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.

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!

Featured image credit: John Schnobrich/Unsplash

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.

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.

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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.

We all know there cannot be a single answer to any given question, and that’s where evolutionary computing comes into play. Inspired by nature’s own processes, evolutionary computing uses smart algorithms to tackle complex challenges in various areas. Now, you might not be a tech expert, but evolutionary computing is important for all of us. It has the potential to transform problem-solving in ways that touch our lives, from healthcare and transportation to finance and the environment.

Let’s imagine a situation where doctors face tricky diagnostic puzzles. Evolutionary computing algorithms can analyze lots of medical information, spot patterns, and optimize diagnostic methods to help doctors make accurate and fast diagnoses. This means quicker treatment, better outcomes for patients, and ultimately, more lives saved.

But it doesn’t stop there. Think about the challenges we encounter in urban planning and transportation. Evolutionary computing can help make traffic flow smoother, reduce congestion, and shorten commuting times. Picture a future where your daily commute becomes easier, with less time spent stuck in traffic and more time for the things you enjoy.

For those who care about the environment, evolutionary computing plays a big role in fighting climate change and promoting sustainability. By optimizing energy use, managing limited resources, and designing eco-friendly systems, we can create a greener and more sustainable world for future generations.

You might be wondering, “How does this actually work”. Don’t worry, the beauty of evolutionary computing lies in its ability to handle complex stuff behind the scenes. Although it might seem complicated, the results are practical and impactful. By mimicking nature’s evolution – where only the fittest survive and the search for the best solutions never stops – evolutionary computing transforms abstract ideas into real-world achievements. It empowers computers to become creative problem-solvers, inspired by nature, to make our lives more efficient and effective.

What is evolutionary computing?

Evolutionary computing, also known as evolutionary computation, is a subfield of artificial intelligence and computational intelligence that draws inspiration from the process of natural evolution to solve complex problems. It is a computational approach that uses principles of natural selection, genetic variation, and survival of the fittest to optimize solutions. So in order to explain and understand evolutionary computing, we must first talk about evolution.

In evolutionary computing, a population of candidate solutions is created, typically represented as a set of individuals called “genomes”. These genomes encode potential solutions to the problem at hand. Each genome is evaluated using a fitness function that quantifies how well it solves the problem.

The evolution process begins with an initial population of randomly generated genomes. Through a series of iterative steps called generations, the population evolves by applying genetic operators inspired by biological evolution, such as reproduction, crossover, and mutation.

During reproduction, individuals with higher fitness are more likely to be selected as parents to produce offspring. Crossover involves combining genetic material from two parents to create new offspring, mimicking the biological process of sexual reproduction. Mutation introduces random changes in the genetic material of individuals to promote diversity and exploration in the population.

After generating new offspring, the population is updated, typically by replacing less fit individuals with newly created individuals. This selection process favors individuals with higher fitness, simulating the natural selection process in biology. The cycle of evaluation, selection, reproduction, and mutation continues for a fixed number of generations or until a termination criterion is met.

Through this iterative process, evolutionary computing explores the search space and gradually converges toward optimal or near-optimal solutions. The underlying assumption is that the fittest individuals in each generation possess better solutions, and by combining and mutating their genetic material, the population evolves towards better solutions over time.

Evolutionary computing has been successfully applied to various problem domains, including optimization, machine learning, scheduling, data mining, and many others. It offers a flexible and robust approach to solving complex problems where traditional algorithmic approaches may struggle.

Outstanding history of evolutionary computing

The history of evolutionary computing can be traced back to the mid-20th century when researchers began exploring the idea of using principles from biological evolution to solve computational problems.
The groundwork for evolutionary computing was laid by Charles Darwin’s theory of evolution in the 19th century. His ideas about natural selection and survival of the fittest provided inspiration for later developments in the field.

In the 1960s, the concept of genetic algorithms was introduced independently by Ingo Rechenberg in Germany and John Holland in the United States. They proposed using simple computational models of genetic processes, such as crossover and mutation, to optimize solutions to complex problems. John Holland’s book “Adaptation in Natural and Artificial Systems” (1975) further popularized genetic algorithms.

In the 1980s, John Koza extended the principles of genetic algorithms to evolve computer programs through a process called genetic programming. GP evolves populations of computer programs in order to solve specific tasks, such as symbolic regression and automatic code generation.

Lawrence Fogel and his colleagues introduced Evolutionary Programming in the 1960s and further developed it in the 1990s. EP is primarily used for optimization problems and control systems.

Evolutionary computing gained popularity and found applications in various fields, including optimization, robotics, data mining, machine learning, financial modeling, and game playing, among others. Researchers continued to refine and develop new evolutionary algorithms to tackle complex and diverse problem domains.

With the advent of parallel and distributed computing, evolutionary algorithms were further advanced to exploit the benefits of parallelism, allowing for more efficient and scalable problem-solving.

Evolutionary computing remains an active and growing area of research and application, continually evolving to tackle increasingly complex real-world problems across different domains. The field continues to explore innovative algorithms, hybrid approaches, and applications in emerging technologies.

How does evolutionary computing work?

The evolutionary computing process starts with a randomly generated population of individuals. The individuals are then evaluated using the fitness function. The selection operator is then used to choose a subset of individuals to be used to create the next generation of individuals. The crossover and mutation operators are then used to create the next generation of individuals. The process repeats until a stopping criterion is met, such as a certain number of generations or a certain level of fitness.

Evolutionary computing uses certain ideas that exist in biology for a long time. This computing technique, which allows us to solve today’s problems in an AI-enhanced way using the techniques we have used for centuries to investigate the origin of life, is inspired by the following techniques in biology:

• Representation: The solutions to the problem are represented as individuals in a population. The individuals can be represented in a variety of ways, such as bit strings, chromosomes, or trees.
• Fitness function: A fitness function is used to evaluate the quality of each individual. The fitness function typically assigns a higher score to individuals that are better at solving the problem.
• Selection: A selection operator is used to choose which individuals will be used to create the next generation of individuals. The selection operator typically chooses the individuals with the highest fitness scores.
• Crossover: A crossover operator is used to combine two individuals to create a new individual. The crossover operator typically swaps some of the genes of the two individuals to create a new individual with a mix of their genes.
• Mutation: A mutation operator is used to randomly change the genes of an individual. The mutation operator can help to introduce new variations into the population and prevent the population from becoming stuck in a local optimum.

Evolutionary computing is a powerful technique that can be used to solve a wide variety of problems. However, it is important to note that evolutionary computing is not a magic bullet. It can be time-consuming and computationally expensive to run evolutionary computing algorithms, and they may not always find the optimal solution.

How is evolutionary computing different from classical computing?

In classical computing, explicit problem representations and prescriptive algorithms are used to solve problems. The focus is on defining the problem explicitly and designing algorithms that provide exact or approximate solutions. classical computing often relies on local search strategies, where the search is conducted in a neighborhood of the current solution. The exploration of solutions is deterministic, meaning it follows a predefined set of rules and does not involve randomness.

On the other hand, evolutionary computing utilizes an implicit problem representation. Instead of explicitly defining the problem, it represents potential solutions as genomes within a population. The algorithms used in evolutionary computing are generative, meaning they generate new solutions through processes like reproduction, crossover, and mutation. This allows for a global search strategy, exploring a larger portion of the solution space.

Evolutionary computing is a stochastic approach, meaning it involves randomness in the selection and generation of solutions. The quality of solutions obtained through evolutionary computing is often approximate, as the focus is on finding good solutions rather than exact solutions. Convergence in evolutionary computing occurs in a population of solutions rather than a single solution, providing a diverse set of potential solutions.

Below, we have prepared a table to show the key differences between classical computing and evolutionary computing.

How have evolutionary computing algorithms been used in data science, artificial intelligence, and analytics?

Evolutionary computing algorithms have found valuable applications in the fields of data science, artificial intelligence, and analytics. These algorithms offer a powerful and flexible approach to solving complex problems, exploring large solution spaces, and optimizing solutions. Let’s explore how evolutionary computing has been used in each of these domains.

Evolutionary computing in data science

Evolutionary computing algorithms have been widely used in data science for tasks such as feature selection, data clustering, classification, and regression. These algorithms can automatically identify relevant features or combinations of features that maximize the predictive power of machine learning models. By applying genetic algorithms, genetic programming, or other evolutionary approaches, data scientists can efficiently search through a large feature space, selecting the most informative features to improve model performance.

Additionally, evolutionary computing has been employed in data clustering, where algorithms such as genetic clustering or evolutionary fuzzy clustering can automatically group similar data points together. These methods explore different cluster configurations and optimize clustering criteria to find the best partitioning of data.

Evolutionary computing in artificial intelligence

Evolutionary computing algorithms have made significant contributions to artificial intelligence, particularly in the areas of optimization, neural network design, and reinforcement learning. Genetic algorithms and evolution strategies have been applied to optimize the parameters of complex models, such as neural networks or deep learning architectures. These algorithms enable the automatic tuning of model hyperparameters, enhancing model performance and generalization.

Moreover, evolutionary computing has been used in the design and evolution of neural networks. Through genetic programming or neuroevolution, researchers have successfully evolved neural network topologies and connection weights, allowing the discovery of novel and effective network architectures. This approach has shown promise in solving complex tasks, such as image and speech recognition, by evolving networks with optimized structures.

In reinforcement learning, evolutionary algorithms have been employed to evolve policies or agents capable of making intelligent decisions in dynamic environments. By combining evolutionary search with reinforcement learning paradigms, researchers have achieved impressive results in challenging tasks, including game playing, robotics, and autonomous systems.

Evolutionary computing in analytics

Evolutionary computing algorithms have also been leveraged in analytics to solve optimization problems, such as resource allocation, scheduling, and portfolio optimization. These algorithms enable the discovery of optimal or near-optimal solutions for complex and dynamic problem domains.

For instance, in resource allocation problems, genetic algorithms or evolution strategies can be used to determine the most efficient allocation of limited resources, maximizing objectives like profit or productivity. Similarly, in scheduling problems, evolutionary computing approaches can find optimal sequences or timetables considering multiple constraints and objectives.

In financial analytics, evolutionary algorithms have been applied to portfolio optimization, where the goal is to determine the optimal allocation of investments to achieve desired returns while considering risk and diversification. Genetic algorithms or other evolutionary methods can explore different combinations of assets and weights, adapting the portfolio to changing market conditions.

Overall, evolutionary computing algorithms have proven to be versatile tools in data science, artificial intelligence, and analytics.

What is the future of evolutionary computing?

Evolutionary computing has emerged as a powerful approach, drawing inspiration from nature’s principles to solve complex problems in various domains. As the field continues to evolve, the future of evolutionary computing holds tremendous potential for advancements and novel applications. Let’s delve into the exciting possibilities that lie ahead.

Hybrid approaches

One avenue of exploration involves integrating evolutionary computing with other computational techniques. Hybrid approaches aim to combine the strengths of different algorithms, such as deep learning or swarm intelligence, to tackle complex problems more effectively. By merging evolutionary algorithms with deep learning, for instance, researchers can achieve improved optimization and design of deep neural networks, leading to enhanced performance and interpretability.

Explainable AI and interpretable models

The demand for explainable artificial intelligence (AI) continues to grow. Evolutionary computing offers a pathway to evolve models that not only exhibit high performance but also provide transparent decision-making processes. Researchers are actively developing techniques to evolve interpretable models, promoting trust and understanding in AI systems. This development is crucial in domains where explainability is essential, such as healthcare, finance, and autonomous systems.

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Evolutionary robotics

The field of evolutionary robotics focuses on automatically designing and optimizing robot morphologies and control systems. As robotics advances, evolutionary computing can play a vital role in evolving adaptable and robust robots capable of navigating complex and dynamic environments. Embodied evolution, allowing robots to autonomously adapt and evolve their behaviors through interactions with the environment, is a fascinating avenue for future exploration.

Multi-objective and many-objective optimization

Evolutionary computing excels in solving multi-objective optimization problems that involve multiple conflicting objectives. Future advancements will address many-objective optimization, where a large number of objectives need to be considered. Researchers are developing innovative algorithms and techniques to efficiently search for diverse and well-distributed solutions in high-dimensional objective spaces. This progress will enable decision-makers to explore a wide range of trade-offs in complex systems.

Evolving complex systems

The optimization and design of complex systems, such as smart cities or transportation networks, present significant challenges. Evolutionary computing offers a powerful tool to evolve solutions that balance multiple criteria and adapt to changing conditions. By integrating evolutionary algorithms into these domains, researchers can contribute to the development of more efficient, sustainable, and resilient systems that cater to the needs of modern society.

Evolving beyond biology-inspired models

While evolutionary computing is rooted in biological evolution, researchers are exploring alternative models of evolution. Concepts like cultural evolution, memetic algorithms, or hyper-heuristics draw inspiration from social and cultural mechanisms to guide the evolutionary process. These innovative approaches expand the capabilities and flexibility of evolutionary computing algorithms, opening up new frontiers for exploration and problem-solving.

Scalability and parallelization

As the scale and complexity of problems increase exponentially, scalability and parallelization of evolutionary algorithms become paramount. Developing efficient parallel and distributed evolutionary computing frameworks will enable faster and more effective exploration of large search spaces. This advancement will facilitate the optimization of complex systems and models, providing practical solutions to real-world challenges.

Ethical and responsible evolutionary computing

As evolutionary computing finds applications in diverse domains, ethical considerations gain importance. Researchers and practitioners are actively working to ensure the responsible and ethical use of evolutionary computing algorithms. Addressing issues like bias, privacy, and accountability ensures that the benefits of evolutionary computing are harnessed while mitigating potential risks and challenges.

The future of evolutionary computing is brimming with potential. Advancements in algorithmic techniques, the integration of multiple computational approaches, and applications in emerging fields will propel the field forward. As evolutionary computing continues to evolve, it promises to reshape problem-solving approaches, optimization strategies, and our understanding of adaptive and intelligent systems.

Featured image created by Kerem Gülen on Midjourney.

Data fidelity, the degree to which data can be trusted to be accurate and reliable, is a critical factor in the success of any data-driven business.

Companies are collecting and analyzing vast amounts of data to gain insights into customer behavior, identify trends, and make informed decisions. However, not all data is created equal. The accuracy, completeness, consistency, and timeliness of data, collectively known as data fidelity, play a crucial role in the reliability and usefulness of data insights.

In fact, poor data fidelity can lead to wasted resources, inaccurate insights, lost opportunities, and reputational damage. Maintaining data fidelity requires ongoing effort and attention, and involves a combination of best practices and tools.

What is data fidelity?

Data fidelity refers to the accuracy, completeness, consistency, and timeliness of data. In other words, it’s the degree to which data can be trusted to be accurate and reliable.

Definition and explanation

Accuracy refers to how close the data is to the true or actual value. Completeness refers to the data being comprehensive and containing all the required information. Consistency refers to the data being consistent across different sources, formats, and time periods. Timeliness refers to the data being up-to-date and available when needed.

Types of data fidelity

There are different types of data fidelity, including:

• Data accuracy: Data accuracy is the degree to which the data reflects the true or actual value. For instance, if a sales report states that the company made $1,000 in revenue, but the actual amount was $2,000, then the data accuracy is 50%.
• Data completeness: Data completeness refers to the extent to which the data contains all the required information. Incomplete data can lead to incorrect or biased insights.
• Data consistency: Data consistency is the degree to which the data is uniform across different sources, formats, and time periods. Inconsistent data can lead to confusion and incorrect conclusions.
• Data timeliness: Data timeliness refers to the extent to which the data is up-to-date and available when needed. Outdated or delayed data can result in missed opportunities or incorrect decisions.

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Examples

Data fidelity is crucial in various industries and applications. For example:

• In healthcare, patient data must be accurate, complete, and consistent across different systems to ensure proper diagnosis and treatment.
• In finance, accurate and timely data is essential for investment decisions and risk management.
• In retail, complete and consistent data is necessary to understand customer behavior and optimize sales strategies.

Without data fidelity, decision-makers cannot rely on data insights to make informed decisions. Poor data quality can result in wasted resources, inaccurate conclusions, and lost opportunities.

The importance of data fidelity

Data fidelity is essential for making informed decisions and achieving business objectives. Without reliable data, decision-makers cannot trust the insights and recommendations derived from it.

Decision-making

Data fidelity is critical for decision-making. Decision-makers rely on accurate, complete, consistent, and timely data to understand trends, identify opportunities, and mitigate risks. For instance, inaccurate or incomplete financial data can lead to incorrect investment decisions, while inconsistent data can result in confusion and incorrect conclusions.

Consequences of poor data fidelity

Poor data fidelity can have serious consequences for businesses. Some of the consequences include:

• Wasted resources: Poor data quality can lead to wasted resources, such as time and money, as decision-makers try to correct or compensate for the poor data.
• Inaccurate insights: Poor data quality can lead to incorrect or biased insights, which can result in poor decisions that affect the bottom line.
• Lost opportunities: Poor data quality can cause decision-makers to miss opportunities or make incorrect decisions that result in missed opportunities.
• Reputational damage: Poor data quality can damage a company’s reputation and erode trust with customers and stakeholders.

Data fidelity is essential for making informed decisions that drive business success. Poor data quality can result in wasted resources, inaccurate insights, lost opportunities, and reputational damage.

Maintaining data fidelity

Maintaining data fidelity requires ongoing effort and attention. There are several best practices that organizations can follow to ensure data fidelity.

Best practices

Here are some best practices for maintaining data fidelity:

• Data cleaning: Regularly clean and validate data to ensure accuracy, completeness, consistency, and timeliness. This involves identifying and correcting errors, removing duplicates, and filling in missing values.
• Regular audits: Conduct regular audits of data to identify and correct any issues. This can involve comparing data across different sources, formats, and time periods.
• Data governance: Establish clear policies and procedures for data management, including data quality standards, data ownership, and data privacy.
• Training and education: Train employees on data management best practices and the importance of data fidelity.

Tools and technologies

There are several tools and technologies that can help organizations maintain data fidelity, including:

• Data quality tools: These tools automate the process of data validation, cleaning, and enrichment. Examples include Trifacta and Talend.
• Master data management (MDM) solutions: These solutions ensure data consistency by creating a single, trusted version of master data. Examples include Informatica and SAP.
• Data governance platforms: These platforms provide a centralized system for managing data policies, procedures, and ownership. Examples include Collibra and Informatica.
• Data visualization tools: These tools help organizations visualize and analyze data to identify patterns and insights. Examples include Tableau and Power BI.

By using these tools and technologies, organizations can ensure data fidelity and make informed decisions based on reliable data.

Maintaining data fidelity requires a combination of best practices and tools. Organizations should regularly clean and validate data, conduct audits, establish clear policies and procedures, train employees, and use data quality tools, MDM solutions, data governance platforms, and data visualization tools to ensure data fidelity.

Applications of data fidelity

Data fidelity is crucial in various industries and applications. Here are some examples:

Different industries

• Healthcare: Patient data must be accurate, complete, and consistent across different systems to ensure proper diagnosis and treatment. Poor data quality can lead to incorrect diagnoses and compromised patient safety.
• Finance: Accurate and timely data is essential for investment decisions and risk management. Inaccurate or incomplete financial data can lead to incorrect investment decisions, while inconsistent data can result in confusion and incorrect conclusions.
• Retail: Complete and consistent data is necessary to understand customer behavior and optimize sales strategies. Poor data quality can lead to missed opportunities for cross-selling and upselling, as well as ineffective marketing campaigns.

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Case studies

• Netflix: Netflix uses data fidelity to personalize recommendations for its subscribers. By collecting and analyzing data on viewing history, ratings, and preferences, Netflix can provide accurate and relevant recommendations to each subscriber.
• Starbucks: Starbucks uses data fidelity to optimize store layouts and product offerings. By collecting and analyzing data on customer behavior, preferences, and purchase history, Starbucks can design stores that meet customers’ needs and preferences.
• Walmart: Walmart uses data fidelity to optimize inventory management and supply chain operations. By collecting and analyzing data on sales, inventory, and shipments, Walmart can optimize its inventory levels and reduce waste.

Final words

The importance of accurate and reliable data cannot be overstated. In today’s rapidly evolving business landscape, decision-makers need to rely on data insights to make informed decisions that drive business success. However, the quality of data can vary widely, and poor data quality can have serious consequences for businesses.

To ensure the accuracy and reliability of data, organizations must invest in data management best practices and technologies. This involves regular data cleaning, validation, and enrichment, as well as conducting audits and establishing clear policies and procedures for data management. By using data quality tools, MDM solutions, data governance platforms, and data visualization tools, organizations can streamline their data management processes and gain valuable insights.

The strategic value of IoT development and data analytics

The applications of accurate and reliable data are numerous and varied. From healthcare to finance to retail, businesses rely on data insights to make informed decisions and optimize operations. Companies that prioritize accurate and reliable data can achieve significant business success, such as improved customer experiences, optimized supply chain operations, and increased revenue.

Businesses that prioritize data accuracy and reliability can gain a competitive advantage in today’s data-driven world. By investing in data management best practices and technologies, organizations can unlock the full potential of their data and make informed decisions that drive business success.

The emergence of the Internet of Things (IoT) has led to the proliferation of connected devices and sensors that generate vast amounts of data. This data is a goldmine of insights that can be harnessed to optimize various systems and processes. However, to unlock the full potential of IoT data, organizations need to leverage the power of data science. Data science can help organizations derive valuable insights from IoT data and make data-driven decisions to optimize their operations.

Coherence between IoT and data science is critical to ensure that organizations can maximize the value of their IoT ecosystems. It requires a deep understanding of the interplay between IoT devices, sensors, networks, and data science tools and techniques. Organizations that can effectively integrate IoT and data science can derive significant benefits, such as improved efficiency, reduced costs, and enhanced customer experiences.

What is an IoT ecosystem?

An IoT (Internet of Things) ecosystem refers to a network of interconnected devices, sensors, and software applications that work together to collect, analyze, and share data. The ecosystem consists of various components, including devices, communication networks, data storage, and analytics tools, that work together to create an intelligent system that enables automation, monitoring, and control of various processes.

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Some key characteristics of an IoT ecosystem include the following:

• Interconnectivity: IoT devices and applications are connected and communicate with each other to share data and enable coordinated actions.
• Data-driven: The ecosystem is built around data, and devices generate and share data that is used to enable automation, predictive maintenance, and other applications.
• Scalable: IoT ecosystems can be scaled up or down depending on the number of devices and the amount of data being generated.
• Intelligent: The ecosystem uses AI and machine learning algorithms to analyze data and derive insights that can be used to optimize processes and drive efficiencies.

What is an IoT ecosystem diagram?

An IoT ecosystem diagram is a visual representation of the components and relationships that make up an IoT ecosystem. It typically includes devices, communication networks, data storage, and analytics tools that work together to create an intelligent system.

The diagram provides a high-level overview of the ecosystem and helps to visualize the various components and how they are interconnected. It can also be used to identify potential areas for improvement and optimization within the system.

Understanding IoT ecosystem architecture

IoT ecosystem architecture refers to the design and structure of an IoT system, including the various components and how they are connected.

There are several layers to an IoT ecosystem architecture, including:

• Device layer: This layer includes the sensors and other devices that collect data and interact with the physical environment.
• Communication layer: This layer includes the communication networks that enable data to be transmitted between devices and other components.
• Data layer: This layer includes the data storage and management systems that store and process the data generated by the IoT system.
• Application layer: This layer includes software applications and tools that enable users to interact with and make sense of the data generated by the system.

Defining IoT ecosystems and their role in data science

IoT ecosystems play an important role in data science, as they generate vast amounts of data that can be used to drive insights and optimize processes.

Some ways that IoT ecosystems contribute to data science include:

• Enabling data collection: IoT devices generate large amounts of data that can be used to train machine learning algorithms and drive predictive models.
• Providing real-time data: IoT ecosystems can provide real-time data that can be used to identify trends and patterns and drive immediate action.
• Facilitating automation: IoT ecosystems can be used to automate various processes, reducing the need for manual intervention and enabling greater efficiency.

IoT ecosystems provide a rich source of data that can be used to drive insights and optimize processes, making them a valuable tool in the data science toolkit.

Components of IoT ecosystems

IoT ecosystems are composed of various components that work together to collect, process, and transmit data.

Hardware and software components of IoT ecosystems

IoT ecosystems consist of both hardware and software components that work together to enable automation, monitoring, and control of various processes. Some of the key hardware and software components of IoT ecosystems include:

• Hardware components: IoT hardware components include devices and sensors, communication networks, and data storage systems. These components are responsible for collecting, transmitting, and processing data.
• Software components: IoT software components include applications, operating systems, and analytics tools. These components are responsible for processing and analyzing the data generated by IoT devices and sensors.

Understanding the role of each component in IoT ecosystems

Each component in an IoT ecosystem plays a critical role in enabling the system to function effectively. Understanding the role of each component is essential in designing and optimizing IoT ecosystems. Some of the key roles of each component in IoT ecosystems include:

• Sensors and devices: IoT sensors and devices are responsible for collecting data from the physical environment. They play a critical role in enabling automation, monitoring, and control of various processes.
• Communication networks: Communication networks enable the transmission of data between IoT devices and other components in the ecosystem. They are responsible for ensuring that data is transmitted securely and reliably.
• Data storage: Data storage is essential in IoT ecosystems, as it is responsible for storing and managing the vast amounts of data generated by IoT devices and sensors. Data storage solutions need to be scalable, secure, and cost-effective.
• Analytics tools: Analytics tools are used to process and analyze the data generated by IoT devices and sensors. They play a critical role in enabling data-driven decision-making and identifying trends and patterns.

Importance of choosing the right components for IoT ecosystems

Choosing the right components for IoT ecosystems is essential in ensuring that the system functions effectively and efficiently. Some of the key reasons why choosing the right components is important to include:

• Scalability: IoT ecosystems need to be scalable, and choosing the right components can ensure that the system can be scaled up or down as needed.
• Reliability: IoT ecosystems need to be reliable, and choosing the right components can ensure that the system is resilient and can operate under various conditions.
• Security: IoT ecosystems need to be secure, and choosing the right components can ensure that data is transmitted and stored securely.

Challenges in designing IoT ecosystems

Designing and implementing IoT ecosystems can be challenging due to various factors, such as the complexity of the system, the diversity of devices, and the need for interoperability. Some of the common challenges in designing and implementing IoT ecosystems include the following:

• Data management: The vast amount of data generated by IoT devices can be overwhelming, making it challenging to store, process, and analyze the data effectively.
• Interoperability: IoT devices and sensors may come from different manufacturers, making it challenging to ensure that they are compatible and can communicate with each other.
• Security: IoT ecosystems are vulnerable to security threats, such as data breaches, hacking, and cyber attacks, making it essential to implement robust security measures.
• Scalability: As the number of devices in an IoT ecosystem increases, the system needs to be scalable and able to handle the increasing volume of data and traffic.
• Lack of standards: The lack of industry-wide standards makes it challenging to ensure that IoT devices and sensors are interoperable and can communicate with each other.
• Data security: IoT ecosystems are vulnerable to security threats, and organizations need to implement robust security measures to protect sensitive data.
• Data management: The vast amount of data generated by IoT devices can be challenging to store, process, and analyze effectively, making it essential to implement effective data management strategies.
• Integration with legacy systems: Integrating IoT ecosystems with legacy systems can be challenging, and organizations need to ensure that the systems are compatible and can work together seamlessly.

Solutions for overcoming IoT ecosystem design and implementation challenges

Overcoming the challenges of designing and implementing IoT ecosystems requires a combination of technical expertise, strategic planning, and effective execution. Some of the solutions for overcoming IoT ecosystem design and implementation challenges include:

• Adopting standards: Adhering to industry-wide standards can help ensure that IoT devices and sensors are interoperable and can communicate with each other.
• Implementing robust security measures: Implementing robust security measures, such as encryption, firewalls, and intrusion detection systems, can help protect sensitive data.
• Leveraging cloud computing: Cloud computing can provide scalable and cost-effective data storage and processing solutions for IoT ecosystems.
• Implementing effective data management strategies: Implementing effective data management strategies, such as data analytics and visualization tools, can help organizations derive insights from the vast amounts of data generated by IoT devices.

Best practices for designing IoT ecosystems for data science

Designing IoT ecosystems for data science requires careful planning and execution. Some of the best practices for designing IoT ecosystems for data science include:

• Identifying use cases: Identifying use cases and defining clear objectives can help organizations design IoT ecosystems that meet specific business needs.
• Choosing the right components: Choosing the right components, such as sensors, communication networks, data storage, and analytics tools, is critical in ensuring that the system is effective and efficient.
• Ensuring interoperability: Ensuring that IoT devices and sensors are interoperable and can communicate with each other is essential in enabling data-driven decision-making.
• Implementing effective data management strategies: Implementing effective data management strategies, such as data analytics and visualization tools, can help organizations derive insights from the vast amounts of data generated by IoT devices.

Designing IoT ecosystems for data science requires a combination of technical expertise, strategic planning, and effective execution, and organizations need to adopt best practices to ensure success.

IoT and machine learning: Walking hand in hand towards smarter future

The role of data science in optimizing IoT ecosystems

Data science plays a critical role in optimizing IoT ecosystems by enabling organizations to derive insights from the vast amounts of data generated by IoT devices and sensors. Data science can help organizations identify trends and patterns, predict future events, and optimize processes.

Some of the key ways that data science can be used to optimize IoT ecosystems include:

• Predictive maintenance: Data science can be used to predict when equipment is likely to fail, enabling organizations to schedule maintenance proactively and avoid costly downtime.
• Optimization: Data science can be used to optimize processes, such as supply chain management, inventory management, and production scheduling, enabling organizations to operate more efficiently.
• Personalization: Data science can be used to personalize products and services, enabling organizations to deliver better customer experiences.

Leveraging data science to optimize IoT ecosystem performance

Leveraging data science to optimize IoT ecosystem performance requires a combination of technical expertise, strategic planning, and effective execution. Some of the key steps involved in leveraging data science to optimize IoT ecosystem performance include:

• Data collection: Collecting data from IoT devices and sensors is the first step in leveraging data science to optimize IoT ecosystem performance.
• Data management: Managing the vast amounts of data generated by IoT devices and sensors requires effective data management strategies, such as data cleansing, data normalization, and data modeling.
• Data analysis: Analyzing the data generated by IoT devices and sensors requires advanced analytics tools, such as machine learning algorithms and artificial intelligence.
• Insights and action: Deriving insights from the data generated by IoT devices and sensors is only useful if organizations can take action based on those insights. This requires effective communication, collaboration, and execution.

IoT ecosystem examples

There are several examples of data science applications in IoT ecosystems. Some of the key examples include:

• Predictive maintenance: Data science can be used to predict when equipment is likely to fail, enabling organizations to schedule maintenance proactively and avoid costly downtime. For example, General Electric uses data science to predict when its engines are likely to fail and schedule maintenance accordingly.
• Optimization: Data science can be used to optimize processes, such as supply chain management, inventory management, and production scheduling, enabling organizations to operate more efficiently. For example, Walmart uses data science to optimize its supply chain and reduce costs.
• Personalization: Data science can be used to personalize products and services, enabling organizations to deliver better customer experiences. For example, Amazon uses data science to personalize its recommendations for customers based on their browsing and purchase history.

Security and privacy concerns in IoT ecosystems

IoT ecosystems pose significant security and privacy challenges due to the sheer volume of data generated by numerous devices and sensors. The data can include highly sensitive information, such as biometric data, personal information, and financial details, making it critical to ensure that it is secured and protected.

One of the significant concerns is device security, where the devices are vulnerable to hacking, compromising their integrity and privacy. Network security is also a concern, where the data transmitted over the networks may be intercepted and compromised. Data privacy is another critical concern where there is a risk of unauthorized access to the vast amounts of sensitive data generated by IoT devices.

Devices and sensors are vulnerable to various types of attacks, including malware, distributed denial-of-service (DDoS) attacks, and phishing scams. These attacks can compromise the security of the devices and data generated, leading to devastating consequences.

Data breaches are another concern where the vast amounts of data generated by IoT devices need to be stored and transmitted securely. Any breach of the data can expose sensitive information, leading to privacy violations, identity theft, and other serious consequences.

Impact of security and privacy concerns on data science in IoT ecosystems

Security and privacy concerns can have a significant impact on data science in IoT ecosystems. Data quality can be compromised due to security and privacy concerns, leading to incomplete or inaccurate data that can affect the effectiveness of data science. The volume of data that is available for analysis may also be limited due to security and privacy concerns. Furthermore, security and privacy concerns can make it challenging to store and transmit data securely, increasing the risk of unauthorized access and misuse.

Building trust in IoT ecosystems: A privacy-enhancing approach to cybersecurity

Best practices for ensuring security and privacy in IoT ecosystems

Ensuring security and privacy in IoT ecosystems requires a combination of technical expertise, strategic planning, and effective execution. Some of the best practices for ensuring security and privacy in IoT ecosystems include:

• Adopting security standards: Adhering to industry-wide security standards can help ensure that IoT devices and sensors are secure and can protect sensitive data.
• Implementing robust encryption: Implementing robust encryption, such as SSL/TLS, can help protect data transmitted between IoT devices and other components in the ecosystem.
• Implementing access controls: Implementing access controls, such as multi-factor authentication and role-based access control, can help ensure that only authorized users can access sensitive data.
• Conducting regular security audits: Conducting regular security audits can help organizations identify vulnerabilities and address security and privacy concerns proactively.

Ensuring security and privacy in IoT ecosystems are essential in enabling organizations to leverage data science to optimize their systems. Implementing best practices can help organizations minimize security and privacy risks and derive maximum value from their IoT ecosystems.

Final words

In closing, the combination of IoT and data science offers a world of endless possibilities for organizations looking to optimize their systems and processes. However, it also presents significant challenges, particularly around security and privacy.

To ensure the coherence of IoT and data science, organizations must take a comprehensive approach to data management and security, adopting best practices and adhering to industry standards. By doing so, they can unlock the full potential of their IoT ecosystems, derive valuable insights from their data, and make data-driven decisions that drive growth and success.

As IoT continues to evolve and expand, organizations that can effectively leverage data science to analyze IoT data will be well-positioned to thrive in the digital age.

In the world of modern computing, containers as a service (CaaS) has emerged as a powerful and innovative approach to application deployment and management. As organizations continue to embrace the benefits of containerization and cloud-based computing, CaaS has quickly gained popularity as a versatile and efficient solution for managing containers without the need to manage the underlying infrastructure.

By offering a range of container management tools and services, CaaS providers have made it possible for users to focus on application development and testing while leaving the complexities of container deployment and management to the experts.

Through increased portability, scalability, and cost-effectiveness, CaaS has transformed the way organizations approach application deployment, empowering them to manage their containerized applications in the cloud. In this article, we will explore the many advantages of CaaS for data science projects and delve into best practices for implementing CaaS in your workflows.

We will also compare and contrast CaaS with other cloud-based deployment models, such as Platform as a service (PaaS), to help you make an informed decision on the best deployment model for your organization’s needs.

What are containers and why are they important for data science?

Containers are a form of virtualization that allows for the efficient and isolated packaging of software applications and their dependencies. Containers encapsulate an application and its dependencies in a self-contained unit, providing consistency in software development, testing, and deployment. Some of the reasons why containers are important for data science are:

• Portability: Containers are portable, meaning that they can run on any system with the same underlying operating system and container runtime. This allows for easier collaboration and sharing of code across different teams and environments.
• Isolation: Containers are isolated from the host operating system and from other containers running on the same system. This helps to prevent conflicts between different software applications and ensures that each application has access to the resources it needs to run effectively.
• Reproducibility: Containers provide a consistent environment for running software applications, making it easier to reproduce and debug issues that may arise during development or deployment.
• Scalability: Containers can be easily scaled up or down to handle varying levels of demand, allowing for more efficient use of resources and reducing costs.

The rise of containers as a service in the data science industry

Containers as a service is a cloud-based container deployment model that enables organizations to easily deploy, manage, and scale containers without havings to manage the underlying infrastructure. CaaS has become increasingly popular in the data science industry due to its many benefits, including:

• Ease of deployment: CaaS providers handle the infrastructure and networking aspects of container deployment, allowing data scientists to focus on developing and testing their applications.
• Flexibility: CaaS providers offer a variety of container management tools and services, giving data scientists the flexibility to choose the tools that best fit their needs.
• Cost-effectiveness: CaaS providers offer a pay-as-you-go pricing model, which can be more cost-effective than maintaining and managing the infrastructure in-house.

How containers as a service are transforming data science workflows?

Containers as a service is transforming data science workflows by providing a flexible, scalable, and cost-effective solution for deploying and managing containers. Some of the ways in which CaaS is transforming data science workflows include:

• Increased productivity: CaaS allows data scientists to focus on developing and testing their applications rather than managing the underlying infrastructure.
• Faster time to market: CaaS allows data scientists to quickly deploy and test their applications, reducing time to market.
• Improved collaboration: CaaS allows for easier collaboration between different teams and environments, increasing the efficiency and effectiveness of data science workflows.

Achieving data resilience with StaaS

What are containers and how do they work?

Containers are a form of virtualization that enable the packaging and deployment of software applications and their dependencies in a portable and isolated environment. Containers work by using operating system-level virtualization to create a lightweight, isolated environment that can run an application and its dependencies.

Containers are similar to virtual machines in that they provide an isolated environment for running software applications. However, containers differ from virtual machines in a few key ways:

It’s important to note that these are general differences between containers and virtual machines, and that specific implementations may have additional or different features. Additionally, while containers have several advantages over virtual machines, virtual machines still have a role to play in certain use cases, such as running legacy applications that require specific operating systems.

Key benefits of using containers for data science projects

Containers offer several key benefits for data science projects, including:

• Portability: Containers can be easily moved between different environments, making it easier to deploy and run applications in different settings.
• Reproducibility: Containers provide a consistent environment for running software applications, making it easier to reproduce and debug issues that may arise during development or deployment.
• Isolation: Containers are isolated from other containers running on the same system, which helps to prevent conflicts between different software applications and ensures that each application has access to the resources it needs to run effectively.
• Scalability: Containers can be easily scaled up or down to handle varying levels of demand, allowing for more efficient use of resources and reducing costs.

What is containers as a service (CaaS)?

Containers as a service is a cloud-based container deployment model that allows users to easily deploy, manage, and scale containers without having to manage the underlying infrastructure. CaaS providers handle the infrastructure and networking aspects of container deployment, providing users with a range of container management tools and services. This allows users to focus on developing and testing their applications, rather than worrying about the underlying infrastructure.

How CaaS differs from other containerization technologies?

Containers as a service differs from other containerization technologies, such as container orchestration platforms like Kubernetes or Docker Swarm. While these platforms provide powerful tools for managing and deploying containers, they require more expertise and effort to set up and maintain. CaaS providers, on the other hand, offer a simpler, more user-friendly solution for deploying containers.

Implementing CaaS for data science

Implementing containers as a service for data science can provide many benefits, including increased portability, scalability, and cost-effectiveness. To implement CaaS in your data science workflows, you will need to choose a CaaS provider and follow best practices for deployment and management.

Choosing a CaaS provider for your data science projects

When choosing a CaaS provider for your data science projects, there are several factors to consider, including:

• Supported platforms: Make sure the containers as a service provider supports the platforms and programming languages you use in your data science projects.
• Container management tools: Look for a CaaS provider that offers a range of container management tools and services that meet your needs.
• Pricing: Compare pricing models and choose a containers as a service provider that offers a pricing model that fits your budget and usage needs.
• Support and documentation: Choose a CaaS provider that offers good documentation and support to help you troubleshoot issues that may arise.

Exploring the strong growth of BaaS in the fintech sector

Best practices for implementing CaaS in your data science workflows

To successfully implement containers as a service in your data science workflows, you should follow best practices for deployment and management, including:

• Use version control: Use version control systems to manage your code and configuration files, which can help with reproducibility and troubleshooting.
• Secure your containers: Make sure your containers are secure by following best practices for container hardening and using secure networking and authentication methods.
• Monitor your containers: Use monitoring tools to keep track of your containers and identify issues before they become critical.
• Optimize your containers: Optimize your containers for performance and resource usage to ensure efficient use of resources.

Common challenges when using CaaS for data science and how to overcome them

Some common challenges when using CaaS for data science include:

• Networking issues: Container networking can be complex, and misconfiguration can cause issues. Use proper networking practices and monitor your containers to identify and resolve networking issues.
• Data management: Containers can make data management more challenging. Use persistent volumes and data management tools to manage your data in containers.
• Compatibility issues: Compatibility issues can arise when moving containers between different environments. Use consistent configurations and container images to reduce the risk of compatibility issues.

To overcome these challenges, use best practices for container networking and data management, and follow best practices for container configuration and deployment. Monitor your containers and use testing and debugging tools to identify and resolve issues quickly.

The future of containers as a service in the data science industry

The future of containers as a service in the data science industry looks bright as more organizations recognize the benefits of containerization for their data-driven workflows. As the industry evolves, we can expect to see more CaaS providers enter the market, offering new tools and services to help data scientists and organizations more effectively manage their containerized applications.

Key takeaways and next steps for implementing CaaS in your data science projects

Some key takeaways and next steps for implementing CaaS in your data science projects include:

• Choose a CaaS provider that meets your needs: Look for a containers as a service provider that offers the tools and services you need for your data science projects.
• Follow best practices for deployment and management: Follow best practices for container deployment and management, including using version control, monitoring your containers, and optimizing for performance and resource usage.
• Monitor your containers: Use monitoring tools to keep track of your containers and identify issues before they become critical.
• Secure your containers: Make sure your containers are secure by following best practices for container hardening and using secure networking and authentication methods.

Why CaaS is a game changer for data scientists and data-driven organizations?

CaaS is a game changer for data scientists and data-driven organizations, providing increased portability, scalability, and cost-effectiveness for deploying and managing containers. By using CaaS, data scientists can focus on developing and testing their applications, while CaaS providers handle the underlying infrastructure and networking aspects of container deployment. This allows organizations to more efficiently manage their containerized applications and scale their infrastructure to meet changing demands.

Containers as a service examples

Here are some examples of popular containers as a service providers:

• Amazon Web ServicesElastic Container Service (ECS): Amazon’s CaaS offering, which supports both Docker containers and AWS Fargate for serverless container deployment.
• Microsoft Azure Container Instances (ACI): Microsoft’s containers as a service offering, which allows users to deploy containers without having to manage any underlying infrastructure.
• Google Cloud Run: Google’s CaaS offering, which supports both Docker containers and serverless containers.
• IBM Cloud Kubernetes Service: IBM’s CaaS offering, which provides a managed Kubernetes environment for container deployment and management.
• Docker Hub: It is a cloud-based registry for storing and sharing container images, which can be used in conjunction with Docker’s other container management tools.
• Red Hat OpenShift: A container application platform that provides both CaaS and PaaS capabilities based on the Kubernetes container orchestration platform.
• Oracle Cloud Infrastructure Container Engine for Kubernetes: Oracle’s containers as a service offering, which provides a managed Kubernetes environment for container deployment and management.

These are just a few examples of the many CaaS providers available today, each offering different tools and services to support container deployment and management in the cloud.

CaaS vs PaaS

Containers as a service and platform as a service are both cloud-based deployment models that offer benefits for application development and deployment. However, they differ in several key ways:

CaaS

CaaS is a cloud-based container deployment model that allows users to easily deploy, manage, and scale containers without having to manage the underlying infrastructure. CaaS providers handle the infrastructure and networking aspects of container deployment, providing users with a range of container management tools and services. This allows users to focus on developing and testing their applications rather than worrying about the underlying infrastructure.

Streamlining operations with IPaaS: A comprehensive guide to Integration Platform as a Service

PaaS

PaaS is a cloud-based platform deployment model that provides a complete platform for developing, testing, and deploying applications. PaaS providers offer a range of development tools, application frameworks, and database management tools, allowing users to develop and deploy applications quickly and easily. PaaS providers also handle the underlying infrastructure and networking aspects of application deployment, making it easy for users to focus on application development and deployment.

Differences between CaaS and PaaS

While both CaaS and PaaS offer benefits for application development and deployment, they differ in terms of their focus, architecture, scalability, portability, flexibility, and complexity. Depending on the specific needs of a given organization or project, one deployment model may be more suitable than the other.

