unseen data, or bridge gaps in historical data.

Care must be taken in supervised machine learning to ensure that the labeled training data is representative of the overall population. If the training data lacks representativeness, the predictive model may become overfit to data that does not accurately reflect new and unseen data, leading to inaccurate predictions upon deployment. Given the nature of regression analysis, it is crucial to select the appropriate features to ensure accurate modeling.

Types of regression in machine learning

There are various types of regression in machine learning can be utilized. These algorithms differ in terms of the number of independent variables they consider and the types of data they process. Moreover, different types of machine learning regression models assume distinct relationships between independent and dependent variables. Linear regression techniques, for example, assume a linear relationship and may not be suitable for datasets with nonlinear relationships.

Here are some common types of regression in machine learning:

• Simple linear regression: This technique involves plotting a straight line among data points to minimize the error between the line and the data. It is one of the simplest forms of regression in machine learning, assuming a linear relationship between the dependent variable and a single independent variable. Simple linear regression may encounter outliers due to its reliance on a straight line of best fit.
• Multiple linear regression: Multiple linear regression is used when multiple independent variables are involved. Polynomial regression is an example of a multiple linear regression technique. It offers a better fit compared to simple linear regression when multiple independent variables are considered. The resulting line, if plotted on two dimensions, would be curved to accommodate the data points.
• Logistic regression: Logistic regression is utilized when the dependent variable can have one of two values, such as true or false, success or failure. It allows for the prediction of the probability of the dependent variable occurring. Logistic regression models require binary output values and use a sigmoid curve to map the relationship between the dependent variable and independent variables.

These types of regression techniques provide valuable tools for analyzing relationships between variables and making predictions in various machine learning applications.

Interaction of regression in machine learning

Regression in machine learning is primarily used for predictive analytics, allowing for the forecasting of trends and the prediction of outcomes. By training regression models to understand the relationship between independent variables and an outcome, various factors that contribute to a desired outcome can be identified and analyzed. These models find applications in diverse settings and can be leveraged in several ways.

One of the key uses of regression in machine learning models is predicting outcomes based on new and unseen data. By training a model on labeled data that captures the relationship between data features and the dependent variable, the model can make accurate predictions for future scenarios. For example, organizations can use regression machine learning to predict sales for the next month by considering various factors. In the medical field, regression models can forecast health trends in the general population over a specified period.

Regression models are trained using supervised machine learning techniques, which are commonly employed in both classification and regression problems. In classification, models are trained to categorize objects based on their features, such as facial recognition or spam email detection. Regression, on the other hand, focuses on predicting continuous outcomes, such as salary changes, house prices, or retail sales. The strength of relationships between data features and the output variable is captured through labeled training data.

Regression analysis helps identify patterns and relationships within a dataset, enabling the application of these insights to new and unseen data. Consequently, regression plays a vital role in finance-related applications, where models are trained to understand the relationships between various features and desired outcomes. This facilitates the forecasting of portfolio performance, stock costs, and market trends. However, it is important to consider the explainability of machine learning models, as they influence an organization’s decision-making process, and understanding the rationale behind predictions becomes crucial.

Regression in machine learning models find common use in various applications, including:

Forecasting continuous outcomes: Regression models are employed to predict continuous outcomes such as house prices, stock prices, or sales. These models analyze historical data and learn the relationships between input features and the desired outcome, enabling accurate predictions.

Predicting retail sales and marketing success: Regression models help predict the success of future retail sales or marketing campaigns. By analyzing past data and considering factors such as demographics, advertising expenditure, or seasonal trends, these models assist in allocating resources effectively and optimizing marketing strategies.

Predicting customer/user trends: Regression models are utilized to predict customer or user trends on platforms like streaming services or e-commerce websites. By analyzing user behavior, preferences, and various features, these models provide insights for personalized recommendations, targeted advertising, or user retention strategies.

Establishing relationships in datasets: Regression analysis is employed to analyze datasets and establish relationships between variables and an output. By identifying correlations and understanding the impact of different factors, regression in machine learning help uncover insights and inform decision-making processes.

Predicting interest rates or stock prices: Regression models can be applied to predict interest rates or stock prices by considering a variety of factors. These models analyze historical market data, economic indicators, and other relevant variables to estimate future trends and assist in investment decision-making.

Creating time series visualizations: Regression models are utilized to create time series visualizations, where data is plotted over time. By fitting a regression line or curve to the data points, these models provide a visual representation of trends and patterns, aiding in the interpretation and analysis of time-dependent data.

These are just a few examples of the common applications whereregression in machine learning play a crucial role in making predictions, uncovering relationships, and enabling data-driven decision-making.