Key takeaways

• Containers as a service is a cloud-based container deployment model that allows organizations to easily deploy, manage, and scale containers without having to manage the underlying infrastructure. By leveraging the expertise of CaaS providers, organizations can focus on application development and testing while leaving the complexities of container deployment and management to the experts.
• The benefits of using CaaS for data science projects are numerous, including increased portability, scalability, and cost-effectiveness. With containers as a service , data scientists can easily manage and deploy their containerized applications, allowing them to focus on data analysis and modeling.
• Choosing the right containers as a service provider is essential for the success of your data science project. When choosing a provider, consider factors such as supported platforms, container management tools, pricing, and support and documentation.
• Best practices for implementing CaaS in your data science workflows include using version control, securing your containers, monitoring your containers, and optimizing for performance and resource usage. By following these best practices, you can ensure that your containerized applications are running smoothly and efficiently.
• While containers as a service is a powerful tool for managing containers in the cloud, it is important to consider other cloud-based deployment models, such as Platform as a Service (PaaS), when choosing the best deployment model for your organization’s needs. By comparing and contrasting different deployment models, you can make an informed decision on the best model to fit your organization’s specific requirements.

Conclusion

Containers as a service is a powerful tool for data scientists and data-driven organizations, providing increased portability, scalability, and cost-effectiveness for deploying and managing containers. As the data science industry continues to grow, the demand for CaaS is likely to increase as more organizations look for efficient and flexible solutions for managing their containerized applications.

Data gravity is a term that has been gaining attention in recent years as more and more businesses are becoming data-driven. The concept of data gravity is simple yet powerful; it refers to the tendency for data and related applications to congregate in one location, similar to how physical objects with more mass tend to attract objects with less mass.

But how does this concept applies to the world of data and technology? Understanding data gravity can be the key to unlocking the full potential of your data and making strategic decisions that can give your business a competitive edge.

What is data gravity?

Data gravity is a concept that was first introduced in a blog post by Dave McCrory in 2010, which uses the metaphor of gravity to explain the phenomenon of data and applications congregating in one location. The idea is that as data sets become larger and larger, they become harder to move, similar to how objects with more mass are harder to move due to the force of gravity.

Therefore, the data tends to stay in one place, and other elements, such as processing power and applications, are attracted to the location of the data, similar to how objects are attracted to objects with more mass in gravity. This concept is particularly relevant in the context of big data and data analytics, as the need for powerful processing and analytical tools increases as the data sets grow in size and complexity.

Data Gravity Index

The Data Gravity Index, created by Digital Realty, a data center operator, is a global forecast that measures enterprise data creation’s growing intensity and force. The index is designed to help enterprises identify the best locations to store their data, which becomes increasingly important as the amount of data and activity increases. Digital Realty uses this index to assist companies in finding optimal locations, such as data centers, for their data storage needs.

The history of data gravity

Dave McCrory, an IT expert, came up with the term data gravity as a way to describe the phenomenon of large amounts of data and related applications congregating in one location, similar to how objects with more mass attract objects with less mass in physics.

According to McCrory, data gravity is becoming more prevalent in the cloud as more businesses move their data and analytics tools to the cloud. He also differentiates between natural data gravity and changes caused by external factors such as legislation, throttling, and pricing, which he refers to as artificial data gravity.

McCrory has also released the Data Gravity Index, a report that measures, quantifies, and predicts the intensity of data gravity for the Forbes Global 2000 Enterprises across different metros and industries. The report includes a formula for data gravity, a methodology based on thousands of attributes of Global 2000 enterprise companies’ presences in each location, and variables for each location.

How does data gravity influence an organization’s cloud strategy?

Data gravity can influence an organization’s cloud strategy in several ways. For example, if an organization has a large amount of data already stored in a specific location, it may be difficult to move that data to a different cloud provider due to the “gravity” of the data in one place. This may make it more difficult for the organization to take advantage of the cost savings and other benefits that can be achieved by using multiple cloud providers.

Additionally, data gravity can also influence where an organization chooses to place its processing power and applications. For example, suppose an organization’s data is stored in a specific location. In that case, it may be more efficient to place the processing power and applications used to analyze the data in that location rather than trying to move the data to a different location.

Enterprise cloud storage is the foundation for a successful remote workforce

Another factor that data gravity can influence on cloud strategy is the decision of choosing where to store the data. Organizations with large data sets may prefer to store their data in a location with high data gravity since the data will be more difficult to move and, therefore, more secure. This can lead to organizations storing their data in data centers or cloud providers located in specific geographic regions or specializing in specific industries.

Overall, data gravity can be a significant factor in an organization’s cloud strategy, influencing decisions around where to store data, where to place processing power and applications, and the overall cost and security of the organization’s cloud infrastructure.

How to deal with data gravity?

There are several ways that organizations can deal with data gravity:

Multi-cloud strategy

One way to deal with data gravity is to adopt a multi-cloud strategy, which involves using multiple cloud providers to take advantage of the different features and benefits that each provider offers. This can help mitigate the effects of data gravity by allowing organizations to move data and processing power between providers as needed.

Edge computing

Another way to deal with data gravity is to use edge computing, which involves placing processing power and applications closer to the location where data is generated. This can help to reduce the need to move large amounts of data over long distances, making it easier to process and analyze data in real time.

Data replication and backup

Organizations can replicate the data and store it in multiple locations. This could be helpful in cases where it is not possible to move the data or if the data is valuable, and it is important to have a backup copy of the data in case of any failure.

Cloud-based data management services

Organizations can also use cloud-based data management services to help manage and move large amounts of data. These services can automate many processes involved in moving data between different locations, making it easier to deal with data gravity.

Data governance

Data governance includes processes and policies that ensure the data’s availability, usability, integrity, and security. Organizations with well-defined data governance are better prepared to deal with data gravity as they can easily identify, locate and move the data if needed.

What are the design requirements for data gravity?

Here are some of the high-level design requirements for data gravity:

• Scalability: The design should be able to scale up or down as the amount of data grows or decreases, allowing organizations to add or remove processing power and storage as needed.
• Data security: The design should ensure that data is secure and protected from unauthorized access, which is especially important when dealing with sensitive or confidential information.
• Network and data transfer speed: The design should be able to handle large amounts of data being transferred over long distances, which can be a challenge when dealing with data gravity.
• Data governance: The design should include a data governance framework that ensures the availability, usability, integrity, and security of the data. This can help organizations to manage better and move large amounts of data.
• Compliance: The design should be in compliance with relevant laws and regulations, such as data privacy laws and industry-specific regulations.
• Flexibility: The design should be flexible enough to accommodate different data types and workloads. This can include support for different data formats, integration with various data sources, and the ability to handle real-time and batch processing.
• Backup and disaster recovery: The design should include a backup and disaster recovery plan to ensure that the data is protected in case of any failure.
• Cost-effectiveness: The design should be cost-effective, considering the total cost of ownership, including the cost of storing, processing, and managing the data, as well as any costs associated with moving data between locations.

How does data gravity often affect customers?

Data gravity can affect customers in several ways, including:

Limited choices

Data gravity can limit customers’ choices when it comes to cloud providers and data storage locations. If a customer’s data is stored in one location, it may be difficult to move that data to a different provider, making it more challenging to take advantage of the features and benefits offered by other providers.

Increased costs

Data gravity can also increase costs for customers, as they may need to pay for additional processing power and storage in order to keep up with the growing amount of data. This can also increase the cost of data transfer and networking between multiple locations.

Reduced performance

Data gravity can also lead to reduced performance, as data may need to be moved over long distances in order to be processed and analyzed. This can lead to delays and increased latency, which can negatively impact the overall performance of applications and services.

Security risks

It can also increase security risks for customers, as the data stored in a specific location may be more vulnerable to attacks or data breaches. This is particularly true for sensitive or confidential data, which may be more vulnerable when stored in one location.

Compliance issues

Data gravity can also lead to compliance issues for customers, as it may be difficult to ensure that data is stored and processed in compliance with relevant laws and regulations.

Complexity

Data gravity can also make data management more complex for customers as they may need to manage multiple data storage locations and transfer data between them.

Overall, it can significantly impact customers, affecting their choices, costs, performance, security, compliance, and complexity of data management. It’s important for customers to understand the implications of data gravity and take steps to mitigate its effects.

Why data redundancy is worth the extra storage space?

Data gravity vs digital realty

Data gravity in the context of digital real estate refers to the tendency for data and related applications to congregate in specific locations, similar to how physical objects with more mass tend to attract objects with less mass.

In the context of digital real estate, data gravity can impact the location of data centers and other infrastructure used to store and process data. As more data is generated and stored in a specific location, it becomes more difficult to move that data to a different location. This can lead to the concentration of data centers and other infrastructure in specific geographic regions and increased demand for real estate in those regions.

Another aspect of data gravity in digital realty is the attraction of other services and providers to the location of data centers, such as cloud providers, internet service providers, and other data-intensive companies. This can lead to the creation of digital clusters in certain areas, where multiple companies and service providers are located in close proximity to one another to take advantage of the large amounts of data that are stored and processed in that location.

To deal with data gravity in digital realty, companies can adopt a multi-cloud strategy, use edge computing or replicate data to multiple locations. It is also important to consider data storage and processing costs, security, and compliance aspects when choosing a location for data centers and other infrastructure.

Conclusion

In conclusion, data gravity is a concept that has become increasingly important for businesses in today’s data-driven world. The term refers to the tendency for data and related applications to congregate in one location, making it difficult to move data to another location. This can have a significant impact on an organization’s cloud strategy, influencing decisions around where to store data, where to place processing power and applications, and the overall cost and security of the organization’s cloud infrastructure.

Understanding the concept of data gravity is crucial for today’s businesses as it can help them make informed decisions about data storage and processing. Adopting a multi-cloud strategy, using edge computing, data replication, data governance, and other solutions can help organizations to better deal with data gravity and make the most of their data.

Furthermore, businesses should be aware of the potential impact of data gravity on digital realty, the location of their data centers and other infrastructure, and the attraction of other services and providers to the location of data centers. Businesses that are able to manage and leverage data gravity effectively will be in a better position to stay competitive in today’s data-driven world.

Do you remember all of your burning questions in 2022? We did that and gathered the top 10 questions you’ve been asking us about in Dataconomy Wrapped 2022. There were numerous additions to our regular routines this year and another year has come to a close. If there is one thing that has stood out above all others in 2022, it is unquestionably artificial intelligence.

Don’t be scared of AI jargon; we have created a detailed AI glossary for the most commonly used artificial intelligence terms and explain the basics of artificial intelligence as well as the risks and benefits of artificial intelligence. But security always comes first, right?

Equifax data breach settlement

Since the breach in 2017, 147 million people have anxiously awaited the settlement and 2022 finally gave what they want.  As much as $425 million of the settlement money would go toward helping anyone who was harmed by the data breach. So people wondered, how the Equifax data breach settlement payment process work and found the answers in Dataconomy.

Reminder:

On September 7th, 2017, Equifax, a credit reporting organization, disclosed that a data leak had occurred within one of its computer networks, compromising the personal information of 143 million clients. This number was later revised upward to 147 million. It held sensitive information such as customers’ names, addresses, dates of birth, Social Security numbers, and credit card details, making it vulnerable to identity theft and other forms of fraud.

With these numbers, the Equifax data breach became the eleventh biggest data breach in the U.S., according to Identity Theft Resource Center.

At the end of 2022, Equifax data breach payments began with prepaid cards. Equifax data breach settlement prepaid card option is being offered to people who are suffering from the massive data breach.

Read our post about the Equifax data breach settlement if you haven’t heard about it yet or if you just need a refresher. After that, it’s time for the biggest trend of the year.

Other settlements that made the news this year: T-Mobile Data Breach Settlement, Epic Games settlement, ATT settlement, Tiktok data privacy settlement, Snapchat privacy settlement, and Google location tracking lawsuit settlement

Lensa AI

Welcome to the AI-driven era! Yes, we said it a lot in 2022. Because nearly every day, a new artificial intelligence (AI) tool is unveiled to further improve our daily lives, and the vast majority of these tools appear to become indispensable. Lensa AI is one of the most loved ones, especially in image generators. Have you not seen those cool profile pictures?

The Lensa AI selfie generator software is a photo editor that uses artificial intelligence to make unique “magic avatars” that can be quickly shared and edited in various ways.

Which is the No 1 photo editing app in the world? Although Lensa AI has been available since 2018, the release of the app’s “magic avatars” feature in late November propelled it to the top of the “Photo & Video” charts on the iOS App Store, despite the app’s competition. On the other hand, Instagram was ranked fourth and YouTube third at the time of writing.

Since we introduced our AI-generated avatars here, the hype has gone over the roof. Among the first and most voted names of whom we should do next, you named Casey Neistat.
So we've braced ourselves, prepared more bandwidth on servers, and are happy to show @Casey as seen by AI pic.twitter.com/FjhLG7WTdU

— Prisma Labs (@PrismaAI) November 29, 2022

How to use Lensa AI selfie generator? Yes, a lot of people asked it and found answers here.

Interior AI

The use of artificial intelligence tools is transforming not just our routines but also our living spaces and Interior AI successfully deliver the assignment. The digital interior designer is helping people that want to redecorate their homes for a while.

With the help of AI, people can give their homes a whole new look and add all sorts of cool, cutting-edge designs. Whether it’s a user-provided photo or a downloaded image, the software takes as input a 2D representation of an indoor space in a theme that you want to try.

One of 32 available interior designs can be applied to an image by the Interior AI:

• Christmas
• Modern
• Minimalist
• Tropical
• Interior AI
• Zen
• Midcentury modern
• Biophilic
• Industrial
• Cottagecore
• Bohemian
• Scandinavian
• Contemporary
• Art deco
• Cyberpunk
• Maximalist
• Ski chalet
• Art nouveau
• Sketch
• Vaporwave
• Baroque
• Rustic
• Tribal
• Japanese design
• Gaming room
• Coastal
• Vintage
• Farmhouse
• French country
• Halloween
• Medieval
• Neoclassic

Quite enjoyable, wouldn’t you agree? There are a large number of people that see things your way, therefore it seems sensible that the Interior AI tool was quite popular.

Dawn AI

Again, this was the year of artificial intelligence. Dawn AI was just one of the tools that got its share of this hype. Dawn AI’s intuitive interface makes it easy to generate high-quality images using artificial intelligence.

With a simple prompt, Dawn AI can produce whole new images with the touch of a button. Simply tell the AI art generator what genre or famous artist to emulate, and it will produce works in that vein.

Is text-to-image boring now? Meet sketch to image. Dawn AI offers a unique and entertaining function called Sketch that lets you design a rough draft of the required artwork rather than just entering it in.

Are you curious about it and wondering how to use Dawn AI? Read our article about it and find out!

Mastodon

One of Elon Musk’s biggest problems is entering our list at number five. After Elon Musk’s $44 billion acquisition of Twitter, chaos emerged, which worked out well for the fediverse Mastodon.

Unlike Twitter, Mastodon is a distributed social network where users register on various servers or nodes, each of which has its own theme, regulations, lingo, and moderation policies. Yet, there is a major issue that needs to be addressed. It has an unusual and sophisticated user interface. Mastodon requires you to join a server, and every new user automatically wonders, “Which server should I join?”

So, we have already explained both what is Mastodon and the best Mastodon servers to join.

Are you hesitant the join Mastodon? Check out our Mastodon vs Twitter comparison and learn all the differences between them.

Do you want to quit Instagram too? Try Mastodon-like open source and decentralized Instagram alternative Pixelfed

NovelAI

As we mentioned above, AI art is the new hype and when NovelAI added its image generation, it became one of the early birds to join the trend.

NovelAI's Image Generation, #NovelAIDiffusion is live on https://t.co/UTsnpZKa6W now!

NovelAI Diffusion Anime image generation is uniquely tailored to give you a creative tool to visualize your visions without limitations, allowing you to paint the stories of your imagination. pic.twitter.com/WZEpQ5idgI

— NovelAI (@novelaiofficial) October 3, 2022

Before it released its image generation feature, NovelAI was already quite popular as an AI writing tool. Even these are some books were written with it, such as The Story of Your Life, My Personable Demon, and more.

To summarize, these are the best NovelAI features:

• The AI generator uses AI to generate new plotlines, characters, and settings based on your chosen criteria.
• You can seek feedback on your work and exchange ideas with other people.
• On the encrypted servers of NovelAI, your work is safe and secure.
• The editor’s typefaces, sizes, and color scheme can all be changed at any moment.
• The NovelAI Diffusion Anime image-generating experience is distinctive and specifically designed to provide you with a creative tool to depict your visions without constraints, enabling you to paint the stories in your head.
• You can produce content with a furry and anthropomorphic animal theme using NovelAI Diffusion Furry (Beta).
  • NovelAI text-to-image generator tools are powered by Stable Diffusion.

For detailed information, we already explained what is NovelAI.

QQ Different Dimension Me

Anime and AI together just make perfect sense. Tencent thought the same way as we did and released the QQ Different Dimension Me anime image generator that people loved so much.

Different Dimension Me is an AI-driven system that can generate images of you, your friends, or even memes. The application went viral after it was released on the Tencent QQ platform in November 2022 and let users create animated versions of themselves or their favored non-anime characters.

https://www.tiktok.com/@oceanvampire56/video/7173175670002961670

You will finally find your anime version: But first, you should learn how to use Tencent’s Anime AI.

Instagram AI trend

Really, do you know how to do the Instagram trend? There are various tools and filters and more that confused the users. So, we gathered all of them in an article and explain how to use them.

Check out the virtual influencers that more popular than humans on the social media

Meitu AI Art

Neither Tencent nor ourselves are the only ones who have made the connection between anime and artificial intelligence. As you may have noticed, the feeds on TikTok and Twitter were full of anime images, and a well-known photo editing tool Meitu was behind it.

China-based photo and video editing software Meitu employs artificial intelligence to produce lifelike images of anime characters. The program features a variety of filters that you can use to transform yourself into anime characters.

meitu AI art is my new obsession lmao pic.twitter.com/uABRH6usLX

— alice 🦕 (taylor's version) (@alicee_lvr) December 4, 2022

The recent online craze allows users to turn selfies into anime images, check out how to use Meitu AI Art.

We have explained some of the best AI tools like OpenAI ChatGPT, Uberduck AI, MOVIO AI, Make-A-Video, and AI Dungeon too. Do you know there are also AI art robots? Check the Ai-Da.

Are you into AI image generation? You can try these tools:

• MyHeritage AI Time Machine,
• Reface app
• Stable Diffusion
• DALL-E 2
• Midjourney
• DreamBooth AI
• Wombo Dream
• NightCafe AI

SQL vs NoSQL

The differences between SQL and NoSQL one the most looked answers in Dataconomy. Check out the overview:

Read our SQL vs NoSQL comparison to find out more information.

This is the end of Dataconomy Wrapped 2022. What do you think awaits us next year? Stay tuned and never miss the trends.

Happy new year!

Data architects are leading experts in data-focused professions. Acquiring the skills necessary to become a professional is like laying the bricks of a wall. If you proceed in a planned and meticulous manner, you will have a solid wall. Otherwise, without an expert opinion of data architecture, a business should be prepared against earthquakes.

An IT specialist known as a data architect outlines the rules, processes, models, and tools that will be utilized for gathering, organizing, storing, and accessing corporate data. It’s common to conflate this role with database architects and data engineers. However, although the previously described professionals apply such links and policies to the architecture of particular databases, data architects concentrate on high-level business intelligence linkages and policies.

In the middle of the big data boom, new organizational positions have evolved those assist businesses in everyday complicated data sourcing, processing, and assimilation from both within and outside the firm. In today’s data-driven environment, one such role that is extremely relevant is data architect or big data architect.

What do data architects do?

The designs for an organization’s data management systems are created by data architects, just like traditional architects.

Data architects develop the blueprints that businesses use to create their data management systems, much like traditional architects create blueprints for the framework needed to make structures. This involves creating a data management framework that satisfies technological and business needs while maintaining data security and legal compliance. Data architects are employed across various sectors, such as technology, entertainment, healthcare, finance, and government.

What exactly is data architecture?

The process of standardizing an organization’s data collection, storage, transformation, distribution, and use is known as data architecture. The objective is to provide pertinent information to those who require it at the appropriate time and assist them in making sense of it.

For many years, an organization’s business strategist had to ask IT for access to certain data. The data engineer would hand-code a specific SQL query to give the solution after receiving a sometimes hazy explanation of what was needed. This was a laborious, time-consuming process that frequently produced something that didn’t satisfy the initial requestor’s requirements or expectations. Accessing the appropriate data at the appropriate time was significantly more challenging in this environment because the bandwidth constrained the business strategy that IT could provide.

Understanding the significance of Data as a Service in a digital-first world

Business strategists now demand more and quicker insights from data to make critical decisions due to the availability and increase of real-time data from both internal and external data sources. In this new context, the outdated method of one-off specialized solutions simply won’t work.

Modern data architecture design makes the promise that a well-designed process will bring business strategists with domain knowledge and data engineers with technical knowledge together at the same table. Together, they may decide what data is required to advance the company, how to obtain it, and how to disseminate it so that decision-makers have access to useful information.

The new data stars, the data architects, are the visionaries who see beyond the company’s needs and are always looking for methods to advance the IT infrastructure’s handling of data to keep up with the surge in data demand.

The expanding importance of the cloud, which offers the kind of quick, simple, and affordable scalability that contemporary data architecture requires has propelled big data into the real world. A common data lake or data warehouse, where ideally, just one master version of the data is accessible to those who need it, is another cloud feature that enables enterprises to pool most or all of their data.

Three levels of data architecture

Experts section three main data architecture levels: physical, conceptual, and external.

Physical level

The three-level architecture’s lowest level is this one. The actual method of data storage in the database is described at the physical level. This data is initially saved as bits on external hard drives, and it is then stated to be stored as files and folders at a slightly higher level. Techniques for compression and encryption are also covered at the physical level.

All businesses need an inclusive approach to their data HQ

Conceptual level

The conceptual level explains the layout of the database for the users as well as the connections between different data tables. How the data is actually kept in the database is unimportant at the conceptual level.

External level

In the three-level architecture, this is the highest and closest to the user. The view level is another name for it. The rest of the data is hidden at the external level, which only presents users with views of the pertinent database content. As a result, various users can see the information in various ways depending on their specific needs.

The roles and responsibilities of data architects

The link between business operations and IT is provided by data architects. Within the company’s technology architecture, business activities gather and use the data, and IT obtains, saves, and retrieves that data from database resources. The data architect is primarily a planner and designer, as is typical of IT positions with the title architect. They frequently conduct business across the entire organization.

Data architects are hands-on practitioners who understand how to construct and optimize the data architecture, facilitating seamless data flow among all users.

Data architects collaborate with a variety of other positions and divisions within the company, including the following:

Line organizations

Data architects frequently communicate with important team leaders and managers. Because of the connection between business requirements, applications, and data, they play a significant role in application design in many organizations.

Chief information officers

In order to express the line organizational data collection and usage requirements and to connect these requirements with the available database and application technologies, data architects will collaborate with CIOs and their employees.

Other data-focused professions

Data engineers, database developers and specialists, database administrators, and application development teams translate high-level data models and management policies into specific applications, database models, and implementations.

The required skills to become one of the best data architects out there

A combination of business and technical abilities is needed for data architects. The main competencies needed are those for converting company needs and operational procedures into data management and gathering procedures. It is especially beneficial to have business architecture experience. Some businesses stretch the term “data architect” to the more inclusive “solutions architect,” signifying a deeper affiliation with EA in those businesses.

Data architect skills

The following are a few essential data architect skills:

• Understanding of system development, including the system development life cycle, project management strategies, and requirements
• Data modeling and design, as well as SQL development and database administration
• Understanding of machine learning, natural language processing, and predictive modeling technologies
• Understanding of the fundamentals of columnar and NoSQL databases, data visualization, unstructured data, and predictive analytics, as well as the ability to integrate common data management and reporting tools.
• Skills in machine learning, visualization, and data mining
• Python, C/C++, Java, and Perl
• Besides, a data architect needs to coordinate and collaborate with users, system designers, and developers in their day-to-day functions. As such, soft skills like effective communication, team management, problem-solving, and leadership are highly desirable traits of a data architect. 

Are there data architect courses?

A bachelor’s degree in computer science, computer engineering, or a closely related discipline is the ideal place to start if you want to become a data architect. Data management, programming, big data advances, systems analysis, and technological architectures should all be covered in the coursework. A master’s degree is frequently preferred for senior roles.

Data architects offer principles for managing data from its initial collection in source systems through business users’ information consumption.

Your experience may be the most important component of your job application. Top employers probably anticipate that job applicants will have knowledge of application architecture, network administration, and performance management.

How to join a data architect certification program?

Those who want to become data architects might enroll in several informative certification programs offered by Udacity. You may also select the following programs:

• IBM Certified Data Architect – Big Data
• IBM Certified Solution Architect – Cloud Pak for Data v4.x
• IBM Certified Solution Architect – Data Warehouse V1
• Salesforce Certified Data Architecture and Management Designer
• Certified Data Management Professional (CDMP)
• Arcitura Certified Big Data Architect
• TOGAF 9 Certification Program

You’ll plan, build, and implement enterprise data infrastructure solutions as well as draft the specifications for a company’s data management system in these data architect certification programs. You’ll develop a scalable data lake architecture that satisfies the requirements of big data, a relational database using PostgreSQL, and an Online Analytical Processing (OLAP) data model to establish a cloud-based data warehouse. Finally, you’ll discover how to use the data management system of a business in accordance with the principles of data governance.

How much do data architects earn?

Let’s review the potential pay for a data architect: according to Payscale, the typical salary for a data architect in Europe is 76,165 euros ($80,306) each year. Similar to how US-based businesses frequently pay slightly more for data scientists and engineers: According to Builtin, the starting wage is an average of 143,573 US dollars.

Data architect salary

If you want more information, you may look at the data architect salaries on Glassdoor in-depth. Informatica, Amazon, and Intel are the organizations that pay professional data architects the highest salaries, according to statistics given by Glassdoor.

The differences between “data professionals”

Make sure you are familiar with the various duties of data architects, data scientists, data engineers, and solution architects before applying to one of these open positions.

Data architects vs. data scientists

The data architect is educated in both statistics and software engineering. Conceptualizing and visualizing data frameworks is part of their responsibility. They also offer information and advice on how to handle different data sources from different databases. While data scientist has a background in statistics, their job entails cleaning and analyzing data before using it to produce metrics and provide answers to questions in order to address business issues.

Data scientists are a new breed of analytical data experts with the technical know-how to address complicated issues and the inquisitiveness to investigate what issues are at stake.

Data architects offer a framework for creating and implementing data governance.

They have elements of mathematicians, computer scientists, and trend-spotters. Additionally, they are in high demand and well-paid due to their ability to bridge the IT and business sectors.

Data architects vs. data engineers

Although there are many overlaps between data science and data architecture, the data architect is more of an expert in hardware technologies than the data scientist is in mathematics, statistics, or software technologies. The data architect creates the model-development framework, which also develops data standards and principles and converts business needs into technical specifications. Data scientists use mathematical, statistical, and computer science techniques to create models.

Managing multi-cloud data is like looking for a needle in a haystack

An enterprise data architecture has multiple layers, often starting at the “information delivery layer” and going all the way up to the data-source layer. A complicated data architecture, which encompasses the underlying hardware, operating system, data storage, and data warehouse, may therefore be designed by various experts.

The modern data architect is frequently a multi-skilled professional with knowledge of data warehouses, relational databases, NoSQL, streaming data flows, containers, serverless, and micro-services. Vendors of technology are still waiting for their products to be widely adopted by businesses, despite the fact that newer technologies are appearing on the data-technology landscape every day. Check out our article, “Data is the new gold and the industry demands goldsmiths” if you want to learn more about data engineers.

Data architects vs solution architects

Both data architects and solution architects work in fields that require technological expertise. The ability to leverage their expertise in database design principles to construct effective databases is a requirement for data architects.

Top 5 data science trends for 2023

Additionally, they must be able to view the data structures they design using modeling tools. Strong technical abilities are also necessary for solution architects since they must comprehend the inner workings of the systems they are developing and be proficient in a variety of programming languages.

Strong problem-solving abilities are necessary for both solution and data architects. Data architects must be able to recognize problems with current databases and provide solutions to solve those problems. Solution architects must be able to take a project’s needs and create a solution that will satisfy them.

Data architects support privacy and security enforcement with their agile problem-solving capabilities.

While solution architects work with more tangible concepts, data architects often work with abstract concepts. Solution architects must be able to think logically in order to create systems that are effective and efficient, while data architects must be able to think swiftly in order to come up with innovative ways to organize data.

Conclusion

Organizations are guided in the proper direction by the capacity to strengthen and enable every corporate decision-making process through a perceptive data-driven approach. The business is evolving quickly; thus agile, more specialized, and targeted data change management that is supplied in close to real-time has replaced the idea of waiting weeks or months for the pending changes in data architecture to enable the release of new software versions.

The development team is under tremendous stress, and the internal data production floor faces daily obstacles due to the ever-increasing demand for adjustments and modifications to data structures and database schemas.

Data architects create and maintain a company’s database by locating structural and installation solutions. They collaborate with database administrators and analysts to ensure simple access to corporate data. Among the responsibilities are making database solutions, assessing needs, and writing design reports.

Even though statistics have always been important in the field, artificial intelligence in sports has greatly impacted how competition advances.

Artificial intelligence and data analytics are used in various sports to develop game strategies and increase audience engagement.

Some claim that data science is destroying traditional football, but football is still a game of mistakes. Remember that those who play on the field are human beings. Artificial intelligence and data analytics are turning points for the transformation and development of sports.

Artificial intelligence in sports

Artificial intelligence in sports is being utilized to plan strategies, coach athletes, market, and much more, from football to Formula 1. In other words, AI greatly impacts how people watch and consume sports information.

Since dawn, the sporting business has utilized statistics and data analytics. Sports provide a rich environment for applying artificial intelligence because they already quantify everything that can be quantified. Artificial intelligence affects us every day, and sports are not immune.

How is AI changing the sports industry?

AI and sensor technology together can assist players in becoming more proficient. AI is being utilized in sports training to design tailored training regimens for players and deliver real-time feedback, increasing the effectiveness of each activity for each individual.

Artificial intelligence in sports examples

AI predictive analysis can be used in sports to enhance fitness and health. Wearable software can tell users about the wear and tear placed on athletes, helping to keep them healthy. During games, AI can spot trends in tactics, methods, and flaws. The Connexion kiosk, introduced by the NBA, employs AI to evaluate player health data and alert teams about injuries and other setbacks.

The artificial intelligence-powered platform Arccos Caddie serves as the player’s virtual caddie and provides information on the wind’s direction and the best club to use. The direction to hit, in addition to other crucial details like location and others

Artificial intelligence in sports is having a significant impact on both pre-game and in-game strategies. Computer analysis is used to influence line-up decisions before and during games. By comprehending many metrics, including spin, speed, serve placement, and even player posture and motion, artificial sports intelligence can enhance sporting performance further. In this regard, AI supports managers and coaches in making better decisions for various games and important competitions.

Best artificial intelligence tools to improve productivity in 2022

AI in sports analytics

AI-powered analytics is utilized to find the top candidates on whom they can place their bets. Finding talent in the sports sector costs millions of dollars. As a result, team owners want to be certain that their choice to scout a certain player is the right one. Artificial intelligence in sports is widely used in player scouting, including cricket, basketball, and football.

Improving players’ talents requires a thorough grasp of their physical and mental preparation. An extensive examination of their strengths and flaws is necessary for effective coaching. These important aspects are better captured by AI sports analytics. For instance, using computer vision and machine learning, NEX technology tracks basketball players’ ability levels. The key performance indicators are calculated, including ball handling, release time, vertical jump, speed, an improvement over time, and shot accuracy.

Similarly to this, tennis shot placement is detected using AI. Contrarily, cricket’s sensor-equipped bats are being used to evaluate the accuracy of shots, the point of impact, the twist at impact, and the speed of the ball. All of this data is gathered, processed, and made available instantly! It is safe to claim that apps in sports use artificial intelligence as a knowledgeable insight source.

Can AI predict sports results?

Predicting a sporting event’s outcome using technology has become increasingly important as both the sports betting business and technology have expanded significantly. In actuality, when digesting a large amount of data, humans have several limitations. However, this problem can be solved using artificial intelligence in sports. Sports are an excellent illustration of an AI problem because there is a lot of data to consider.

Artificial intelligence in sports prediction

As technology advances, artificial intelligence is becoming more and more popular. It is feasible to forecast the outcome of a sports match with high accuracy, even better than the domain experts, with the right data set and technique for the chosen sport. A model and feature selection for forecasting soccer results were proposed based on the examination of the relevant research. Additionally, by utilizing science on our side, these techniques might help generate revenue in betting businesses.

The information provided will be the foundation for a future project to develop a soccer match prediction model. In order to gather a sizable amount of data for this assignment, including details of various leagues and seasons around the world, computer science methods will be used as discussed in the discussion. The techniques like ANN enhance artificial intelligence in sports. This will also be used to create a model with a high degree of accuracy.

Advantages of artificial intelligence in sports

Artificial intelligence in sports is advantageous in the following fields:

• Coaching and training
• Healthcare and safety
• Fan engagement
• Scouting
• Journalism

Coaching and training

Performance is how athletes are constantly judged on their success. And the ability to employ AI to evaluate individual player performances is the key to transforming the sports sector. Coaches and analysts can pinpoint a player’s advantages and areas for growth and track their development over time.

Metrics can also be used to compare player statistics and play execution flaws. By identifying trends in techniques and developing intricate game plans to counter them, artificial intelligence in sports can offer one team an advantage over another when playing defense.

AI in sports training

Artificial intelligence is applied in sports through predictive analysis to improve performance and health. Athletes can prevent major injuries thanks to the development of wearables that collect data on degrees of strain and wear. But that’s only the start. Teams can use AI to develop strategies and tactics and play to their strengths.

Thanks to AI, player performance analysis has advanced beyond all previous levels. Coaches can alter their teams’ tactics and strategies to take advantage of their opponent’s shortcomings by using data and graphics to get insights about their teams’ strengths and weaknesses on any given day. This holds true for all sports, including swimming and handball, as well as tennis and football. For instance, tracking and analyzing human movements uses computer vision.

New artificial intelligence can diagnose a patient using their speech

In the meantime, single stationary cameras above and below the water filters have been utilized to analyze swimmers’ performance using human pose estimation. The quantitive evaluation method, which previously relied on manually annotating body parts in each video frame, will eventually be replaced by this approach.

Healthcare and safety

Rethinking how we approach and treat a player’s health and safety is another significant way artificial intelligence in sports is transforming the sector. Coaches and managers can maintain a player’s health, fitness, and safety thanks to AI’s predictive and diagnostic skills. Additionally, more physical and mental conditions are identified and treated more quickly than ever before as a result.

Additionally, wearables like watches and heart rate monitors contain artificial intelligence. These tools monitor players’ whereabouts off the field to ensure safety and track player movements for the best workout sessions. AI can even keep drivers safe in competitive sports like NASCAR by spotting problems before they become dangerous.

Fan engagement

Giving fans the finest possible experience has become more important than ever in recent years. Fortunately, AI is revolutionizing the sports sector by assisting clubs in providing unrivaled client engagement. Fans may communicate with and learn more about their favorite players more easily, thanks to chatbots and virtual assistants.

Artificial intelligence can increase fan engagement by providing fans with access to their favorite teams through an app. They may track their tickets, receive alerts when new stuff is available, find check-in locations on gameday, and keep an eye on the schedule.

Scouting

Teams have painstakingly kept track of player statistics and made predictions about who the next great athlete will be for decades. However, scouting and recruiting take on a whole new dimension with the integration of AI.

The team staff can record and monitor complex data, then compare it to historical data, instead of using straightforward indicators like home runs and goals scored. They can then determine whether a player would be a good match for their squad or determine their market value.

Journalism

Last but not least, artificial intelligence in sports has the unintended effect of spawning a new subset of media called automated journalism. Using tools like Wordsmith, authors may create narratives using natural language while including statistics and data.

In order to cover more games, journalists will be able to reach a wider audience thanks to artificial intelligence. This means selecting games for press coverage won’t be done any longer. Minor League Baseball is the only sport where automated journalism is used, but it shows how useful it may be for all sports.

Disadvantages of artificial intelligence in sports

Artificial intelligence in sports is disadvantageous in the following fields:

• High expenses
• Layoffs

High expenses

Artificial intelligence in sports has a very hefty price tag. Its widespread application across nearly all facets of sports management raises the cost of operation even further. Furthermore, as technology advances, it will be necessary to continually update and upgrade the hardware and software of artificial intelligence systems. The price of maintaining and fixing an AI machine is another consideration. It is a sophisticated mechanism; hence the cost of construction and upkeep or repair is high.

Layoffs

This has been a key criticism of this technology, and even in sports, it will inevitably lead to job loss or unemployment. These devices automate manual tasks that would have required human labor, eliminating the need for workers.

Scouting and recruiting are two instances of this. It is now simpler to gather information about a player’s capabilities, limitations, and how they fit into the team without actually watching them play, as opposed to having scouts go and observe specific players repeatedly during games to identify their primary strengths and weaknesses. Scouts are becoming redundant as a result of the reduction in their workload. Few teams will use scouts for recruitment as time passes, and more teams will replace scouts.

Can AI replace sports referees?

A game’s referees are an essential component. They participate in training sessions and seminars to become familiar with various rules and serve as a neutral third parties to ensure fair play for all parties. However, with the development of artificial intelligence (AI), some individuals are unsure whether this technology will completely replace human referees.

Can an artificial intelligence-enabled invention be patented?

It goes without saying that being a referee is stressful work. They are under constant pressure to make the proper decision because even a minor error can have serious repercussions. Referees can frequently make incorrect choices under pressure, damaging the game for everyone.

Significant difficulties with referees’ mental health also result from this. Injuries and criticism from the media and fans are a few examples of the main stressors. Therefore, we can relieve some of their strain by using AI to help umpires and referees make the final decisions.

Naturally, we don’t solely rely on referees to determine if a goal is valid. There are presently technology tools in place to reduce referees’ errors by spotting penalties that would otherwise be too hard to catch. For instance, to stop officials from making poor choices, the International Federation of Association Football (FIFA) supported Goal-Line Technology, an automated tool to evaluate whether a goal is in or out.

Many, however, question if VAR is completely correct. Since there is no agreed-upon definition of a “deliberate” handball, minor mistakes may be made while using VAR from various viewpoints or positions.

By using comparative judgment, AI could be a good solution to these disputes in light of human error. It can be done by gathering a few contentious football plays and deciding whether or not it warrants a penalty. Although it hasn’t been done yet, this approach is thought to help lessen football-related controversies.

However, AI refereeing is unquestionably a creative creation that will eventually replace human referees for the purpose of accuracy. Although there are legitimate reasons for concern about AI referees, developers still have many opportunities to adapt and improve it.

Conclusion

AI offers endless options, and newer systems are always being developed, which can expand the range of possibilities for data processing. Humans have long tried to foresee the future, and with the development of artificial intelligence (AI), we are now better able to do so. We can now tackle complicated problems and examine data from many angles thanks to the capability of speedier information processing.

With the use of AI systems, the human race has improved its opinions on various topics, and in turn, intelligent systems have helped us understand more about ourselves. Artificial intelligence is being used in every industry, not just sports, and it has a profound impact on our daily lives that we cannot even begin to fathom. Ai has improved our ability to function and advance quickly and helped reshape the world as we currently know it. The power to decrease noise and inaccuracy while making important decisions has been provided to us by artificial intelligence in sports with its limitless potential.

Business intelligence dashboards are effective management tools essential for internal and external decision-making in a firm. Company users can obtain a unified view of pertinent KPIs and trends for operational decision-making and long-term business planning using business intelligence dashboards, also referred to as data dashboards.

What is a business intelligence dashboard?

The status of key performance indicators (KPIs) and other significant business metrics and data points for a company, department, team, or process are displayed on one screen via a business intelligence dashboard, which is a data visualization and analysis tool. Most BI software platforms include dashboards as a core component, frequently used to provide analytics data to business executives and employees.

Business intelligence dashboards, also known as data dashboards, may include several data visualizations to provide business users with a comprehensive view of pertinent KPIs and trends for both operational decision-making and long-term planning. Usually allowing viewers to access the data that underlying charts and visuals for further study, they are more interactive than static reports. Dashboards can be developed by business analysts and other users of self-service BI technologies, as well as by members of a BI team in particular situations.

Business intelligence dashboard types

There are 4 types of business intelligence dashboards:

• Operational: It displays operational processes and shorter time horizons.
• Analytical: It includes enormous volumes of data that analysts have produced.
• Strategic: It is concentrated on high-level measurements and long-term strategies.
• Tactical: Mid-management employs it to speed up decision-making.

Let’s delve deeper into the specifics of each of these business intelligence dashboards!

Operational business intelligence dashboard

Operational dashboards are used to keep an eye on shorter-term activities. Junior levels of management typically handle these dashboards because they are used to monitor operational processes.

Due to their focus on tracking and analyzing a company’s operations in a specific business area, operational dashboards are businesses’ most prevalent sort of dashboards. They are based on real-time data and allow operational managers to interactively and visually highlight a problem that needs to be fixed immediately.

Operational reports that offer a more in-depth look at specific data sets are also made using operational dashboards.

Analytical business intelligence dashboard

A huge quantity of data is contained in analytical dashboards, and their main function is to give an organization a thorough overview of the data.

The task of acquiring the data and providing it to executives for support falls to analysts.

All businesses need an inclusive approach to their data HQ

Analytical dashboards are highly helpful when a business is working with complicated and broad information and needs visualization to analyze the given data.

Analytical dashboards use historical data to find trends, compare them with various variables, and make forecasts instead of operational dashboards that concentrate on real-time. These dashboards are, in a sense, where the operational and strategic dashboards meet.

Strategic business intelligence dashboard 

A strategic dashboard is a reporting tool used to keep track of a company’s long-term plan. Since they offer a business’s entire enterprise-wide insight, these dashboards are typically quite complicated.

Senior-level management typically uses strategic dashboards.

Tracking performance metrics against corporate-wide strategic goals is the primary goal of strategic dashboards. Strategic dashboards may include a review of business performance over predetermined time periods, such as the previous month, quarter, or even year.

When created properly, a strategic dashboard can significantly shorten the time required to achieve a given business KPI while reducing operating costs.

Tactical business intelligence dashboard

Mid-level management analyzes and keeps an eye on processes using tactical dashboards. Their main objective is to assist people in making decisions.

This kind of dashboard is excellent for keeping an eye on the operations that support the company’s strategic ambitions.

A tactical dashboard’s level of detail lies somewhere between an operational and a strategic dashboard.

Additionally, because they frequently incorporate more data visualization than operational dashboards, these dashboards maximize the interactive nature of dashboards.

The importance of dashboards in business intelligence

A business intelligence strategy for a corporation must include dashboards. They ought to be created with the specific goal of analyzing data from important datasets to enhance company decisions. Modern BI tools can access, analyze, present, and share data via web-based dashboards in place of analysts manually assembling spreadsheets. Stakeholders can create dashboards to review, make decisions, and take action using a strong, automated business intelligence platform.

What are the advantages of a business intelligence dashboard?

Organizations can utilize business intelligence dashboards to make complex data approachable and clear for non-technical users. Business users are able to construct and see their own dashboards, with content created by IT as a starting point. Non-technical individuals are given the ability to engage with data by self-service BI. For instance, Chipotle optimized its analytical procedures and produced a uniform image of all of its restaurant locations using dashboards. Business users that employ dashboard-driven data visualizations might find trends. They can provide forecast insights, isolate unfavorable trends, and alert to positive trends.

In essence, the advantages of a business intelligence dashboard can be summarized:

Discovering trends: They enable organizations from a variety of industries to recognize and assess favorable trends relating to a wide range of business operations while identifying and reversing unfavorable trends for increased organizational effectiveness.

Top 5 data science trends for 2023

Maximizing efficiency: Always make decisions on the appropriate facts for the greatest outcomes; a business analytics dashboard can help you do this. They increase efficiency by providing pertinent real-time insights that help you make decisions that will help you succeed.

Accurate data usage: Accurate data must be used in planning, analysis, and reporting in order to outperform the competition. Real-time access makes this possible by giving you immediate access to information about how your company is doing from an operational or strategic standpoint. Guesswork is fully eliminated when all employees receive the appropriate information at the appropriate time, resulting in information that can be used to make informed decisions.

Visualization of data: There is a critical need to gather a centralized point of access where data may be presented in a clear manner with immediate insight as more data sources become available. Making a business choice may require endless scrolling and search for the appropriate data due to the crowded nature of traditional spreadsheets like Excel. Since visual content is processed by humans more quickly than textual content, visuals are increasingly used in presentations in the modern era. Not just regular graphs and charts but also interactive reports that show every stage of a business process, forecast results, and give business users insights immediately.

Self-service functions: Modern self-service BI can be simply implemented without the requirement for specialized IT knowledge. This results in a level of agility and mobility that traditional data processes just cannot match, giving everyone in the firm quick access to priceless performance measures.

Communication enhancement: There is no need to rely on email communication or static reports when there are interactive elements available. These strong analytical tools may be simply distributed to coworkers, managers, clients, and other important stakeholders to keep everyone up to date on the most recent developments. This will improve collaboration and foster a data-driven culture within the company while also improving communication.