Regression vs classification in machine learning

Regression and classification are two primary tasks in supervised machine learning, but they serve different purposes:

Regression focuses on predicting continuous numerical values as the output. The goal is to establish a relationship between input variables (also called independent variables or features) and a continuous target variable (also known as the dependent variable or outcome). Regression models learn from labeled training data to estimate this relationship and make predictions for new, unseen data.

Examples of regression tasks include predicting house prices, stock market prices, or temperature forecasting.

Classification, on the other hand, deals with predicting categorical labels or class memberships. The task involves assigning input data points to predefined classes or categories based on their features. The output of a classification model is discrete and represents the class label or class probabilities.

Examples of classification tasks include email spam detection (binary classification) or image recognition (multiclass classification). Classification models learn from labeled training data and use various algorithms to make predictions on unseen data.

Creating an artificial intelligence 101

While both regression and classification are supervised learning tasks and share similarities in terms of using labeled training data, they differ in terms of the nature of the output they produce. Regression in machine learning predicts continuous numerical values, whereas classification assigns data points to discrete classes or categories.

The choice between regression and classification depends on the problem at hand and the nature of the target variable. If the desired outcome is a continuous value, regression is suitable. If the outcome involves discrete categories or class labels, classification is more appropriate.

Fields of work that use regression in machine learning

Regression in machine learning is widely utilized by companies across various industries to gain valuable insights, make accurate predictions, and optimize their operations. In the finance sector, banks and investment firms rely on regression models to forecast stock prices, predict market trends, and assess the risk associated with investment portfolios. These models enable financial institutions to make informed decisions and optimize their investment strategies.

E-commerce giants like Amazon and Alibaba heavily employ regression in machine learning to predict customer behavior, personalize recommendations, optimize pricing strategies, and forecast demand for products. By analyzing vast amounts of customer data, these companies can deliver personalized shopping experiences, improve customer satisfaction, and maximize sales.

In the healthcare industry, regression is used by organizations to analyze patient data, predict disease outcomes, evaluate treatment effectiveness, and optimize resource allocation. By leveraging regression models, healthcare providers and pharmaceutical companies can improve patient care, identify high-risk individuals, and develop targeted interventions.

Retail chains, such as Walmart and Target, utilize regression to forecast sales, optimize inventory management, and understand the factors that influence consumer purchasing behavior. These insights enable retailers to optimize their product offerings, pricing strategies, and marketing campaigns to meet customer demands effectively.

Logistics and transportation companies like UPS and FedEx leverage regression to optimize delivery routes, predict shipping times, and improve supply chain management. By analyzing historical data and considering various factors, these companies can enhance operational efficiency, reduce costs, and improve customer satisfaction.

Marketing and advertising agencies rely on regression models to analyze customer data, predict campaign performance, optimize marketing spend, and target specific customer segments. These insights enable them to tailor marketing strategies, improve campaign effectiveness, and maximize return on investment.

Insurance companies utilize regression to assess risk factors, determine premium pricing, and predict claim outcomes based on historical data and customer characteristics. By leveraging regression models, insurers can accurately assess risk, make data-driven underwriting decisions, and optimize their pricing strategies.

Energy and utility companies employ regression to forecast energy demand, optimize resource allocation, and predict equipment failure. These insights enable them to efficiently manage energy production, distribution, and maintenance processes, resulting in improved operational efficiency and cost savings.

Telecommunication companies use regression to analyze customer data, predict customer churn, optimize network performance, and forecast demand for services. These models help telecom companies enhance customer retention, improve service quality, and optimize network infrastructure planning.

Technology giants like Google, Microsoft, and Facebook heavily rely on regression in machine learning to optimize search algorithms, improve recommendation systems, and enhance user experience across their platforms. These companies continuously analyze user data and behavior to deliver personalized and relevant content to their users.

Wrapping up

Regression in machine learning serves as a powerful technique for understanding and predicting continuous outcomes. With the ability to establish relationships between independent variables and dependent variables, regression models have become indispensable tools in the field of predictive analytics.

By leveraging labeled training data, these models can provide valuable insights and accurate forecasts across various domains, including finance, healthcare, and sales.

The diverse types of regression models available, such as simple linear regression, multiple linear regression, and logistic regression, offer flexibility in capturing different relationships and optimizing predictive accuracy.

As we continue to harness the potential of regression in machine learning, its impact on decision-making and forecasting will undoubtedly shape the future of data-driven practices.

The presence of conspiracy theories on social media is not uncommon, and the TikTok diesel truck fires videos are no exception. Some users on the platform are notorious for circulating misinformation and fabricating narratives that can be deceptively convincing. The recent trend of videos showing diesel trucks on fire falls into this category. These videos feature semi and heavy-duty trucks exploding into flames without any apparent cause, leaving viewers perplexed and concerned.