Forecasting: Predicting future results is another excellent advantage. Predictive analytics tools provide you with a glimpse into the future in a number of areas by evaluating your history and current data to uncover patterns and trends. In this way, you can receive precise estimates regarding things like product demand and plan out your plans and production in advance.

Real-time information: You require the most recent data accessible in order to make the greatest strategic judgments. In order to do this, business intelligence dashboards deliver real-time information as soon as it becomes available. There’s no need to manually update everything by sifting through countless databases. You may quickly and easily access the most recent results for precise decision-making.

Adaptability: To build on what we’ve said thus far, a business intelligence dashboard’s centralized and entirely portable design makes it feasible to access and evaluate priceless information from a variety of devices around the clock, no matter where you are in the world. This degree of autonomy and adaptability consistently corresponds to higher production and better business intelligence, an essential element of success.

Business intelligence dashboard examples 

Different operational and analytical dashboards are used by organizations to aid in departmental and enterprise-level decision-making. Here are some typical business intelligence dashboard designs for various purposes, along with descriptions of what they contain and how to use them.

Sales and marketing business intelligence dashboards

 Corporate leaders, company managers, and sales and marketing teams frequently use these dashboards. A sales dashboard allows users to assess progress toward sales goals and uncover possible trouble spots by providing information on a product or retail sales, the cost of sales activities, and other KPIs. A marketing dashboard functions similarly, providing information on expenses, response rates, lead generation, and other marketing analytics.

Which way you choose to go matters to marketers

Customer business intelligence dashboards

Users of a custom dashboard can view and evaluate information about a business’s customer base, including its size, churn and retention rates, revenue per customer, lifetime value, and other customer metrics that can be used to help plan marketing campaigns and sales operations.

IT business intelligence dashboards

 Dashboards are widely used by IT departments, BI, data management, data warehousing, and data science teams. Their use of networks, systems, databases, and applications, as well as the availability of IT resources, performance difficulties, security concerns, and technological expenses, are all monitored using IT dashboards.

Mobile business intelligence dashboards

 Many business information dashboards are made for both PCs and mobile devices. Therefore this isn’t really a different type of dashboard. However, dashboard designers must make sure that dashboards are readable on smartphones and tablets if they want to enable mobile BI customers. As a result, mobile dashboards are frequently quite basic and only contain a small number of data visualizations that are legible on a small screen.

Financial business intelligence dashboards

 This type of dashboard for the CFO, other executives, and employees in the finance department present information on financial KPIs. To aid a company in keeping track of business performance and doing financial planning and analysis, a financial dashboard includes revenue, operating costs, and profits in addition to cash holdings, assets, liabilities, working capital, and profit margins.

Project business intelligence dashboards

 The status and development of business projects are shown in data on a project dashboard or project management dashboard. One can help project managers monitor work, spot issues, and maintain projects on schedule and within budget by keeping track of activities, deadlines, costs, and other metrics.

HR business intelligence dashboards

 HR managers and business leaders can utilize an HR dashboard to access data on a company’s employees. It also includes hiring and recruiting analytics and KPIs on things like employee happiness, turnover, and expenses to help with talent management and employee experience efforts. Basic workforce data includes things like the number of employees, compensation information, and demographics.

Operational business intelligence dashboards

 These dashboards continuously monitor the state of operations, business procedures, and equipment for use in management and day-to-day monitoring. To guarantee that production targets are reached and to spot any issues or bottlenecks that need to be resolved, plant managers may use an operational dashboard to provide metrics on the output of manufacturing machines, for instance.

How to create a business intelligence dashboard?

Anyone across functional domains can glean insights from a well-designed dashboard even if they are not statisticians or data gurus.

A well-designed business intelligence dashboard serves as a quick snapshot of management, giving a user a high-level picture of the company, a department, or a particular process without entangling them in a web of data. They convey a complex set of facts quickly and exceptionally clearly by making full use of visual perception.

Navigate through the rough seas of retail with business intelligence as your compass

Fast adoption of analytics is facilitated by well-designed business intelligence dashboards, establishing a model of data democracy within the enterprise.

But creating the greatest analytics dashboards requires both art and science, and mistakes in design might result in failure to provide the user with what they would have expected. When this occurs, the value of analytics to enterprises may be constrained.

Always strive to incorporate useful KPIs in your dashboard that can assist you in providing precise answers to your company’s inquiries. You must first conduct research on the KPIs that are pertinent to your company. Each KPI has a lifecycle that calls for users to identify, define, redefine, and remove them as necessary.

KPIs must be regularly tracked and assessed by business managers if they are to attain a real-time business inside.

Always strive to incorporate useful KPIs in your dashboard that can assist you in providing precise answers to your company’s inquiries. You must first conduct research on the KPIs that are pertinent to your company. Each KPI has a lifecycle that calls for users to identify, define, redefine, and remove them as necessary.

KPIs must be regularly tracked and assessed by business managers if they are to attain a real-time business inside.

The next step is to include charts or graphs for your data visualization after identifying your KPIs. Create a dashboard that is well-presented with quality data visualizations to aid in the audience’s quicker and more accurate understanding of the business. Reduce the amount of text on your dashboard that is unnecessary and unattractive. Dates, bar charts, and figures with highlights are common elements used in visual designs.

The data on the business dashboard should enable you to decide what to do and how to do it. It should therefore provide the details you require in order to carry out those actions. You should find it simple to compare and analyze business performance statistics using the dashboard. You’ll be able to learn something new or intriguing that will help you change or stabilize the course of your company.

Data is constantly changing and being added. Thus your dashboard should be able to update in real time in this situation. Having analytics tools and software that automatically gather information, show it to you in an automated dashboard, and do all of these things is the ideal approach to achieve this aim.

The challenges of creating a business intelligence dashboard

One could be tempted to provide a comprehensive collection of data while using business intelligence dashboards. While it could guarantee that no important metric that the user might feel critical is missing from the analytics dashboard, this is likely to result in a cluttered user experience. When the dashboard contains an excessive number of widgets and information is presented in a crowded manner, it can become confusing for the user.

Analytics dashboards frequently make the error of using excessive visualization to present information in novel and sophisticated ways.

If people aren’t trained, they might think it’s just a flashy interface with no real utility. They will then turn to other potentially isolated reporting frameworks that are more trustworthy in this situation.

Lack of alignment with the user persona is a typical sign of dashboards that have not been successfully implemented. Such dashboards are created without a deep grasp of the user’s needs, abilities, and expectations. Such reports are likely to be ignored by users.

If these errors aren’t properly avoided, analytics dashboards will quickly turn into yet another upgrade that isn’t really useful. Such errors are surefire ways to fail.

A poorly designed business intelligence dashboard could diminish your company analytics project’s value.

I’ll go through a few best practices for business information dashboards below. An analytics framework’s goal is served by well-designed analytics dashboards, which act as a single source of truth for pertinent data spanning departments and processes.

Conclusion

Business intelligence dashboards are a pleasant method to present data for your organization and share insights for decision-making. You may make a dashboard that your target audience can readily understand and conveys the message you’re trying to get across by using the rules and suggestions in this article.

• Data science trends are common in the market and advantageous to data scientists. This industry’s projections and trends are crucial for organizations to prosper in the global technology market.
• To be successful in this field, data scientists must be knowledgeable of numerous data science trends.
• We will assist you in locating and gaining insights into the top five Data Science trends that will dominate the coming year.

Data science trends are prevalent in the industry and beneficial to data scientists. Projections and trends in this industry are critical for firms to thrive in the global technology market. Data science and machine learning are important in business and marketing since they increase a company’s growth rate.

What are these 5 data science trends?

Data scientists must be aware of various data science trends in order to be successful in this industry. These forthcoming developments will provide major benefits to the sector and its enterprises. This post will help you find and get insights into the top 5 data science trends that will dominate the future year.

Augmented analytics

The first of our picks for the top data science trends is Augmented analytics. Augmented analytics is a vital data science concept that is becoming more popular by the day. It transforms how data analytics is handled, manufactured, and generated by utilizing machine learning algorithms and artificial intelligence. Augmented analytics tools are now popular because they provide automated chores and insight solutions by using complicated algorithms to enable conversational analytics.

Furthermore, augmented analytics contributes to the evolution of data science platforms and embedded analytics. This trend is likely to undergo a variety of developments in 2023 or the following years, becoming an important role in the growth of BI platforms.

Data-as-a-Service (DaaS)

Data-as-a-Service (DaaS) is a technology that encourages users to use and access digital assets over the internet. It is based on cloud technology. DaaS sectors have risen significantly since the epidemic, and it is anticipated that by 2023, they will be worth $11 billion. DaaS is a top data science concept that boosts corporate efficiency. So it naturally finds a spot in our list of 5 data science trends.

This industry has a good awareness of the benefits data offers for corporate success, particularly in terms of marketing. The following are the primary features of this data science trend:

• This data stream is available on demand, which makes data sharing a breeze.
• It is highly convenient and advantageous to use because there are no specific fees for accessibility.
• DaaS subscribers may receive high-speed data and cover a greater area.
• Because of the availability of resources and the affordability of data storage, the financial demand for DaaS is growing by the day.

Big Data Analysis Automation

Automation plays a crucial part in the transformation of the planet. It has sparked different company reforms, leading to long-term proficiency. The industrialization of big data analytics has delivered the finest automation capabilities in recent years.

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Analytic Process Automation (APA) promotes growth by giving firms prescriptive and predictive capabilities, as well as other insights. Businesses have benefited from this by receiving quality with efficient outputs and reasonable expenses.

APA mostly improves computing capability to make better judgments. Automation of data analytics is an ideal disruptive force. Big data analysis may significantly boost useful data usage and production.

A survey found that 48% of CEOs felt data analytics is critical. Global information has begun to double every 17 months thanks to the substantial data science trend of big data analysis. Apache Hadoop, SAP Business Intelligence Platform, IBM Analytics, Sisense, and others are among the most well-known big data analysis software. All this is the reason Big Data Analysis automation is on our list of 5 data science trends.

In-Memory Computing

In-Memory Computing is also one of the most important data science and machine learning innovations that will emerge in 2023. It offers numerous technology solutions while providing various benefits in data and analytics.

Data was formerly saved on centralized servers, but thanks to In-Memory Computing, a significant quantity of data may now be stored in Random Access Memory (RAM). In-Memory Computing is extremely advantageous in several ways and has its own worth and significance. It provides a very resilient and competent mass memory for performing vivid business tasks and quick execution of business-related operations.

Data Governance

The last entry on our picks for 5 data science trends is Data Governance. Data Governance manages data access globally. Compliance with the General Data Protection Regulation (GDPR) has numerous organizations and enterprises that emphasize data governance and manage consumer data.

Data governance has played a crucial role in increasing consumer data safety. A new policy has been implemented to improve data protection, data management, and consumer profiling. The California Consumer Privacy Act is the name of this statute (CCPA).

All of these policies, when combined, raised the firm to a higher level. The CCPA has an impact on many corporate activities and regulates personal consumer data. It also ensures the security and safety of data.

Data science conquers the customer journey and sales funnel

Data governance as a whole allows for bias, but it also has its benefits and is relatively easy to utilize. Many firms utilize data governance to ensure that their subordinates are competent. The CCPA is new and encompasses all privacy regulations; it was approved in 2020.

Conclusion

Analytics and data are helping to alter the commercial world, and it is impossible to overstate the importance of these 5 trends in data science for the coming year. We hope that we were able to provide some insight into the topic.

A technique called location-based marketing enables data-driven methods to their fullest potential. This is essential for any business as the world gets more digital. Marketing organizations may geotarget clients with amazing accuracy thanks to location data.

A pillar of the marketing industry in the twenty-first century is location-based marketing. It locates customers using a range of techniques and technology. It also aids in identifying and analyzing customer preferences. This marketing tactic significantly increases both in-store and online sales when used effectively.

What is location-based marketing?

With location-based marketing (LBM), businesses may specifically target customers with online or offline messaging based on their precise geographic position. Marketing teams can target customers based on criteria such as proximity to a store, local events, and more by using location data.

Location-based marketing has been proven successful throughout the whole consumer lifecycle, from engagement and retention through discovery and purchase. When done right, location-based marketing enables businesses to target particular client segments with offers while enhancing the customer experience for a society that places an increasing emphasis on quick satisfaction. For instance, location-based marketing could inform a potential customer that a product they’ve been eyeing is available at a nearby store, enabling them to buy it right away.

How does location-based marketing work?

Location-based marketing is a direct marketing tactic that notifies the owner of a mobile device about a deal from a nearby company by using the device’s location.

Location-based alerts are typically sent to mobile devices via SMS text messages. An alert could contain details on a local company’s offer of the day or a purchasing incentive, like a coupon code for a discount.

The consumer must consent to receive location-based marketing. When a user downloads a mobile app and selects “ok” when the app asks permission to access the device’s location, the opt-in procedure often occurs. The LMS’s technology makes use of geofencing, a form of software that employs triggers to deliver notifications whenever a device crosses a predetermined geographic border. As with any mobile marketing campaign, the objective of LMS is to attract the end user’s attention and convert him into a customer.

Location-based advertising is praised by proponents as a means of bridging the gap between online and offline customer interactions and encouraging impulsive purchases. If the data obtained by LBM is not used, shared, secured, and preserved properly, critics wonder whether it would lead to customer burnout and a breach of their privacy. Companies using LBM should provide strict opt-in rules and security measures to protect client privacy.

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Types of location-based marketing

Let’s review the types of location-based marketing:

• Geotargeting
• Geofencing
• Beaconing
• Mobile targeting
• Geo-conquesting

Geotargeting

A user’s location is determined through geotargeting, which then delivers customized messages to them based on their location. If a user has given permission for an app to access their location, they may receive push notifications or in-app messaging based on their position or how close they are to a store.

Geofencing

The act of drawing a line inside a particular area is known as geofencing. They will turn into active targets of the marketing plan whenever target audiences cross that line. This could imply that the brand sends them material, deals, or other kinds of messages. An area that includes a well-known shopping center where the business has a store is an illustration of a border.

Beaconing

Beacons are electronic devices that can connect to certain applications that are running within the beacon’s range using Bluetooth or WiFi. In order to create a target audience in a limited geographic area, beacons are really effective tools.

Mobile targeting

When advertisers target customers with adverts on their mobile devices, this is known as mobile targeting. The goal of marketers is to make their ads context-specific, which might be based on time, device, or location because consumers normally want to avoid advertising.

Geo-conquesting

Geo-conquesting employs location information to steer potential customers away from rival venues. For instance, auto dealerships might erect a wall around a rival’s parking area. An offer to users will be delivered to a target consumer when they enter that zone, enticing them to visit the other dealer.

Location-based marketing strategy

Location-based marketing can benefit consumers and businesses alike in a variety of ways. Marketers may communicate with clients and prospects more precisely, raising awareness and fostering relationships. These ads’ targeted nature also frequently results in less money being spent. Customers receive customized offers at convenient times, improving their overall experience as they become more selective about the branded messaging they connect with. The following criteria is vital for a successful location-based marketing strategy:

• Increase traffic
• Create accurate ads
• Drive customers away from rival brands
• Create a better UX

Increase traffic

By alerting consumers in the market to the closeness of a business and luring them with an offer, location-based marketing can increase foot traffic for local establishments like retail stores or food services.

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Create accurate ads

Real-time location information can be used by marketers to develop more relevant, customized adverts. This is not always limited to a person’s actual location. Timing and messages can also be affected by location data. For instance, data may reveal that a customer is more likely to interact with an advertisement when riding the train or commuting, assisting marketers in choosing the best time to display an advertisement.

Copy and creative work can benefit from location data. Instead of using generic photos, marketing organizations could decide to use pictures of the city where the target consumer resides. Relevance and context have emerged as essential elements of messaging that are absorbed rather than dismissed. These requirements are satisfied by utilizing these real-time insights.

Drive customers away from rival brands

Marketers can gain market share by encouraging people to visit their store by focusing on users who are heading to a competitor’s business.

Create a better UX

You may give clients a better user experience by reaching out to them when they are most likely to require your services.

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Bottom line

Marketers must take into account both their own location and the location of their customers for location-based marketing to be successful. Businesses situated on the street with little foot traffic, for instance, might not want to spend money on location strategies. Similar to this, establishments that are not on the ground floor, such as those in hotels or apartment complexes, should be cautious to only advertise to customers who are physically present in the building. Regular reminders may annoy people who have no interest in visiting the store and harm the reputation of the brand.

Before spending money on advertising, assess your consumer base and important external elements to understand how these strategies will affect ROI.

What are the advantages of location-based marketing?

Because you may target ads to customers who are nearby a specific business or competition using some of the technologies outlined previously, location targeting can help businesses increase in-store visits.

Marketers can leverage the results of advertising campaigns to gain deep insights into the purchasing habits of their clients by using visitor, audience, and trade area data. In the future, this can help to develop further consumer personas for even more pertinent and successful marketing.

Using location-based marketing strategies, you can fine-tune and segment your advertising to target potential customers based on a variety of factors, including time of day, shopping habits, weather, and more. This enables you to tailor your adverts, increasing conversion and enhancing return on ad investment.

Imagine simply paying for outcomes. Marketers can now use new performance models, like cost-per-visit (CPV) advertising, by utilizing location-based advertising. That implies that you only receive payment when customers enter the store. That increases your return on ad spend once more.

Location-based marketing examples

Below there are some brands that utilize location-based marketing strategies.

Yelp

A location-based local push recommendation checks off two crucial boxes: it’s personalized and catered to the user’s current tastes. Both short-term and long-term user retention may benefit from this. It’s conceivable that a Yelp user will turn to the same company for a comparable recommendation on their next journey if they receive a useful tip from that business in one city.

Don’t be put off by firms that lack access to Yelp’s huge restaurant database. Regardless of whether you are connected to one or a hundred recommendations, this strategy demonstrates the worth of users both inside and outside of the app.

Google Maps

When is a user most likely to provide insightful criticism? When a brand encounter comes to an end? The client is considering how they feel about their most recent encounter. Google Maps is one tech company that effectively uses this.

After determining the user has finished their contact at a local business based on device location, Google Maps provides the customer a proactive request to rate their visit. The star choice is simple and efficient compared to having a form or boxes to fill out. The opportunity to interact with a captive audience is now.

Sephora

Who doesn’t love free goods, especially when it comes from a well-known retailer like Sephora? This is the kind of push message that might be sent to app users who are close to a nearby store (within a particular range). This incentive is intended to boost the possibility of a full shopping basket as well as in-store traffic.

This alluring offer capitalizes on two crucial factors: It is succinct, and it emphasizes the numerous benefits of an in-person encounter.

How effective is location-based marketing?

Any retailer, supermarket, eatery, or other places where people go is a wonderful fit for location-based marketing. The physical footprint of these businesses syncs with location data, which makes for excellent use cases. Customers are encouraged to return, and their loyalty is increased when marketers build an audience based on previous actual visitors.

While location-based marketing works well for most businesses, there are some drawbacks. To reach their consumers, these brands frequently employ demographic targeting techniques. Other restrictions are imposed by regulatory bodies that have rules about audiences drawn from delicate areas, like points of interest in the health care system, and against the creation of discriminatory audiences.

The geotargeting tools that both Google and Facebook offer to make it simple for marketers to get started exploring location-based marketing. Their tools streamline the procedure and instantly distribute advertisements to audiences in specific places or geographic regions.

They switch to more sophisticated choices when their needs get more complex. These offer more alternatives for distributing such campaigns across the digital ecosystem, greater personalization when generating geotargeted audiences, and the capacity to generate audiences based on previous visits, such as visitors to their own sites or competitors’ locations.

Similar to any targeting strategy, testing, measuring, and refining are the keys to success with location-based marketing. While it might initially seem difficult, any marketer can easily plan and carry out effective geotargeting campaigns with a little practice and fundamental information.

Why is location-based marketing so attractive to marketers?

Because it enables them to deliver the consumers pertinent and targeted offers and messages, marketers adore location-based marketing. Location-based mobile marketing has been quite successful for many businesses. That comes as no surprise. It is user-relevant, data-driven, and tailored. Businesses experience a rise in engagement, conversions, and sales as a result.

Sales, according to marketing experts, are the main benefit of location-based marketing. Additionally, the Businesswire analysis demonstrates that location-based advertising strategies are 20 times more successful than non-location-specific ones.

Location-based marketing companies: Best data providers

Below we reviewed some of the best data providers for those who are looking for location-based marketing companies.

LocationSmart

Through APIs and Cloud Location Services, LocationSmart offers location information. The data provided by LocationSmart covers 160 million cell towers and 15 billion devices worldwide. Mobile location data from the business improve location-based marketing techniques like geofencing and in-app messaging.

SafeGraph

A provider of location data, SafeGraph has over 6.1 million POIs in the US and Canada. As a result of the company’s information on visit attribution trends, advertisers can create location-based audiences that can be modified in real-time.

Skyhook

The leading source of geolocation information is Skyhook. Geospatial Insights from the business provides location-based intelligence for over 20 million verified locations, visits from signal-confirmed devices, and dynamic behavioral and demographic segments. Marketers may build OOH ads that are based on the intent and loyalty of consumers by using Skyhook’s Context SDK to comprehend consumer visits.

The future of location-based marketing

The future for location-based marketing contains three significant elements: regulation, a proliferation of new data sources, and attribution.

Three important factors will affect location-based marketing in the future: regulation, the expansion of new data sources, and attribution.

New federal and state laws will require marketers and data providers of all kinds to adapt and change, starting in 2020 with the California Consumer Privacy Act. The advertising industry anticipates that more US states will follow California’s lead, with possible federal legislation as well. Similar regulations already exist in the European Union. The ultimate objective is a consistent framework that organizations and consumers can readily accept and that will increase transparency and control over data practices throughout the whole data ecosystem.

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With the deployment of billions of new sensors via the Internet of Things, the implementation of 5G will produce enormous sources of extremely precise location data. Data is vital for a location-based marketing strategy. Compared to current cell phone towers, 5G towers must be grouped together more tightly. Due to the tighter clustering, cell phone operators can more precisely triangulate location than they can at the moment.

Faster upload and download rates provided by 5G will encourage the use of more internet-connected sensors across a variety of goods and businesses. As these zillions of sensors come online, they’ll produce data that can be used to evaluate product usage, consumption, and life cycles in addition to location data. This more detailed data will likely be viewed, analyzed, and used in different ways by regulators, marketers, and researchers.

Finally, advertisers will demand proof that their advertising expenditures increase foot traffic and sales as they hold their advertising spending more accountable. We are a long way from being able to demonstrate how particular in-store sales were influenced by digital campaigns or the majority of other marketing media. The number of disconnected data silos makes it impossible to combine meaningful and statistically significant results. While the point-of-sale system or online checkout can’t let those prior touch points know the sale happened, the TV advertisement can’t let your phone or laptop know it was also seen.

Given the difficulties in connecting internet ads to offline sales, marketers are employing location data to adopt a macroeconomic view of attribution. The effectiveness of their location-based marketing spending is assessed by looking at how their campaigns affect foot traffic at both their own locations and those of their rivals.

Ethical considerations in location-based marketing

All businesses involved in the advertising ecosystem should place the protection of customer privacy as their top priority. With location-based marketing, businesses only gather location data from customers who have given their consent before aggregating and depersonalizing the information.

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Since a single audience offers little to no value, marketers do not use this information to target specific individuals. Delivering advertisements to huge groups as opposed to solitary individuals is significantly more effective at reaching audiences who exhibit comparable habits and features.

Furthermore, the industry self-regulates through organizations like Network Advertising Initiative and TrustArc, despite the fact that there is little federal or state regulation governing what data can and cannot be gathered or how it is utilized. They are essential to the industry’s efforts to safeguard consumers and to keep them informed about impending legislation.

Conclusion

It isn’t easy to undervalue the power of location-based marketing. Around 83% of marketers claim that using location data allows them to conduct more effective ads, according to MarTech Series.

They gain from it in a variety of ways. This technology not only assists marketers in acquiring new customers but also strengthens their existing ones. This is so that they may better meet the needs of their customers and increase response and customer engagement by using location-based advertising and marketing strategies.

New opportunities are presented by each of these shifts. Because of the effectiveness of location-based marketing, marketers will keep spending money on it. The cornerstone of any effective campaign is having an understanding of what works and what doesn’t.

Data analytics and artificial intelligence are changing how businesses decide their paths today. All we hear about is data these days. We hear about how much is produced, how crucial it is, and how corporations use it to further their objectives and boost profits. Yet how? Yes, much of it is technologically based, but how will businesses keep up with the enormous amount of data generated daily? The answer is data science and artificial intelligence.

To extract hidden patterns from unstructured data, data scientists utilize a variety of instruments, algorithms, formulae, and machine learning techniques. These patterns can then be utilized to inform decision-making and improve understanding of various aspects. Data science reveals the “why” underlying your data, going beyond simple number crunching.

Data science is the secret to turning information into useful information by analyzing vast amounts of facts to predict actions and derive meaning from meaningfully connecting data. Data science assists businesses in maximizing innovation by assisting them in locating the best clients, setting the appropriate rates, effectively allocating expenditures, and decreasing work-in-progress and inventory.

A helping hand: Enterprise SEO tools are crucial for data analytics

Although data science technology and techniques have advanced significantly, no advancement was more significant than the advent of artificial intelligence (AI). AI is the capacity of computers to carry out tasks that were previously only performed by humans. AI previously required just human programming, but computers can now learn from data and hone their skills with machine learning. As a result, AI tools are now able to read, write, listen, chat, and even listen like a human – but at a scale and rate that is significantly greater than those of any one person. That is why data analytics and artificial intelligence are both crucial for the future of technology.

Data analytics and artificial intelligence definition

Let’s examine each term’s definition, beginning with a data analytics definition. Fundamentally, data analytics is the science of examining large data sets to identify patterns, provide answers to inquiries, and draw inferences. It’s an intricate and diverse field that frequently makes use of specialized software, automated processes, and algorithms.

Nearly every industry can benefit from using data analytics and artificial intelligence in some way. Organizations of all types employ data analysts to assist them in making defensible judgments regarding various aspects of their company. Usually, historical data from events are analyzed, allowing for the identification of current trends.

Descriptive analytics, diagnostic analytics, predictive analytics, and prescriptive analytics are just a few of the several forms of data analytics.

The idea of artificial intelligence (AI) has been around for a long time. But it wasn’t until recently that we actually had the computing capacity to make it a reality. The capacity to enable computers to mimic human intelligence is the essence of artificial intelligence.

It is possible to teach computers through experience by building machines capable of learning. These artificial intelligence systems have three characteristics: intelligence, adaptability, and intentionality. These characteristics enable them to make decisions that ordinarily require a human degree of knowledge and experience.

Where do data analytics and artificial intelligence overlap?

So, as we’ve said, we have three different areas of competence. They are all quite separate fields with unique applications, sets, and specializations. There are undoubtedly certain locations where they overlap, as you may have already seen.

What is the future of data analytics?

In the future, data analytics and artificial intelligence are anticipated to alter how we live and conduct business. We already make a lot of decisions in our daily lives using the analytics built into our technology. We use these tools to find waste in corporate processes and learn how to drive from point A to point B while avoiding traffic jams.

Machine learning makes life easier for data scientists

It is obvious that while organizations are making efforts to turn data into insights, they are still having trouble with data quality and locating the resources necessary to convert these insights into real value and move toward a more data-driven approach.

Although the data era is still in its infancy, expectations are that data analytics will enable the impossible. In essence, all businesses are increasingly making investments in data analytics capabilities to stay abreast of both known and unknowing trends and competition.

The decision-making process was initially “assisted” by data analytics, but it is now enabling “better” decisions than we can make on our own. Here, examples of using analytics to merge several data sources that yield new and better insights come to mind. For instance, you could combine sales, location, and weather data to determine why sales are increasing in certain stores and enhance the restocking process.

How is data analytics used in AI?

Of course, there are a lot of fields that have direct connections to data analytics and artificial intelligence. There are common approaches and technologies used in subjects as disparate as statistics, mathematics, computer science, and information science. Other related areas of specialization include some of the following:

• Robot science: The pinnacle of artificial intelligence is thought to be the creation and programming of robots that can function in actual environments. Here, machine learning is especially crucial since it enables computers to respond appropriately to visual and aural signals.
• Cloud services: Artificial intelligence and machine learning often demand a significant amount of processing power. That power may come from cloud computing, which is the technique of providing on-demand computing services through the internet.
• Big data analytics: The idea of big data is fundamental to many of these professions. Large datasets of organized and unstructured data that are challenging to process through conventional methods are referred to by this term.
• Data mining and statistics: Massive and intricate data sets are used in data mining. Some machine learning principles are applied to analyze this data and draw inferences and predictions from it.

Automating data analysis using artificial intelligence

With the aid of automation, the most recent developments in data analytics and artificial intelligence significantly contribute to increasing the effectiveness and power of commercial processes. Because of AI, analytics is also becoming more automated and accessible. Here are a few examples of how AI is advancing analytics:

• AI systems may automatically evaluate data and find hidden trends, patterns, and insights that can be used by staff to make more educated decisions. This is done with the aid of machine learning algorithms.
• By employing natural language generation, AI automates report creation and simplifies data.
• AI improves data literacy and frees up time for data scientists by enabling everyone in the business to intuitively find solutions and extract insights from data using natural language query (NLQ).
• By automating data analytics and generating insights and value more quickly, AI aids in the streamlining of BI.

Instead of using rule-based programs like traditional business intelligence (BI) did to generate static analytics reports from data, enhanced analytics makes use of AI tools like machine learning and natural language generation.

Machine learning extracts patterns, trends, and connections between data points by learning from the data itself. To adjust to changes and make adjustments based on the data, it might draw from past examples and experiences.

Using language, natural language generation transforms the results of machine learning data into understandable insights. All the insights are derived by machine learning, and NLG transforms them into a format that is legible by humans.”

Users can ask questions and receive replies from augmented analytics in the form of text and images. Non-technical individuals may easily evaluate data and find insights because of the automated nature of the entire process of generating insights from data.

What is the role of machine learning in data analytics?

In essence, machine learning automates the process of data analysis and makes real-time predictions based on data without the need for human interaction. A data model is automatically created and then trained to provide predictions in real-time. The data science lifecycle is when machine learning algorithms are applied.

The standard machine learning process begins with you providing the data to be studied, followed by you defining the precise features of your model and the construction of a data model in accordance with those features. The original training dataset is then used to train the data model. The machine learning algorithm is prepared to make a prediction the next time you upload a fresh dataset once the model has been trained.

What is the difference between data analytics and artificial intelligence?

While AI involves data analysis, making assumptions, and seeking to make predictions that are beyond the capacity of humans, data analytics works by detecting patterns based on historical data to anticipate future events.

Finding patterns in the data is the goal of data analytics, but artificial intelligence (AI) tries to automate the process by giving robots human intelligence.

Will artificial intelligence replace data analytics?

Data analytics and artificial intelligence are co-living creatures. The requirement to handle and evaluate data as soon as someone in an organization comes into contact with it is great because today’s world is data-driven, and technology is evolving quickly. In order to deploy the abundance of information while still keeping it all secure and well-guarded, new tools and procedures must be deployed. In this current technology era, artificial intelligence and data science are the two most significant data managers.

While most machines are replacing people, it is a known fact that artificial intelligence will never be able to replace data analysis. Instead, they complement one another to increase each other’s efficiency. This is true for a number of noteworthy reasons.

Every server may be connected by machines, increasing efficiency, but if one server is connected to the wrong network, everything could be irreparably damaged. This is mostly handled by data analytics, which has extensive knowledge of all networks and servers and carefully examines each system before making judgments on equipment.

New artificial intelligence can diagnose a patient using their speech

No matter how many computers enter the tech hub, they will never be able to replace the importance of expert data analytics because these tools offer critical evaluation and suggestions for improving outcomes in their respective fields. Yes, machines supply the relevant knowledge, and necessary facts needed to make informed decisions. Although data analytics has the knowledge to use these techniques, artificial intelligence has the right instruments.

Although artificial intelligence is gaining popularity, this does not mean that it will completely dominate the data analytics industry. Instead, all the tasks in the systems are performed by data science and artificial intelligence using far more virtuosic and sophisticated methods. Data analytics is used to create conclusions utilizing logical reasoning and expository thinking, while artificial intelligence is used to interpret stored data.

Data analytics are ideal for controlling artificial intelligence, which results in the longer continuation of the business. Machines will always need a command to function properly in all areas. But only a qualified data analyst with the necessary certificates can operate this equipment and carry out essential tasks for the company.

Who earns more, data scientist or artificial intelligence expert?

A data scientist can expect to make about $116,654 a year on average, according to Indeed. Big data is powerful, and the companies paying these hefty salaries are keen to use it to improve business decisions. Even entry-level pay is beginning to look appealing in this expanding sector. Entry-level data scientists can make up to $93,167 per year, while seasoned data scientists can make up to $142,131 annually.

Similar to that, an artificial intelligence engineer makes well over $100,000 per year on average. With an average low of $90,000 and a high of $304,500, the average national wage in the United States is $164,769 each year. The salary of AI engineers will rise as the number of job options for them dramatically increases.

What is an AI analyst?

An AI data analyst is a computer expert tasked with gathering, processing, and locating statistical data from the datasets that already exist. Due to its promising future, the career path of an AI data analyst is in high demand. You can investigate careers in AI & ML, data science, and related areas after you are on the learning path for AI data analysts.

AI data analyst salary

On average, an artificial intelligence analyst makes $105,456 a year in the US\ according to ZipRecruiter.

Suppose you need a quick pay estimator. That comes out to be about $50.70 per hour. This is the same as $2,028 every week or $8,788 per month.

Data analytics and artificial intelligence jobs

If these data-driven fields of interest intrigue you, you may be thinking about a job in a related field. But what jobs are available in the various fields? We’ve only chosen a handful of instances of each.

Data analytics jobs

• Data analyst: The primary responsibility of a data analyst is to transform raw data into insightful knowledge. They strive to recognize trends and convey them in a useful and clear manner.
• BI analyst: Business intelligence analysts strive to offer data insights that can help with decision-making in the workplace. They employ a range of methodologies and tools to empower organizations to make data-driven decisions.

Artificial intelligence jobs

• Robotics engineer: This position focuses on creating and designing machines that can perform work for us. AI is necessary for robotics when building robots that can carry out difficult jobs. It is a very important job for data analytics and artificial intelligence.
• AI programmer: An artificial intelligence programmer works to develop software that’s used for AI applications. It’s a role very much focused on the software development perspective. 

Machine learning jobs

• ML engineer: In this position, there are overlaps between software engineering and data science. Computer programs and algorithms are developed by machine learning engineers to aid in the autonomous learning of computers.
• NLP scientist: The technology known as natural language processing (NLP) enables computers to comprehend ordinary spoken language. In order to aid in the process of comprehending human language, NLP researchers develop algorithms. It is a vital profession for data analytics and artificial intelligence.

Conclusion

Data analytics and artificial intelligence have applications in all sectors of the economy and in all types of services, and they will continue to develop new uses in business, government, and academia. Healthcare, banking, education, media, and customer service are among the sectors likely to use AI in increasingly potent and widespread ways.

Build a wall around your sensitive data with advanced threat protection

Leveraging data to innovate is a cornerstone of the financial services industry and includes anything from financial modeling to risk and fraud detection to performing customer and credit analytics. Many businesses are using data science and machine learning to keep up with industry standards and rivals. Data science assists businesses in deriving knowledge from their data so they may make fact-based business choices while safeguarding private consumer data. Data analytics and artificial intelligence will change how we do business.

Today we are here to show you the best enterprise SEO tools, solutions, biggest SEO mistakes, and a lot more. Because saying that data is at the core of every business has become a cliche, but the truth goes deeper. Data is the business itself for the majority of enterprises.

Data management, analysis, and protection are every business’s top priorities because it is their most valuable and irreplaceable asset. Utilizing your data for search engine performance optimization is part of SEO analytics. You may pinpoint spots on your website that need work and learn how to fix them with SEO analytics. You may monitor statistics like your site’s crawlability, page speed, and conversion rate optimization.

Enterprise SEO is getting more challenging and time-consuming as there are millions of pages and keywords to manage and report on. For organic search marketing teams in a large firm managing organic search activities, using enterprise SEO tools can boost efficiency and productivity.

What are enterprise SEO tools?

Enterprise SEO has no accepted definition; however, an enterprise is typically a big company. The goal of SEO is to have your website appear at the top of search results on Google and other search engines. Therefore, enterprise SEO helps Fortune 1000 or Global 2000 firms’ organic (unpaid) search ranks, which in turn boosts sales. These strategies are frequently both high-level and low-level, and big teams are devoted to aligning them with business objectives.

Enterprise SEO definition

A group of SEO techniques aimed at enhancing a major company’s organic visibility is referred to as enterprise SEO.

Businesses with websites with thousands of pages need specialist SEO teams and advanced tactics to increase traffic. Enterprise SEO teams provide major enterprises with practical recommendations that increase income and a scalable, strategic method of expanding the business.

What is SEO and how it works?

“Search Engine Optimization,” often known as SEO, is the practice of obtaining visitors via unpaid, editorial, or natural search results in search engines. It seeks to raise the position of your website on search results pages. Keep in mind that the more individuals will view a website, the higher it appears on the list.

Machine learning changed marketing strategies for good and all

Why is enterprise SEO important?

Enterprise SEO has become more difficult and time-consuming due to the hundreds of thousands, tens of thousands, or even millions of pages, sites, and keywords that must be managed and optimized. By utilizing enterprise SEO tools, you can manage organic search campaigns more quickly, accurately, and with fewer mistakes.

SEO marketing

Your company will gain trust organically if your SEO and user experience are good. The search results that are solely the product of a user’s search are known as organic searches. Over time, you will continue to develop and refine your digital marketing strategy as well as your entire organization by naturally building trust.

If you provide customers with what they want, you establish yourself as a reliable source they can rely on. Users are aware of their wants. So, your SEO will suffer if you don’t live up to user expectations. Customers will start to trust your brand if your platform delivers the content they need, whether it’s information, or solutions to their questions, goods, or services. Your digital marketing will perform better in terms of UX and SEO as you build more trust.

Benefits of using enterprise SEO tools

The following advantages can be achieved thanks to enterprise SEO tools:

• One place for many tasks. In a single system, enterprise SEO tools handle a variety of activities. When compared to employing single-function point solutions, the integration of tasks, reporting, and user rights gives significant advantages to enterprise-level SEO operations.
• Better localization across the globe. Enterprise SEO tools come with built-in diagnostics that can be quite helpful in locating site-wide issues across languages, nations, or regions on a worldwide scale. These tools reveal both large-scale and small-scale problems with infrastructure, templates, and pages.
• Golden insights regarding the current state of search engines. Dedicated teams and engineers are employed by SEO software manufacturers to monitor algorithm updates and their effects on ranking elements.
• Instant data reports. Many businesses try to update spreadsheets with a ton of data manually. However, it doesn’t give a comprehensive picture of the facts. In order to make reporting quick and simple, several business SEO platforms provide highly configurable reporting tools that are widget- and wizard-driven.

Understanding your present marketing procedures, knowing how to assess success, and being able to pinpoint areas that require development are all essential components of the decision-making process for the enterprise SEO tools you choose.

Enterprise SEO solutions

From rank-checking tools and keyword research toolkits to full-service systems that manage keywords, links, competitive intelligence, foreign rankings, social signal integration, and workflow rights and roles, enterprise SEO tools come in a variety of forms and sizes.

The bulk of the SEO programs evaluated in this list provide the following fundamental features:

• Keyword ranking and research
• Backlink analysis
• Tracking of social signals
• Rank tracking
• Data management APIs

Enterprise SEO tools might also offer more thorough link and site analytics, including predictive scoring systems to find possibilities to boost brand websites or link authority. Vendors also start to set themselves apart by providing more frequent data updates or more modules, sometimes at an additional cost, such as local or mobile SEO.

How enterprise SEO is different than regular SEO?

There is no question that enterprise SEO and traditional SEO share the same basics. But there are some significant distinctions between strategy and execution. Traditional SEO doesn’t typically require a large staff with members committed to various tasks. Your team will be a lot smaller because each employee can concentrate on several tasks at once. Increases in organic search traffic are necessary for businesses adopting traditional SEO.

Each member of the digital marketing team for an enterprise SEO project will have a very narrow focus. They will delve deeply into the regions they have been given to work in and search for ways to boost profits. The gains in traffic typically have significantly smaller percentage increases because these businesses are so big and well-established. Adding just.02% more organic search traffic could result in thousands of additional website visitors.

Enterprises, caution your “data in motion”

However, just because your business is large does not imply that you require a corporate website. How many pages, products, and services your website has is more important. This is one of the causes for hiring staff or a company that specializes in managing niche websites.

Enterprise SEO and traditional SEO have some differences between them.

• As you would have imagined, traditional SEO is used on websites that have fewer than 100 pages and as many as 1,000 pages.
• Larger websites with thousands of pages often use enterprise SEO. Major corporations with thousands of products and a separate page for each product require the assistance of expert enterprise SEO tools and teams.

And when it comes to keywords:

• In general, small business SEO focuses on long-tail keywords with less competition.
• Whereas enterprise SEO targets short-tail keywords with higher competition.

The long tail is a business technique that enables businesses to make huge profits by selling small quantities of difficult-to-find goods to lots of clients as opposed to only selling big quantities of a small number of popular goods. The phrase was first used in 2004 by Chris Anderson, who suggested that if the store or distribution channel is large enough, low-demand or low-volume products can collectively command a market share that approaches or exceeds the very few current bestsellers and blockbusters.

You may read this theory in Anderson’s book called “The Long Tail: Why the Future of Business is Selling Less of More.”

Enterprise SEO employs the fundamental SEO principles as well as more sophisticated, time-consuming strategies that can significantly affect the number of conversions that take place on the website.

Large websites employ enterprise SEO because it aids in developing a specialized company strategy that links SEO, content marketing, public relations, and social media.

Best enterprise SEO tools

A large business needs a comprehensive tool that can do research, carry out activities, and manage an SEO strategy. The enterprise-level marketing departments of today are supported by these four apps, which offer centralized, all-in-one SEO administration. To make it easier for you to choose the ideal one for you, we’ve outlined each one’s capabilities. Let’s delve into the best enterprise SEO tools.

BrightEdge

As a general indicator of your visibility based on your local carousel, videos, photos, links, and e-commerce signals, BrightEdge provides a special proprietary metric called Share of Voice. It’s simpler to use for teams with less SEO expertise and it helps you prioritize work as you take on your SEO issues.

In-depth competition analysis is another service that BrightEdge offers, providing you with information on the pages, page templates, and inbound links that are helping your competitors succeed in the search engine results pages. The discovery tools from BrightEdge can help you identify chances you’ve missed as well as terms that are successful for your rivals. BrightEdge enables you to develop a 360-degree perspective of your digital marketing plan by fusing social data, domain analytics, and SEO statistics. Basically, BrightEdge is one of the best enterprise tools you can find out there.

Linkdex

As implied by the name, Linkdex provides excellent link-building tools. You can make notes for each connection you’re fostering to show your team where you are in the process in addition to seeing which sites link to your competitors and your pages.

From a management standpoint, task management is one of Linkdex’s coolest features. Within one handy dashboard, you can allocate, check off, and analyze various SEO duties. The potent tracking and forecasting features provided by Linkdex can also be used to determine which optimization adjustments will have the most impact. For local visibility, you can then refine your study to the zip code level.

seoClarity

seoClarity has to be in the best enterprise SEO tools list for numerous reasons. Your entire marketing team may use the customized SEO dashboards you create with seoClarity. To find duplicate content and site issues, you can do site audits and deep crawls.

With the help of seoClarity’s Local Clarity feature, businesses that divide their clientele according to location can benefit from local keywords. Find out which of your domain pages can currently generate the highest SEO benefits by using the Keyword Clarity tool. Link Clarity will highlight the sites that require inbound connections the most, as well as notify you of broken links and modifications to the page rank of related domains.

SearchMetrics

SearchMetrics offers everything we expect from enterprise SEO tools: SEO and content research, SEO-optimized content briefs, competitor research, data reports, research cloud, content experience, search experience, and site experience…

This all-inclusive tool aids in your planning, execution, monitoring, and reporting.

The user interface is quite simple to use, and it’s a fantastic platform for team collaboration. Without leaving the Suite, create content on the platform, tag other users, and follow projects and workflows. Increase your progress by integrating APIs and using consumer insights.

Conductor Searchlight

To give you a detailed daily snapshot of your search rankings, Conductor Searchlight makes use of interfaces with Adobe Omniture and Moz OpenSite Explorer. To generate and market content that affects your rankings, it also offers tools for analyzing which content is most in demand. Surely, it is one of the most interesting enterprise SEO tools to consider.

Additionally, Conductor Searchlight can assist you in finding simple adjustments that will raise page ranks. For instance, Conductor Searchlight may advise you to add an internal link with your keyword as the anchor text to your chosen landing page if a page is performing well for a keyword but isn’t your ideal landing page.

To use Conductor Searchlight, you’ll need a team with some SEO expertise; while it makes suggestions for jobs and provides excellent insights into what needs to be fixed, it doesn’t always assist you in determining your top priorities. Your employees will value the thorough analysis and the lovely user interface if they are informed about SEO.

MarketMuse

MarketMuse offers levels to meet your budget and objectives, ranging from straightforward optimization tests to comprehensive content strategy development. It uses mountains and mountains of data to inform you about content planning and is referred to as an “AI Content Intelligence and Strategy Platform.” Heavy lifting that would take you hundreds of hours is handled by machine learning.

Before your material goes online, you may use this tool to predict how well it will do. Even optimal content briefs can be generated by it to help your production workflow. Additionally, it contains a natural language generator that can even try generating your content for you!

Surfer

Surfer is a content optimization platform that automates tasks that many SEO specialists previously had to perform manually. It evaluates the search results and chooses the most effective way to optimize content.

In essence, this algorithmically-driven tool checks the top URLs for a certain keyword and collects insightful data on what makes these pieces of content successful as well as how to apply this information to your material.

Because Surfer’s Business subscription level permits up to 140 audits every month, the participation of 10 team members and 70 content editors, it is excellent for businesses.

This entails that your company can use the Content Editor to edit up to 70 pieces of content while auditing up to 150 pages or posts each month.

SparkToro

An audience research tool called SparkToro assists companies in producing more user-friendly content. It is one of the best enterprise SEO tools out there without a doubt.

And without writing with your target audience in mind, you cannot produce amazing content. In order to help you develop content that appeals to your customers’ interests and behavior, SparkToro examines the websites your customers visit, the social accounts they follow, the hashtags they use, and more.

The idea is to “forget audience surveys” in favor of letting AI generate precise and beneficial audience insights with a few simple clicks.

Even while the Agency subscription bundle is targeted toward marketing agencies, the same tools might be useful to businesses as well. This pricing option offers unlimited searches, up to 250 social media results, demographic data, and up to 50 users for your team.

Every enterprise should utilize Spark Toro since it can search through millions of social and web results to identify the interests of your audience.

What’s the difference between SEO and local SEO?

Traditional and local SEO both concentrate on raising your ranks in internet search results so that more people can discover, get in touch with, and buy from your company.

Local SEO enables you to seize local search territory to interact with searchers in your area, unlike traditional SEO, which concentrates on increasing your site’s visibility on a national or international scale. Many of the tactics used by SEO and local SEO are similar. However, local SEO strategies use unique techniques to connect you with local searchers.

Biggest enterprise SEO mistakes

Not doing channel optimization is one of the worst enterprise SEO mistakes. Digital marketing strategies at the corporate level are frequently spread over several channels. SEO, paid search, social media, sponsored social media, syndication, and any other channel via which a company may be able to connect with and attract its target market to its website. While some of these methods can be quite expensive, SEO’s cost per acquisition is frequently less than that of these other channels.

In order to grow their operations effectively, many enterprise-level brands—if not most—need to use numerous channels. Both are trying to expect organic to carry everything and ignoring it are mistakes in enterprise SEO. In most circumstances, expecting a single channel to satisfy the organization’s demand-generating needs is just neither practical nor efficient.

Don’t forget to optimize page templates. It’s crucial to have a well-managed taxonomy for categorizing content on your site and determining where various categories of content will live, as well as structurally sound site architecture. In addition, the template that the content is placed in determines numerous on-page SEO elements. The placement of the h1 tag title, how it displays on the page, and how the end user inputs and edits the copy are all determined by templates.

An additional illustration is internal linking, a crucial on-page SEO strategy. A useful template for blog posts or product pages will have an on-page widget that shows links to similar content and can even be set up to automatically fill in information based on semantic or related meta tagging. Due to the variety of content types that an enterprise organization may need to include in its catalog, it is simple to make the SEO mistake of neglecting to include SEO in the design of new templates as well as ignore reviewing legacy templates to ensure that new content is being set up for long-term success.

Cloud costs have started to become a heavy burden for the IT sector

Update your posts instead of expanding aggressively. Enterprise websites typically have a lot of history and are quite huge websites. “A lot of history” includes years’ worth of content creation, website updates, product introductions, product discontinuations, site relocations, and other activities. The website and its contents probably went through multiple digital teams throughout that time, each of which brought their own set of standards and procedures to the table. It’s very simple to get off track at an enterprise scale.

Maintaining an active sitemap.xml file and monitoring the number of indexed pages in Google Search Console can help. That said, it frequently happens that older pages on a related topic on existing URLs could have been rewritten, re-optimized to fit the new keyword targets, and republished. An example of this would be when the need to capitalize on a specific group of high-value keywords arises, and significant resources are invested in creating new content.

Understand your aim. The idea of “enterprise” SEO is generally associated with website size rather than just business size, though this isn’t always the case. According to this guideline, enterprise SEO tools typically comprise thousands of pages dispersed over numerous categories, including product pages, blog posts, asset landing pages, about us pages, and more. Having so many pages raises an issue of scale.

Most of the time, it just isn’t practical or cost-effective to give each page the same level of care. Enterprise SEOs and digital marketers will instead concentrate their time and efforts on website areas that generate the most value for the company. Although there is nothing fundamentally wrong with this method, it does run the danger of ignoring possible SEO growth or risk areas.

What is a backlink in SEO?

Links leading back to a page on your website from other websites are called backlinks. Because they reflect traffic flowing to your own website from another website, backlinks are also known as inbound links. Your backlink profile’s quantity and quality both play a role in how Google and Bing will rank you.

Backlinks are seen as a sign of how well-liked your website is by consumers. An essential component of search engine optimization (SEO) and SEO strategies is the implementation, management, and analysis of backlink performance.

How many types of links are there?

A link, usually referred to as a hyperlink, is a word, phrase, or image that can be clicked to move from one online page to another.

There are three types of links:

• Internal links: Hyperlinks that take users from one page of your website to another.
• External links: Hyperlinks that direct users to another site.
• Backlinks: Hyperlinks that other pages lead to your page.

For your website’s SEO, each of these links is crucial. That is why you need enterprise SEO tools because it is not the easiest thing to follow all these important data.

When it comes down to it, backlinks come in two types: dofollow and nofollow. A web page reader won’t be able to tell the difference between a backlink that is dofollow and one that is nofollow. The source code is where the difference lies. The effect of a backlink on your SEO profile is determined by a special element in the source code that controls how Google and other search engines evaluate the backlink.

What is an example of a backlink?

As an illustration, suppose you wrote an article about artificial intelligence and included a link to a page in Dataconomy to either cite the source of the original information or to substantiate your argument.

However, if the backlink has a do-follow code, it is more valuable.

You would have to look at the specific code to see how it is written, but a do-follow link will look like this:

<a href=“dataconomy.ru”>dataconomy</a>

A nofollow tag would look like this:

<a href=“dataconomy.ru” rel=“nofollow”> dataconomy</a>

But why is this vital?

A backlink effectively informs the search engine that the link is natural and that the other website wants Google to give it link equity for being the original source.

As a result, a backlink or do-follow link helps to transmit that information and aid in the SERPs’ assessment of the authority of your site.

A nofollow link may also be beneficial, but the search engine has the last say.

Therefore, a backlink that lacks the nofollow tag is a good example.

How many backlinks should a website have?

According to experts, in order for a website to compete for SEO, it needs between 40 and 50 backlinks to the homepage and between 0 and 100 backlinks to each web page. The PageRank ratings of those backlinks are crucial, though, because the more valuable they are, the fewer links are required to improve overall ranks.

Conclusion

For any enterprise, using organic search as a lead and revenue generator is crucial. Organic search can generate up to 50% of leads for an e-commerce website, compared to paid adverts, which only account for 5% of leads. Enterprise SEO analytics can undoubtedly boost revenue when used efficiently, without requiring significant outlays for paid advertising.

A complete guide for marketing automation software

For example, if you have an e-commerce page, your main goal will be to create leads for the most conversions and sales possible. However, accomplishing this goal is difficult given the crowded industry and the difficulty that potential buyers have found your website in search engines. This is where enterprise SEO tools come into play. It assists you in analyzing the data of your site and makes recommendations for specific adjustments that can help you enhance organic traffic to your site, which will eventually increase sales and revenue as well.

Machine learning pipeline architectures streamline and automate a machine learning model’s whole workflow. Once laid out within a machine learning pipeline, fundamental components of the machine learning process can be improved or automated. Models are created and applied in an increasing range of contexts as more businesses take advantage of machine learning. This development is standardized through the use of a machine learning pipeline, which also improves model accuracy and efficiency.

Machine learning pipeline architecture explained

Defining each phase as a separate module of the overall process is a crucial step in creating machine learning pipelines. The end-to-end process may be organized and managed by organizations using this modular approach, which enables them to understand machine learning models holistically.

Streamlining a machine learning process flow: Planning is the key

But because individual modules can be scaled up or down inside the machine learning process, it also offers a solid foundation for scaling models. A machine learning pipeline’s various stages can also be modified and repurposed for use with a new model to achieve even greater efficiency gains.

What is a machine learning pipeline?

The machine learning pipeline architecture facilitates the automation of an ML workflow, and it enables the transformation and correlation of sequence data into a model for analysis and output. The purpose of an ML pipeline is to enable the transfer of data from a raw data format to some useful information.

It offers a way to create a multi-ML parallel pipeline system to analyze the results of various ML techniques. Its goal is to exert control over the machine learning model. The implementation can be made more flexible with the aid of a well-planned pipeline. Identifying the error and replacing it with the appropriate code is similar to having an overview of the code.

What is an end-to-end machine learning pipeline?

Building a machine learning model can benefit greatly from machine learning pipelines. Data scientists can concentrate on preparing other stages of the sequence, while some of the sequences can be conducted automatically (such as data import and cleaning) thanks to clearly documented machine learning pipelines. Parallel execution of machine learning pipelines is another option for increasing process effectiveness. Machine learning pipelines may be reused, repurposed, and modified to fit the needs of new models because each stage is precisely specified and optimized, making it simple to scale the process.

A greater return on investment, quicker delivery, and more accurate models are all benefits of improved end-to-end machine learning procedures. Less human error results from replacing manual procedures, and delivery times are shortened. A good machine learning pipeline must also have the ability to track different iterations of a model, which requires a lot of resources to execute manually. Additionally, a machine learning pipeline offers a single point of reference for the entire procedure. This is significant because different experts frequently lead several difficult phases in machine learning training and deployment.

What is a pipeline model?

The process of developing, deploying, and monitoring a machine learning model is called a “machine learning pipeline.” The method is used to map the entire machine learning model development, training, deployment, and monitoring process. The process is frequently automated using it. In the overall machine learning pipeline architecture, each stage of the process is represented by a separate module. Then, each component can be automated or optimized. The orchestration of these many components is a crucial factor to take into account while developing the machine learning pipeline.

Machine learning changed marketing strategies for good and all

Machine learning pipelines are iteratively developed and improved upon, making them cyclical in essence. The workflow is divided into multiple modular stages that can each be upgraded and optimized independently. The machine learning pipeline then unites these separate steps to create a polished, more effective procedure. It can be viewed as a development guide for machine learning models. The machine learning pipeline can be enhanced, scrutinized, and automated after it has been created and developed. An effective method for increasing process efficiency is an automated machine learning pipeline. It is end-to-end, starting with the model’s initial development and training and ending with its final deployment.

The automation of the dataflow into a model can also be thought of as a component of machine learning pipelines. This relates to how the term “data pipeline” is used more frequently in organizations. Instead, the preceding definition—one of the modular elements in the entire machine learning model lifecycle—is the subject of this guide. It includes all phases of model creation, deployment, and continuous optimization. A machine learning pipeline architecture will also consider static elements like data storage options and the environment surrounding the larger system. Machine learning pipelines are beneficial because they enable top-down understanding and organization of the machine learning process.

Data scientists train the model, and data engineers deploy it within the organization’s systems; these are only two of the numerous teams involved in the development and deployment of a machine learning model. Effective collaboration across the various parts of the process is ensured by a well-designed machine learning pipeline architecture. It is related to the idea of machine learning operations (MLOps), which manages the full machine learning lifecycle by incorporating best practices from the more mature field of DevOps.

The best practice approach to the various components of a machine learning pipeline is what is known as MLOps. The MLOps life cycle includes model deployment, model training, and ongoing model optimization. However, the machine learning pipeline is a standalone product that serves as a structured blueprint for creating machine learning models that can later be automated or repeated.

Why pipeline is used in ML?

A machine learning pipeline serves as a roadmap for the steps involved in developing a machine learning model, from conception to implementation and beyond. The machine learning process is a complicated one that involves numerous teams with a range of expertise. It takes a lot of time to move a machine learning model from development to deployment manually. By outlining the machine learning pipeline, the strategy can be improved and comprehended from the top down. Elements can be optimized and automated once they are laid out in a pipeline to increase overall process efficiency. This allows the entire machine learning pipeline to be automated, freeing up human resources to concentrate on other factors.

ML engineers build the bridge between data and AI

The machine learning pipeline serves as a common language of communication between each team because the machine learning lifecycle spans numerous teams and areas.

To enable expansion and reuse in new pipelines, each stage of a machine learning pipeline needs to be precisely specified. This characteristic of reusability allows for the efficient use of time and resources with new machine learning models by repurposing old machine learning processes. The machine learning pipeline architecture can be optimized to make each individual component as effective as feasible.

Active learning overcomes the ML training challenges

For instance, at the beginning of the machine learning lifecycle, a stage of the pipeline often involves the collection and cleaning of data. The movement, flow, and cleaning of the data are all taken into account throughout the stage. Once it has been precisely defined, a process that may have initially been manual can be improved upon and automated. For instance, a certain section of the machine learning pipeline could include triggers that would automatically recognize outliers in data.

It is possible to add, update, change, or improve specific steps.

How do you build a pipeline for machine learning?

There are several steps in a machine learning pipeline architecture. Each stage in a pipeline receives the processed data from the stage before it, or the output of a processing unit provided as an input to the following step. Pre-processing, Learning, Evaluation, and Prediction are its four primary phases.

Pre-processing

Data preprocessing is a data mining approach that entails converting unstructured data into something that can be interpreted. Real-world data typically lack specific behaviors or trends, is inconsistent, and is incomplete, making it more likely to be inaccurate. Following procedures like feature extraction and scaling, feature selection, dimensionality reduction, and sampling, acceptable data is obtained for a machine learning algorithm. The final dataset used to train the model and conduct tests is the byproduct of data pre-processing.

Learning

Data is processed using a learning algorithm to uncover patterns suitable for use in a new circumstance. Utilizing a system for a certain input-output transformation task is the main objective. Select the top-performing model for this from a group of models created using various hyperparameter settings, metrics, and cross-validation strategies.

Evaluation

Fit a model to the training data and forecast the labels of the test set to assess the effectiveness of the machine learning model. To determine the model’s prediction accuracy, also tally the number of incorrect predictions made using the test dataset.

When it comes to evaluating ML models, the machine learning pipeline architecture understanding of AWS is key:

“You should always evaluate a model to determine if it will do a good job of predicting the target on new and future data. Because future instances have unknown target values, you need to check the accuracy metric of the ML model on data for which you already know the target answer, and use this assessment as a proxy for predictive accuracy on future data.”

AWS

Prediction

No training or cross-validation exercises were performed using the model after it had successfully predicted the results of the test data set.

Understanding a machine learning pipeline architecture

Before beginning the build, it is helpful to comprehend the typical machine learning pipeline architecture design. Overall, the actions necessary to train, deploy, and constantly improve the model will make up the machine learning pipeline components. Every single section is a module that is outlined and thoroughly investigated. The architecture of the machine learning pipeline also comprises static components, such as data storage or version control archives.

Each machine learning pipeline will seem different depending on the kind of machine learning model that is applied or the various final purposes of the model. For instance, an unsupervised machine learning model used to cluster consumer data will have a different pipeline than a regression model used in finance or a predictive model, especially because system architectures and structures vary across various organizations.

However, a machine learning pipeline architecture will typically move between correspondingly distinct phases, as the machine learning process does. This includes the initial intake and cleaning of the data, the preprocessing and training of the model, and the final model tuning and deployment. As part of the post-deployment process, it will also incorporate a cyclical approach to machine learning optimization, closely observing the model for problems like machine learning drift before invoking retraining.

The following are typical components of the machine learning pipeline architecture:

• Data collection and cleaning
• Data validation
• Model training
• Model evaluation and validation
• Optimization and retraining

To understand, optimize, and if at all possible, automate each phase, it must be carefully defined. There should be tests and inspections at every stage of the machine learning process, and these are typically automated as well. In addition to these numerous stages, the machine learning pipeline architecture will also include static elements like the data and feature storage, as well as various model iterations.

The following are some instances of the machine learning pipeline architecture’s more static components:

• Feature storage
• Data and metadata storage and data pools
• Model version archives

Architecting a machine learning pipeline

Pipelines typically require overnight batch processing, which entails gathering data, transferring it across an enterprise message bus, and processing it to produce pre-calculated results and direction for the operations the following day. While this is effective in some fields, it falls short in others, particularly when it comes to machine learning (ML) applications.

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Machine learning pipeline diagram

The diagram that follows illustrates a machine learning pipeline used to solve a real-time business problem with time-sensitive features and predictions (such as Spotify’s recommendation engines, Google Maps’ estimation of arrival time, Twitter’s follower suggestions, Airbnb’s search engines, etc.) is shown.

It comprises two clearly defined components:

• Online model analytics: The operational part of the application, or where the model is used for real-time decision-making, is represented by the top row.
• Offline data discovery: The learning component, which is the examination of historical data to build the ML model in batch processing, is represented by the bottom row.

How do you automate a machine learning pipeline architecture?

Every machine learning pipeline architecture will vary to some extent depending on the use case of the model and the organization itself. The same factors must be taken into account when creating any machine learning pipeline, though, as the pipeline often follows a typical machine learning lifecycle. The first step in the process is to think about the many phases of machine learning and separate each phase into distinct modules. A modular approach makes it simpler to focus on the individual components of the machine learning pipeline and enables the gradual improvement of each component.

The more static components, such as data and feature storage, should then be mapped onto the machine learning pipeline architecture. The machine learning pipeline’s flow, or how the process will be managed, must then be established. Setting the order of modules as well as the input and output flow is part of this. The machine learning pipeline’s components should all be carefully examined, optimized, and, whenever possible, automated.

The following four actions should be taken while creating machine learning pipeline architectures:

• Create distinct modules for each distinct stage of the machine learning lifecycle.
• Map the machine learning pipeline architecture’s more static components, such as the metadata store.
• Organize the machine learning pipeline’s orchestration.
• Each step of the machine learning pipeline should be optimized and automated. Integrate techniques for testing and evaluation to validate and keep an eye on each module.

Create distinct modules for each distinct stage 

Working through each stage of the machine learning lifecycle and defining each stage as a separate module is the first step. Data collection and processing come first, followed by model training, deployment, and optimization. It is important to define each step precisely so that modules can be improved one at a time. The scope of each stage should be kept to a minimum so that changes can be made with clarity.

Map the machine learning pipeline architecture’s components 

The more static components that each step interacts with should also be included in the overall machine learning pipeline architecture map. The data storage pools or a version control archive of a company could be examples of these static components. To better understand how the machine learning model will fit inside the larger system structure, the system architecture of the organization as a whole can also be taken into account.

Organize the machine learning pipeline’s orchestration 

The machine learning pipeline architecture’s various processes should then be staged to see how they interact. This entails determining the order in which the modules should be used as well as the data flow and input and output orientation. The lifetime can be managed and automated with the aid of machine learning pipeline orchestration technologies and products.

Data science conquers the customer journey and sales funnel

Automate and optimize

The overall objective of this strategy is to automate and optimize the machine learning workflow. The process can be mechanized, and its components can be optimized by breaking it down into simple-to-understand modules. Using the machine learning architecture described above, the actual machine learning pipeline is typically automated and cycles through iterations. Models can be prompted to retrain automatically since testing and validation are automated as part of the process.

What are the benefits of a machine learning pipeline architecture?

The benefits of a machine learning pipeline architecture include:

• Creating a process map that gives a comprehensive view of the entire series of phases in a complex process that incorporates information from various specialties.
• Concentrating on a single stage at a time, allowing for the optimization or automation of that stage.
• The initial stage is turning a manual machine learning development process into an automated series.
• Providing a template for more machine learning models, with each step’s ability to be improved upon and altered in accordance with the use case.
• There are tools for orchestrating machine learning pipelines that increase effectiveness and automate processes.

What are the steps in a basic machine learning pipeline architecture?

As we explained above, a basic machine learning architecture looks like this:

• Data preprocessing: This step comprises gathering unprocessed, erratic data that has been chosen by an expert panel. The pipeline transforms the raw data into a format that can be understood. Techniques for processing data include feature selection, dimension reduction, sampling, and feature extraction. The result of data preprocessing is the final sample that is utilized for training and testing the model.
• Model training: In a machine learning pipeline architecture, it is essential to choose the right machine learning algorithm for model training. How a model will find patterns in data is described by a mathematical method.
• Model evaluation: To create predictions and determine which model will perform the best in the following stage, sample models are trained and evaluated using historical data.
• Model deployment: Deploying the machine learning model to the manufacturing line is the last stage. In the end, forecasts based on real-time data can be obtained by the end user.

Challenges: Updating a machine learning pipeline architecture

While retraining can be automated, coming up with new models and improving existing ones is more difficult. Version control systems handle updates in conventional software development. All the engineering and production branches are divided. It is beneficial to roll back software to an earlier, more reliable version in case something goes wrong.

Advanced CI/CD pipelines and careful, complete version control are also necessary for updating machine learning models. The complexity of upgrading machine learning systems, however, increases. If a data scientist creates a new version of a model, it almost always includes a variety of new features and additional parameters.

Changes must be made to the feature store, the operation of data preprocessing, and other aspects of the model for it to operate correctly. In essence, altering a relatively small portion of the code that controls the ML model causes noticeable changes in the other systems that underpin the machine learning process.

Additionally, a new model cannot be released right soon. If the model supports some customer-facing features, it may also need to go through some A/B testing. It must be compared to the baseline during these studies, and model measurements and KPIs might even be re-evaluated. Finally, the entire retraining pipeline needs to be set up if the model is successful in going into production.

Machine learning pipeline architecture creation tools

Typically, a machine learning pipeline is constructed to order. However, you can lay the groundwork for this using certain platforms and technologies. To get the idea, let’s take a brief look at a few of them.

• Google ML Kit: The ability to use the Firebase platform to exploit ML pipelines and close connection with the Google AI platform allows for the deployment of models in the mobile application through API.
• Amazon SageMaker: You may complete the entire cycle of model training on a managed MLaaS platform. ML model preparation, training, deployment, and monitoring are all supported by a range of tools in SageMaker. One of the important aspects is that you may use Amazon Augmented AI to automate the process of providing model prediction feedback.
• TensorFlow: Earlier, Google created it as a machine learning framework. You can use its core library to implement in your pipeline even though it has expanded to become the entire open-source ML platform. TensorFlow’s strong integration possibilities via Keras APIs are a clear benefit.

Key takeaways

• Data scientists can be relieved of the maintenance of current models thanks to automated machine learning pipelines.
• Automated pipelines can reduce bugs.
• A data science team’s experience can be improved by standardized machine learning workflows.
• Data scientists can readily join teams or switch teams because of the standardized configurations, which allow them to work in the same development environments.

Using an automated machine learning pipeline architecture is key for a data science team because:

• It provides more time for unique model development.
• It brings simpler techniques for updating current models.
• It will take less time to reproduce models.

Stephan Schnieber, Sales Leader IBM Cloud Pak for Data – DACH at IBM Deutschland, explains four ways to gain more trust in AI in our interview.

You and your team monitored a workshop on the topic of trustworthy AI today – at Europe’s biggest data science and AI EVENT, the Data Natives Conference in Berlin, DN22. How was that?

It was very, very good- I think it was really successful overall. Successful in the sense that collaboration can be extremely difficult in a workshop setting. We had a lot of interaction with participants, a full house, so to speak. The room was completely booked, occupied and everyone really participated.

We had an interactive session at the beginning to collect peoples’ points of view. There were some important ground rules that we had to set together as a group- How do we define trust? How do participants touch base if they need to? And once we established that, the workshop became a dynamic, flexible environment to collaborate within. What’s more is that we were actually fully subscribed, which I didn’t expect at all. The workshop was run by three of us from IBM and we were all excited by what we saw.

Afterwards, we held another meeting so we could explore a few of the themes we unearthed during the workshop. There were a lot of questions that participants still wanted answers for. So we really facilitated a forum for that. What are their views on trustworthy AI? What are their challenges, their encounters with it? We also used the opportunity to discuss a few recurring ideas that we picked up on; how do we understand our participants’ answers? What solutions can we offer? Because many of the points they raised have been ongoing conversations around AI for some time.

As well as this, IBM has a product offering that supports trustworthy AI, and we were able to present some of it during the workshop and at the same time collect feedback for it. So it was a really exciting, interesting 90 minutes.

What was the workshop participant structure like?

What I like about Data Natives is the diversity of attendees, which is actually better than what I experience in some client meetings. It’s that same lack of diversity that can make the IT sector boring. And of course, the relaxed atmosphere at the conference really helped. In terms of age structure, we had a lot of younger participants, Generation X and even Gen Z, which was something else I found pretty interesting.

‘Trustworthy AI’ means different things to different people. What does trustworthy AI mean at IBM?

My personal opinion happens to align with IBM. So the objective of Trustworthy AI is to integrate AI into business, to make sure that IBM supports their decision making with AI methods, both automated and partially automated. That’s the basic idea. The real question is how might we achieve this? The most important thing we can do is to build “trust” in AI as a decision maker or stakeholder. It’s ultimately about proving AI trustworthiness, so that when a decision is made, you don’t feel the need to second-guess it and say, “Hm, help, why do I feel unsure about this?” So that means I have to be able to say confidently that I’d be willing to allow an increasing amount of automatisms in my work and have my business processes supported by AI. That’s the real idea behind it.

And that’s why, as IBM sees it, AI begins with data. So first and foremost, we need to ensure the quality of data flowing into the AI systems. Meaning that we don’t just look at AI and the model, the modeling and subsequently the distribution of the models, but that the success of all of these prior steps begins with the data and the quality of said data. That’s why collaboration is so important in the field of AI.

As I said before, diversity massively enriched Data Natives as a conference, and my workshop really depended on a wide variety of influences within our audience, listeners, and participants to broaden the conversation around AI. As you might imagine, my team found that stimulating discussion during the workshop was directly impacted by these diverse backgrounds and ideas.

So in the end, the very foundations upon which our workshop was conducted came from understanding the value of a truly collaborative platform.

I believe that collaboration is one of our most important tools for supporting the development and acceptance of AI. There are many approaches to AI within the sector that tend to run in a stand-alone format and fail to utilise the innovation offered by teamwork. So for us at IBM, collaboration is an essential component to AI advancement. In team-based projects, you can bring people in, take them away. I can control who sees which data on which project, or who can see which AI models, and so on. So collaboration is really key to everything. Of course, collaboration across roles is equally important, because, if you think about all the different job titles involved in checking the quality of data- I have to prepare my data, develop it, maybe even transform it. And that’s just the beginning. We have to work really closely with data scientists, so it’s crucial that their attitudes to inter-role collaboration are just the same as ours- he or she has to get accurate preliminary work and be able to coordinate with colleagues. It’s these human skills that dictate the success of AI. And then, of course, we’ve got our new tool, IBM’s Cloud Pack for Data Platforms. The Pack enables you to collect, organise and analyse data no matter where it’s stored, so it really supports the collaborative environment we’ve worked hard to create at IBM.

Auto AI: This is a tool we use for a platform approach, dealing not only with data science, but data transformations too. Data science is pretty much a catch-all term, used lovingly by those amongst us who are what I like to call “no code people”. Which is basically anyone who isn’t a data scientist. Of course, “no-code people” need AI too, so for them, we’ve created a software feature called Auto AI. Our competitors call it Auto ML, but we call it Auto AI.

Auto AI makes it very, very easy to input data. I simply tell it what I need it to do, give it my target values, and the Auto AI automatically formulates a model for me, which, as research suggests, is highly likely to be accurate. After using the initiative myself, I’ve seen firsthand that the results generated by the AI are so reliable that I’d be confident in using them in a productive setting. We’ve also had major customers, mainly DAX companies, who have enjoyed remarkable successes with our Auto AI.

We also deal with Low-Code, which is obviously well-known as a component of graphic design. Our SPSS software can be considered a part of this platform, and it enables people to work graphically and develop stronger data and AI models. Of course, this fits into the broader idea of coding as a programmatic approach, seen in recent years with Teyssen, Jupyter Notebooks, AR and CoR Studio. Visual Code means we can improve data integration and API to enhance our flexibility and interconnect the different spheres of data. What’s more, being able to demystify concepts like Low-Code gives more people individual access to machine learning.

Trustworthy development models

This creates an environment for development which allows me to collaborate with other people, which is really crucial in the data science sector. Of course, it’s all about controlled collaboration: when it comes to data, privacy is everything. Whilst collaboration can enrich our understanding of data, it’s equally important that I can control which data I should mask, and who should or shouldn’t have access to it.

Creating a trustworthy development model means that I can protect personal data like e-mail addresses and telephone numbers in a secure way. That I’m able to control everything in a clean, reliable manner is essential, in turn, for establishing trust in the AI systems we use at IBM. When it comes to operations, I have to be confident in deploying my models. There’s been times where I’ve asked customers, “Have you started with machine learning? How many of the models are integrated into processes?” And it’s very few. Several companies are on their way, but in operation it’s a big construction site.. That’s where we can really start developing our processes.

What are the most important points to gain more trust in AI?

There are 4 points: Bias Detection, Data Drift, Transparency, Explainability.

Bias Detection: I’ll start with the topic of bias detection. Bias is ultimately the question: Are women and men treated identically? Are young people treated the same as old people? We also discussed this in the workshop. For example, with car insurance, it’s always the case that women get cheaper car rates. This has to be reflected and I have to ensure fairness in my modelling, and make sure that I get usable models and results that reflect reality. I have to build trust and acceptance in such a machine learning model.

Data Drift: After I created the model and put it into production, I had to factor into my machine learning model events for which we could never have planned. Take Covid, or the war in Ukraine, for example. Consumer behaviour changes completely. My entire machine learning model may become unusable because the behaviour of the users has changed. So I have to retrain my models and adapt them to the corresponding framework conditions. Detecting this automatically is done via data drift. I can set threshold values and then there is an alarm signal, which we can then monitor- nowadays, I can even ensure that I have automatic re-modelling and redeployment. So I can operationalise that too, if you like.

Transparency and explainability go hand in hand. I need transparency. Which decisions were made when? On the basis of which model, for example? So if I’m talking about data drift, how can I find out which machine learning model was used the day before yesterday? What data was used? How was the decision made? I need to know all of these things. It has to be transparent and sustainable. We always need to know what values were inputted, what model was used, what came out, in order to be able to trace it. There’s no use in saying, “Oh, something happened and I don’t know what to do.” We need to have total transparency in what we’re doing, and everything needs to be traceable. I always have to be able to explain the decisions I’ve made.

Do you have an example of this?

Say I apply for a bank loan and they say no because I’m just too old. The bank advisor who makes the decision thinks I won’t live long enough to pay back the 5 million I asked for. If I disagree with him, however, there’s a chance that I might be able to get the decision reversed.

With an AI model, I have to be able to map similar things. This means that, in principle, when I make a call, I don’t have to talk to the AI within the machine, but I do have to talk to the human at the end. But my friend at the other end of the line has to be able to understand the system, Mr Schnieber was not allowed to take out the bank loan due to his age. These are things that system transparency allows us to deliver. In principle, our system gives an indication that if the parameter of age is changed, then it’s very likely that different values would have emerged and that our bank manager would have reached a different conclusion.

With neural networks, it’s now quite important to have the ability to explain processes. Meaning that it’s something we have to factor in. And the bank advisor would then be able to tell me that the result on my loan was decided by a machine, that he decided against it, but that age is the main factor. And there’s nothing the bank can do about my age. It’s difficult. But if I offered them more equity now, for example, or if I were willing to reduce my loan amount, then the bank could reduce five million to three and a half. And we both make compromises. So I can use this example to present the issue of transparency, the need to understand AI systems, and how this applies to customer interactions with our software.

Of course, I have to be careful with data. What I’m really saying is that, if I build a model, I have to create something that I’m able to trust. And trust takes a lifetime to build but a second to lose. And that’s why I have to create a stable environment for innovation and development. Because when we do that, that’s when we can really create a secure platform for democratising and expanding knowledge around AI, as well as increasing software accuracy. 

The process of copying data to guarantee that all information remains similar in real-time between all data resources is known as data replication, also known as database replication. Consider database replication as a net that prevents your information from slipping through the cracks and disappearing. Data rarely remain constant. It changes constantly. Thanks to an ongoing process, data from a primary database is continuously replicated in a replica, even if it’s on the opposite side of the world.

The common goal of data replication is to reduce latency to sub-millisecond periods. Pressing the refresh button on a website and waiting for what seems like an eternity (seconds) to see your information refreshed is a scenario we have all experienced. A user’s productivity is reduced by latency. The objective is achieving near-real-time. For whatever use scenario, zero time lag is the new ideal.

What is data replication?

Data replication is the process of creating numerous copies of a piece of data and storing them at various locations to improve accessibility across a network, provide fault tolerance, and serve as a backup copy. Data replication is similar to data mirroring in that it can be used on both servers and individual computers. The same system, on-site and off-site servers, and cloud-based hosts can store data duplicates.

Modern database solutions frequently leverage third-party tools or built-in features to replicate data. Although Microsoft SQL and Oracle Database actively provide data replication, some traditional technologies might not come with this feature by default.

Data replication in distributed database

Data replication is the process of making several copies of data. These copies, also known as replicas, are then kept in some places for backup, fault tolerance, and enhanced overall network accessibility. The replicated data might be kept on local and remote servers, cloud-based hosts, or even all within the same system.

Data replication in a distributed database is the process of distributing data from a source server to other servers while keeping the data updated and in sync with the source so that users can get the data they need without interfering with the work of others.

What do you mean by data replication?

For instance, your standby instance should be on your local area network in case you need to recover from a system outage (LAN). You can then replicate data synchronously from the primary instance over the LAN to the secondary instance for essential database applications. Because it is now in sync with your active instance and “hot,” your backup instance is prepared to take over immediately in case of a breakdown. High availability (HA) is the term used for this action.

Make sure your secondary instance is not positioned where your primary instance is in the event of an emergency. This means that you should locate your backup instance in a cloud environment connected by a WAN or at a location far from the first instance. Data replication on a WAN is asynchronous to prevent adversely affecting throughput performance. As a result, updates to standby instances will be made later than updates to active instances, delaying the recovery process.

A study paper titled “Efficient privacy-preserving data replication in fog-enabled IoT” outlines the dependency of cloud computing on network performance:

“According to research, 41.6 billion Internet of Things (IoT) devices will be generating 79.4 zettabytes of data by the year 2025. A high volume of data generated by IoT devices is processed and stored in cloud computing. Cloud computing has a strong dependency on network performance, bandwidth, and response time for IoT devices’ data processing and storage. Data access and processing can be a bottleneck due to long turnaround delays and high demand for network bandwidth in remote cloud systems.”

What is the purpose of data replication?

You should duplicate your data to the cloud for five reasons:

• As we described previously, cloud replication keeps your data off-site and away from the business’s site. Although a significant disaster, such as a fire, flood, storm, etc., can destroy your primary instance, your secondary instance is secure in the cloud. It can be utilized to restore any lost data and applications.
• Replicating data to the cloud is less expensive than doing so in your own data center. You may eliminate the expenses of running a second data center, such as hardware, upkeep, and support fees.
• Replicating data to the cloud for smaller firms can be safer, especially if you don’t have security expertise on staff. The network and physical security offered by cloud providers are unrivaled.
• On-demand scalability is provided by replicating data to the cloud. You don’t have to spend money on more hardware to maintain your secondary instance if your business expands, contracts, or let that hardware lie idle if the business picks up. You don’t have any long-term contracts either.
• You have a wide range of geographic options for replicating data to the cloud, including having a cloud instance in the next city, across the country, or another country, depending on your business’s requirements.

How do you replicate a database?

Database replication can be a one-time event or a continuous procedure. It involves every data source in the dispersed infrastructure of a company. The data is replicated and fairly distributed among all the sources using the organization’s distributed management system.

Data mature businesses are more profitable than others

DDBMS, or distributed database management systems, generally make sure that any alterations, additions, or deletions made to the data in one place are automatically reflected in the data kept at all the other locations. The system that oversees the distributed database, which results from database replication, is known as a distributed database management system, or DDBMS.

One or more applications that link a primary storage location with a secondary location—often off-site—represent the traditional situation of database replication. Individual source databases like Oracle, MySQL, Microsoft SQL, and MongoDB, as well as data warehouses that combine data from these sources and provide storage and analytics services on greater amounts of data are most frequently used as the primary and secondary storage locations. Cloud hosting is frequently used for data warehouses.

What are the types of data replication?

There are three main types of data replication.

Data replication types

Data replication can be categorized into 3 main types: Transactional, snapshot, and, merge data replication.

Transactional replication

Transactional replication automatically distributes frequent data changes amongst servers. Changes are replicated from the publisher to the subscriber almost instantly. It captures each stage of the transaction and the sequence in which the changes occur, rather than just copying the outcome.

For instance, in the case of ATM transactions, the replication from the publisher to the subscriber includes all of the individual transactions made between the start and end balances. The fact that data changes at the publisher are duplicated at the subscriber but not the other way around is another important aspect of transactional replication. By default, data updates don’t take place at the subscriber level.

If only you knew the power of the dark data…

Snapshot replication

Data is synchronized between the publisher and subscriber at a specific moment via snapshot replication. A single transaction transfers data chunks from the publisher to the subscriber. As opposed to transactional replication, updates in a snapshot replication happen less often. To create a baseline state for the two servers before transactional replication is possible though. Both the order of data changes and every transaction between servers are not updated.

Data that changes over time is synchronized via this process. For instance, many businesses clone information from a cloud CRM to a local database for reporting purposes, such as accounts, contacts, and opportunities. Depending on how frequently the data changes, this might be done once every 15 minutes, once every hour, or once every day. Instead of taking a complete snapshot at each replication period, the replication process may recognize when data has changed in the publisher and duplicate only the changes.

Merge replication

Merge replication is a little more complicated than standard replication. Snapshot replication is used for the initial synchronization from the publisher. However, data modifications can take place at the publisher and subscriber levels in this fashion. The merging agent, installed on all servers, receives the updated data after that. The merging agent uses algorithms for resolving conflicts to update and distribute the data.

For instance, transactional replication would occur if a worker was online and revising a document directly stored on a cloud server (publisher) on their laptop or phone (subscriber). This is possible since the content is saved almost instantly. However, since the data was updated at the subscriber’s end, there would be contradictions if the document was downloaded from the cloud server and updated offline on the laptop or phone. Once online again, it would travel through a merging agent, which would compare the two files to update the document at the publisher using a conflict resolution procedure.

Merge replication is utilized in a variety of situations where a user does not always have direct access to the publisher, such as with mobile users who may go offline while the data is being updated. It would also be utilized if many subscribers had access to, updated, and synced the same data with the publisher or other subscribers at different periods. It might also be utilized when numerous subscribers are simultaneously updating the same publication data in pieces.

What is the data replication strategy?

The majority of database-based solutions monitor all database changes from the very beginning. Additionally, it creates a record for the same that is referred to as a log file or changelog. Every log file functions as a collection of log messages, each of which contains information such as the time, user, change, cascade effects, and manner of the change. The database then gives each of them a distinct position ID and keeps them in a chronological sequence depending on their IDs.

Enabling customer data compliance with identity-based retention

What are the three data replication strategies?

Although businesses may use many methods for replicating data, the following are the most typical replication strategies:

Full-table replication

Every transaction involves full-table replication, which ensures that all data, including new, updated, and existing data, is replicated. Hard-deleted data can be successfully recovered using this replication strategy, as can data from databases without replication keys.

Key-based incremental replication

Data that has changed since the last update is captured incrementally using keys. Keys are components found in databases that start data replication. This method works for databases that hold data records on distinct elements and concentrate on recent changes rather than historical values.

Log-based incremental replication

Log-based data replication is a technique in which essential changes are made and recorded in a log file or changelog. Only the backend databases for MySQL, PostgreSQL, and MongoDB support this data replication mechanism.

Database replication schemes

For database replication, the following replication schemes are employed:

Full replication

Full replication entails replicating the entire database across all nodes in the distributed system. This plan improves worldwide performance and data accessibility while maximizing data redundancy.

Partial replication

Based on the importance of the data at each site, partial replication happens when specific portions of a database are duplicated. As a result, the number of replicas in a distributed system can be anywhere between one and the precise number of nodes.

No replication

There is no replication when there is only one fragment on each distributed system node. The easiest data synchronization may be accomplished with this replication strategy, which is also the fastest to execute.

What are some advantages and disadvantages of data replication?

Data replication enables extensive data sharing among systems and divides the network burden among multisite systems by making data accessible on several hosts or data centers.

Data replication advantages 

Consistent access to data can be provided by using data replication. Additionally, it expands the number of concurrent users with data access. By combining databases and updating slave databases with partial data, data redundancies are eliminated. Additionally, databases are accessible faster with data replication.

• Reliability: A different site can be used to access the data if one system is unavailable due to malfunctioning hardware, a virus attack, or another issue.
• Better network performance: Having the same data in various places might reduce data access latency since the data is retrieved closer to the point where the transaction is being executed.
• Enhanced support for data analytics: Replicating data to a data warehouse enables distributed analytics teams to collaborate on shared business intelligence projects.
• Performance improvements for test systems: Data replication makes it easier to distribute and synchronize data for test systems that require quick data accessibility.

Data replication disadvantages

Large amounts of storage space and equipment are needed to maintain data replication. Replication is expensive, and infrastructure upkeep is complicated to preserve data consistency. Additionally, it exposes additional software components to security and privacy flaws.

Cloud costs have started to become a heavy burden for the IT sector

Organizations should balance the advantages and disadvantages of replication, even though it has many advantages. Limited resources are the key barrier to maintaining consistent data across an organization:

• Costs: It costs more to store and process data when copies are kept in several places.
• Time: A team within the organization must commit time to setting up and maintaining a data replication system.
• Dense network: Consistency across data copies necessitates new processes and increases network traffic.

Data replication tools

Data replication on your end may occur for a variety of reasons. You might require application migration to the cloud or be searching for a hybrid cloud solution. It depends on whether you need real-time setup analysis or replication for synchronization in your instance Understanding why you want to perform a replication in the first place is the first step.

Database replication technologies frequently provide a variety of replication functions, as well as other ancillary functionality. Your needs and expectations for the tool must be written down. It might depend on how many sources and targets are involved, how much data you’ll be dealing with, etc.

You must choose the best mix of appropriate elements for your circumstance. Your choice of database replication tools may be influenced by cost, features, and accessibility factors. A business should invest in its budget. Finding the best Database Replication solutions for the job within your allocated budget is the objective.

These are some of the best data replication tools:

Rubrik

Rubrik is a solution for managing and backing up data in the cloud that provides quick backups, archiving, immediate recovery, analytics, and copy management. It provides streamlined backups and incorporates cutting-edge data center technologies. You may assign tasks to any user group with ease, thanks to an intuitive user interface. The integration of different clusters into a single dashboard, which is necessary depending on the use case, has some limitations.

SharePlex

Another database replication tool that uses real-time replication is called SharePlex. The program is very flexible and works with many different databases. Fast data transport is available and extremely scalable thanks to a message queuing mechanism. Both the tool’s change data collecting process and its monitoring services have some shortcomings.

Hevo Data 

Data teams are essential to driving data-driven decisions as firms’ capacity to collect data grows exponentially. Even yet, they find it difficult to combine the disparate data in their warehouse to create a single source of truth. Data integration is a headache because of faulty pipelines, problems with the data quality, glitches, errors, and a lack of control and visibility over the data flow.

Hevo’s Data Pipeline Platform is used by 1000+ data teams to quickly and seamlessly combine data from more than 150 sources. With Hevo’s fault-tolerant architecture, billions of data events from sources as diverse as SaaS apps, databases, file storage, and streaming sources may be duplicated in almost real-time.