These alarming clips of the TikTok diesel truck fires videos have caught the attention of users, who are left questioning the safety of their own vehicles. The TikTok algorithm, which prioritizes engaging content, has contributed to the virality of these videos. However, it is important to approach such content with caution, as these videos are part of an elaborate hoax.

See an example of the TikTok diesel truck fires videos by @quiet_shvdow below.

@that_94_xlt

its true . . . . . #dieselfires #dieseltruckfire #truckfire #fire #biden #fyp #fypシ #fypage

♬ original sound – 🔧 94 XLT 🔧

Trolls misuse old clips of Biden on the TikTok diesel truck fires videos

To lend credibility to their misleading videos, trolls have exploited an unrelated clip featuring President Joe Biden. The clip, taken from a 2019 speech in which President Biden discusses the government’s plans to reduce emissions by transitioning to electric vehicles, has been skillfully manipulated to insinuate a connection between diesel truck fires and the US government.

By showing the clip together with the TikTok diesel truck fires videos, trolls attempt to create a false narrative that the government is somehow involved in these incidents.

It is important to emphasize that there is no factual basis for these claims. The manipulated clip of President Biden has been taken out of context and falsely attributed to the recent TikTok truck fires videos.

This deliberate misrepresentation aims to deceive viewers and exploit their concerns regarding environmental issues and government involvement. By spreading such misinformation, trolls seek to garner attention and engagement on social media platforms like TikTok.

Some heroes don’t wear capes

Thankfully, not all TikTok users have fallen prey to this orchestrated hoax. Some users have been quick to question the authenticity of the videos and critically analyze the claims being made. They have pointed out the use of photoshopped images and outdated footage in these viral videos. By conducting their own investigations and relying on personal experiences, these users have been able to debunk the false claims on the TikTok diesel truck fires videos and shed light on the deceptive nature of the content.

Many TikTok users have expressed skepticism regarding the alleged connection between diesel truck fires and the government. They have shared personal anecdotes, highlighting alternative explanations such as faulty wiring or unrelated incidents. By engaging in open discussions and challenging the misinformation, these users have played a crucial role in countering the viral hoax. Their critical thinking and fact-checking have helped to expose the lack of credibility behind these sensationalized videos.

Trolling is never OK

So, why are we telling you about all these TikTok diesel truck fires videos?

It is very difficult even to describe the power of social media in 2023 standards. Countries are now saturating our subconscious minds with their political campaigns, huge technology companies with their advertisements, and even artists with their music through many agreements that are no longer visible to us, under the name of ”going viral”.

The proliferation of trolling, especially in the context of spreading misinformation and fabricating narratives, has had a significant impact on today’s society. This intentional act of provoking and deceiving others for personal gain or amusement has led to several concerning effects.

The most important effect of trolling is the erosion of trust in online information. With the abundance of false claims and manipulated content, it becomes increasingly challenging for individuals to discern what is true and what is fabricated. Those who accept the truth of information without questioning it are already doomed to fall into a great misconception.

In 2012, believers of the Mayan apocalypse, which support that the end of the world is coming by 12.12.12, looted supermarkets and built underground bunkers, creating a vague sense of panic around the world. When the countdown site reached 0, it performed perhaps the most “successful” trolling in the history of the world and welcomed us with a meme song. But what about the panic it created in society?

Those who are aware are also unlucky because we have to double-check everything we read, as trolling not only spreads misinformation but also undermines the credibility of legitimate sources. This skepticism and lack of trust in information can lead to confusion and a decreased willingness to engage with online content.

Bottom line

The rise of internet trolling has found a distinct connection to the viral TikTok diesel truck fires videos. These videos, which have captured widespread attention and sparked concerns among viewers, are intertwined with the phenomenon of trolling that has permeated online platforms.

The term “trolling” describes the behavior of individuals who deliberately provoke reactions and manipulate discussions for personal amusement or gain. In the context of TikTok, trolls have taken advantage of the platform’s vast reach and the captivating nature of truck fires to create sensational content. By sharing videos of diesel trucks engulfed in flames and amplifying the dramatic effects with accompanying music, trolls have managed to captivate audiences and generate significant attention.

However, these videos, as we see on TikTok diesel truck fires videos, often perpetuate misinformation and false claims. Trolls go to great lengths to fabricate narratives surrounding the involvement of the US government or other entities in the truck fires. They manipulate clips, including unrelated speeches from politicians like Joe Biden, to insinuate a connection between diesel trucks and government conspiracies.