Key takeaways

• By synchronizing cloud-based reporting and facilitating data migration from many sources into data stores, such as data warehouses or data lakes, to provide business intelligence and machine learning, data replication supports advanced analytics.
• Users can retrieve data from the servers nearest to them and experience lower latency because data is stored in various locations.
• Data replication enables businesses to distribute traffic over several servers, improving server performance and reducing the strain on individual servers.
• Data replication offers effective disaster recovery and data protection. Millions of dollars can be lost each hour that a critical data source is down due to data availability.
• Depending on the use case and the current data architecture, businesses can employ a variety of data replication approaches.
• A data replication method can be expensive and time-consuming to invest in. To obtain a competitive edge and protect their data from downtime and data loss, it is crucial for businesses that wish to use data for a variety of analytical and business use cases.

FAQ

What is the difference between replication and backup?

For many companies that must maintain long-term data for compliance reasons, backup remains the go-to option.

But data replication focuses on business continuity—providing mission-critical and customer-facing programs with uninterrupted operations following a disaster.

What is data replication in DBMS?

The technique of storing data across multiple sites or nodes is known as data replication. It helps increase the accessibility of data. It merely entails copying data from a database from one server to another so that every user can get the same information without any discrepancies. As a result, users can access data pertinent to their duties without interfering with the work of others using a distributed database.

Data replication includes the continuous copying of transactions to keep the replicate up to current and synced with the source. In contrast, data replication makes use of several locations for data availability, but each relation only needs to be stored in one place.

What is data replication in SQL?

A group of technologies known as replication is used to distribute and replicate data and database objects from one database to another, then synchronize data between databases to ensure consistency. Replication allows you to send data through local and wide area networks, dial-up connections, wireless connections, and the Internet to many locations as well as distant or mobile users.

What is data replication in AWS?

By automatically transforming the source database schema, AWS SCT facilitates heterogeneous database migrations. The majority of custom code, including views and functions, are also converted by AWS SCT into a format appropriate for the target database.

There are two steps involved in heterogeneous database replication. You can duplicate data between the source and target using AWS DMS, or you can use AWS SCT to convert the source database schema (from SQL Server) to a format compatible with the target database, in this case, PostgreSQL.

Conclusion

Instances of database replication defined as master-slave configurations in the past are now more commonly described as master-replica, leader-follower, primary-secondary, and server-client configurations.

With the development of virtual machines and distributed cloud computing, replication techniques originally focused on relational database management systems have been broadened to cover nonrelational database types. Once more, different non-relational databases like Redis, MongoDB, and others use different replication techniques.

While horizontally scaling distributed database configurations, both on-premises and on cloud computing platforms, as well as remote office database replication may have emerged as drivers of replication activity, and remote office database replication may have been the canonical example of replication for many years. Relational databases like IBM Db2, Microsoft SQL Server, Sybase, MySQL, and PostgreSQL all have different replication specifications.

Data replication design always involves striking a balance between system performance and data consistency. At least three methods exist for database replication. In snapshot replication, data from one server is simply moved to another server or a different database on the same server. Data from two or more databases are merged into one database during merging replication. In addition, user systems receive full initial copies of the database in transactional replication and periodic updates as data changes.

• IDC defines data maturity as the degree to which an organization successfully uses data and incorporates it into decision-making.
• The IDC white paper investigates the relationship between an organization’s data maturity and its financial performance.
• In contrast to organizations with lower degrees of data maturity, those with the greatest levels reported 3.2 times as much revenue and 2.4 times as much profit.
• The study found that data maturity may increase every corporate result by up to 2.5 times, including net promoter scores, profits, operational efficiency, and customer loyalty and value.

Heap, a provider of digital insights, has released findings from its recently published sponsored IDC white paper, “How Data Maturity and Product Analytics Improve Digital Experiences and Business Outcomes.” The paper discovered that businesses with the highest levels of data maturity saw 3.2 times as much revenue and 2.4 times as much profit as businesses with lower levels of data maturity.

What is data maturity?

How effectively a business uses data and incorporates it into decision-making is defined by IDC as data maturity. Many firms are evaluating their data maturity to compare their data capabilities and advancement to their competitors and meet objectives like increased operational effectiveness and consumer loyalty.

Data maturity evaluations can be used to develop best practices for data and spot process gaps that prevent the achievement of these objectives. Additionally, product analytics technology can assist businesses in making better use of their data and advancing data maturity.

Heap-sponsored IDC white paper examines how people, procedures, and technologies affect an organization’s data maturity and business results. It is based on a survey of 626 product builders and data scientists.

According to the study, data maturity can lead to up to a 2.5x improvement in all company outcomes, including greater net promoter scores, earnings, operational efficiency, and higher customer loyalty and value.

Benefits of data maturity

Heap describes the following as the report’s main conclusions:

• Only 29% of the least mature organizations reported having a strong to excellent understanding of customer journey friction points, compared to 98.4% of the most mature firms.
• Compared to only 3% of less data-mature enterprises, over 84% of teams at mature organizations receive answers in minutes or hours.
• Compared to those who are lagging, leaders are more than twice as likely to say that using data for customization was either “easy” or “somewhat easy.”
• Only 3% of the least mature businesses have a single source of truth for data compared to 76% of top teams.
• Over 89% of top teams concur that their firm appreciates learning via experimentation, while 77% of lagging businesses feel the opposite.
• More than 80% of top-performing teams have completely automated data validation, well-defined access control procedures, and the capacity to manage data.

Heap’s CEO Ken Fine said, “In today’s challenging market, efficient growth drivers are more important than ever. [The]‘How Data Maturity and Product Analytics Improve Digital Experiences, and Business Outcomes’ white paper indicates that data-driven insight is a catalyst for business progress leading to profit and revenue increases, shorter time to market, better NPS scores, and improved operational efficiency.”

The article identifies areas for development while highlighting data maturity’s potential benefits. Data access is one problem, and while leading companies claimed to have “strong” or “complete” access to data, just slightly more than half of the businesses in the early part of their data maturity journeys said the same.

Business processes need data management for their continuous improvement

Another noteworthy point is the underutilization of data, with 73% of businesses saying they believe they might use their data more effectively. Data is only used by 70% of participants to gauge the success or failure of key efforts, and 69% said that the highest-paid individual makes decisions with little to no respect for data.

According to the study, less developed firms are paradoxically more content with the data they have access to, and 70% of companies that are falling behind think they are on the level with or even ahead of their rivals regarding data maturity. The organizations falling behind also lack the tools to catch up, with 65% claiming they cannot pinpoint specific user-friction points.

Heap was established to improve user data consumption. The company’s founders, Matin Movassate and Ravi Parikh, created an end-to-end data collecting and analytics system to allow businesses to learn from the user information associated with their web and mobile domains.

Machine learning makes life easier for data scientists

The platform uses an ontological framework to semantically designate which interface elements need to be monitored after automatically gathering data on user interaction. Data is visually represented on the same interface where it is collected for simpler querying.

Users can use multiple analytical methods, such as funnel, retention, and cohort analysis, to apply the labels retroactively on top of their raw datasets to reveal insights they may not have been looking for. Additionally, the software provides an “effort analysis” tool that automatically identifies the interface elements with which users are having the greatest difficulty and explains why.

Heap claims that because data gathering and labeling are separated on its platform, data governance—a driving force in data maturity—is simpler to achieve. The business claims that because data from every event automatically fit into a data structure as soon as it is acquired, these characteristics ensure that data is maintained accurately and orderly.

According to Heap’s sponsored IDC white paper, businesses must invest in digital product analytics technologies to improve their data maturity and business outcomes to compete in today’s digital landscape with demanding customer experience expectations.

Although there are still some data access and governance issues, these solutions can offer the insight and automation required to get the most out of digital projects.

David Wallace, IDC Research Director, Customer Intelligence and Analytics, said,  “These findings should be a wakeup call for businesses who want to efficiently grow their business and retain their customers for longer periods of time. As reported, businesses that are most data mature enjoy a 3x increase in revenue versus companies that are the least data mature. Our study illustrates the importance of data maturity and why a data-driven culture is critical to success today, under any market conditions.”

Active learning (AL) is a key technique for most supervised machine learning models because they need a lot of data to be trained in order to operate properly. Most businesses have trouble giving data scientists access to this data, specially tagged data. The latter is essential for any supervised model to be trained and can end up being the main bottleneck for any data team.

Data scientists are frequently given large, unlabeled data sets and asked to use them to develop effective models. It becomes very difficult for data teams to train strong supervised models with that data since the volume of data is typically too enormous to label manually.

What does active learning mean in machine learning?

The abundance of unlabeled data is a major issue in machine learning since it is becoming increasingly affordable to collect and store data. Data scientists are now faced with more data than they can ever process. Active learning can come in handy at this point.

The algorithm actively chooses the subset of instances from the unlabeled data that will be labeled next in active learning. The basic idea behind the active learner algorithm is that if an ML algorithm were given free rein to select the data it wishes to learn from, it could achieve greater accuracy while utilizing fewer training labels.

As a result, during the training phase, active learners are welcome to ask questions interactively. These requests are typically sent as unlabeled data instances, and a human annotator is asked to label the instance. As one of the most effective examples of success in the human-in-the-loop paradigm, AL is now included in that paradigm.i

What is active learning?

A form of machine learning known as “active learning” allows learning algorithms to engage with users to categorize data with desired outcomes.

Interesting academical research called “A Survey of Deep Active Learning” stresses the efficiency provided by the AL techniques:

“Active learning (AL) attempts to maximize the performance gain of the model by marking the fewest samples. Deep learning (DL) is greedy for data and requires a large amount of data supply to optimize massive parameters, so that the model learns how to extract high-quality features.”

What are active learning and passive learning in machine learning?

Most adaptive systems in use today are constructed using active or passive learning strategies. Using a change or shift detection test, the learning model is updated using the active learning approach depending on the identified shift in the data stream. The learning system is continuously updated in a passive learning approach since it is assumed that the environment is constantly changing. There is no shift detection test necessary.

The touchstone of machine learning: Epoch

The phrase “active learning” is typically used to describe a learning system or problem where the learner has some control over selecting the data that will be utilized to train the system. This contrasts with passive learning, in which the learner is merely given access to a training set without other options.

What are the 3 types of learning in machine learning?

To teach a machine to learn and make predictions, detect patterns, or classify data, a lot of data must be fed to it. The type of machine learning is determined by the algorithm, which functions somewhat differently. Supervised, unsupervised, and reinforcement learning are the three different types of machine learning.

Supervised learning

According to Gartner, supervised learning will continue to be the most popular machine learning technique among enterprise IT professionals in 2022. This kind of machine learning feeds historical input and output data into machine learning algorithms, with processing added in between each input/output pair to enable the system to change the model and provide outputs that are as feasible to the intended outcome. Typ supervised learning techniques are typical neural networks, decision trees, linear regression, and support vector machines.

This type of machine learning is called “supervised” learning because you feed the algorithm information to aid in learning while it is being “supervised.” The remainder of the information you supply is used as input features, and the output you give the system is labeled data.

For instance, you might provide the machine with 500 instances of clients who defaulted on their loans and another 500 examples of clients who didn’t if you were seeking to learn about the connections between loan defaults and borrower information. The machine determines the information you’re looking for under the “supervision” of the tagged data.

This ML algorithm identifies undiagnosable cancers

Several commercial goals, such as sales forecasting, inventory optimization, and fraud detection, can be addressed with supervised learning:

• Determining the degree of fraud in bank transactions
• Assessing the riskiness of potential borrowers for loans
• Estimating the price of real estate
• Identifying disease risk elements
• Predicting the failure of mechanical components in industrial equipment

Unsupervised learning

Unsupervised learning doesn’t employ the same labeled training sets and data as supervised learning, which requires humans to assist the machine in learning. Instead, the machine scans the data for less evident patterns. This type of machine learning is particularly useful when you need to find patterns and use data to make judgments. Hidden Markov models, k-means, hierarchical clustering, and Gaussian mixture models are common unsupervised learning algorithms.

Let’s imagine, using the supervised learning scenario, and you had no idea which clients had defaulted on their debts or not. Instead, after receiving borrower data, the machine would analyze the data to identify patterns among the borrowers before clustering them into different groups.

Predictive models are frequently developed using this form of machine learning. Additionally, clustering and association, which identify the rules that exist between the clusters, are common uses. Clustering builds a model that groups things based on particular attributes. Several instances of use cases include:

• Grouping customers based on their buying habits
• Grouping inventories based on manufacturing and/or revenue metrics
• Identifying relationships in customer data

Reinforcement learning

The machine learning method that most closely resembles human learning is reinforcement learning. By interacting with its environment and receiving good or negative rewards, the algorithm or agent being employed learns. Deep adversarial networks, Q-learning, and temporal differences are common algorithms.

Recalling the bank loan client example, you may examine customer data using a reinforcement learning system. The algorithm receives a benefit if it labels them as high-risk and they go into default. They receive a negative reward from the algorithm if they don’t default. Both examples ultimately aid machine learning by improving its awareness of the issue and its surroundings.

This self-driving car remembers the past using neural networks

Since most firms lack the necessary computing power, most ML platforms do not have reinforcement learning capabilities, according to Gartner. Reinforcement learning can be used in situations that can be fully simulated, are immobile, or have a lot of pertinent data. This machine learning type is simpler to utilize when working with unlabeled data sets because it involves less management than supervised learning. This kind of machine learning is still being used in actual applications. Examples of certain uses are as follows:

• Teaching vehicles how to park and drive autonomously
• Adjusting traffic lights dynamically to ease congestion
• Using unprocessed video as input to teach robots how to follow the rules so they can copy the behaviors they observe

Bonus: Semi-supervised learning

A learning problem known as semi-supervised learning uses many unlabeled instances and a limited number of examples with labels.

These learning problems are difficult because neither supervised nor unsupervised learning algorithms can effectively handle combinations of labeled and untellable data. This necessitates the use of specific semi-supervised learning methods.

Semi-supervised learning bridges supervised and unsupervised learning techniques to address their main problems. With it, you initially train a model on a small sample of labeled data before applying it repeatedly to a larger sample of unlabeled data.

• Semi-supervised learning, as opposed to unsupervised learning, is effective for a wide spectrum of problems, including clustering, association, regression, and classification.
• Contrary to supervised learning, the method uses both large amounts of unlabeled data and small amounts of labeled data, which lowers the cost of manual annotation and shortens the time required for data preparation.

Is active learning supervised or unsupervised?

The technique of prioritizing the data that has to be labeled to have the most influence on training a supervised model is known as active learning. When there is too much data to label, and smart labeling needs to be prioritized because there is too much data to label, AL can be utilized.

So yes, active learning is a form of supervised learning and aims to perform as well as or better than “passive” supervised learning while being more efficient with the data gathered or used by the model.

What are the benefits of active learning?

Labeling data costs less time and money than active learning. Across a wide range of applications and data sets, from computer vision to NLP, active learning has been shown to yield significant cost savings in data labeling. This alone should be a sufficient explanation because data labeling is one of the most expensive components of training contemporary machine learning models.

Natural language processing fosters new protein designs

Model performance feedback is provided more quickly with active learning. Typically, data is labeled before any models are trained, or feedback is received. It frequently takes days or weeks of iterating on annotation standards and re-labeling before it is realized that the model’s performance is woefully inadequate or that differently labeled data are necessary. AL allows for frequent model training, which enables feedback and error correction that would otherwise have to wait until much later.

With active learning, your model will be more accurate in the end. People are frequently taken aback by the fact that active learning models can converge to superior final models while learning more quickly (with less data). It’s simple to forget that the data quality is just as important as the number because we are constantly informed that more data is better. The performance of your final model may actually suffer if the dataset contains samples that are difficult to categorize appropriately.

How the model interprets the examples is also a crucial point. Curriculum learning is a whole branch of machine learning that investigates how teaching simple concepts first rather than complex ones can enhance model performance. For instance, “arithmetic before advanced calculus” comes to mind. Your models will automatically adhere to the curriculum through AL, improving overall performance.

What are the challenges of active learning?

Active learning is a potent technique for solving contemporary machine learning challenges. However, applying conventional active learning approaches in real-world settings comes with several difficulties because of the underlying presumptions. There are some obstacles, such as the active learning algorithm’s initial lack of labels, the querying process’ reliance on unreliable external sources of labels, or the incompatibility of the processes used to assess the algorithm’s effectiveness.

The cold-start problem, which happens when labels are missing during the training of an initial model, is a struggle in active learning frameworks. A suggestion is to combine uncertainty and diversity sampling into a single procedure to choose the best representative samples, as the initially labeled dataset is one of the choices.

If you want to learn more about the challenges of active learning, we recommend you to read this article called “Addressing practical challenges in Active Learning via a hybrid query strategy.”

Key takeaways

• Active learning uses your model to choose which points you should label next to improve that model the most.
• The main objective is to increase the value of each data point we manually classify in terms of accuracy down the line.
• It performs only relatively better than a manual labeling strategy that is more conventional.
• Many additional techniques, such as weak supervision, data augmentation, and semi-supervised learning, provide far higher performance benefits in terms of man-hours to downstream accuracy.
• Finally, to improve those approaches, we can also combine AL with weak supervision, data augmentation, and semi-supervised learning.

Conclusion

Deep learning has transformed how computer vision issues are solved. However, the key barrier preventing applications in numerous sectors is the lack of high-quality labeled data (like biomedicine). Unstructured data are abundant in the current era of information technology, although data labels are not easily accessible. This necessitates the requirement for a productive semi-supervised strategy that can make use of this huge pool of unlabeled data as well as a few labeled samples.

The answer to this is active learning, which, based on the reinforcement learning literature, enables the machine to choose which sample from a pool of unlabeled data points a user can label to improve predicted performance. There are many ways to weigh the value of labeling samples, and each has its advantages and disadvantages.

Silicon image sensors enable faster image processing

Several AI paradigms, including NLP and audio processing, as well as challenging computer vision tasks like image segmentation, scene identification, etc., have successfully utilized AL. The current focus of research on active learning systems is on requiring even fewer labeled samples while retaining prediction performance equivalent to or superior to traditional supervised learning techniques (or selecting the best sample with the lowest computational cost).

For its first post-pandemic, fully on-site comeback, Data Natives Conference will guide 5000 expected visitors on a hybrid tour to explore the galaxy from the vantage point of data science. On five stages, the event will be hosted by Dataconomy, Europe’s leading portal for news, content and expert opinion from the world of data-driven technology.

Superstars of the data science and artificial intelligence ecosystem, startup enthusiasts, innovative corporations, decision-makers and thought leaders will meet in Berlin from 31 August to 2 September.

The best-known data science unicorns and enthusiasts from around the world, young aspiring founders and data scientists, will embark on a unique journey to the galaxy of Data Science and AI. They will gather for learning, connecting, and collaborating with a particular focus on Blockchain and Web3 on day three.

Three days of content

• Day One: AI Ethics and Sustainability
• Day Two: Healthcare and Social Issues
• Day Three: New Content on the verge of data and blockchain, web three, and the Metaverse

Data Natives’s mission is to connect data experts, inspire them, and let people become part of the equation again.

The story of Data Natives began in 2015 when Greek entrepreneur Elena Poughia found her purpose in Berlin: to create a place to go for the European Data Science Community. As a data enthusiast herself, she believes in leveraging data to make our world a better place. During the past few years, she established and shaped a unique conference concept: Data Natives became an annual 3-day data experts festival with keynote speakers, panels, networking concepts, startup pitches, and an epic Berlin party finale. On its growth journey, the Data Natives team successfully onboarded the important players needed to build a future society with good use of Data and AI: corporations, science, academia, politics, media, the design industry, and the startup community. It also endeavoured to bridge the gaps between corporations and startups, matching data scientists and AI experts with companies, and making an impact through Data Design Thinking and Data Visualisation.

The Data Natives community is diverse, but united by a shared purpose: to contribute to building a sustainable future with the help of data and machine learning.

DN22 confirmed speakers

For this year, Data Native’s confirmed speakers already include: Caroline Lair, Founder at The Good AI, Mike Butcher, Editor-at-large at TechCrunch; Clara Rodríguez Fernández, deep tech reporter at Sifted, Mina Saidze, Lead Data Analytics & Tech Evangelist at Axel Springer SE, Lubomila Jordanova, CEO & Co-Founder Plan A & Co-Founder at Greentech Alliance, Berlin Chief Digital Officer Ralph Kleindieck, and many more international data natives and AI experts, leaders, and thought leaders in data science, Artificial Intelligence, Sustainability, Blockchain and Web3.

The conference will kick off from its main stage with the topic of Ethical AI, with Kenza Ait Si Abbou, Director Client Engineering DACH at IBM, followed by workshops, networking, conference sessions, matchmaking, and an exhibition.

What can you expect from this year’s conference? Here are the for tracks designing the content

Data Natives 2022 will delve into how data can transform economies and create opportunities, hosting thinkers, doers, and innovators whose focus is shaping a brighter future for all. Also, we will talk about Phyton, NLP, transformer networks, and augmented analytics. All this while interacting with the freshest startups in the ecosystem.

We aim to bring data and tech professionals and established companies together to collaborate, diversify, and ease the transition into the Blockchain space by sharing their thoughts on the future of tech trends, changes, and inspirational stories. Four tracks design the conference’s content:

1. Future Society

The Future Society track hosts thinkers, doers, and innovators whose focus is shaping a brighter future for all. Whether through impact initiatives, data, and AI governance, or any other emerging tech, this stage is for those who want to change the world.

2. StartUp

Cutting through all the usual conference jargon in startup presentations, we bring you unfettered insights from the freshest startups throughout the conference.

3. Dataconomy

Let’s talk business – the debates of the Data Economy track are meant for those who want to know how to ethically use data to transform economies and create opportunities for business growth.

4. Data Science

Deep dives into Python, NLP, transformer networks, AutoML, graph databases, augmented analytics & smart passion projects live in our Data Science Track. Most of our attendees flock to the Data Natives conference to learn something new, and this track is where that happens.

DN22 Full agenda

Please check the full agenda on our website:

https://agenda.datanatives.io

Data Natives is made possible by the generous help and support of our sponsors and community partners: IBM, SAP, Big Bang Food, Siemens Energy, Neti, Penfabric, Popsinc, Paretos, HowtoHealth, SMP,  Factory Berlin, Hands on Data, Imdena, ICT Spring, Sesamers, Sigma Squared, Uhlala Group, Women in Data, Developer Nation, Helsinki Data Science Meetup, Womens Authors of Achievement, W all Woen, Contextual Solutions, Nections and many more.

We can’t wait to see you!

WHEN?

Wednesday, August 31, 2002, Thursday, September 1, 2002 and Friday  September 2, 2022, 9:00 AM –6:00 PM

WHERE?

Kühlhaus Berlin
Luckenwalder Straße 3, 10963 Berlin info@kuehlhaus-berlin.com

Kühlhaus is not only beautiful, but extremely easy to get to. It’s a one-minute walk from U1+U2 Gleisdreieck, and a few minutes away from U7, various S-Bahn, and buses.

Press Contact: ch@harthcommunications.com

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

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

Machine learning vs data science: What is the difference?

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

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

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

What is data science?

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

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

Check out and learn about data science:

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

Data scientist skills

Skills needed to become a data scientist:

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

What is machine learning?

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

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

Check out and learn about machine learning:

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

Machine learning engineer skills

Skills needed to become a machine learning engineer:

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

Comparison: Data science vs machine learning

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

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

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

Data scientists vs machine learning engineers

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

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

Machine learning vs data science salary

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

Similarities: Data science vs machine learning

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

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

Check out what programming language for artificial intelligence is the best

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

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

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

What is machine learning in data science?

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

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

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

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

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

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

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

Should you learn data science or machine learning first?

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

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

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

Which is better, data science or machine learning?

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

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

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

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

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

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

Can a data scientist become a machine learning engineer?

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

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

Conclusion

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

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

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

The lengthy and prone-to-failure communication between the provider, intermediary, and customer in the insurance industry makes the advancement of digitization a crucial debate. In the German insurance market alone, more than 500 insurance companies and over 45,000 brokers are in operation.

The several branches are in charge of managing policies totaling 1.7 trillion euros. However, the insurance industry uses a lot more paper documents than almost any other industry.

Insurance firms can no longer limit themselves to handling and processing claims that have already happened. Long-term success is ensured through foresight and adaptability. Additionally, hackers and cyberattacks are increasingly focusing on the massive volumes of data gathered. And the insurance industry is not the only one at risk; we’ve also discussed how rising cybersecurity risks threaten the healthcare industry.

Established insurers can succeed in keeping up with market demands and winning customers’ trust by embracing digitization and the newest information technologies.

The measures also impact the internal structure in the form of cost savings and process improvement, in addition to the favorable impact on the client.

More than 15 years ago, the business initiative for process improvement (BiPro EV) was established. Their ultimate objective is to standardize processes and interfaces while streamlining interaction between brokers and insurers.

The project is supported by a sizable number of insurance providers and producers of brokerage products. Together, standards are formed and defined to digitize cross-company procedures eventually.

EU employers shouldn’t miss the workplace digitization opportunity

The 430.0 standard is one of the most well-known standards. The correspondence to the brokers as well as to the end clients was effectively automated with the cross-company mailboxes. Documents, policies, and other correspondence are processed independently of the different portals inside a single interface.

The role of data analytics

Data is at the heart of every insurance policy. They are practically worthless if they are left unfiltered. The subject of data analytics is receiving a lot of funding as a result of digitization and big data. This division’s responsibility is to make the data useful.

They must be used to develop decision templates, risk analyses, and the ability to assess contract and damage data. However, due to the extremely heterogeneous structure of the current system landscape, interfaces between the systems must first be developed.

Big data is futuristic from a variety of angles. Based on repeating trends, a working piece of software can identify incidents of fraud early on and sound the alert. Security is boosted by automatic image recognition and database comparison.

On the other hand, the pricing may be more flexible and individualized for each client. It is feasible to divide the previous insurance industry history into several risk or damage classes by examining it.

Data science conquers the customer journey and sales funnel

Last but not least, using artificial intelligence and data analysis benefits customers. Response times lengthen as the manual processing of questions requires less effort. Insurance providers can take far more initiative and offer lasting value.

Mobile services are driving digitization

The age of digital transformation has seen a tremendous change in consumer behavior. Video calls are more popular than ever as a replacement for consultations in dingy offices. The sector is being revolutionized by quick methods of communication like chatbots with artificial intelligence or even mobile chat apps.

Since several startups have realized this, they are operating as entirely online insurance. All current company procedures and data must be transformed into user-friendly digital products. All tariffs, premiums, and special services are summarized for the brokers. They can still provide consumers with professional advice using this and a tablet.

Customers can easily view and sign their contracts online. The private customer business, however, considers even more. Existing clients can obtain invoices, make damage complaints, and compare offers thanks to the digitization of operations.

The insurance industry will undergo numerous changes in the future. Accident reconstruction is made possible by cutting-edge software and virtual reality. The blockchain allows for the safe keeping of private contract information, and the sensor-driven Internet of Things provides previously unheard-of insights into consumer behavior.

Data science is crucial for marketing, and Konnecto uses different instruments to analyze consumer behavior. Suggesting COVID-19 has permanently changed consumer attitudes and habits would be an understatement. A startling 67 percent of consumers claim that since the pandemic began, their online spending has increased. Additionally, there were 900 million more online users in 2021 than in 2020, an increase of around 4.5 percent annually.

However, one concern remains as marketers look ahead to a world without pandemics: how can businesses stay up with the always-evolving consumer journey?

Konnecto uses data science to identify weaknesses in a customer journey

The pandemic and recent privacy law reforms in the EU and the US have altered how marketers track their online customers. The pandemic drove more customers online, upending the traditional sales funnel. As a result, there is now a market for companies like Konnecto, a platform for consumer journey analytics that tracks customer journeys using data science rather than third-party cookies.

From telemedicine to financial services, consumer experiences that used to take place offline are now taking place online. And because more customers are searching online, on social media, and various other places to get answers to their questions, brands don’t really have any idea at which point in the journey the customer decided to leave and choose their competitor, “Erez Nahom, the CEO of Konnecto, told VentureBeat.

Brands are using consumer intelligence solutions to comprehend market dynamics and take preventative action to avoid playing the guessing game. These tools can assist organizations in determining the most effective ways to interface and communicate with their customers to satisfy growing customer demands and preserve client loyalty.

However, Konnecto, according to Nahom, identifies the most important weaknesses in a brand’s customer journey and offers precise, prescriptive advice to maximize business results, as opposed to piecing together data and metrics from several platforms.

“Brands that work with Konnecto won’t need to run queries or take a deep dive into their data.” They’ll get daily recommendations across their digital marketing investments that will tell them what to do and why, with complete compliance with global privacy regulations, “said Nahom.

Reverse engineering customer journeys that led to conversions with a brand, its competitors, or on a marketplace accomplishes this.

“We essentially go from the moment of transaction backward all the way to the early funnel to the first interaction that consumers have with the brand,” Nahom added.

Konnecto has assisted several Fortune 500 organizations, including Coca-Cola, MassMutual, eToro, Lego, and Mercedes-Benz, by providing them with crucial behavioral data and highly targeted recommendations to increase online sales and enhance marketing ROI.

Konnecto’s clients have tripled, and its revenue has grown by more than 500% in the last six months. The Israel-based company recently received $21 million in series A fundraising from a group of investors that included TPY Capital, Mindset Ventures, Differential Ventures, SeedIL Ventures, and Magna Capital Partners. PeakSpan Capital served as the lead investor in the deal. The business intends to use the money from its most recent investment round to keep investing in R&D and expand its infrastructure to keep up with demand for its expanding platform.

“The main goal for us right now is to improve the existing models that we have and build additional models that can essentially find more vulnerability points in more datasets and create more accommodations for different teams,” explained Nahom. Check out the most popular data science techniques of 2022 before you leave to seek more insightful information on your customers’ journey.

Data science techniques, applications, and tools allow organizations to extract valuable insights from data.

The evolution of data science and advanced forms of analytics has created significant change for companies. The conditions were created for the emergence of various applications that provide deeper insights and business value.

While data science was once considered the risky and even more nerdy side of IT, it has now become the cornerstone of the working principles of any organization.

How are data science techniques used today?

Modern data science techniques offer the capabilities needed to crunch and analyze large data pools for a wide variety of applications, including predictive modeling, pattern recognition, anomaly detection, personalization, speech-based AI, and autonomous systems.

Many organizations today rely on data science-based analytics applications, mostly focused on areas that have proven their worth over the past decade. By using the power of data, organizations can gain competitive advantages against their competitors, serve their customers better, and gain the ability to react more effectively to rapidly changing business environments that require constant adaptation.

Let’s take a closer look at the most popular data science techniques that have already become the cornerstone of the business world:

Anomaly detection

Anomaly detection, one of the most popular data science techniques, uses statistical analysis to detect anomalies in large data sets. While it is a simple practice to fit data into clusters or groups, then spot outliers when dealing with small amounts of data, this task becomes a real challenge when it is petabytes or exabytes.

Financial services providers, for example, are finding it increasingly difficult to detect fraudulent spending behavior in transaction data, which continues to grow tremendously in volume and diversity. Anomaly detection applications are also used to eliminate outliers in datasets to increase analytical accuracy in tasks such as preventing cyber-attacks and monitoring the performance of IT systems.

Pattern recognition

Recognizing repetitive patterns in datasets is a fundamental data science task. For example, pattern recognition helps retailers and e-commerce companies detect trends in customer purchasing behaviors. Organizations need to make their offerings more relevant and ensure the credibility of their supply chain to keep their customers happy and prevent customer churn.

Giant retailers serving tens of millions of customers today have long used data science techniques to discover purchasing patterns. In one of these studies, a retailer noticed that many customers shopping in anticipation of a storm or tropical storm bought a particular brand of strawberry biscuits and took advantage of this invaluable information to change its sales strategy. This resulted in increased sales. Such unexpected correlations are made possible by recognizing data patterns. The insights created from data help create more effective sales, inventory management, and marketing strategies.

Pattern recognition also helps improve technologies such as stock trading, risk management, medical diagnosis, seismic analysis, natural language processing (NLP), speech recognition, and computer vision.

Predictive modeling

Data science makes predictive modeling more accurate by detecting patterns and outliers. While predictive analytics has been around for decades, data science techniques today create models that better predict customer behavior, financial risks, and market trends. It also applies machine learning and other algorithms to large datasets to improve decision-making capabilities.

Predictive analytics applications are used in various industries, including financial services, retail, manufacturing, healthcare, travel, utilities, and many others. For example, manufacturers use predictive maintenance systems to help reduce equipment failures and improve production uptime.

Aircraft manufacturers rely on predictive maintenance to improve their fleet availability. Similarly, the energy industry is using predictive modeling to improve equipment reliability in environments where maintenance is costly and difficult.

Organizations are also leveraging the predictive ability of data science to improve business forecasting. For example, formulaic approaches to purchasing by manufacturers and retailers have failed in the face of the sudden shifts in consumer and business spending caused by the COVID-19 pandemic. Innovative companies have overhauled these fragile systems with data-driven forecasting applications that can better respond to dynamic customer behaviors.

Recommendation engines and personalization systems

Customers are very satisfied when products and services are tailored to their needs or interests and when they can get the right product at the right time, through the right channel, with the right offer. Keeping customers happy and loyal gives them enough reasons to choose you again. However, tailoring products and services to the specific needs of individuals has traditionally been very difficult. It used to be a very time-consuming and costly task. This is why most systems that customize offers or recommend products need to group customers into clusters that generalize their features. While this approach is better than no customization, it is still far from optimal.

Fortunately, combining data science, machine learning, and big data allows organizations to build a detailed profile of individual customers and users. Systems can learn people’s preferences and match them with others with similar preferences. This is the working principle of the hyper-personalization approach.

Popular streaming services, as well as the largest retailers today, are using data science-driven hyper-personalization techniques to better focus their offerings on customers through recommendation engines and personalized marketing. Financial services companies also offer hyper-personalized offers to clients, while healthcare organizations use this approach to provide treatment and care to patients.

Investing heavily in its recommendation engine and personalization systems, Netflix uses machine learning algorithms to predict viewer preferences and deliver a better experience. The streaming service uses a recommendation engine that influences critical data touchpoints such as browsing data, search history, user ratings on content, and device information to provide customers with relevant recommendations through a hyper-personalized homepage that differs for each user.

Emotion, sentiment, and behavior analysis

Data scientists probe data stacks to understand the emotions and behaviors of customers or users using the data analysis capabilities of machine learning and deep learning systems.

Sentiment analysis and behavioral analysis applications allow organizations to more effectively identify customers’ buying and usage patterns, understand what people think about products and services, and how satisfied they are with their experience. These approaches can also categorize customer sentiment and behavior and reveal how they change over time.

Travel and hospitality organizations are developing strategies for sentiment analysis to identify customers with very positive or negative experiences so they can respond quickly. Law enforcement also uses emotion and behavior analysis to detect events, situations, and trends as they arise and evolve.

Classification and categorization

Data science techniques effectively sort large volumes of data and classify them according to learned features. These capabilities are especially useful for unstructured data. While structured data can be easily searched and queried through a schema, unstructured data is very difficult to process and analyze. Emails, documents, images, videos, audio files, texts, and binary data are unstructured data formats. Until recently, searching this data for valuable insights was a huge challenge.

The advent of deep learning, which uses neural networks to analyze large data sets, has made it easier for organizations to perform unstructured data analysis, from image, object, and voice recognition tasks to classifying data by document type. For example, data science teams can train deep learning systems to recognize contracts and invoices among document stacks and identify various types of information.

Government agencies are also interested in classification and categorization practices powered by data science. A good example is NASA, which uses image recognition to reveal deeper insights into objects in space.

Chatbots and voice assistants

One of the earliest applications of machine learning was the development of chatbots that could communicate like real humans without any intervention. Designed by Alan Turing in 1950, the Turing Test used the speech format to determine whether a system could mimic human intelligence. So it’s hardly surprising that modern organizations are looking to improve their existing workflows by using chatbots and other conversational systems to delegate some tasks previously handled by humans.

Data science techniques have been extremely useful in making speech systems useful for businesses. These systems use machine learning algorithms to learn and extract speech patterns from data. Powered by advanced natural language processing technology, chatbots, smart agents, and voice assistants now serve people everywhere, from phones to websites and even cars. For example, it provides customer service and support to find information, assist with transactions, and engage in both text-based and voice-based interactions with people.

Autonomous systems

Speaking of cars, one of the dreams that the artificial intelligence field has been trying to achieve for a long time is driverless vehicles. Data science plays a huge role in the ongoing development of autonomous vehicles, as well as AI-powered robots and other intelligent machines.

There are numerous challenges in making autonomous systems a reality. In a car, for example, image recognition tools must be trained to identify all elements. The list goes on and on, in the form of roads, other cars, traffic control devices, pedestrians, and anything else that can affect a safe driving experience. Moreover, driverless systems must know how to make snap decisions and accurately predict what will happen based on real-time data analysis. Data scientists are developing supporting machine learning models to help make fully autonomous vehicles more viable.

The following article explains how data scientists can access datasets distributed through Web 3.0 technology, earning bounties of up to $25,000 USD for data science work. Engage with a global data science community to explore blockchain data exchange, solve data challenges, and learn how to become part of a Web 3.0 data economy powered by Ocean Protocol.

Asset Tokenization on the blockchain

Blockchain technology disrupted and evolved the Web 2.0 internet to Web 3.0. While Web 2.0 offers an exchange of information through intermediaries, Web 3.0 allows for a trustless exchange of value between two parties. On blockchain networks, the mean of exchange is tokens. They can represent various forms of value, including money (Bitcoin), ownership (NFTs), and utility (OCEAN).

The superpower of blockchains to tokenize assets helps to regulate proof-of-ownership and improves transparency. The recent multi-billion-dollar speculation around non-fungible tokens (NFTs) representing unique ownership over digital art and collectibles proves that Web 3.0 is experiencing unprecedented attention. While digital art was likely just the beginning for blockchain tokens, many use-cases still lie in the dark, waiting to be explored.

One such largely unknown use case for blockchain is the valuation, monetization, and exchange of datasets that Ocean Protocol initiated. 

The Web 3.0 data economy

Blockchains can be used to tokenize non-traditional assets, like data and intellectual property. These tokens can then value, monetize and transfer assets that couldn’t be exchanged previously. 

Ocean Protocol is a pioneer in this domain and deployed token standards across many blockchains to incentivize data owners to publish their datasets and intellectual property to an open-source data marketplace called the Ocean “Onda” V4 Data Market. Ocean’s technology pairs blockchain tools like decentralized finance (DeFi) to allow data to become a decentralized asset class. Effectively, Ocean Protocol’s technology makes datasets investable. Data owners can create ‘Datatokens’ for their datasets, a form of blockchain tokens that regulate the value and the access to their data. Speculators can also trade data tokens. Therefore, the Ocean Data Marketplace could be compared to a stock exchange for data assets, while it also provides the opportunity for data scientists to access data.

In summary, tokenizing datasets on the Ocean Marketplace help data owners to offer their data assets to buyers and investors. While data consumers can spend Datatokens to gain access to the underlying dataset for their purpose, speculators can trade Datatokens for profit. The supply and demand of the Datatokens determine the price of the underlying dataset. This unique concept gathered attention from various institutions, including Mercedes-Benz and the World Economic Forum. 

Blockchain data challenges

Publishing data openly is not a common practice among companies around the world. While many executives believe their data holds the company’s dearest secrets and needs to be protected, they miss out on untapped potential economic value. One example is Goldcorp, a near-bankrupt business that released a 400MB .ZIP file of geographic drilling data to the world with the plea to submit potential mining sites. In return, Goldcorp offered data scientists a reward for every submission. More than 80% of the submissions led to the exploration of profitable sites, and Goldcorp grew to become a leading multi-billion-dollar mining business. Releasing open data to a global audience of data scientists with the right incentives creates incredible economic value.

Ocean Protocol, a pioneer within the Web 3.0 data economy, shares the vision of unlocking data and aims to bridge the gap between blockchain technology and data scientists.

Currently, there are several tokenized datasets on the Ocean ONDA Data Market, including a dataset related to consumer browsing data with over 230M data points from Decentr Network. 

In collaboration with data publishers, Ocean Protocol recently released data bounties that incentivize the global data science community to solve real-world problems and create economic value for data owners that tokenize data on the Ocean Marketplace. Challenges include writing an algorithm for a dataset, establishing insights into the data, or determining whether the data could be valuable for other use-cases. Data challenges will generate real-world economic value on top of Ocean Protocol’s blockchain data marketplace. While exploring a variety of datasets, data scientists can also use other Ocean Protocol features, such as Compute-to-Data, and receive additional rewards.

Learn how to engage with the Ocean Marketplace with Data Whale’s Tutorials or visit Ocean Protocol’s website. 

In today’s article we will explain what are data points and their synonyms. We’ll also clarify how unit of observation is utilized in addition to types of data points. For digital marketing analytics, there are some important data point categories professionals need to be aware of. Finally we will learn differences between a data point, data set, data field and so on.

Data points are the same for big data, just as we define “family” as the smallest social unit. When we look at data processing technology history, it is all about how we use data points. Accordingly, professions such as data architect and data engineer are on the rise. They appear so basic at first glance that many experts simply ignore them. Data points, however, can be challenging because of restricted visibility at the level of data collection and ineffective exclusion through aggregations.

What are data points?

Any fact or piece of information is a data point, generally speaking.

A discrete unit of information is called a data point. Any single fact is a data point, broadly speaking. A data point can be quantitatively or graphically represented and is typically produced from a measurement or research in a statistical or analytical context. The singular form of data, or datum, is roughly equal to the term “data point.”

Be careful; data points should not be confused with informational tidbits obtained by data analysis, which frequently combines data to derive insights but is not the actual data point.

Another data point definition

A data point (also known as an observation) in statistics is a collection of one or more measurements made on a single person within a statistical population.

Data points synonym

Here’s a list of similar words for data points; data, facts, detail, points, particularity, particulars, niceties, circumstance, elements, specifics, statistics, components, traits, instances, counts, aspects, technicalities, units, specifications, facets, features, specialties, members, schedules, things, ingredients, singularity, portions, characteristics, accessories, nitty-gritty, respect, structure, factors, dope, and more.

What is the unit of observation?

The context of units of observation provides the best understanding of data points. The “objects” that your data depicts are observation units. Consider gathering information on butterflies. An observational unit is a butterfly.

You could compile data on the butterfly’s weight, speed, and wing color, as well as the continent on which it lives. Each of these pieces of information is referred to as a dimension, and a cell’s entry is referred to as a data point. Each observational unit is described by a data point (aka each butterfly).

Types of data points

Words, numbers, and other symbols are all examples of data points. These are the kinds of data points that we store in data tables and perform queries on. The standard five types of data points in software are:

• Integer: Any number without a decimal point is an integer.
• Date: The date is a particular year’s and month’s date.
• Time: The time of day is time.
• Text: Text, sometimes known as “string,” simply refers to any collection of letters rather than numerals or other symbols.
• Boolean: Boolean is a data type that can be TRUE, FALSE, YES, NO, 1, or 0 in numbers. Simply put, it is binary data.

The big-picture data points kinds mentioned above are straightforward, but they are not all-inclusive. Let’s look at some examples.

How are data points represented?

Point format is the most typical way to express a data point. When graphing points along a coordinate axis, point format is used. When using two coordinate axes, a point is written as (x, y), and when using three, it is written as (x, y, z). It is possible to number the values of x, y, and z, but this is not a guarantee. To see if there is a pattern in the data, data points are frequently graphed. Numbers, dates (12/10/2001), times (0730), words (green), and binary values are all examples of data points (1 or 0). An example of a data point would be (3, 4, 5), or (blue, 06252004), or (1, 1200).

Data points examples

An observation or data point is a collection of one or more measurements made on a single member of the observational unit. An example of a data point would be the values of income, wealth, age of the individual, and the number of dependents in a study of the factors that influence the desire for money with the individual as the unit of observation. A statistical sample made up of various such data points would be used to draw conclusions about the population using statistics.

Additionally, a “data point” in statistical graphics can refer to either a single person within a population or a summary statistic produced for a certain subpopulation; such points might be related to both.

For example, the data points that you should pay attention to during digital marketing analysis will help you explain your subject.

Important data points for digital marketing analytics

Statistics and analytics are what we mean by “social media data.” It is the data gathered from social media platforms that demonstrate how people view or interact with your profiles or content. This information offers insights into your social media strategy and expansion. Raw social data includes the following metrics:

• Shares
• Mentions
• Comments
• Likes
• New followers
• Impressions
• Keyword analysis
• Hashtag usage
• URL clicks

These significant data points demonstrate your growth on social media.

What is a good number of data points?

When an energy reading is taken, a data point is produced. A data point is a discrete string of data transmitted by a device, meter, or sensor inside a structure or other site. Not counting the meters and devices themselves, mind you! Consider data points as the variables in an algebraic equation.

For some key reasons, data points are a fundamental notion in energy management:

• They are essential for developing a clear budget for your energy platform.
• They are important to create a watertight energy savings strategy and assist in creating a strong energy monitoring structure.

The fact that the amount of data points always relies on the various variables that must be monitored in each unique energy-saving project is a given. Every energy project is special and different when it comes to the necessary quantity of data points, just like each snowflake is. As a result, until now it has been challenging to generalize when customers inquire about the normal number of data points needed for a project. But, you can try some tools such as Data Point Calculator and calculate.

Data point categories

There are several data point categories for customers such as:

• Aging: Information on open customer balances.
• Bank references: Information regarding consumer bank accounts is provided by bank references.
• Payments and billing: A history of customer transactions and payments.
• Business data and credit: Information on past credit histories of customers, both inside and outside your own company, with external credit agencies and monitoring services.
• Collateral: Information on client collateral as it relates to creating or obtaining credit is known as collateral.
• Financial information: Information on a customer’s company’s health, including profits, losses, and cash flow.
• Guarantors: Information on third parties who are prepared to guarantee customer credit.
• References: Details about the individuals who act like the customer’s references.
• Trade references: References from businesses in the same industry that offer statements about the customer’s creditworthiness.
• Venture financing: Details about customer investment financing.
• Additional: For user-defined categories and values, additional data points are accessible.

Comparison: Data point vs data set

Data sets are collections of one or more data objects (including tables) that are grouped together either because they are kept in the same location OR because they are connected to the same subject. Data sets are not just collections of data tables.

We’ve already discussed data points in data tables and demonstrated how one point equals one cell. All of the data objects that make up a data collection are subject to the same logic.

One point corresponds to one cell in an array, record, or set. Points also stand-in for 1 cell when an object with pointers is expressed as a dimension. A scalar object’s single scalar value is referred to as a data point.

There are no data points in files or schemas. This is because these things are of that sort. In certain ways, a file could be seen as a non-data object because it is code created to guarantee the correct structure of another data item.

Schemas are summaries of other things, and they completely ignore points in order to convey object information fast.

Comparison: Data point vs data attribute

A data dimension and a data attribute are the same things. It is the column header in a table. Wing color is an attribute in the butterfly data example.

Consequently, a data point is a single value entry for an attribute.

Comparison: Data point vs data field

The terms “data field” and “data attribute” are interchangeable, however, they are applied in slightly different contexts. In a table, “field” typically refers to the column itself, whereas “attribute” typically refers to the column when we’re discussing a particular row.
As opposed to saying “the Color of Wings attributes for Monarch butterflied is orange,” you may say “the Color of Wings is a data field.”

In the context of programming languages, “field” also has a technical meaning that “attribute” does not.

Comparison: Unit of observation vs a unit of analysis

The distinction between units of observation and units of analysis is the most frequent source of misunderstanding regarding data points.

After data has been analyzed and aggregated, the single rows that remain in a data table are the units of analysis. 

Each row that acts as a grouping of data points in the basic data set is a unit of observation.

Big data requires the removal of original data for analytical reasons, however, there is disagreement over when this should and shouldn’t be done.

Conclusion

The word “point” serves as a reminder that any dataset is essentially a type of “space.” A “data point” would actually be a spot within a conventional three-dimensional space that has specified coordinates so that you could “point” to it. You may indicate that location and the precise moment at which it was thereby including a time coordinate. We advise you to check out and learn how object storage helps address unstructured data’s increased security risks.

The UK is preparing to relax further data mining laws to support its thriving AI business.

We all know that data mining is crucial to AI development. Tech companies are in a good position since they already have enormous datasets or have the resources to sponsor or pay for the needed data. The majority of startups rely on data mining to launch.

Data mining laws are under a change to make UK more “competitive”

The data-mining laws in Europe are infamously rigorous. While detractors claim that data mining laws like GDPR drive innovation, investment, and employment out of the Eurozone and towards nations like the USA and China, proponents of such laws contend that they are required to safeguard consumers.

This week’s announcement outlines the intellectual property support the UK will provide for its National AI Strategy.

Following a two-month cross-industry consultation session with individuals, large and small firms, and a variety of organizations, the announcement was made by the nation’s Intellectual Property Office (IPO).

Researchers can use text and data mining (TDM) to copy and use various datasets for their algorithms. The UK claims in the release that it will now permit TDM “for any purpose,” which offers much more freedom than the 2014 exception, allowing AI researchers to utilize such TDM for non-commercial purposes.

In striking contrast, a TDM exception is exclusively provided for scientific research under the EU’s Directive on Copyright in the Digital Single Market.

“These changes make the most of the greater flexibilities following Brexit. They will help make the UK more competitive as a location for data mining firms,” explained the IPO.

Can we count AI systems as inventors?

The UK maintains its earlier positions elsewhere, notably that AI systems cannot be given credit for the patent invention.

The instance of US-based Dr. Stephen Thaler, the creator of Imagination Engines, is the most well-known one in this area. Dr. Thaler has been at the forefront of the movement to acknowledge machines as creators.

Dr. Thaler’s AI creation DABUS was utilized to develop various products, including a food container with improved grip and heat transfer.

After Dr. Thaler’s applications were submitted in the nation by Ryan Abbott, a professor at the University of Surrey, a federal court in Australia decided in August 2021 that AI systems can be recognized as inventors under patent law. Identical applications were also submitted in the US, New Zealand, and the UK.

The UK’s IPO denied the requests at the time because only people can be recognized as inventors under the country’s Patents Act. Later appeals were similarly denied.

“A patent is a statutory right, and it can only be granted to a person. Only a person can have rights. A machine cannot,” said Lady Justice Liang.

The IPO reiterates in its most recent statement:” For AI-devised inventions, we plan no change to UK patent law now. Most respondents felt that AI is not yet advanced enough to invent without human intervention.”

According to the IPO, the UK is one of just a few nations that protects computer-generated works. The rights to a computer-generated work belong to whoever makes “the arrangements essential for the creation of the [computer-generated] work” for 50 years following the creation.

The aim is to boost the AI industry

With pioneers like DeepMind, Wayve, Graphcore, Oxbotica, and BenevolentAI, the UK has emerged as Europe’s powerhouse for AI despite being subject to stringent data mining laws. For instance, the EU AI Act is under progression toward regulating the future of artificial intelligence. The nation’s top universities produce more sought-after AI talent and tech investments than any European nation.

In general, the UK is consistently ranked as one of the top countries in the world to start a business. All eyes are on how the nation will use its freedoms to deviate from EU regulations after Brexit to strengthen its industry further.

“The UK already punches above its weight internationally and we are ranked third in the world behind the USA and China in the list of top countries for AI. “We’re laying the foundations for the next ten years’ growth with a strategy to help us seize the potential of artificial intelligence and play a leading role in shaping the way the world governs it,” explained Chris Philp, DCMS Minister.

There will surely be discussions over the UK’s decisions to advance its AI business, particularly concerning TDM. Still, the policies published so far will encourage entrepreneurship and the nation’s attractiveness for pertinent investments. Some others are defending the creation of a “Tech NATO.”

A new research collaboration between China and the US offers a way of detecting malicious ecommerce reviews designed to undermine competitors or facilitate blackmail by leveraging the signature behavior of such reviewers.

Machine learning algorithm has managed to detect PMUs

The paper describes how a system called the malicious user detection model (MMD) analyzes the output of such users to determine and label them as Professional Malicious Users (PMUs). Using Metric Learning, a method used in computer vision and recommendation systems, and a Recurrent Neural Network (RNN), the system identifies and categorizes the output of these critics.

User experience may be evaluated using star ratings (or a score out of ten) and text-based comments, which usually make sense in a typical scenario. PMUs, on the other hand, frequently go against this thinking by submitting a negative text evaluation with a high rating or a poor rating with a good review.

It’s a lot more pernicious because it allows the user’s review to inflict reputational harm without setting off e-commerce sites’ rather simple filters for identifying and addressing maliciously bad comments. If an NLP filter detects invective in a review, the high star (or decimal) rating assigned by the PMU effectively cancels out the negative content, making it seem ‘neutral,’ statistically speaking.

The new study states that PMUs are often used to demand money from internet retailers in exchange for amending negative comments and a promise not to post any more bad reviews. Some individuals seeking discounts are sometimes employed by the victim’s rivals, albeit most of the time, the PMU is being unethically utilized by the victim’s competitors.

The newest variety of automated detectors for such examinations employs Content-Based Filtering or a Collaborative Filtering approach, seeking unequivocal ‘outliers’. These are dismal negative reviews in both feedback modes and differ significantly from the overall trend of review sentiment and rating.

A high posting frequency is a typical sign that such filters look for. In contrast, a PMU will post strategically but seldom since each review may be an individual commission or a component of a longer plan to obscure the ‘frequency’ statistic.

Because of this, the paper’s researchers have incorporated the unusual polarity of expert malicious comments into a separate algorithm, giving it nearly identical capabilities to a human reviewer in detecting fraudulent reviews.

Previous studies

According to the authors, there are no comparable prior works to compare MMD against because it is the first technology to try to detect PMUs based on their schizophrenic posting style. As a result, the researchers compared their method against various component algorithms previously used by conventional automatic filters, including; Hysad; Semi-sad; Statistic Outlier Detection (SOD); K-means++ Clustering; CNN-sad; and Slanderous user Detection Recommender System (SDRS).

“[On] all four datasets, our proposed model MMD (MLC+MUP) outperforms all the baselines in terms of F-score. Note that MMD is a combination of MLC and MUP, which ensures its superiority over supervised and unsupervised models, ” the researchers said.

The paper further states that MMD could be used as a pre-processing method for standard automatic filtering systems, and it presents experimental results on several datasets such as User-based Collaborative Filtering (UBCF), Item-based Collaborative Filtering (IBCF), Matrix Factorization (MF-eALS), Bayesian Personalized Ranking (MF-BPR), and Neural Collaborative Filtering (NCF).

According to the article’s conclusions, the authors say that in terms of Hit Ratio (HR) and Normalized Discounted Cumulative Gain (NDCG), these investigated augmentations resulted in improved results:

“Among all four datasets, MMD improves the recommendation models in terms of HR and NDCG. Specifically, MMD can enhance the performance of HR by 28.7% on average and HDCG by 17.3% on average. By deleting professional malicious users, MMD can improve the quality of datasets. Without these professional malicious users’ fake [feedback], the dataset becomes more [intuitive].”

The paper is called Professional Malicious User Detection in Metric Learning Recommendation Systems and was published by researchers at Jilin University’s Department of Computer Science and Technology; the Key Lab of Intelligent Information Processing of China Academy of Sciences in Beijing; and Rutgers University’s School of Business.

Method

It is hard to detect PMUs because two non-equivalent parameters (a numerical-value star/decimal rating and a text-based review) must be considered. According to the new paper’s authors, no similar research has been done before.

The review subject is divided into content chunks using a Hierarchical Dual-Attention recurrent Neural network (HDAN). HDAN uses the emphasis to assign weights to each word and each sentence. In the picture above, the authors state that the term “poorer” should be given greater importance than other words in the review.

The MMD algorithm uses Metric Learning to estimate an exact distance between items to characterize the entire set of connections in the data.

MMD uses a Latent Factor Model (LFM) to select the user and item, which gets a base rating score. HDAN, on the other hand, incorporates reviews into the sentiment score as supplementary information.

The MUP model generates the sentiment gap vector, which is the difference between the rating and the predicted sentiment score of the review’s text content. For the first time, it was possible to detect PMUs using this method.

The output labels are used in Metric Learning for Clustering (MLC) to establish a metric against which the probability of a user review being malicious is calculated.

The researchers also performed a user study to see how effectively the system identified malicious reviews based only on their content and star rating. The participants were asked to assign the evaluations a score of 0 (for ordinary users) or 1 (for an experienced malevolent user).

On average, the students identified 24 true positives and 24 false negatives out of a 50/50 mix of good and bad reviews. MMD was able to label 23 genuine positive users and 24 genuine negative users on average, operating almost at human levels, surpassing the task’s baseline rates.

“In essence, MMD is a generic solution that can detect the professional malicious users that are explored in this paper and serve as a general foundation for malicious user detections. With more data, such as image, video, or sound, the idea of MMD can be instructive to detect the sentiment gap between their title and content, which has a bright future to counter different masking strategies in different applications,” the authors explained. If you are into ML systems, check out the history of Machine Learning, it dates back to the 17th century.

A new machine learning framework developed at the U.S. Department of Energy’s Brookhaven National Laboratory can hone in on which parts of a multistep chemical conversion should be altered to increase productivity. The technique may assist researchers in determining the shape of catalysts, also known as chemical dealmakers that speed up reactions.

Machine learning framework is used to increase productivity

The method to examine the carbon monoxide (CO) to methanol conversion using a copper-based catalyst was devised by the team. The reaction has seven simple elementary procedures.

“Our goal was to identify which elementary step in the reaction network or which subset of steps controls the catalytic activity,” said Wenjie Liao, one of the study’s authors, who is a Stony Brook University graduate student involved with Brookhaven Lab’s Chemistry Division’s Catalysis Reactivity and Structure (CRS) group. The paper is published in the Catalysis Science & Technology journal.

“We used this reaction as an example of our ML framework method, but you can put any reaction into this framework in general,” said Ping Liu, the leader at the CRS team.

Consider a rollercoaster with lots of different-sized hills. The height of each peak reflects the energy needed to go from one step to the next. Catalysts reduce “activation barriers” by making it simpler for reactants to combine or allowing them to do so at lower temperatures and pressures. A catalyst must target the step or steps that have the most influence in order for the reaction to move forward more quickly.

Traditionally, scientists attempting to improve a reaction have tried to figure out how varying one activation barrier at a time might affect the overall yield. This form of inquiry may reveal which stage is “rate-limiting” and which steps determine reactant selectivity, that is, whether the reactants proceed to the intended product or down an alternative route toward an unwanted by-product.

“These estimations end up being very rough with a lot of errors for some groups of catalysts. That has really hurt for catalyst design and screening, which is what we are trying to do,” Liu explained.

The team is attempting to improve these estimations by developing a new machine learning framework that will allow chemists to predict how catalysis would impact reaction processes and chemical output more accurately.

“Now, instead of moving one barrier at a time we are moving all the barriers simultaneously. And we use machine learning framework to interpret that dataset,” said Liao.

The researchers said that this approach, in which reactivity is inferred from the structure of a product rather than its activity or chemical makeup, provides considerably more trustworthy outcomes, including how components in a reaction interact.

“Under reaction conditions, these steps are not isolated or separated from each other; they are all connected. If you just do one step at a time, you miss a lot of information — the interactions among the elementary steps. That’s what’s been captured in this development,” said Liu.

How the ML framework has been built?

The researchers began by preparing a data set to train their machine learning framework. The activation energy needed to convert one arrangement of atoms to the next through the seven steps of the reaction was modeled using DFT (density functional theory) calculations. Then the researchers conducted computer modeling to evaluate what would happen if they adjusted all seven activation barriers at once. Chemistry isn’t the only field of science that benefits from machine learning, if you want to learn more about quantum machine learning, we have an article for you.

“The range of data we included was based on previous experience with these reactions and this catalytic system, within the interesting range of variation that is likely to give you better performance,” explained Liu.

The researchers used a model to simulate variations in 28 “descriptors,” including the activation energies for the seven steps as well as pairs of steps changing two at a time, resulting in a comprehensive data set of 500 points. This dataset predicted how all those individual changes and pairs of changes would influence methanol production. The model then assessed each descriptor based on its relevance in determining methanol output.

“Our model ‘learned’ from the data and identified six key descriptors that it predicts would have the most impact on production,” said Liao.

After the most essential words had been identified, the researchers retrained the machine learning framework with just those six “active” criteria. This enhanced machine learning framework was able to predict catalysis activity solely based on DFT calculations for those six variables.

“Rather than you having to calculate the whole 28 descriptors, now you can calculate with only the six descriptors and get the methanol conversion rates you are interested in,” explained Liu.

According to the team, they can also apply the model to screen catalysts. The model predicts a maximum methanol production rate if they can produce a catalyst that improves the value of the six active identifiers.

The researchers compared the model’s predictions with the catalyst’s real-world performance and alloys of various metals with copper, finding that they matched up. The machine learning framework’s comparisons to previous methods for predicting alloy performance demonstrated that it was far superior.

The findings also illuminated how shifts in energy hurdles might impact the reaction mechanism. The data revealed how various steps within the process operate together, especially that lowering the energy barrier in rate-limiting step alone would not always improve methanol production if done alone. However, changing the energy barrier of a prior reaction network step while maintaining the rate-limiting step’s activation energy within an acceptable range would boost methanol yield.

“Our method gives us detailed information we might be able to use to design a catalyst that coordinates the interaction between these two steps well,” explained Liu.

With data-driven machine learning frameworks, Liu is most intrigued by the potential to apply such techniques to more complicated reactions.

“We used the methanol reaction to demonstrate our method. But the way that it generates the database and how we train the machine learning framework and how we interpolate the role of each descriptor’s function to determine the overall weight in terms of their importance — that can be applied to other reactions easily,” said Liu.

The study was made possible by a grant from the Department of Energy Office of Science (BES). The calculations were done with computational resources at the Center for Functional Nanomaterials (CFN), a DOE Office of Science User Facility at Brookhaven Lab, and the National Energy Research Scientific Computing Center (NERSC), a DOE Office of Science User Facility at Lawrence Berkeley National Laboratory.

By the way, if you are interesed in ML methods, you can check out the history of machine learning, it dates back to the 17th century.

The industrial revolution, which took place in the last several years, has been the most significant transformation ever faced by the industrial sector. It covers all of today’s cutting-edge technology trends, including autonomous cars, smart connected devices, sensors, computer chips, and other technologies. This metamorphosis was caused by advances in manufacturing technology that has always welcomed new ideas. One of them is AI in manufacturing.

Why do we need AI in manufacturing?

Manufacturing’s main cost is the ongoing maintenance of plant equipment and machinery, which has a major influence on any business. Also, billions of dollars in lost production line downtime occur each year due to unanticipated shutdowns. As a result, manufacturers are turning to advanced artificial intelligence-aided predictive maintenance to minimize these costs.

Manufacturers are finding it difficult to maintain high quality on agreed standards and regulations in today’s extremely short market deadlines with the heavy burden of products. Manufacturers may achieve the greatest degree of product quality through artificial intelligence in manufacturing.

Workers will be prepared for more advanced jobs in programming, design, and maintenance as millions of occupations are taken over by robots. Human-robot integration will have to be quick and secure during this periodic phase as robots entered the manufacturing floor alongside human employees, and artificial intelligence may meet this need.

Artificial intelligence methods are currently being used in the manufacturing sector for a variety of purposes:

• Neural Networks: In an input layer, a neural network receives information. The input is sent to a hidden layer, which assigns a value and sends it to the output layer. For example, there’s a new neural network that is able to read tree heights using satellite images.
• Machine Learning: An artificial intelligence procedure in which a computer program learns from training data and applies this learning to make decisions and detect patterns in actual-world data. You can learn more about the history of Machine Learning, by visiting our article.
• Deep Learning: A technique of employing machine learning that mimics the human brain, like a neural network, but the information is passed from one layer to the next for greater processing. You can find out how Deep Learning can solve the problem of global climate change here.

How is AI in manufacturing transforming the industry?

Artificial intelligence is unquestionably the key to future growth and success in the manufacturing industry. Because AI aids with problems such as decision making and information overload, almost 50% of manufacturers considered it highly important in their factories for the next five years. Using artificial intelligence in industrial firms allows them to completely revolutionize their operations.

Future of AI in manufacturing

In the near future, AI will have an enormous influence on the industrial sector in ways that we cannot yet predict, but which we can already observe. There are two intriguing developments on the horizon that include using AI with IoT to improve manufacturing and AI with computer vision.

Manufacturers in numerous sectors, such as pharmaceuticals, automobiles, food and beverages, and energy and power, have adopted artificial intelligence. The worldwide AI in the manufacturing market is attributed to growing venture capital investments, rising demand for automation, and rapidly changing industries.

According to the latest trends, increasing demand for hardware platforms and a growing need for high-performance computing processors to execute a variety of AI software are all expected to propel the worldwide artificial intelligence in the manufacturing market. AI in manufacturing is also beneficial in collecting and analyzing big data.

As a result, it is extensively utilized in numerous production applications such as machinery inspection, cybersecurity, quality control, and predictive analytics. All of these elements are anticipated to help drive the global AI in manufacturing sector forward.

Conclusion

AI is simply a more advanced version of automation, which is the inevitable consequence of the industry 4.0 transformation. It might be useful in generating new things and lowering manufacturing costs by improving quality. There’s no way that humans can be replaced, though.

The ability to adapt to changing conditions and generate higher margins is one of the most important advantages of AI in manufacturing. Companies that have embraced AI early, such as Google, have far outpaced their peers and grown rapidly, owing in large part to their superior capacity to anticipate and continually modify to ever-changing circumstances.

This week, Nvidia laid out its strategy for moving forward with the next generation of computing. Nvidia Graphics Technology Conference (GTC) saw CEO Jensen Huang reveal a variety of technologies that he claims will power the future wave of AI and virtual reality worlds.

Even though Nvidia’s GPU Technology Conference, more popularly known as GTC, was held virtually this year, it still offered exciting news. A keynote speech by Nvidia CEO Jensen Huang was the event’s main highlight. So, what are the newest goods, and how do they relate to data science?

At Nvidia GTC 2022, the company revealed its next-generation Hopper GPU architecture and Hopper H100 GPU and a new data center chip that integrates the GPU with a high-performance CPU, dubbed the “Grace CPU Superchip” and, of course, Nvidia Omniverse. 

Nvidia H100 “Hopper GPU”

Nvidia is launching a slew of new and enhanced Hopper features. Still, the emphasis is on transformer models, which have become the machine learning technique of choice for many applications and which power models like GPT-3 and asBERT may be the most important.

The new Transformer Engine in the H100 chip promises to speed up model learning by up to six times. Because this new architecture incorporates Nvidia’s NVLink Switch technology for linking many nodes, massive server clusters powered by these chips will be able to scale up to support enormous networks with less overhead.

Customers’ Tensor Cores, which can handle up to 16 bits of precision while maintaining accuracy, are utilized in the new Transformer Engine.

Nvidia Grace CPU

The Nvidia GPU Superchip is Nvidia’s first foray into the data center CPU market. The Arm-based chip will have a staggering 144 cores and one terabyte per second of memory bandwidth, according to Intel leaks. It combines two Grace CPUs connected via Nvidia’s NVLink interconnect, comparable to Apple’s M1 Ultra architecture.

The new CPU, powered by fast LPDDR5X RAM, is expected to arrive in the first half of 2023 and will deliver 2x the performance of previous servers. Nvidia expects the chip to score 740 on the SPECrate®2017_int_base benchmark, which is against high-end AMD and Intel data center processors.

The firm is collaborating with “leading HPC, supercomputing, hyper-scale and cloud customers,” implying that these systems will be accessible on a cloud provider near you.

What is Nvidia Omniverse?

At GTC 2022, Nvidia is releasing Omniverse Cloud, a suite of cloud services that allows artists, creators, architects, and developers to collaborate on 3D design and simulation from any device.

The Nucleus Cloud, a one-click-to-collaborate sharing software, is included in Omniverse Cloud’s portfolio of services. It enables artists to access and change enormous 3D environments from anywhere without sending large data files. The View is an app for non-technical users that streams full Omniverse scenes with Nvidia GeForce Now technology powered by Nvidia RTX GPUs in the cloud.

The other app, Create, is for technical designers, artists, and creators to create 3D worlds in real time are among the suite of services provided by Omniverse Cloud. Users will be able to stream, create, view, and collaborate with other Omniverse customers from any device.

GPU Accelerated Data Science with RAPIDS

These new hardware and cloud service initiatives are for handling big data. “Big data” refers to more varied data that arrive in greater numbers and move faster. The three Vs are also known as this.

Big data is a bigger, more complex set of data, especially from new sources. Traditional data processing software cannot deal with these massive amounts of information. However, employing these vast quantities of data may solve issues that you couldn’t previously address. But Nvidia has a RAPIDS solution and is the heart of all data science-related GTC 2022 sessions.

What is RAPIDS?

At GTC 2022, there were 131 data-related sessions, and the first one was Fundamentals of Accelerated Data Science, which explained what RAPIDS is.

RAPIDS is a collection of open-source GPU code libraries that can be used to develop end-to-end data science and analytics pipelines. RAPIDS boosts data science pipeline speeds to help organizations produce more productive workflows.

The GPU-only library of open-source software called RAPIDS is the heart of data processing, and Nvidia may produce maybe one of the best hardware for this. It helps accelerate the processing speed of machine learning algorithms without incurring data serialization expenses. RAPIDS also supports multi-GPU deployments for end-to-end data science pipelines on large data sets.

How does RAPIDS work?

RAPIDS uses GPU acceleration to accelerate the entire data science and analytics processes. A GPU-optimized core data frame aids in database and machine learning application development.

RAPIDS is a Python-based framework that allows you to write your code in C++ and run it entirely on GPUs. It includes libraries for executing a data science pipeline completely on GPUs.

Relation between RAPIDS and data science

RAPIDS strives to reduce the time it takes to get data in order, often a significant barrier in data science. RAPIDS helps you develop more dynamic and exploratory workflows by improving the data transfer process.

RAPIDS advantages

RAPIDS brings several advantages into the mix:

• Integration — Create a data science toolchain that is as easy to maintain.
• Scale — GPU scaling on various GPUs, including multi-GPU configurations and multi-node clusters.
• Accuracy — Allows for the rapid creation, testing, and modification of machine learning models to improve their accuracy.
• Speed — Increased data science productivity and reduced training time.
• Open source — The world’s first GPU-optimized SPV chain architecture. It is compatible with NVIDIA and built on the Apache Arrow open-source sonviftware platform.

The new GPU and CPU introduced at GTC 2022 will make a significant contribution to data processing with the support of RAPIDS. Also, you can use these systems on every PC, thanks to Nvidia Omniverse. 

GTC 2022 was a virtual conference that offered over 900 sessions, including keynote addresses, technical instructionals, panel discussions, and roundtable talks with industry experts. Despite the lack of audience and the opportunity to interact with the diverse array of attendees from both private and public sectors, there was plenty of information at this event. Even if you miss out on the actual event live, many sessions, such as Jensen Huang’s keynote speech, will be archived for later viewing.

Nvidia’s ecosystem continues to expand and scale at breakneck speed, as seen at GTC 2022. This article outlined some of the new features and how they were connected to data science. Do you think the further improvements are enough to move data science forward?

There’s no doubt that data scientists are in high demand right now. Companies are looking for people who can help them make sense of all the data they’re collecting and use it to make better decisions.

Being a data scientist is a great way to start or further your career. It’s a field rising, and there are many opportunities for those with the right skills. We talked with Ken Jee, Head of Data Science at Scouts Consulting Group, about how to build a career in data science.

With a goal-oriented approach to problem solving, data science evangelist Ken Jee is admired for his work in the field. He is the Head of Data Science at Scouts Consulting Group, and creates and shares content via his podcast, website, YouTube channel, and 365 DataScience course offering; frequently contributes to Kaggle; and is a Z by HP global ambassador. He recently helped design data science challenges featured in “Unlocked,” an interactive film from Z by HP. The film and companion website present data scientists with the opportunity to participate in a series of problem-solving challenges while showcasing the value of data science to non-technical stakeholders, with a compelling narrative. We spoke with Jee about how he built a successful career as a data scientist.

What is your background and how did you get started in data science?

All my life, I played competitive sports, and I played golf in college. One of the ways that I found I could create a competitive edge was by analyzing my data and looking at the efficiencies that could be created by better understanding my game, and allocating time towards practice more effectively. Over time I became interested in the data of professional athletes, particularly golfers, so I started to analyze their performance to predict the outcome of events. I tried to play golf professionally for a bit, but it turns out I am better at analyzing data than playing the game itself.

What advice do you give young people starting out or wanting to get into the field?

If they’re just starting out learning data science, I recommend that they just choose a path and stick to it. A lot of times people get really wrapped up in whether they’re taking the right course and end up spinning their wheels. Their time would be better spent just learning, whatever path they take. I will also say that the best way to land a job and get opportunities is by creating a portfolio by doing data science. Create or find data, whether it’s on Kaggle or from somewhere else, like the “Unlocked” challenge, show your work to the world, get feedback and use that to improve your skills.

“Unlocked” is a short film that presents viewers with a series of data science challenges, that I along with other Z by HP Data Science Global ambassadors helped to design. There are challenges that involve data visualization using environmental data; natural language processing or text analysis using a lot of synthesized blog posts and internet data; signal processing of audio information; and computer vision to analyze pictures, along with accompanying tutorials and sample data sets. We wanted to highlight a variety of things that we thought were very exciting within the domain.

There’s a lot of fun in each of these challenges. We’re just really excited to be able to showcase it in such a high production value way. I also think that the film itself shows the essence of data science. A lot of people’s eyes glaze over when they hear about big data, algorithms and coding. I jump out of bed in the morning happy to do this work because we see the tangible impact of the change that we’re creating, and in “Unlocked,” you’re able to follow along in an exciting story. You also get to directly see how the data that you’re analyzing is integrated into the solutions that the characters are creating.

How has technology opened doors for you in your career?

I would argue that technology built my entire career, particularly machine learning and AI tech. This space has given me plenty to talk about in the content that I create, but it has also helped to perpetuate my content and my brand. If you think about it, the big social media companies including YouTube all leverage the most powerful machine learning models to put the right content in front of the right people. If I produce content, these algorithms find a home for it. This technology has helped me to build a community and grow by just producing content that I’m passionate about. It is a bit meta that machine learning models perpetuate my machine learning and data science content. This brand growth through technology has also opened the door for opportunities like a partnership with Z by HP as a global data science ambassador. This role gives me access to and the ability to provide feedback on the development of their line of workstations specifically tuned to data science applications–complete with a fully loaded software stack of the tools that my colleagues and I rely on to do the work we do. Working with their hardware, I’ve been able to save time and expand my capabilities to produce even more!

What educational background is best suited for a career in data science?

I think you have to be able to code, and have an understanding of math and programming, but you don’t need a formal background in those areas. The idea that someone needs a master’s degree in computer science, data science or math is completely overblown. You need to learn those skills in some way, but rather than looking at degrees or certificates, I evaluate candidates on their ability to problem solve and think.

One of the beautiful things about data scientists is that they come from almost every discipline. I’ve met data scientists from backgrounds in psychology, chemistry, finance, etc. The core of data science is problem solving, and I think that’s also the goal in every single educational discipline. The difference is that data scientists use significantly more math and programming tools, and then there’s a bit of business knowledge or subject area expertise sprinkled in. I think a unique combination of skills is what makes data science such an integral aspect of businesses these days. At this point, every business is a technology company in some respect, and every company should be collecting large volumes of data, whether they plan to use it or not. There’s so much insight to be found in data, and with it, avenues for monetization. The point is to find new opportunities.

What’s an easy way to describe how data science delivers value to businesses?

At a high level, the most relevant metric for data science in the short term is cost savings. If you’re better able to estimate how many resources you’ll use, you can buy a more accurate number of those resources and eventually save money. For example, if you own a restaurant and need a set amount of perishable goods per day, you don’t want to have excess inventory at the end of the week. Data science can be used to very accurately predict the right quantity to buy to satisfy the need and minimize the waste, and this can be on-going and adjusted for new parameters. Appropriate resourcing is immensely important, because if you have too much, you’ll have spoilage, and too little, you’ll have unhappy customers. It’s a simple example but when your sales are more accurate, even by a small percentage, those savings compound. At the same time, the data science models get better, the logic improves, and all these analytics can be used for the benefit of the business and its profitability.  

Is being a data scientist applicable across industries?

You can have success as a data scientist generalist, where you bounce across different subject area expertise and industries, like finance, biomedical, etc.; you just have to be able to pick up those domains relatively quickly. I also think that if you’re looking to break into data science from another field, the easiest path for you would be to do data science in that field. It all sort of depends on the nature of the problems you would like to solve. There are verticals where subject area expertise is more important, maybe even more so than data skills, like for sports and you need to understand a specific problem. But generally, someone could switch between roles.

Any final notes?

I’m a huge believer of setting goals and accountability. A good goal is measurable, you control the outcome, and set a time constraint. Once you’ve set your goal, write it down or tell people about it. Also, never forget that learning is a forever journey.

From February 28 to March 7, 2022, the Applied Data Hackathon welcomed data scientists with entrepreneurial spirits from multiple backgrounds. Participants tackled three challenges or brought their own, intending to solve some of the most pressing real-world problems we face today.

The satisfaction of coming up with original and inspiring solutions was incentive enough, but to add to the available rewards, the winner and runners-up had the chance to share over €120,000 in prizes and a place at the Applied Data Incubator in Berlin.

Challengers formed 17 teams, creating their solutions throughout the weekend, culminating in a Pitch Day event hosted online and at The Drivery Berlin on March 7.

An all-star judging panel

Joining the event both virtually and in-person, the judges brought a huge amount of experience and knowledge to the table. Selecting the winners were:

• Carla Penedo of Celfocus
• Michael Durst of ITONICS GmbH
• Claudia Pohlink of Deutsche Bahn
• Michael Leyendecker of VITRONIC Machine Vision
• Peter Ummenhofer of GO Consulting GmbH 
• Thomas Brüse of QuickMove GmbH
• Simon Mayer of University of St. Gallen
• Timon Rupp of The Drivery Berlin
• Judith Wiesinger, DeepTech Entrepreneur
• Norbert Herrmann of Berliner Senatsverwaltung für Wirtschaft, Energie und Betriebe 
• Maren Lesche of Applied Data Incubator and Startup Colors

“We already have an incubator in the healthcare field, and I’ve seen the impact that it creates,” Lesche, Founder at Applied Data Incubator said. “There is so much unstructured data around, and we produce, produce, produce, and we don’t even know what to do with it. So I hope that we can empower entrepreneurs and teams to use the available data. This is the reason why I wanted to do it; these hundred hackers that registered for the hackathon can help to shape the future.”

Pitch it to win it

Of the 17 teams that entered, nine presented their solutions during Pitch Day, intending to win those coveted places at the Applied Data Incubator.

The first pitch tackled a problem that will become significant as more electric trucks take to the roads.

Hyperfleet supports logistics with a data-driven multi-variant decision model for order taking and fleet route optimization, helping organizations make decisions that improve their total cost of ownership and the environment.

Three projects tackled an expensive problem. AI Anomaly Detector, Archimedes, and MoveQuickly all created an intelligent anomaly detector for industrial applications, assisting with the predictive maintenance of costly and critical machinery to ensure they stay up and running.

Panos.AI is a digital advisor that helps companies identify, manage and scale their process automation initiatives more successfully, powered by data-driven, self-learning algorithms.

Hyperspace analyzes scientific papers and news articles to extract insights about emerging technology milestones and breakthroughs.

Composite Vision is an automated system for detecting particular types of defects in data acquired by non-destructive testing, such as ultrasound, x-ray, and more.

ClearCO2 maps the cause and effect of carbon emissions in food production and logistics to reverse climate change.

Kapsola empowers health tech companies to label data for use in their AI applications, providing them with services like image classification, object detection, semantic segmentation, and more.

And the winner is?

After a great deal of discussion, the judges selected their winners, with the results appearing for all participants and interested attendees on the Applied Data Hackathon portal, powered by Taikai.

Hyperfleet, Composite Vision, Panos.AI, ClearCO2, Kapsola, Hyperspace, and AI Anomaly Detector all won the opportunity to go through the eligibility criteria process and join Applied Data Incubator either in April or October 2022.

And the winner of the €500 Conference Voucher is MoveQuickly, with Archimedes taking home four hours of special coaching, worth €400.

It was a challenging but fantastic and inspiring week. And it was wonderful to see so many participants, both in-person in Berlin and online.

Congratulations to all the hackathon participants and the partners, mentors, judges, and organizing team for making it all possible. 

Although Big Data is primarily thought of as a technology used by large companies, many benefits can be derived from big data technologies by small and medium-sized companies (SMEs).

Big Data benefits for SMEs at a glance

One of the primary benefits of Big Data is the ability to gain insights into customer behavior that would not be possible with traditional data analysis methods. Big Data technologies make it possible to quickly analyze large volumes of data, which can be used to identify trends and patterns that would not be detectable with smaller data sets. SMEs can then use this insight to improve their products and services and better meet the needs of their customers. Additionally, SMEs can use Big Data to enhance marketing efforts, target customers more effectively, and create a personalized customer experience.

SMEs can also use Big Data to improve their operations. For example, companies can use Big Data to optimize business processes, identify areas of waste or inefficiency, and improve decision-making. Additionally, Big Data can help SMEs better understand their customers and the markets in which they operate. This understanding can then be used to make more informed business decisions and improve the competitiveness of SMEs.

Overall, big data provides small and medium enterprises with many opportunities to improve their businesses and compete more effectively in today’s economy. While Big Data technologies may seem daunting at first, they can be quickly learned and used to significant effect by SMEs. With the right tools and resources, SMEs can harness the power of Big Data to improve their bottom line and stay ahead of the competition.

You may be wondering whether these Big Data approaches are that complicated and elaborate and if they’re only appropriate for big businesses. The answer is no. Let’s take away the idea of volume (or amount of data) from the definition of Big Data, and it becomes transferable and applicable in a small or medium-sized business setting. Thanks to the decline in technology costs and innovative tools that provide new methods to interact with databases, SMEs can obtain much more valuable insights data from their data.

Deeply understand customers

With the help of a range of communication channels and in-house data we have today, it is possible to capture consumer behavior and interpret it. For example, to anticipate their future purchases, all you have to do is analyze their buying habits. Information shared on social media must also be considered.

It’s also critical to understand how to ask the right questions when conducting Big Data analyses. Answers to these questions might enhance your existing services or develop your following best-selling product.

Optimize operations

Data analysis allows for better management of the distribution chain, allowing you to redirect your efforts to sectors that need it. In a nutshell, using Big Data means modifying your company’s operations plan.

Some companies will alter their services to consumers, while others will sell their data to third parties. And here we are talking about 80 percent of unstructured data that companies will have in 2025 according to an IDC report.

Just remember, it’s critical to figure out what the issue is and how you’ll address it before beginning a data analysis project. I should also mention that you may need to enhance your company strategy.

Discover upcoming trends

One of the Big Data benefits for SMEs is to discover the trends. Big Data is replacing the gut instinct for good. It takes out the guesswork and helps identify and track behaviors and patterns to forecast where things are heading, how demand will change over time, and what will influence it.

Social networks create trending topics by mining their data. And as such, Big Data can form trends by looking at retail, online, and offline customer behaviors, comparing them to external conditions, and capturing patterns between them.

Know your competitor

Understanding your competition is another area where Big Data is better than your gut instinct. Today you have a better chance to predict some of the things your competitors are doing, as financial data, product trends, or social media analysis results can be easily accessed through the internet. But, Big Data offers more by examining more than a team of people can do faster and more precisely.

It would be best to keep in mind that your competitors can do all these about your business. So being first and prioritizing your Big Data investment might give you a headstart.

Remember that it’s also easy for your competitors to glean more information on your business than ever before. There’s no way around this, but you can stay one step ahead by keeping up-to-date on the latest big data technologies and uses.

There is more to Big Data benefits for SMEs from an industry perspective, and I plan to cover these topics soon. Until then, I would recommend reading these related articles:

• Top 6 trends in data analytics for 2022
• Unstructured Data Will Be Key to Analytics in 2022

Energy and the environment were significant threads covered at Web Summit and garner additional importance when considering the travel and accommodation footprint created by almost 44,000 attendees. It calls for greater awareness of the CO2 produced by the event and its participants. While Web Summit took place on the same days as COP26, it still managed to attract energy and environmental industry pioneers.

Being selected at the Web Summit Alpha program means something. It means your startups are disrupting their industries. The Alpha program offers the chance to join a global community of startups, each selected by the Web Summit team for potential, uniqueness, and world-changing ideas. If chosen, they get tickets to Web Summit, access to the world’s most influential investors, and can apply for pitch workshops, startup masterclasses, and mentor hours.

Given the importance of the energy and environment sector, we took a look at some of the more exciting Alpha program participants in Lisbon at the end of 2021.

Emax – a Belgian peer-to-peer energy trading platform 

Emax offers a trading platform that allows users to trade electricity, including providers who supply electricity to homes, grid operators that provide consumers, businesses, and industry with electricity, and service providers that develop, design, build, and fund projects that save energy, reduce energy costs and decrease operations costs at their customers’ facilities.

In short, its users can sell excess renewable energy to other network participants through automated smart contracts using blockchain technology. What’s unique about Emax is that it makes energy trading as smooth as booking a hotel room. 

All transactions are recorded instantly, with minimal effort, and safely and transparently through a blockchain. One observation, however; this technology may face growth challenges in that all participants need to have smart meters and be registered in the network.

Bio-Carbon International – a US startup focused on producing biocarbon for all industries and helping mitigate climate change

Bio-Carbon International utilizes waste biomass from timber and agriculture and creates quality biocarbon products at an industrial scale. The biocarbon production from waste biomass sequesters CO2 and prevents methane and other harmful gasses from being generated during the decay process.  Biocarbon can be used in dozens of industries in different forms and can help restore arable land and increase global biomass organically.

What is unique about their patented Terra Preta Mix is that it could revolutionize agriculture, reforestation efforts, and water conservation by increasing water retention in soil, promoting microbial activity, and eliminating the need for chemical fertilizers that pollute earth and groundwater.

In addition, Biocarbon international is thinking ahead and will tokenize its biocarbon production and the associated carbon credits and allowances. After tokenizing its products, it will develop an asset-backed crypto commodity exchange and project platform. This will allow other carbon and climate-friendly companies to list their products and credits and connect a global project network that token holders can donate to or set up token farming on to develop new businesses.                                                                            

Blue Planet Ecosystem – an Austrian startup turning sunlight into seafood

Blue Planet Ecosystem has miniaturized an entire ecosystem in a closed-loop, recirculating aquaculture systems facilitated by computer vision to turn sunlight into seafood.

The sun feeds the algae, which provides fuel to the zooplankton, feeding the fish with no fish meal or pesticides. Quality is controlled by computer vision, which monitors every fish’s health, resulting in healthy fish free of any harmful additives. 

ANNEA – a German startup that makes renewable energy machines intelligent

ANNEA is the leading next generation of condition-based predictive and prescriptive maintenance platforms for wind turbines, solar farms, and hydropower plant machines. Its end-to-end solutions enable you to create digital twins of each component of your devices through artificial intelligence, physical modeling, and normal behavior modeling. It then analyzes any damaged parts and replicates them.

Laava Tech – an Estonian startup that makes indoor farming more efficient through artificial intelligence

Laava Tech uses smart lamps, sensors, and IoT controllers to manage the indoor farming growing process. The CEO, Tatsiana Zaretskaya, claims that Laava Tech reduces energy consumption up to 88%. Their AI solution is trained to automatically monitor the types and states of crops and adjust physical surrounding parameters, such as light wavelength, pulsation, temperature, humidity, and CO2 level. It’s like having an indoor greenhouse on your phone. 

Ecofye – a UK startup that evaluates the sustainability performance of businesses and organizations

Ecofye grades a company’s level of sustainability while generating tailored solutions for improvement. Ecofye uses algorithms to analyze a client’s entire value chain, including emissions and social impact. 

Its 360º Assessment evaluates its clients’ value chain and provides an ESG score – a measurement of a company’s level of sustainability – free of charge. Using that information, Ecofye develops solutions aligned with the client’s business objectives, designed to minimize environmental impact. It looks into waste, material use, supply chain, distribution, and energy to create solutions that make commercial sense and have a high potential to reduce emissions. 

KEME – a Portuguese company that aims to expand renewable energy generation in companies and society

KEME implements individual and collective self-consumption projects in renewable energy communities, offering a service that allows any citizen, group of citizens, or companies to produce, consume, share, store, and sell surplus energy, connecting production units to one or more points of consumption. 

What is different about KEME is its business model. KEME Energy’s services follow the ESCo (Energy Service Company) model, where KEME plays the role of a Self-Consumption Management Entity during the contract period agreed with the client. During this period, the savings obtained from implementing the project are shared. After the contract period, the equipment ownership is transferred wholly to the customer, and all the savings obtained in energy costs. This means that clients gain immediate benefits after implementing the project without any initial outlay.

Big events need big energy and environment solutions

These were some of the outstanding energy and environment startups from the Web Summit Alpha program. It behooves such huge events always to take this sector seriously, and help find the next generation of solutions, since the energy cost of bringing so many people together is difficult to explain without equal attention on how to solve the problem, not just for the event, but the planet as a whole.

For decades, managing data essentially meant collecting, storing, and occasionally accessing it. That has all changed in recent years, as businesses look for the critical information that can be pulled from the massive amounts of data being generated, accessed, and stored in myriad locations, from corporate data centers to the cloud and the edge. Given that, data analytics – helped by such modern technologies as artificial intelligence (AI) and machine learning – has become a must-have capability, and in 2022, the importance will be amplified.

Enterprises need to rapidly parse through data – much of it unstructured – to find the information that will drive business decisions. They also need to create a modern data environment in which to make that happen.

Below are a few trends in data management that will come to the fore in 2022.

Data lakes get more organized, but the unstructured data gap still exists

There are two approaches to enterprise data analytics. The first is taking data from business applications such as CRM and ERP and importing it into a data warehouse to feed BI tools. Now those data warehouses are moving to the cloud, with technologies like Snowflake. This approach is well understood, as the data has a consistent schema.

The second approach is to take any raw data and import it directly into a data lake without requiring any pre-processing. This is appealing because any type of data can be funneled into a data lake, and this is why Amazon S3 has become a massive data lake. The trouble is, some data is easier to process than others. For instance, log files, genomics data, audio, video, image files, and the like don’t fit neatly into data warehouses because they lack a consistent structure, which means it’s hard to search across the data. Because of this, data lakes end up becoming data swamps: it is too hard to search, extract and analyze what you need.

The big trend now and a continuing data trend for 2022 is the emergence of data lake houses, made popular by DataBricks, to create data lakes with semi-structured data that does have some semantic consistency. For example, an Excel file is like a database even though it isn’t one, so data lake houses leverage the consistent schema of semi-structured data. While this works for .csv files, Parquet files, and other semi-structured data, it still does not address the problem of unstructured data since this data has no obvious common structure. You need some way of indexing and inferring a common structure for unstructured data, so it can be optimized for data analytics. This optimization of unstructured data for analytics is a big area for innovation, especially since at least 80% of the world’s data today is unstructured.

Citizen science will be an influential, related 2022 trend

In an effort to democratize data science, cloud providers will be developing and releasing more machine learning applications and other building block tools such as domain-specific machine learning workflows. This is a seminal trend, because, over time, the level of what individuals will need to code is going to decrease. This will open up machine learning to many more job roles: some of these citizen scientists will be within central IT, and some will live within lines of business. Amazon Sagemaker Canvas is just one example of the low-code/no-code tools that we’re going to see more of in 2022. Citizen science is quite nascent, but it’s definitely where the market is heading and an upcoming data trend for 2022. Data platforms and data management solutions that provide consumer-like simplicity for users to search, extract and use data will gain prominence.

‘Right data’ analytics will surpass Big Data analytics as a key 2022 trend

Big Data is almost too big and is creating data swamps that are hard to leverage. Precisely finding the right data in place no matter where it was created and ingesting it for data analytics is a game-changer because it will save ample time and manual effort while delivering more relevant analysis. So, instead of Big Data, a new trend will be the development of so-called “right data analytics”.

Data analytics ‘in place’ will dominate

Some prognosticators say that the cloud data lake will be the ultimate place where data will be collected and processed for different research activities. While cloud data lakes will assuredly gain traction, data is piling up everywhere: on the edge, in the cloud, and in on-premises storage. This calls for the need to, in some cases, process and analyzes data where it is, versus moving it into a central location because it’s faster and cheaper to do so. How can you not only search for data at the edge, but also process a lot of it locally, before even sending it to the cloud? You might use cloud-based data analytics tools for larger, more complex projects. We will see more “edge clouds,” where the compute comes to the edge of the data center instead of the data going to the cloud.

Storage-agnostic data management will become a critical component of the modern data fabric

A data fabric is an architecture that provides visibility of data and the ability to move, replicate and access data across hybrid storage and cloud resources. Through near real-time analytics, it puts data owners in control of where their data lives across clouds and storage so that data can reside in the right place at the right time. IT and storage managers will choose data fabric architectures to unlock data from storage and enable data-centric vs. storage-centric management. For example, instead of storing all medical images on the same NAS, storage pros can use analytics and user feedback to segment these files, such as by copying medical images for access by machine learning in a clinical study or moving critical data to immutable cloud storage to defend against ransomware.

Multicloud will evolve with different data strategies

Many organizations today have a hybrid cloud environment in which the bulk of data is stored and backed up in private data centers across multiple vendor systems. As unstructured (file) data has grown exponentially, the cloud is being used as a secondary or tertiary storage tier. It can be difficult to see across the silos to manage costs, ensure performance and manage risk. As a result, IT leaders realize that extracting value from data across clouds and on-premises environments is a formidable challenge. Multicloud strategies work best when organizations use different clouds for different use cases and data sets. However, this brings about another issue: moving data is very expensive when and if you need to later move data from one cloud to another. A newer concept is to pull compute toward data that lives in one place. That central place could be a colocation center with direct links to cloud providers. Multicloud will evolve with different strategies: sometimes compute comes to your data, sometimes the data resides in multiple clouds.

Enterprises continue to come under increasing pressure to adopt data management strategies that will enable them to derive useful information from the data tsunami to drive critical business decisions. Data analytics will be central to this effort, as well as creating open and standards-based data fabrics that enable organizations to bring all this data under control for analysis and action.

This article on data analytics was originally published in VentureBeat and is reproduced with permission.

“The Olympics of Tech,” Web Summit, took place 1-4 November in Lisbon, Portugal. Despite Europe being in the middle of a pandemic, the world’s premier tech conference gathered 42,751 attendees. At a time of great uncertainty for many industries and, indeed, the world itself, “the grand conclave of the tech industry“ as the New York Times has named them, gathers the founders and CEOs of technology companies, fast-growing startups, investors, policymakers, and heads of state for an exchange. 

Web Summit is a company from Dublin, Ireland, that holds events worldwide: Web Summit in Lisbon and Tokyo, Collision in Toronto, and RISE in Hong Kong. No matter who was the speaker, no matter that COVID  is in the air, for most attendees, just being back at a big tech conference in person was a celebration in itself. Which testifies again that the human need for connection is as significant as the desire to live, and bigger than the fear of dying from the virus. 

Dataconomy belonged to the risk-takers and was present for four days to see, speak, observe, talk, feel, and connect with those redefining global technologies using data science and AI. Read on as we contextualize this for our audience, whether these startups are making good use of data or if they abuse our information. 

We met some fascinating startups advancing the human race by using data science. They all have three things in common:

1. They ask for your data, sometimes without clarity on how they’ll use it
2. They ask for permission to use your data 
3. They ask for permission to share your data with third parties (for a greater good, or maybe just to make money)

Dashbike is a german startup that has developed Dashcam, a crash detector and distance measurement device for bicycles. In cooperation with the city of Leipzig, the urban data platform Dashtrack was developed. On a voluntary and anonymous basis, cyclists can now make the sensor data of the Dashbike available to their city. The cities can use the data for their planning purposes to improve the infrastructure for cyclists in an efficient and determined way. This is an example of data used for the greater good.

Radix wants to decentralize the $360 trillion global financial market. They have been working on a DeFi solution since 2013 and have built a core network, Olympia, which already has over 200 million of its tokens staked. According to the founder Dan Hughes, Radix has launched and built a network that is already faster than Ethereum.

The question raised is where they got the 200 million tokens because a token is nothing else but personal data that has been de-identified. To re-identify a token can be possible depending on the company’s technical and business processes to help prevent reidentification.

However, Radix has announced Scrypto, an asset-oriented programming language for decentralized finance, so that developers have all the tools they need to build a more secure and faster DeFi platform.

We think that Thomas Hobbes would be very disappointed with the idea of decentralizing the finance sector.

FlyAgData is an IoT solution that helps farmers make the most effective decisions. To collect, store, visualize, and analyze in one place allows farmers to make data-driven decisions.

It is a device connected to agricultural machinery that collects agronomic and machine data from the equipment transforming it into valuable insights for the farmers through AI.

The FlyAgData device collects and processes the data from the machinery. The data is processed through APIs (Cropio, Wialon, Power BI, Google Data Studio, etc.), and then through Artificial Intelligence, it gathers insights with a high level of data accuracy.

Tebrio is an agtech “unicorn” startup. They’re building the largest negative carbon footprint industrial insect farm globally, transforming mealworms into biodegradable plastic, animal and fish feed, and biofertilizer. Its machinery is patented in 150 countries.

Global Mindpool is an initiative built in partnership between the United Nations Development Programme (UNDP) — UN’s development agency — and Mindpool, the Danish technology firm Mindpool specializing in collective intelligence. 

Do you remember the viral video of dinosaurs that entered the United Nations and gave a speech about climate change? That was from Global Mindpool. 

Global Mindpool uses the collective intelligence of people around the globe to take action against climate change. You can make your voice heard and contribute your knowledge, experience, and solutions through the platform. But how exactly can collective intelligence help tackle this crisis? 

Global Mindpool asks questions such as “Do you think climate change is a threat to your country?” and “How do you think climate change might affect you?”. You provide answers, and UNDP will channel your answers and amplify your voices to critical decision-makers around the globe.

YData is a data development platform. It offers a set of tools that helps data science teams to discover and unlock data sources, fast prototyping, experimentation with data, and productization of models. The cofounder Fabiana Clemente, a speaker at Web Summit, found an Achilles heel. Everybody talks about and is doing AI, but the truth is 80% of AI companies never reach production, and of those, 40% are not profitable. 80% of the time a data scientist spends doing their job is not building algorithms but data cleaning and preparation. That’s why Fabiana founded YData, a data development platform built for and by data scientists that helps them build a better dataset for AI. 

The question we raise is, do they use or abuse the data for the greater good? Stay tuned for more reports from our trip to Web Summit, where we’ll expand on this question and offer some answers. 

Big data, specifically with the help of artificial intelligence (AI), empowers insurance companies to make better financial decisions. Data science can help mitigate fraudulent claims, enhance risk management, optimize customer support, and predict future events, among many other benefits. The result is higher profits for insurance companies and lower premiums for their customers. 

In this article, we’ll look at three ways big data can help insurance companies manage their losses and protect their customers and why this is so beneficial for both parties. 

Detecting insurance fraud

Insurance fraud causes an estimated $34 billion worth of lost revenue for insurance companies. Detecting insurance fraud is difficult, as a thorough investigation can be very time-consuming and yield vague results. Typically, insurance fraud involves deliberate damage to an insured item or a staged event to trigger an insurance payout.

Insurance companies must consider this lost revenue when pricing out premiums for customers, which results in a higher overall price for insurance coverage. Unfortunately, like in many aspects of life, law-abiding citizens end up paying the price for the actions of a few dishonest individuals. 

In some cases, the cost of insurance prohibits some individuals from having it at all. In Canada, for instance, only 33% of adults with children report having a life insurance policy. Life insurance ownership is higher in the US at 52%, but this is still barely half of the country. 

But now, with technology giving insurance companies the tools to avoid losing money on fraudulent claims, life insurance can be more affordable for everyone. For example, big data combined with AI can create a virtual catalog of legitimate insurance claims and those discovered to be fraudulent. 

By using algorithms, you can detect similarities between fraudulent claims to “red flag” potentially fraudulent claims for further investigation. Image analysis can also pinpoint whether photos have been altered or time stamps have been changed in any way. 

Furthermore, AI can detect anomalies in a customer’s claim by providing an in-depth look at a variety of factors. For example, for an automobile insurer, AI can quickly and accurately analyze the reported location of an accident, the position of the vehicles, the speed of the crash, and the time of the incident. They can also detect inconsistencies by factoring in additional data such as reports from involved parties, injury details, vehicle damages, weather data, doctor’s notes, and prescriptions, and notes from law enforcement or auto body shop workers. 

Predictive analytics for risk management

In the past, insurance companies relied on broad-scale data for risk assessments. One commonly known fact is that young men pay higher insurance rates than young women or older men. This is based on statistics that show that teenagers, specifically those that are male, are more likely to drive above the speed limit or engage in risky behavior when behind the wheel.

Basing premiums on factors such as gender has met with some pushback for being discriminatory. However, developments in predictive analytics can help eliminate this issue by creating insurance rates that are customized for the individual. 

For example, the Snapshot device by automobile insurer Progressive can be hooked up to a customer’s car to provide personal data about the driver. Data like the rate of speed, amount of short stops, and the average amount of driving time and distance covered can be used to create a more accurate risk assessment for the individual driver. 

However, using big data to assess the lifestyle and habits of individuals comes with legitimate data privacy concerns for consumers. Insurance companies who want to use telematics devices such as Snapshot must take care to protect customer data privacy as they gather, store, and utilize user data. Depending on the country or even state the insurance company operates in, data breaches or compromised customer data can result in legal action or hefty fines.

Big data in health insurance

Big data is perhaps the most useful in health insurance scenarios when a variety of different factors can influence a patient’s risk of health concerns. For example, in the Affordable Care Act, federal legislation regarding health insurance premiums in the United States, health insurance companies can charge smokers a premium up to 50% higher than other patients. This is based on statistics that show that smokers are more likely to need extensive medical treatment due to the damage tobacco smoke causes to the lungs.

Health insurance companies can now gather sensitive health data through many other methods, such as smartwatches (such as FitBit) or health apps on mobile phones. They can also factor in a customer’s online behavior when paying out claims or detecting potential fraud. If, for example, a client reported having an expensive medical procedure on a particular day during which he was also very active on social media, this may raise red flags for further questioning. 

A group of former NBA players recently revealed how easy it is to commit health insurance fraud, racking up $3.9 million in fake claims, $2.5 million of which were paid out. The group’s scheme was discovered when one filed a claim for a pricy dental procedure in Beverly Hills during the same week he was playing televised basketball in Taiwan. Digital travel itineraries, email correspondence, and publicly available box scores helped prosecutors prove the fraud in court. 

Conclusion

The amount of data gathered by governments and corporations about individuals is a cause of concern for many. However, when placed in good hands and used for beneficial purposes, big data and AI can increase insurance companies’ profits and lower premiums for customers.  

By leveraging the power of AI to interpret large swathes of data, insurance companies can more accurately pinpoint fraud. They can also use this information to engage in predictive analytics that can help accurately assess risk levels. This all results in an insurance plan that is genuinely custom-fit for your lifestyle, providing rewards for your good behavior and ensuring you are covered for whatever life may throw at you in the future – as predicted by AI.

Our world revolves around data. Data science, machine learning, and AI have become some of the most critical technologies and industries in the 21st Century, penetrating almost every aspect of our lives, including fitness and wellbeing.

The impact of technology on fitness industry

On the consumer side, this technology triumvirate helps us determine the next movie we’ll likely want to watch on Netflix, the products we might enjoy from Amazon, and how a Google Home smart speaker recognizes and replies to our spoken questions.

On the supplier side, AI is considered a top strategic technology trend, according to Gartner. And according to a report from ResearchAndMarkets (COVID-19 Growth and Change), the global artificial intelligence market is expected to grow more than twice in just a couple of years – from $40.74 billion in 2020 to $99.94 billion in 2023.

While all three technologies have been shown to improve almost every aspect of healthcare – and provide patients with unparalleled advice and results – let’s focus on how data science, ML, and AI are being used in the fitness industry and what the future holds.  

The current state of data science, machine learning, and AI in fitness

Consumer apps

The global pandemic has caused a massive increase in the use of fitness apps as a way for people to both relieve lockdown boredom and to provide instruction and inspiration while gyms and personal training sessions are unavailable.

Whether it is the increased use of online training programs or virtual fitness studios from traditional gyms, fitness apps’ download and usage metrics have increased dramatically. According to an article published in the World Economic Forum in September 2020, the global downloads of health and fitness apps increased by 46%. The growing awareness regarding health and wellness is also driving the market.

And at the center of many of these apps is AI and all the technologies related to it.

FitnessAI

FitnessAI provides simple exercise tracking to pinpoint your strengths and weaknesses and work towards getting better in the areas that need attention. Your trainer uses this information, and the AI-powered recommendations, to inform you of the best exercises and routines for your specific needs with step-by-step tutorials on how to stay safe. With fitness data from more than 6 million workouts, the algorithm gives its users a tailored workout plan.

Aaptiv

Aaptiv includes a virtual AI fitness trainer, plus personalized workouts from real-life trainers. It takes data from eating habits, workout performance, fitness goals, your current fitness level, and information from wearable devices. A personal trainer in your pocket, it provides you with daily, weekly, or monthly personalized exercise plans to achieve your goals.

In addition to these, many of the long-established, big brand fitness apps use some level of predictive analytics, data science, machine learning, and AI to personalize programs, including those from Nike, Adidas, Samsung, Under Armour, and Google.

But not all AI fitness apps are smartphone-based. A new breed of services and products entered the scene recently: custom hardware.

Tempo

Tempo is an AI-powered personal trainer with a large screen, a raft of sensors, and a collection of weights. Looking a little like a cabinet with a TV on top, the built-in 3D sensors alert you any time your form isn’t correct, and the subscription delivers classes that help you get in shape and increase strength. Like Peloton and Mirror, Tempo joins the category of AI fitness solutions that include custom hardware purchasable through monthly finance.

Gyms, studios, and personal trainers

In a recent speech at World Health, Fitness & Wellness Week 2021, Harry Konstantinou, CEO and managing director at Viva Leisure, explained how they use data science to optimize its 100+ gyms.

Viva Leisure’s data showed a gym that had significantly more females as members than males. The company aims to have 45-55% equality between genders.

“When you have a club that has 70% females, you’re going to have the males following at that club,” Konstantinou said. “We made a quick change. We moved the cardio area out of the strength area. And we added more strength equipment in.”

That brought the numbers more in line with the company’s goals. It is now at 58% to 42%.

“We didn’t lose female members; we gained more male members,” Konstantinou said. “A $500k upgrade, once-off, created $500k in new yearly recurring revenue.”

Viva Leisure also uses technology to monitor each section of their gyms with its app and IoT beacons, giving them a “hot mapping” capability that helps them to optimize each studio to improve services for its members and increase revenues.

And solutions like Egym provide a mixture of gym management, smart devices, and white-label fitness apps to help you run a more efficient, profitable studio and customize workouts for every member to help them reach their personal goals.

Along with an array of off-the-shelf data analysis tools and custom-built solutions (Viva Leisure, for example, has an entire department dedicated to data science and analytics), it’s clear that the future of gyms lies in data analysis and insight creation.

Personal trainers are benefitting from these innovations too. Trainerize, for example, allows personal trainers to manage every aspect of their offering and analyzes user data at an individual level to provide a better, more personalized service. It connects to consumer apps like Mindbody, Evolution Nutrition, Fitbit, and MyFitnessPal to gather client performance information usually reserved for big-ticket gym management software.

The future of data-based fitness

In every category, and for individuals, gym owners, and personal trainers alike, a new breed of apps, software, sensors, and hardware is on the horizon.

Computer vision imitating real-life experience

Several AI coach apps are being developed for imminent launch that promise they will create the same customer experience as having a trainer right next to you. Instead of pre-recorded workouts or access to trainers via video calls, these apps will use AI to develop – in real-time – programs that not only push you to your limits and help you hit your goals but that adjust to your performance within that workout.

In other words, you might be doing the same workout as your friend, but these apps will instruct you differently based on fitness data captured from smartphone sensors, computer vision (using the phone camera to determine form), wearable devices, spoken feedback, and more.

Speaking of computer vision, it will become commonplace for our smartphones, laptops, and custom hardware solutions to use “human pose estimation.” This branch of AI assesses your skeletal system, contours, and volume, giving the ability for fitness products to help correct your form and measure your body shape. While there are a few examples of this technology in use now – such as in the yoga app Zenia – you can expect almost every fitness app and gym equipment to feature human pose estimation soon.

Computer vision won’t stop there, either. While there will need to be serious consideration regarding data privacy and security – hot topics when dealing with something as personal as health information – regular cameras are already being used for gym entry via facial recognition. It isn’t a giant leap of faith to see a future where everything from an in-studio camera to a high-quality, street-installed CCTV is used to measure your fitness activity and feed that data into your choice of app.

Wearables

We also see a significant increase in the production of wearable devices designed to be worn like regular clothes but that feed information into fitness solutions throughout the day. While smartwatches and fitness trackers are now commonplace – from manufacturers such as Fitbit, Apple, Huawei, Samsung, and Oura – the future promises smart jackets, legwear, and hats.

Purdue University has developed a method to transform existing cloth into a wirelessly chargeable wearable, resistant to stains and laundry. One application of this spray is to include a miniaturized cardiac monitoring system capable of monitoring the wearer’s health status in everything from a sweatband to a vest.

The future

In addition to these more obvious uses of AI and machine learning, we’ll see everything from new supplement and drug identification to improve performance, medical imaging (which is already in use with professional athletes but will filter down to regular consumers), and even voice analysis to understand how well you’re responding to your training and whether you are picking up ailments from over-training.

One thing is sure. The future of fitness, both on the consumer side and for those running wellness companies, will be data-driven. Artificial Intelligence, machine learning, and data science will become utterly ubiquitous in the industry, and any fitness solution that doesn’t focus on big data will become obsolete within the next five years.

This article was originally published at Neoteric and is reproduced with permission.

If there’s one topic that has captured the public’s attention consistently over the decades, it is this: have aliens visited Earth, and have we caught them in the act on camera? Unidentified Flying Objects (UFOs) and Unidentified Aerial Phenomena (UAPs) tick all the boxes regarding our love of conspiracy theories, explaining the unexplainable, and after-hours conversation starters.

As with many things in life, data may have the answer. From Peter Sturrock’s survey of professional astronomers that found nearly half of the respondents thought UFOs were worthy of scientific study, to the SETI@Home initiative, which used millions of home computers to process radio signal data in an attempt to find alien communications, UFOs and UAPs continue to fascinate the world.

However, the scientific community seems to have a dim view of studying these phenomena. A search of over 90,000 grants awarded by the National Science Foundation finds none addressing UFOs, UAPs, or related topics.

But the tide may be turning.

A US Intelligence report released in June 2021 (on UAPs specifically – the US military is keen to rebrand UFOs to avoid the “alien” stigma associated with the UFO acronym) has rekindled interest within a broad audience.

Among other findings, the report noted that 80 of the 144 reported sightings were caught by multiple sensors. However, it also stated that of those 144 sightings, the task force was “able to identify one reported UAP with high confidence. In that case, we identified the object as a large, deflating balloon. The others remain unexplained.”

UAP data requires new ways of working. The ability to fuse, analyze, and act on inherently spatial and temporal data in real-time requires new computing architectures beyond the first generation of big data. 

Vectorization and the quest to identify UFOs/UAPs

Enter “vectorization.” A next-generation technique, it allows for the analysis of data that tracks objects across space and time. Vectorization can be 100 times faster than prior generation computing frameworks. And it has the attention of significant players, such as Intel and NVIDIA, which are both pointing towards vectorization as the next big thing in accelerating computing.

NORAD and USNORTHCOM’s Pathfinder initiative aims to better track and assess objects through the air, sea, and land through a multitude of fused sensor readings. As part of the program, it will be ‘vectorizing’ targets. One company helping to make sense of this is Kinetica, a vectorization technology startup, which provides real-time analysis and visualization of the massive amounts of data the Pathfinder initiative monitors.

“After a year-long prototyping effort with the Defense Innovation Unit, Kinetica was selected to support the North American Aerospace Defense Command and Northern Command Pathfinder program to deliver a real-time, scalable database to analyze entities across space and time,” Amit Vij, president and cofounder at Kinetica, told me. “The ability to fuse, analyze, and act across many different massive data streams in real-time has helped NORAD and USNORTHCOM enhance situational awareness and model possible outcomes while accessing risks.”

The platform allows data scientists and other stakeholders to reduce the technology footprint and consolidate information to increase operational efficiency.

“Military operators can deepen their data analysis capabilities and increase their situational awareness across North America by combining functions currently performed by multiple isolated systems into a unified cloud database producing intelligence for leadership to act on in real-time,” Vij said. “Kinetica quickly ingests and correlates sensor data from airborne objects, builds feature-rich entities, and deepens the analysis capabilities of military operators. Teams of data scientists can then bring in their machine learning models for entity classification and anomaly detection.”

Parallel (data) universe

Vectorization technology is relatively new in data science and analysis and shows promise for specific applications. Vectorization is different from other data processing methodologies.

“Vectorization, or data-level parallelism, accelerates analytics exponentially by performing the same operation on different sets of data at once, for maximum performance and efficiency,” Nima Negahban, CEO and cofounder at Kinetica, told me. “Previous generation task-level parallelism can’t keep pace with the intense speed requirements to process IoT and machine data because it is limited to performing multiple tasks at one time.” 

The way we have dealt with these problems is unsustainable from a cost standpoint and other factors such as energy use.

“Prior generation big data analytics platforms seek to overcome these inefficiencies by throwing more cloud hardware at the problem, which still comes up short on performance and at a much higher cost,” Negahban said. “In an almost industry-agnostic revelation, companies can implement this style anywhere their data requires the same simple operation to be performed on multiple elements in a data set.”

How does that apply to the Pathfinder program and its objectives?

“For the Pathfinder program, vectorization enables better analysis and tracking of objects throughout the air, sea, and land through a multitude of fused sensor readings much faster and with less processor power,” Negahban said. “The technology’s speed and ability to identify the rate of change/direction attributes algorithms that can disguise planes, missiles and potentially help the government better understand what these UAPs or UFOs really are. This means that NORAD can understand what they see in the sky much faster than before, and with much less cost to the taxpayer!”

Vectorization technology is known for its high-speed results, and recent investments in the supporting infrastructure from some of the world’s most significant hardware manufacturers have helped advance the field.

“Every five to 10 years, an engineering breakthrough emerges that disrupts database software for the better,” Negahban said. “The last few years have seen the rise of new technologies like CUDA from Nvidia and advanced vector extensions from Intel that have dramatically shifted our ability to apply vectorization to data operations.”

Negahban likens the process, and the resulting speed vectorization achieves, to a symphony. 

“You can think of vector processing like an orchestra,” Negahban said. “The control unit is the conductor, and the instructions are a musical score. The processors are the violins and cellos. Each vector has only one control unit plus dozens of small processors. Each small processor receives the same instruction from the control unit. Each processor operates on a different section of memory. Hence, every processor has its own vector pointer. Vector instructions include mathematics, comparisons, data conversions, and bit functions. In this way, vector processing exploits the relational database model of rows and columns. This also means columnar tables fit well into vector processing.”

Data has the answer

We can’t have an article about UFOs and UAPs without talking about the sizeable grey lifeform in the room. I’ve been fascinated by the subject of flying objects and aliens since I was a child, but if I were an X-Files character, I’d be the ever-cynical Scully. So here’s one of my many hypotheses.

Throughout the 1980s and into the 90s, newspapers regularly featured “martian invaders” and other alien visitors, with front-page blurry photos and tabloid headlines. Caught mainly on 35mm cameras and basic video cameras, the images of cigar and saucer-shaped objects in the sky would always be blurry and debunked a few weeks later.

There are 3.6 billion smartphone users today. The majority of these devices have incredibly high-quality cameras. Not only that, but taking photos, capturing Instagram Stories, and recording TikTok videos is now so ubiquitous, the smartphone has become an extension of our arms.

Yet, we do not see countless videos or photos of UFOs and UAPs anymore. Sightings are rare compared to when there were significantly fewer cameras in use at any given time and when we used them with specific intention instead of part of our daily lives. So just how likely is it that any of these sightings are alien in origin versus human-made objects and natural phenomena? I couldn’t resist posing this to Kinetica.

“What we know from government-issued statements is that no conclusions have been drawn at this time,” Vij said. “The June 25th preliminary assessment of UAPs by Director of National Intelligence calls for an effort to ‘standardize the reporting, consolidate the data, and deepen the analysis that will allow for a more sophisticated analysis of UAP that is likely to deepen our understanding.'” 

If we are going to find an answer, it will be data-driven and not opinion-based, that’s for sure. 

“What’s interesting is that much of the data from radar, satellites, and military footage has been around for decades, but it was previously an intractable problem to fuse and analyze that volume and type of data until recently,” Vij said. “The answer to this question now feels within reach.”  

Vectorization technology certainly offers the performance and flexibility needed to help find the answers we all seek. How can the data science community take advantage?

“What has recently changed is that the vectorized hardware is now available in the cloud, making it more of a commodity,” Negahban said. This has allowed us to offer Kinetica as-a-service, reducing the traditional friction associated with what was traditionally viewed as exotic hardware, requiring specialized and scarce resources to utilize. Our goal is to take vectorization from extreme to mainstream, so we’ll continue to make it easier for developers to take advantage of this new paradigm.”

The truth is out there, and it’s being processed in parallel.

No industry is immune from the impact of technological, social, or economic disruption. Today, Chief Data Officers are playing a crucial role in navigating the complexities of the ever-changing world and engineering response to the changing demands of markets. 

So, what’s next for the Chief Data Officer role? How can CDOs navigate disruption, bring business value to their organizations, and, ultimately, get an invitation to the board of directors? 

In this article, you will learn:

1 – A brief overview of the Chief Data Officer role

2 – The current state of play & challenges for Chief Data Officers

3 – How to extract the true value of data & use cases

4 – What the future holds for CDOs

What are Chief Data Officers?

Chief Data Officer is in charge of developing and implementing how the organization acquires, manages, analyzes, and governs data. Chief Data Officers are responsible for putting data on the business agenda instead of treating data as a by-product of running a business.

Until the 1980s, the role of the data manager was far from being a senior position. The Chief Data Officer first appeared in the early 2000s. One of the first appointed Chief Data Officers was Cathryne Clay Doss of Capital One in 2002. Five years later, Usama Fayyad took the CDO role at Yahoo!. Today, Chief Data Officers are the driving force that leverages data to drive business outcomes.

Even though businesses recognize the importance of data leadership, data value is still a vague concept for many. Therefore, there’s a lack of meaningful metrics to measure the effectiveness of the Chief Data Officer role. 

Not only are CDOs expected to provide a 360-degree view of the company data that is usually scattered across multiple silos, but they are also expected to use data for the transformation of business models and, ultimately, increase revenues. 

When the inevitable business disruption occurs, CDOs are expected to drive the transformation, put in place the right tools and strategies for innovation and execution.

The two opposing trends

Last year, The World Economic Forum found that 84% of business leaders are currently “accelerating the digitization of work processes” and automating tasks. For many Chief Data Officers, the pandemic is the first significant disruption and a growth catalyst that is yet to be embraced and leveraged. 

Martin Guther (SAP’s VP, Platform&Technologies CoE MEE) says that the pandemic acted as an accelerator that enforces current trends rather than a disruptor by itself. He sees the current trends for CDOs coming from two very different perspectives – legislation and innovation. 

“CDOs need to look into how the data is taken care of. This is actually something that has been mandated by law for quite some time, and now it’s enforced – companies need to put a lot of work into how they treat the data that customers give them,” says Guther. 

On July 13th, we will be discussing the future of the CDO role together with the experts from SAP, Lufthansa Industry Solutions, HelloFresh & idealo. Apply to join our free CDO Club webinar.

The other trend is innovation – newer techniques to learn from mass data are booming. The usage of breakthrough technologies like AI and machine learning opens up the possibilities to extract insights from previously unavailable data.

The clash of legislative restriction and the increased technical capability for innovation challenges Chief Data Officers to keep a balance between compliance and drive innovation while reinforcing customer trust. In a data-driven world, a growing amount of business data contains personal or sensitive information. If applications use this data for statistical analysis, it must be protected to ensure privacy.

Extracting the value of data

We’ve all heard the saying circulating in the business world for the last decade – “data is the new oil.” But is it possible to measure the value of data to back up this statement? And if it is, then why do Chief Data Officers are struggling to measure their success?

According to SAP’s Martin Guther, data is an intangible asset that is often not valued with accounting standards. 

“Many companies know more about the value of office furniture than about the value of their data. Data is not represented anywhere in the company’s balance sheet – that presents a big challenge for CDOs as they need to find ways to prove the value of their work.”

So how can CDOs measure the value of data? 

“There are three quantifiable elements that drive the data value – incremental revenue, cost reduction, and risk mitigation,” says Guther. Simply collecting more data does not necessarily create more value. 

To extract the data value, CDOs need to look at a combination of three factors: data volume, data quality & data use. Chief Data Officers must actively manage each aspect. 

“All three drivers need to come together as they are multiplied by each other. If one element is out of the equation, the value won’t be extracted.” For example, the data volume and quality are top on the technical side, but the findings are not applied across departments on the organizational side. Ensuring the formula is used correctly on both technical and organizational levels is probably the most complex challenge for a CDO. 

How data helped Saturday Night Live show gain more viewership

For example, the media and entertainment industry quickly realized that extracting the data value from their content is key to long-term growth. In 2015, Michael Martin (SVP Product, Technology and Operations at NBC Entertainment Digital) encountered a challenge – even though much of the SNL show’s library was online, the audience still wasn’t able to access the content they liked. As a result, only recent shows got the most viewership. 

To let fans discover SNL’s content in full, Martin’s team realized they need to address data to drive the viewer’s experience. SNL library wasn’t getting enough visibility because of a mismatch between the content and the metadata. The most reliable data consisted of dates, titles, and characters. The problem was that this data didn’t consider that titles were often vague to conceal a joke, fans didn’t know when the shows were aired, and character names were not always known.

Martin’s team used a mixture of metadata and semantics to model data and capture every character, cast member, season, impersonation, sketch, and each item’s characteristics. This reformulated approach to data mapped the library of the SNL show in much more detail and allowed the fans to discover and access their favorite content. 

Why letting data guide the way is critical

As for recent examples, both the freight and aviation industries experienced accelerated disruption due to the pandemic. With the worldwide approvals of the Covid-19 vaccine, the global distribution of vaccines in extremely demanding circumstances was a challenge for Lufthansa Industry Solutions. 

Susan Wegner, VP Artificial Intelligence & Data Analytics at Lufthansa Industry Solutions, is convinced that following the principle of “letting data guide the way” was crucial for adapting to novel and demanding circumstances. 

The overall network was severely affected by the pandemic, with borders closing and planes being grounded, since a considerable share of the cargo volume is usually transported in the bellies of passenger airplanes. 

“Besides transforming some of the passenger airplanes into cargo freighters, we have algorithms,  which optimize the production planning. With our freight forecast AI we had a clear competitive advantage and the ability to adapt quickly, because we knew we had the algorithms on our side”, says Wegner. 

AI algorithms Lufthansa Industry Solutions deploys were initially designed to learn and retrain themselves continuously. This was particularly beneficial since an unusual event like a pandemic does not paralyze the AI algorithms as they are not solely based on historical data. Thus, within a very short time of taking the pandemic into account, these algorithms adjusted their forecasts and calculations accordingly. 

Another critical element for successful adaptation was the way data and AI leaders managed their teams. AI and data teams worked closely together with the process and business departments. 

“With these cross-functional and diverse teams, we make sure that decisions in the process and business departments are based on the optimal amalgamation of data and experience. This proved a valid approach as we all knew quite early that not many of us have a lot of experience with pandemics, and data never lies”, says Wegner. 

While some departments analyzed data for decision-making purposes on demand, others utilized AI to automate processes and tasks, not only in terms of speed but process efficiency and costs, since the latter one is also quite important.

“From a leadership position, Covid changed a lot,” says Wegner. She took the role at the beginning of Covid-19 and could not meet the whole team in person. It was a huge challenge to build up relationships and trust, encourage and motivate the team virtually. 

“I was very lucky since the team is basically digital native and had great ideas on how to connect virtually. But personal meetings count, and therefore I tried to arrange additional one-to-ones always when it was safely possible.” 

From Decentralized Data Teams to Data Evangelism

Mina Saidze, Data Evangelist at idealo (a German price comparison service), shared how her company approached generating the most value from data. “The advantage of having a decentralized data team structure is that each unit can specialize in a certain domain. However, the pitfall is that silos can evolve over time and, hence, the lack of communication and collaboration hinders innovation”, says Saidze.

The company decided to combine the best of two worlds – a hybrid approach between decentralized domain specialization and inspiring an understanding of how important data is for an organization as a whole. 

The Data Leadership Team and the CTO approved the centralized Centre of Excellence and the Data Evangelist role to help strengthen the collaboration among tech and business units within the organization. 

Mina’s role is developing best practices, identifying relevant use cases, and building up an Analytics Community to tackle the current challenges.

What’s next for the CDO role? 

Even though organizations have woken up to the fact that data deserves a place on the board, the CDO role is not yet defined. Gartner estimates that by 2025, 90% of large organizations will have a CDO. So what does the future hold for the Chief Data Officer role?

“If a CDO wants to go into the direction of being a senior business leader, being a part of the board of directors, the data assets that she or he is managing and the value must be clear,” says Martin Guther.

Chief Data Officer is one of the most critical roles right now as it brings together innovation, compliance, technical & business perspectives. There’s a lot of risk yet a tremendous opportunity if you get it right.

According to Mina Saidze, a Chief Data Officer role should not be seen as encroachment by CIOs and CTOs. In fact, a CDO is a person that underpins the company strategy with data and ensures the data quality and generates value out of this asset.

“A future trend I observe is that CDOs do not treat data as a liability but as an opportunity. Until recently, many CDOs were focused on limiting the downsides of data, such as GDPR policies or ensuring data quality. Soon, we will need CDOs who have a vision and the know-how to generate new revenue streams for the company by developing data-driven products, services, and processes. An entrepreneurial mindset, paired with a data background can help the CDO of the future to succeed in this role”, said Saidze.

As Martin Guther states, “CDOs should ask themselves what they truly want to achieve. Do they want to be innovative thinkers and risk their ideas being too progressive to be applied in the organization? Do they want to be a technical master working behind the scenes? Or do they want to become a transformational leader acting across all these domains of expertise? I think that there’s a great opportunity to grow and take their place at the board of directors.”

You can meet the experts featured in this article on July 13th at 6 PM CET at the SAP & Data Natives CDO Club event. Apply to participate by filling out the form below:

Data science is now essential to e-commerce success. Targeting the right audience through advertising platforms is highly necessary to boost online sales as customers only want to look at relevant products or items they need. Artificial intelligence (AI), with the assistance of machine learning (ML), helps determine the target audience based on customer preferences and past browsing data, which help bring potential buyers and score inbound sales. 

Similarly, suggesting the right products to customers on a platform also helps bring in more sales. E-commerce services like Amazon and Alibaba use data science to power predictive recommendations which help in suggesting various products that users will like. 

For advertising products on platforms like Facebook and Google that act as mediums through which e-commerce companies can run ads, there is heavy dependency on data science to show relevant ads to potential buyers. For instance, when users search for specific products on Google, it would show relevant ads for the same product from different companies.

The accuracy of AI in determining potential buyers for specific products goes a long way in suggesting to them the product they would need immediately, resulting in immediate predicted sales. Without this, the chances of buyers stumbling upon the product they would definitely like and buy are relatively lower unless they are actively looking for a product.

Data Science in E-commerce

Data science powers predictive forecasting using various data sources, such as the historical data of sales, economic shifts, customer behavior, and searches. This empowers e-commerce companies by promoting relevant products to potential buyers. Machine learning (ML) and artificial intelligence (AI) make it possible to provide shoppers with predictions based on what they like even before deciding to look for a product or if they need something in particular.

ML and AI get this done by analyzing the behavioral trends of customers and creating a relation between the past purchases. Customer sentiment analysis plays a significant role in identifying future sales prospects and the target audience, enabling direct marketing tactics and sales promotions.

Data science plays a significant role in investigating trends and discovering patterns in customer behavior and brand sentiments.

Analysts can use data science to analyze purchase patterns and develop strategies to increase sales and effectively stock the inventory. Businesses can further utilize data analytics to predict sales and demand, which helps companies make better decisions to advertise or stock up on specific products.

How is Data Science Boosting Sales in E-commerce?

There are many ways in which data science is boosting sales in the e-commerce domain. Some of these are: 

Recommendation Systems:

Data science powers recommendation systems that are entirely based on the past data of users alongside the heavy use of ML and AI to help e-commerce services give more relevant and accurate recommendations. This works like a charm and seems almost to recommend products that users will always wish to buy or at least show interest in. This translates to increased sales by producing the right product in front of the right buyer.

Recommendation systems are personalized according to customers and modeled with the help of user information, such as products a user is buying and pages a user is clicking on. Amazon’s recommendation system and Amazon Personalize have helped improve sales; both are an integral part of Amazon’s armory, which now controls 40% of total US e-commerce revenues.  Notably, according to Barilliance, product recommendations account for up to 31% of eCommerce site revenues.

Customer Feedback Analysis:

Data science allows e-commerce companies to work on their shortcomings by collecting the relevant feedback for each product or service and then taking action based on the collective analytics. Methods such as sentiment analysis and brand image analytics help companies understand what a customer or the target audience requires, increasing sales significantly.

E-commerce giants and startups use NLP or natural language processing, text analysis, text analytics, and computational linguistics to power analytics of this kind.

Inventory Management:

Data science allows established e-commerce companies and startups to manage their inventory more effectively. This also indirectly helps them not waste capital on unpopular products which are not selling well and have no need for restocking. Since e-commerce companies work with tons of customers and thousands of products daily, advanced data science is highly necessary to conduct accurate inventory management and predictive forecasting for future requirements.

Room and Board used predictive analysis to get around 2900% return on investment.

Customer Experience and Customer Service:

Data science helps ease and improve customer experience by automating a lot of functionalities and making regular things hassle-free with the help of feedback and analytics. These implementations can range from automated experiences to easier navigation.

As per reports, around 80% of customers are of the opinion that customer experience is also important and helps them come back to a specific site. In addition, determining preferences via social media can also improve customer service, and recommendations as many millennials and Gen Z have discovered products via social media platforms like Instagram.

ML is especially useful in customer service as it leads to better IVR and chatbot services which help solve customer issues more effectively with time.

Tools like Sentiment Analysis are quite good at understanding customer experience and helping companies retain them.

Does data science help e-commerce companies advertise better?

Yes, data science helps in advertising analytics as well. Also, advertising platforms run on AI and ML, using data science to perform various functions like audience targeting through behavior and other factors, such as demographics. Notably, data science allows e-commerce companies to run relevant advertising campaigns. 

How is machine learning used in online sales?

Machine learning promotes online sales in various ways, from virtual assistants to personalized recommendation engines. For example, ML helps convert more browsers or prospects into immediate buyers with the help of customized recommendations increasing the chances of conversion. Also, it helps in gathering new customers based on historical data. 

In Conclusion

Data science arms e-commerce giants with the power to reach out to their customers and provide them with a personalized experience.

This is quite certainly leading to an enhanced shopping experience for customers and increasing online sales for many e-commerce companies.

Data science has proved itself to be highly useful to gather customers as well as increase profits. 

When we imagine the future of AI, we may think of the fiction we see in cinema: highly advanced robots that can mimic humans so well as to be indistinguishable from them. It is true that the ability to quickly learn, process, and analyze information to make decisions is a key feature of artificial intelligence. 

But what most of us have come to know as AI actually belongs to a subdiscipline called machine learning. Artificial intelligence has become a catch-all term for several algorithmic fields of mathematics and computer science. There are some key differences between them that are important to understand to maximize their advancement potential. 

Experts predict that investment in AI will continue to grow, including the adoption of AI as a Service platforms, which will make machine learning programs more accessible to users without advanced technical expertise. Therefore, it’s important to take a deeper dive into how these technologies work and how they can be used to positively impact the future of data science. 

AI vs. ML

In short, AI can be thought of as a field or a class of technology that aims to simulate human intelligence in machines. Machine learning, in contrast, is the subfield in which computers are taught to learn from past data. 

Things we may call AI, like facial recognition, speech recognition, and anomaly detection, all belong to the deep learning and reinforcement learning categories of machine learning. In these disciplines, computers are taught to learn patterns so they can eventually perform recognition or categorization tasks without human intervention.

A potential key to unlocking the next level of AI is through the continued development of reinforcement learning. While traditional machine learning programs learn through historical data, reinforcement learning programs learn through trial and error. RL can be thought of as a “mature” learning technology adept at optimization, that is, maximizing or minimizing a particular outcome. 

A program takes a series of actions, and subsequent actions are informed by the best outcomes of previous ones. This trial and error takes time, but technology is always getting faster. In the future, we can expect reinforcement learning programs to operate at a level that produces efficient results much faster. 

Although the dystopian fears about rogue AI are largely overblown, like any technology, AI and ML are not without implications and limitations. But these technologies can also provide great advantages for companies by offering them innovative ways of organizing and analyzing data.

The benefits of AI and ML

The benefits of AI and ML include:

Security

Identifying opportunities and risks through machine learning has become critical in the field of cybersecurity. Machine learning programs can be used to help protect private data and keep security architecture operating smoothly. A good example of ML in cyber is Dynamic Application Security Testing (DAST), a program which communicates with web applications to identify potential security vulnerabilities in the app and the underlying architecture. 

According to the security analysts at Cloud Defense, “DAST is a type of black-box application testing that can test applications while they are running. When testing an application with DAST, you don’t need to have access to the source code to find vulnerabilities. You’ll then get notified if your project’s dependencies are affected by newly disclosed vulnerabilities.” This means vulnerability detection is becoming more efficient and comprehensive than ever. 

Once the scanner has identified a vulnerability, humans can then intervene and mitigate the issue. As “smart” as computers can be, ML programs do not have intuitions; they make decisions according to strict parameters and learned data. So, it’s still important for an IT expert to audit the scan after the process is complete to ensure maximal benefit.

Business logistics

The ability for a computer program to learn, organize, and analyze data on its own has led to the development of many business tools and applications. Market predictions, customer behaviors, and target demographics are just a few of the analytical areas in which machine learning can assist humans. 

Internally, companies can rely on machine learning algorithms to catch manual mistakes, increase speed and accuracy, and streamline business operations. Additionally, the prevalence of Big Data makes AI-driven marketing analytics a must for companies seeking to maximize their data analysis potential.

Customer outreach

With cloud data storage solutions increasing productivity and accessibility, more businesses are asking themselves how best to use customer data. As more data is collected, AI-powered analysis becomes more accurate, and B2B marketing efforts will see benefits from the information collected over time. 

We can expect to see customer interactions and preference-detection tailored with increasing speed. AI-based predictive analysis will give tech-savvy companies an undeniable advantage over their competition. 

The risks of AI and ML

The risks of AI and ML include:

The myth of sentient machines

There is a foreboding feeling that often accompanies the wonder at the speed and innovation of AI. Big names like Stephen Hawking, Elon Musk, and Bill Gates all warn of the potential dangers of AI  if humans don’t properly manage advancing technology. Popular books and movies have stoked the fear that machines will one day have minds of their own. There is some concern that destructive AI programs such as autonomous weapons could end up in the wrong hands. These concerns are not altogether misplaced. 

The two most recent US presidential elections, for example, brought to light how effective data mining algorithms can be in targeting social media users and the consequences of tampering with technology. 

But at its core, these interventions were not sentient machines; they were people using advanced technology for questionable purposes. The convenience and ubiquity of automation makes AI a powerful presence in our everyday lives, and, like anything, it must be managed through policy and ethics. 

Bad actors 

Another potential area of concern is cybersecurity. Cyberattacks are becoming increasingly complex and innovative. Just like any other artificial intelligence, AI-based malware AI is learning how to go up against AI-based cybersecurity tools too. We are entering into an era where the cybersecurity space may be a battle between good and bad machines. Fortunately, ML algorithms are good at anomaly detection. Cybersecurity professionals will have to continue to innovate in order to keep up with bad actors.

The future of data science

Currently, the limitations of artificial intelligence are related to the learning mechanism itself. Machines learn incrementally by basing future decisions on past data to produce a specific output. Humans, in contrast, are able to think abstractly, use context, and unlearn information that is no longer necessary.

Therefore, future machine learning algorithms will hopefully be able to engage in machine unlearning as well, particularly for digital assets like financial and personal data. This may be the next step in increasing security with AI and ameliorating some of its risks. 

Advances in AI will have a substantial impact on the future of data science, but machines are still not truly “intelligent” in the way humans tend to think of intelligence. Computers can put us to shame in terms of processing speed, but we have yet to create a program that is able to capture our own creative and logical abilities. Machines are a significant asset, but they are still only complimentary to human innovation. 

As we get ever closer to making fiction a reality, developments in AI are likely to happen in the disciplines of deep learning and reinforcement learning. These are some of the areas to watch when asking what’s next in the pursuit of artificial intelligence. 

Data science and AI are among the best (and highest paying) careers in the world right now, so it makes sense to keep increasing your knowledge, and learning from the best. But doing that in an age of information overload isn’t easy.

One way to stay ahead of the game is to ensure you’re reading the best material, and being inspired by the greats while you work from home (or wherever is safe and possible right now), and that means picking the best books and podcasts.

But with a dizzying amount of choice on these two important subjects, it can be hard to know what to read or listen to.

So we’ve done the hard work for you, and chosen the best recent books, and the top podcasts, on both data science and AI, so that you can save time and become better, faster.

Whether you want to brush up on data structures and algorithms, understand the intricacies of machine learning, gain direction and discover good processes, hear from giants in the industry, or be inspired with new ideas, the books and podcasts featured here will accelerate your learning.

Books on data science and AI

The Essential AI Handbook for Leaders presented by Peltarion, with a foreword by Marcus Wallenberg

The book is organized into three sections. The first reveals the possibilities of AI and how we can use it for business and society. The second explains the fundamentals of AI and how it works. The third presents how we can operationalize AI in the business world.

You can read a full review of this title here at Dataconomy, and Peltarion has been gracious enough to offer a free e-book download for all our readers and Data Natives community members.

A Common-Sense Guide to Data Structures and Algorithms: Level Up Your Core Programming Skills 1st Edition by Jay Wengrow

This excellent book is for those that find it difficult to grasp what is going on thanks to other texts being heavy on math jargon and obtuse concepts. It sets out to demystify computer science fundamentals, and does a fantastic job of doing so.

Machine Learning: 2 Books in 1: An Introduction Math Guide for Beginners to Understand Data Science Through the Business Applications by Samuel Hack

Broken into two distinct books, this resource breaks everything down into simple, easy-to-follow explanations of the foundations behind machine learning, from mathematical and statistical concepts to the programming behind them.

Introduction to Computation and Programming Using Python, third edition: With Application to Computational Modeling and Understanding Data by John V. Guttag

This book will take you from little or no Python experience to being able to use it and various Python libraries, including numpy, matplotlib, random, pandas, and sklearn, for problem solving. It covers computational techniques, and some data science tools and techniques, as well as machine learning.

The Atlas for the Aspiring Network Scientist Paperback by Michele Coscia

Billed as an “atlas” rather than focusing on helping you understand just one aspect of computer intelligence and data science, this books aims to help you chart your path to encompass all of the aspects of our field, and ultimately become something different; a pure network scientist.

AI and data science podcasts

Lex Fridman Podcast

While not solely about pure data science and AI, this podcast – which is billed as “conversations about the nature of intelligence, consciousness, love, and power” – is a real treasure trove of amazing discussions and interviews that will keep you inspired and engaged.

Talking Machines

While the team behind this podcast is taking a break to reflect on important issues such as Black Lives Matter, hosts Katherine Gorman and Neil Lawrence have built up an impressive library of episodes that include discussions with experts in the field, industry news, and useful answers to your machine learning questions.

Concerning AI

Ted Sarvata and Brandon Sanders are (at the time of writing) 70 episodes into their deep dive on how AI is affecting our daily lives, and whether it presents a risk to humanity, as many have suggested in recent history.

Data Skeptic

Centered on data science, machine learning, and artificial intelligence, Data Skeptic digs into each topic in detail. For example, a recent episode is a conversation with Yuqi Ouyang, who in his second year of PhD study at the University of Warwick in England, gives details on his work “Video Anomaly Detection by Estimating Likelihood of Representations.”

So there you have it. Four books and four podcasts to help you get ahead in data science and AI. Enjoy, and grow.

When you build products and launch them, are you – and be honest here – making decisions on each stage of development using data?

While for many years, everyone from development teams, product managers, founders, and marketers have touted they’re using a data-driven approach to everything; the truth is often starkly different.

Partly, that’s because we’re just creating what we technically can. Partly it’s because the wrong strategies, tactics, and foundations have been put in place, and processes aren’t followed.

With this in mind, The Tesseract Academy is delivering a free short introductory workshop on April 19, 2021, designed to help you make a real impact with your current or next product.

This is a free short introduction to the data-driven product boot camp delivered by Noam Auerbach. The full boot camp’s goal is to help product managers and other product practitioners level their decision-making skills. The free intro will present some case studies, such as how Soundcloud is building data-driven products. A Q&A session will follow where the participants can ask any question they like or get help with any issue related to data-driven product development.

Noam Auerbach is a Data Product Manager and consultant with 10+ years of experience in the field.

Noam is focused on scaling and monetizing platforms. He has been on the fine line between product management and growth throughout his career, driving retention and revenue. At the moment, he is the head of product & growth at Enhancv – the world’s leading resume builder and career development platform. Before that, he was the product lead at Tourlane (a Soquia Berlin-based traveling start-up), head of product at YEAY (Berlin-based video social shopping app), and the Growth PM at SoundCloud.

This event is perfect for CEOs, founders, managers, entrepreneurs, and product managers, which will equip you with tools and techniques to build agreement as you create your product strategy and roadmap.

Anyone interested in the free introductory workshop by The Tesseract Academy can simply register on Eventbrite.

When we think of data science, we rarely think beyond those people with the technical ability, knowledge, training, and qualifications necessary for the job.

But decision-makers need to be involved in data science too.

Whether you need to understand data science, know how to approach solution providers, or be better positioned to hire data scientists, having a solid foundation in data science and business strategy can be crucial to your organization.

The Tesseract Academy helps educate decision-makers on topics like what data science and AI are, how to think like a data scientist without being one, the fundamentals of hiring and managing data scientists, and building a data-centric culture. The Tesseract Academy runs a free event called the Data Science and AI clinic, to help decision-makers better understand how they can utilize data science in their companies.

“The attendees of our programs immerse themselves into the workshop and then come out of it with a clear, actionable plan,” Dr. Stylianos Kampakis, CEO and instructor at The Tesseract Academy, told me. “So, our workshops are crash courses for any non-technical professional who is thinking to use data science and doesn’t understand how. The most important part is the interactive exercises, which help drive the data strategy plan.”

Dr. Kampakis has been in data science and AI for many years and has worked with companies of all sizes, from solopreneurs to big corporates such as Vodafone. He is also a data science advisor for London Business School and works with various universities, including UCL and Cambridge University’s Judge Business School. He is also a published author.

For CEOs, founders, managers, entrepreneurs, and product managers, taking a data science workshop from a strategic and business perspective could give their businesses a competitive edge. After all, 2021 is looking to be a pivotal year in staying ahead of the game with data science.

“I know that executives are busy people,” Kampakis said. “That’s why I wanted to create something which can give them results as fast as possible. It’s a win-win because even I’ve seen people and companies grow due to my teachings, and they will always come back to me for further coaching later down the line. There is nothing more rewarding than seeing a client get ahead of the competition, as a result of the methods and tools I teach.”
Anyone interested can visit The Tesseract Academy’s website for further details and register for the event.

Data science is one of the hottest jobs in IT and one of the best paid too. And while it is essential to have the right academic background, it can also be crucial to back those up with the proper certifications.

Certifications are a great way to give you an edge as a data scientist; they provide you with validation, helping you get hired above others with similar qualifications and experience.

Data science certifications come in many forms. From universities to specific vendors, any of the following are recognized by the industry and will help you hone your skills while demonstrating that you fully understand this area of expertise and have a great work ethic.

Certified Analytics Professional

The Certified Analytics Professional (CAP) is a vendor-neutral certification. You need to meet specific criteria before you can take the CAP or the associate level aCAP exams. To qualify for the CAP certification, you’ll need three years of related experience if you have a master’s in a related field, five years of related experience if you hold a bachelor’s in a related field, and seven years of experience if you have any degree unrelated to analytics. To qualify for the aCAP exam, you will need a master’s degree and less than three years of related data or analytics experience.

The CAP certification program is sponsored by INFORMS and was created by teams of subject matter experts from practice, academia, and government.

The base price is $495 for an INFORMS member and $695 for non-members. You need to renew it every three years through professional development units.

Cloudera Certified Associate Data Analyst

The Cloudera Certified Associate (CCA) Data Analyst certification shows your ability as a SQL developer to pull and generate reports in Cloudera’s CDH environment using Impala and Hive. In a two-hour exam, you have to solve several customer problems and show your ability to analyze each scenario and “implement a technical solution with a high degree of precision.”

It costs $295 and is valid for two years.

Cloudera Certified Professional Data Engineer

Cloudera also provides a Certified Professional (CCP) Data Engineer certification. According to Cloudera, those looking to earn their CCP Data Engineer certification should have in-depth experience in data engineering and a “high-level of mastery” of common data science skills. The exam lasts four hours, and like its other certification, you’ll need to earn 70 percent or higher to pass.

The cost is $400 per attempt, and it is valid for three years.

DAMA International CDMP

The DAMA International CDMP certification is a program that allows data management professionals to enhance their personal and career goals.

The exam covers 14 topics and 11 knowledge areas, including big data, data management processes, and data ethics. DAMA also offers specialist exams, such as data modeling and design, and data governance.

Data Science Council of America Senior Data Scientist

The Data Science Council of America Senior Data Scientist certification program is for those with five or more years of research and analytics experience. There are five tracks, each with different focuses and requirements, and you’ll need a bachelor’s degree as a minimum. Some tracks require a master’s degree.

The cost is $650, and it expires after five years.

Data Science Council of America Principal Data Scientist

The Data Science Council of America also offers the Principal Data Scientist certification for data scientists with ten or more years of big data experience. The exam is designed for “seasoned and high-achiever Data Science thought and practice leaders.”

Costs range from $300 to $950, depending on which track you choose. Unlike the other certifications so far, this does not expire.

Google Professional Data Engineer Certification

The Google Professional Data Engineer certification is for those with basic knowledge of the Google Cloud Platform (GCP) and at least one year of experience designing and managing solutions using GCP. You are recommended to have at least three years of industry experience.

It costs $200, and the credentials don’t expire.

IBM Data Science Professional Certificate

The IBM Data Science Professional certificate comprises nine courses, covering everything from data science to open-source tools, Python to SQL, and more. In an online course, you’ll create a portfolio of projects as part of the certification, which is useful for employers who need to see practical examples of your work.

There is no charge for this course and no expiry.

Microsoft Azure AI Fundamentals

Microsoft’s Azure AI Fundamentals certification focuses on machine learning and AI but specific to Microsoft Azure services. A foundational course, it is suitable for those new to the field.

It costs $99 with no credentials expiry.

Microsoft Azure Data Scientist Associate

Microsoft also provides the Azure Data Scientist Associate certification focused on machine learning workloads on Azure. You’ll be tested on ML, AI, NLP, computer vision, and predictive analytics, and it requires more advanced knowledge of the field than its other certification program.

The cost is $165, and again, credentials don’t expire.

Open Group Certified Data Scientist

The Open Group Certified Data Scientist (Open CDS) certification is markedly different from the other programs listed here. There are no traditional training courses or exams. Instead, you gain levels of certification based on your experience and a board approval process.

The cost depends on which level you are applying for, but the minimum fee is $1,100 to reach level one. Credentials don’t expire.

TensorFlow Developer Certificate

The TensorFlow Developer Certificate is for those who want to show their machine learning skills using TensorFlow. You will need experience with ML and deep learning’s basic principles, building ML models, image recognition, NLP, and deep neural networks.

This certification costs $100 per exam, and credentials don’t expire.

Artificial intelligence saw rapid growth in 2020. The global pandemic and the resulting digital transformation forced upon companies and individuals as we moved from offices to homes accelerated AI usage and development.

And it seems that this pace will not slow down in 2021. Here are a few predictions on how AI will continue to dominate in the coming year.

AI investment will skyrocket

As noted in PWC’s annual AI predictions survey, this is probably the most straightforward prediction to make.

In that report, PWC noted that 86% of its respondents say that AI will be a “mainstream technology” at their company in 2021. Whether it is being used to offer better customer service, help stakeholders make better business decisions, innovate existing products and services (and create new ones), achieve significant cost savings, or increase productivity, a majority of organizations will use AI to give them a competitive advantage.

Indeed, you could argue that the use of AI within your company will be mandatory soon to survive, in the same way that Gartner once claimed that every business – large or small – needed a website by the year 2000 to stay alive.

This leads us to another prediction: one that allows all businesses to take advantage of what AI offers without hiring specialists.

AI as a Service will explode in 2021

While demand for AI is ever increasing, the talent pool is reducing. It isn’t easy to hire data scientists at this time, and it will become even more difficult as the need to incorporate AI in your business becomes more urgent.

One solution is to make use of the many AI as a Service platforms that are appearing. Using these platforms is a cost-effective and fast way to adopt artificial intelligence and integrate it into existing systems for smaller businesses. 

We’ve already witnessed these solutions scale dramatically in 2020, and we expect that to accelerate further in 2021. Indeed, the future of digital transformation is more likely to come from easy-to-use, easy to adopt platforms that bring the power of AI to every business, rather than just those that can afford to hire experts and build their own solutions.

Initially, we expect AIaaS platforms to be adopted for improved customer service, data analysis, and financial reporting. Still, we expect a wide range of AI capabilities to join the ever-growing AIaaS industry.

We’ll see more deep fake legislation, but it won’t fix the problem

Deep fake audio and video have been on the rise over the last three years. Famous examples, such as the fake Obama video that circulated in 2018, have highlighted the dangers of deep fake videos in the political sphere. A recent incident in India showed how deep fakes could have a real effect on voting and elections.

And while the state of California in the US passed a bill that made it illegal to circulate deep fake videos of politicians within 60 days of an election, it is clear that laws won’t deter the perpetrators and distributors of such videos.

In 2021, we expect to see more legislation around deep fakes – both punishing production and distribution – but what is needed is a better way to identify them. The Rochester Institute of Technology (RIT) in New York has built its own deep fake detection software, and a browser plugin called Reality Defender is helping to identify fake videos.

That being said, the answer may be non-technical. In the case of India’s deep fakes, a group of people noticed a slight anomaly in the mouth movements and raised the alarm. As more negative deep fakes are being circulated, it may be that a simple awareness campaign will be the best way to counter the effects.

There will be considerable advances in Federated Learning

With privacy and security becoming mainstream topics after several high profile cases and documentaries such as The Great Hack, it is becoming essential to ensure data privacy at all times.

Federated Learning helps to achieve that. Google describes how FL works in this way concerning mobile phones:

It works like this: your device downloads the current model, improves it by learning from data on your phone, and then summarizes the changes as a small, focused update. Only this update to the model is sent to the cloud, using encrypted communication, where it is immediately averaged with other user updates to improve the shared model. All the training data remains on your device, and no individual updates are stored in the cloud.

FL enables devices like mobile phones to collaboratively learn a shared prediction model while keeping the device’s training data instead of requiring the data to be uploaded and stored on a central server.

FL moves model training to the edge; smartphones, tablets, IoT devices, or even “organizations” such as hospitals that are required to operate under strict privacy constraints. Having personal data remain local is a substantial security benefit.

And since models sit on the device, the prediction process works even when there is no internet connectivity.

The number of papers written about FL has exploded in the last two years, and it looks like 2021 will be the year that FL will become a mainstay for anyone working in machine learning.

Data Natives Unlimited – Europe’s biggest data science and AI event – was forced out of its regular, “so Berlin” home this year thanks to the Covid-19 pandemic. What followed was an endeavor that exceeded our expectations.

With 5,000 attendees, over 150 speakers, incredible partners, fantastic volunteers, and a team that put their heart and soul into the event, we set out to create something unique; an online conference that felt every part as good as the offline events of last year.

“2020 has been the year where an unprecedented event changed our lives, and we had to change the way we work, live, and communicate,” CEO and founder at Dataconomy and Data Natives, Elena Poughia, said. “Digital transformation happened faster and accelerated more than ever expected, and similarly Data Natives Unlimited was the experience that we put together as a reaction to the situation.”

And the journey didn’t just start with the conference.

“It was more than an event,” Poughia said. “It was an experience that started from September, with a hackathon, which we found was the best way to find quick, innovative solutions to societal problems and challenges, and then it continued with discussion roundtables tackling important topics before coming to an end with the DN Unlimited conference.” 

And while we’re a little biased, we think we achieved exactly what we set out to do. Over three days, we brought you keynotes, discussions, networking, community features, and exclusive access that came as close to an in-person event as is possible across a screen. 

Opening the event on day one, Poughia set the scene with a talk that sparked some interesting discussions. Looking at a year we lived online, for the most part, Poughia’s keynote turned to the privacy, security, and transparency of our data, a commodity that is now more valuable than ever.

“The world produced an immense amount of data over the past months. It is our responsibility to handle this data with care – staying both private and transparent, sharing our data while protecting it, and always keeping in mind that impact is the new money,” Poughia said.

Chris Wiggins of the New York Times then explained the ins and outs of data-driven journalism and how the world’s oldest newspaper became the forerunner in the media industry by developing a data strategy for its core activities.

Speaking of news, two themes emerged across the first two days of DN Unlimited. Fake news and deep fakes; both of great concern to many.

Juan Carlos Medina Serrano presented his research on Tik Tok as the new gatekeeper of political information and social media’s power to increase societal polarisation. Weifeng Zhong fascinated us by NLP’s applications, predicting the next major political moves by analyzing propaganda content. And we heard from the likes of Thorsten Dittmar, Kathrin Steinbichler, and Alexandra Garatzogianni on deep fakes and fake news too.

“Based on the lessons learned in the last five years of tackling fake news, we can design the proper policy response to deepfakes, but we need to spot the risks early on,” said Areeq Chowdhury of WebRoots Democracy.

Of course, the battle for the presidency in the United States made its way into the conversation, especially given it was happening at the same time as the conference.

“Take the US election; 98% percent of disinformation didn’t require any AI at all to create a very compelling conspiracy. We need to learn to think critically about the media messages we are receiving,” added Kathryn Harrison of FixFake.

There is some hope for the future, however, as we start to see more data sovereignty solutions come to fruition worldwide.

“We are entering the third phase of internet development, where citizens create control over their data. This year is all about personal privacy and internet reliability,” said John Graham-Cumming

In addition to the discussions around how our data is used, we dove deep into another topic that keeps AI and machine learning advocates awake at night; ethics and data bias.

The opening keynote from Mia Shah-Dand, CEO at Lighthouse3 and Founder at Women in AI Ethics, talked about the crisis of ethics and diversity in AI.

“We can draw a direct line between a lack of diversity and AI bias. The questions we should ask ourselves before implementing algorithms are who participated, who was harmed, and who benefitted from our solutions,” Shah-Dand said.

Listening to Jessica Graves of Sefleuria, we realized that there is no technical reason algorithms can’t eventually learn to generate creative output if we give them access to the same inputs and feedback as humans.

The conversation around data also extended to governmental and regulatory policy. Anu Bradford, Alexander Juengling, Sebastien Toupy joined us on the main stage to talk about the “Brussels effect” – the EU’s unique power to influence global corporations and set the rules of the game while acting alone. We found out that neither a ‘hard’ or ‘soft’ Brexit will liberate the UK from the EU’s regulatory reach.

One of our main partners, IBM, had a stage that was booming with insightful talks. Noel Yuhanna and Kip Yego discussed how trust in AI should start with the concrete data foundation, and Jennifer Sukis and Dr. Robin Langerak walked us through the AI lifecycle.

“I haven’t been prouder than before when it comes to our content,” Poughia said. “We really managed to get very high level quality speakers, focused on a lot of interesting topics such as the pandemic, data monopolies, deep fakes, fake news, and other areas that impact our lives.” 

In addition to the conference content, we announced the winners of the DN Unlimited Hackathon we ran in September. Three winners created solutions to help bring adaptive learning to places where internet connectivity is weak, to accelerate precision medicine, and to help people measure their environmental impact.

Our EUvsVirus colleagues Michael Ionita, Urska Jez & Jesus del Valle concluded that realizing our wildest dreams is possible not only through hackathons but entirely online. The trend is here to stay.

Speaking of online, our attendees took full advantage of speaker Ask Me Anything (AMA) sessions, where participants could meet and greet our distinguished experts. Our Slack channels were buzzing with activity, and the various networking tools on offer helped to connect the masses.

“I’m glad we had a way to bring everyone together and to be connected on different mediums and formats,” Poughia said. “And it’s ‘DN Unlimited’ because there was no limit to the communication, and the connections that were made. We really were crossing all borders.”

We would love to bring Data Natives back in an offline capacity for 2021, and pandemic permitting, we’ll make that a reality. And while COVID-19 forced our hand, we couldn’t have wished for a better online event this year.

Thank you to everyone that participated, in whatever capacity – you made it special. We’ll see you all, in either two or three dimensions, next year.

If you are a Data Scientist wondering what companies could have the most career opportunities or an employer looking to hire the best data science talent but aren’t sure what titles to use in your job listings — a recent report using Diffbot’s Knowledge Graph could hold some answers for you.

According to Glassdoor, a Data Scientist is a person who “utilizes their analytical, statistical, and programming skills to collect, analyze, and interpret large data sets. They then use this information to develop data-driven solutions to difficult business challenges. Data Scientists commonly have a bachelor’s degree in statistics, math, computer science, or economics. Data Scientists have a wide range of technical competencies including: statistics and machine learning, coding languages, databases, machine learning, and reporting technologies.”

DATA SCIENCE COMPANIES: IBM tops the list of employers

Of all the top tech companies, it is no surprise that IBM has the largest Data Science workforce. Amazon and Microsoft have similar amounts of Data Science employees. Despite their popularity, Google and Apple are in the bottom two. Why is this the case? It could have something to do with their attitude to how to attract and retain a data scientist. The report does not clearly mention the reasons for these rankings. 

However, Data Scientists want to work for companies that provide them with the right challenges, the right tools, the right level of empowerment, and the right training and development. When these four come together harmoniously, it provides the right space for Data Scientists to thrive and excel at their jobs in their companies.

TOP FIVE COUNTRIES WITH DATA SCIENCE PROFESSIONALS: USA, India, UK, France, Canada

The United States contains more people with data science job titles than any other country. Glassdoor actually names “Data Scientist as the best job in the United States for 2019.”  After the United States are the following countries in this order:

• India
• United Kingdom
• France
• Canada
• Australia
• Germany
• Netherlands
• Italy
• Spain
• China

China has the least amount of data science job titles at 1,829 compared to the United States’ number of 152, 608. But what is the scenario for Data Scientists in Europe? What is the demand and supply? 

Key findings indicate that demand for Data Scientists far outweighs supply in Europe. The existence of a combination of established corporations and up-and-coming startups have given Data Scientists many great options to choose where they want to work. 

MOST SOUGHT AFTER DATA SCIENCE JOB ROLES: Data Scientist, Data Engineer and Database Administrator.

Among all companies, the most common job roles are Data Scientist, Data Engineer and Database Administrator. Data Scientist is the most common job role among all companies, with Database Administrator coming in at second place. If you remove Database Administrator, you find that Microsoft leads the way in terms of data science employees. This means that the reason for IBM’s lead in its data science workforce could largely be due to its sheer amount of Database Administrators. Unsurprisingly, across every job title in data science, males outnumber females 3:1 or more.  It is also interesting to note that this ratio only exists within the Database Administrator category. At the Data Scientist category, the ratio reads 6:1.

It also comes to no surprise that Data Scientist ranks number 1 in LinkedIn’s Top 10. It has a job score of 4.7, job satisfaction rating of 4.3 with 6,510 open positions paying a median base salary of $108,000 in the U.S. However, it is important to note that these positions do not work in isolation. A move towards Data Science collaboration is increasing the need for Data Scientists who can work alone and in a team as well. By utilizing the strengths of all the different job roles mentioned above, data science projects in companies remain manageable and their goals become more attainable. The main takeaway is that despite the vast amount of job titles, each role brings its own unique expertise to the table. 

DATA COLLECTION AND ANALYSIS

Diffbot is an AI startup whose Knowledge Graph automatically and instantly extracts structured data from any website. After rendering every web page and browser, it interprets them based on formatting, content, and web page type. With its record linking technology, Diffbot found the people currently employed in the data science industry at a point in time to provide an accurate representation of the statistics mentioned in this article. 

Whether you are experienced or thinking about getting into Data science, in this guide you will find out:

• Which cities top the chart when it comes to the highest data scientist salary available?
• Do Data Scientists like working with startups?
• Do they want to stick to a job for more than a couple of years?
• What tools should you learn to get the highest data scientist salary?

We reveal all.

The Data Scientist is often a storyteller presenting data insights to decision-makers in a way that is understandable and applicable to problem-solving.  

Investopedia

The role of Data Scientists has dramatically evolved over the last five years from mere data miners to complex problem solvers. From entertainment companies like Netflix to retail brands like Walmart – all have business models that now heavily rely on data intelligence.

Data Scientists collect, analyze, and interpret large volumes of data, in many cases, to improve a company’s operations. They develop statistical models that analyze data and detect patterns, trends, and relationships in data sets. This information is vital to predict consumer behavior or to identify business and operational risks. 

Data Scientist Salary – Is data scientist a high paying job?

For the year 2020, Glassdoor named Data Scientist as the third most desired job in the United States with more than 6500 openings and a median base data scientist salary of $107,801with a job satisfaction rate of 4.0.

When Glassdoor had named Data Scientist as the best job in the U.S, we published a scenario of Data Scientists’ jobs and salaries in Europe based on a report by Big Cloud.

Amidst this high demand of Data Scientists across the globe, it is not only difficult to hire Data Scientists, but also challenging to retain them.  Undoubtedly, salary is one of the major components when Data Scientists look at jobs and decide what is going to be their next big gig. A lot of experienced data scientists are increasingly freelancing to increase their salary.

Data scientist salary trends we picked up from a recent report by Big Cloud titled European Salary Report. This report delves into insights from over 1300 responses and 33 questions asked to professionals of all backgrounds, ages, and locations within the European region — the largest share of contributions being from Germany, France, UK, Netherlands, and Switzerland. 

Data Scientists prefer learning new skills on the job. Retain them!

Pay is a strong motivator although employees are willing to stay at their current company for longer – more so than before. This could be due to companies starting to understand the serious talent gap in the market and offering more competitive salaries to retain employees with new skills and increasing the overall average salary in the United States and Europe.

Comparing this to just 2% of employees staying with the same company for 10+ years, this data solidifies the ideology that Data Science is a fast-paced industry where Data Scientists and others in similar roles see value in learning new technologies and skills elsewhere. this comes as no surprise, as more companies recruit data scientist teams, salaries rise, and more specialist skills become sought after.

Python: The most popular modelling coding language for Data Scientists

70% of respondents said they use Python as their primary modelling coding language. This is a 10% increase from last year. A key skill to command at least an average data scientist salary. However, to go above an average salary more coding languages may be needed.

Broadly 9% use R, 4% use SQL, and 4% use Java. There were also 3% of respondents that said they don’t code.

In addition, 66% of respondents said their primary production coding language is also Python. These charts only highlight the top five primary modelling and production coding languages from the respondents. There were also 2% of use cases for C++ in modelling and 2% in Matlab. 

Most popular tools and methods used by Data Scientists

As high as 90% of participants chose Python as a tool they use regularly across Europe, which highlights just how universally accepted this data science tool is. Another 65% chose Jupyter notebooks and 60% chose SQL. When it came to Data Science methods, logistic regression, neural networks, and random forests were the top three most popular choices with roughly 56% of respondents claiming to use them.

Compared to a Data Scientists tool preferences, there is a much greater variety amongst their chosen data science methods. Other options in the survey (that didn’t make it to the top seven shown above) were 34% ensemble, 31% Bayesian techniques and 28% SVMs.

Expected Salary Increase by European Data Scientists

The Big Cloud survey asked its scientist respondents, ‘if you were moving jobs, what percentage salary increase do you think is realistic?’ A 23% majority expect to see an 11-15% increase in salary per year, with a further 19% expecting anywhere between 16-20%.

Upon comparing our 2019 results to the 2020 results, free food and equity/shares are more popular benefits now than they were than last year, with car/transport allowance and gym and leisure moving down the majority list instead.

If you are wondering where would a data scientist want to relocate for a new job? The top five places to relocate as a data scientist amongst European citizens were the United States, Germany, Switzerland, the UK, and France – the places with the highest data analytics salary on offer.

How much can you make as a data scientist?

Switzerland offers the highest salaries for Data Scientists

Here is a look at salaries in different cities in Europe (including the UK for now)- Switzerland leading the way!

Data Science salary in the United States

If you are on either coast this makes a difference, according to indeed the average data science salary is $123,785 and according to glassdoor its $113,436.

Entry-level can range the widest from $50,000 to $90,000. this probably depends on your prior background and education in your career and of course, location.

Seeing that the data analytics industry is young, its not surprising to see professionals more active in moving employers often as anyone with experience becomes far more valuable to companies and can reach manager status quickly.

Data Scientists are more than willing to work with startups

Across all industries in Europe, 68% of people do not currently work in start-ups. Despite this, the data science market remains open-minded, with 83% saying they would consider joining one in the future.

The respondents that currently work in start-up industries are primarily Technology/IT and Consulting (37%). Respondents from these two industries are also respectively the majority that would consider working for a start-up in the future (38%).

Which industry do Data scientists work in?

Consulting was the number one spot in the O’Reilly salary survey, followed by software and banking. No surprises there as these companies have the resources and huge amounts of data to go through. We fully expect data in insurance to be one of the biggest growth areas in 2021.

Final thoughts on a data scientist salary in 2020

Python is the most used although to increase your salary data science skills should continually be enhanced, especially start-ups looking for more rounded skills with SQL and Spark, for example, will only add to boost a data scientists salary.

O’Reilly’s Data Science Survey found that learning D3, visualization library in javascript can boost a salary by $8,000 a year.

SQL, Excel, R and Python are the most commonly used tools although we would suggest learning another to make your resume really stand out.

Lastly, familiarity and experience with cloud computing will also boost salaries, with respondents who use Amazon Elastic Mapreduce getting a boost of about $6,000 in their salaries.

Big data in 2020 is still growing with lots of companies still to recruit we think scientist salaries will continue to rise, and the remote opportunities will also grow as companies compete for experience, skills and need to widen their job search to all geographies to recruit this in-demand skill set.

Disclaimer: The content of this article is from Data Science Salary Report 2020 Europe by Big Cloud & O’Reilly Data Science Salary Survey

A sneak-peek into a few AI trends we picked for you from Data Natives 2019 – Europe’s coolest Data Science gathering.

We are about to enter 2020, a new decade in which Artificial Intelligence is expected to dominate almost all aspects of our lives- the way we live, the way we communicate, how we sleep, what we do at work and more. You may say it already does- and it is true. But I assume the dominance will magnify in the coming decade and humans will become even more conscious of tech affecting their life and the fact that AI is now living with them as a part of their everyday existence. McKinsey estimates AI techniques have the potential to create between $3.5T and $5.8T in value annually across nine business functions in 19 industries. The study equates this value-add to approximately 40% of the overall $9.5T to $15.4T annual impact that could be enabled by all analytical techniques. Something or the other makes us a part of this huge wave in the tech industry, even if we don’t realize it. Hence, the question we asked this year at Data Natives 2019, our yearly conference was “What makes us Tech?”– consciously or subconsciously. 

Elena Poughia, Founder and Head Curator at Data Natives and Managing Director Dataconomy Media  defines this move towards the future in a line,

“We are on a mission to make Data Science accessible, open, transparent and inclusive.”  

It is certainly difficult to capture the excitement and talks at this year’s Data Natives in one single piece as it included 7 days of 25+ satellite events, 8.5 hours of workshops, 8 hours of inspiring keynotes, 10 hours of panels on five stages and a 48 hours-long hackathon, over 3500 data enthusiasts and 182+ speakers. Hence, I decided to pick up a few major discussions and talks that define critical trends in AI for this year and the coming decade from Data Natives 2019. Here is a look: 

How human intelligence will rescue AI?

In the world of Data Scientists, it is now fashionable to call AI stupid. Unable to adapt to change, to be aware of itself and its actions, a simple performer of the algorithms created by the human hand; and especially supposed to be unfit to reproduce the functioning of a human brain. According to Dr Fanny Nusbaum, Chercheur Associé en Psychologie et Neurosciences, there is a form of condescension, of snobbery in these allegations.

“Insulting a machine is obviously not a problem. More seriously, this is an insult to some human beings. To understand, we must ask ourselves: what is intelligence?”

Fanny Nusbaum explains that intelligence is indeed a capacity for adaptation, but adaptation can take many forms. There is a global intelligence, based on the awareness allowing adaptation to new situations and an understanding of the world. Among the individuals demonstrating an optimal adaptation in this global thinking, one can find the great thinkers, philosophers or visionaries, called the “Philocognitives”. 

But there is also a specific intelligence, with adaptation through the execution of a task and whose representatives the most zealous, the “Ultracognitives”, can be high-level athletes, painters, musicians. This specific intelligence strangely looks like what AI does. A swim lane, admittedly, with little ability to adapt to change, perhaps, but the task is usually accomplished in a masterful way. Thus, rather than gargling a questionable scientific knowledge of what intelligence is, perhaps to become the heroes of an AI-frightened population, some experts would be better off seeking convergence between human and artificial intelligences that can certainly work miracles hand in hand.    

The role of AI in the Industrial Revolution

Alistair Nolan, a Senior Policy Analyst at the OECD, spoke about AI in the manufacturing sector. He emphasized that AI is now used in all phases of production, from industrial design to research. However, the rate of adoption of AI among manufacturers is low. This is a particular concern in a context where OECD economies have experienced a decline in the rate of labor productivity growth for some decades. Among other constraints, AI skills are everywhere scarce, and increasing the supply of skills should be a main public-sector goal. 

“All countries have a range of institutions that aim to accelerate technology diffusion, such as Fraunhofer in Germany, which operates applied technology centers that help test and prototype technologies. It is important that such institutions cater to the specific needs of firms that wish to adopt AI. Data policies, for instance, linking firms with data that they don’t know how to use to expertise that can create value from data is also important. This can be facilitated through voluntary data-sharing agreements that governments can help to broker. Policies that restrict cross-border flows of data should generally be avoided. And governments must ensure the right digital infrastructure, such as fiber-based broadband,” he said.

AI, its bias and the mainstream use

The AI Revolution is powerful, unstoppable, and affects every aspect of our lives.  It is fueled by data, and powered by AI practitioners. With great power comes great responsibility to bring trust, sustainability, and impact through AI.   

AI needs to be explainable, able to detect and fix bias, secure against malicious attacks, and traceable: where did the data come from, how is it being used?  The root cause of biased AI is often biased human decisions infused into historic data – we need to build diverse human teams to build and curate unbiased data.

Leading AI platforms offer capabilities for trust & security, low-code build-and-deploy, and co-creation, also lowering the barrier of entry with tools like AutoAI.  Design Thinking, visualization, and data journalism are a staple of successful AI teams.   Dr. Susara van den Heever, Executive Decision Scientist and Program Director, IBM Data Science Elite said that her team used these techniques to help James Fisher create a data strategy for offshore wind farming, and convince stakeholders of the value of AI.  

“AI will have a massive impact on building a sustainable world.  The team at IBM tackled emissions from the transport industry in a co-creation project with Siemens.  If each AI practitioner focuses some of their human intelligence on AI for Good, we will soon see the massive impact,” she says. 

The use of Data and AI in Healthcare 

Before we talk about how AI is changing healthcare, it is important to discuss the relevance of data in the healthcare industry. Bart De Witte, Founder HIPPO AI Foundation and a digital healthcare expert rightly says,

“Data isn’t a commodity, as data is people, and data reflects human life. Data monetization in healthcare will not only allow surveillance capitalism to enter into an even deeper layer of our lives. If future digital medicine is built on data monetization, this will be equivalent to the dispossession of the self. “

He mentioned that this can be the beginning of an unequal new social order, a social order incompatible with human freedom and autonomy. This approach forces the weakest people to involuntarily participate in a human experiment that is not based on consensus. In the long run, this could lead to a highly unequal balance of power between individuals or groups and corporations, or even between citizens and their governments. 

One might have reservations about the use of data in healthcare but we cannot deny the contribution of AI to this industry. Tjasa Zajc, Business Development and Communications Manager at Better emphasized on  “AI for increased equality between the sick and the healthy” in her talk. She noted that researchers are experimenting with AI software that is increasingly able to tell whether you suffer from Parkinson’s disease, schizophrenia, depression, or other types of mental disorders, simply from watching the way you type. AI-supported voice technologies are detecting our mood and help with psychological disorders, and machine vision technologies are recognizing what’s invisible to the human eye. Artificial pancreas — a closed-loop system automatically measuring glucose levels and regulating insulin delivery, is changing diabetes into an increasingly easier condition to manage.

“While a lot of problems plague healthcare, at the same time, many technological innovations are improving the situation for doctors and patients. We are in dire need of that because the need for healthcare is rising, and the shortage of healthcare workers is increasing,” she said.

The Future of AI in Europe 

According to McKinsey, the potential of Europe to deliver on AI and catch up against the most AI-ready countries such as the United States and emerging leaders like China is large. If Europe on average develops and diffuses AI according to its current assets and digital position relative to the world, it could add some €2.7 trillion, or 20 percent, to its combined economic output by 2030. If Europe were to catch up with the US AI frontier, a total of €3.6 trillion could be added to collective GDP in this period.

Why are some companies absorbing AI technologies while most others are not? Among the factors that stand out are their existing digital tools and capabilities and whether their workforce has the right skills to interact with AI and machines. Only 23 percent of European firms report that AI diffusion is independent of both previous digital technologies and the capabilities required to operate with those digital technologies; 64 percent report that AI adoption must be tied to digital capabilities, and 58 percent to digital tools. McKinsey reports that the two biggest barriers to AI adoption in European companies are linked to having the right workforce in place. 

The European Commission has identified Artificial Intelligence as an area of strategic importance for the digital economy, citing it’s cross-cutting applications to robotics, cognitive systems, and big data analytics. In an effort to support this, the Commission’s Horizon 2020 funding includes considerable funding AI, allocating €700M EU funding specifically. This panel of “future of AI in Europe”  was one of the most sought after panels at the conference by Eduard Lebedyuk, Sales Engineer at Intersystems, Alistair Nolan, Organisation for Economic Co-operation and Development at OECD and Nasir Zubairi, CEO at The LHoFT – Luxembourg House of Financial Technology, Taryn Andersen President & co-founder at Impulse4women & a jury Member at EIC SME Innovation Funding Instrument, Dr. Fanny Nusbaum Fondatrice et directrice du Centre PSYRENE, PSYchologie, REcherche, NEurosciences and moderated by Elena Poughia, Founder & CEO of Datanatives. 

AI and Ethics. Why all the fuss? 

Amidst all these innovations in AI that are affecting all sectors of the economy, the aspect that cannot and should not be forgotten is ‘Ethics in AI’. A talk by Dr. Toby Walsh, Professor of AI at the TU Berlin emphasized the need to call out bad behavior when it comes to ethics and wrongs in the world of AI. The most fascinating statement of his talk was when he said that the definition of “fair” itself is questionable. There are 21 definitions of ‘fair’ and most definitions are mutually incompatible unless the predictions are 100 percent accurate or groups are identical. In Artificial Intelligence, maximizing profit will give you a completely different solution “again” and a solution that is unlikely to be seen as fair. Hence, while AI does jobs for us, it is important to question what is “fair” and how we define it at every step. 

(The views expressed by the speakers at Data Natives 2019 are their own and the content of this article is inspired by their talks) 

Read a full event report on Data Natives 2019 here. 

What algorithms do dating apps use to find your next match? How is your personal data impacting your decision to go on a date? How is AI affecting your dating life?  Find out below.

Technology has changed the way we communicate, the way we move, and the way we consume content. It’s also changing the way we meet people. Looking for a partner online is a more common occurrence than searching for one in person. According to a study by Online Dating Magazine, there are almost 8,000 dating sites out there, so the opportunity and potential to find love is limitless. Besides presenting potential partners and the opportunity for love, these sites have another thing in common — data. Have you ever thought about how dating apps use the data you give them?   

How are dating apps using your data?

All dating applications ask the user for multiple levels of preferences in a partner, personality traits, and preferred hobbies, which raises the question: How do dating sites use this data? On the surface, it seems that they simply use this data to assist users in finding the best possible potential partner. Dating application users are frequently asked for their own location, height, profession, religion, hobbies, and interests. How do dating sites actually use this information as a call to action to find you a match? 

• Natural Language Processing (NLP) looks at social media feeds to make conclusions about users and assess potential compatibility with others. AI programs use this input to look for other users with similar input to present to the user. Furthermore, these programs learn user preferences based on profiles that they agree to or reject. Simply put, the application learns the types of people you are liking and will subsequently put more people like that in front of you to choose from. 
• Deep Learning (DL) sorts through facial features of profiles that you have “liked” or “disliked.” Depending on how homogenous your “likes” are, the variety of options presented to you will change. 

What algorithms are these dating apps using?

Hinge calls itself “the dating app that was designed to be deleted.” It uses a Nobel Prize winning algorithm to put its users together. Known as the Gale-Shipley algorithm, this method looks at users’ preferences, acceptances, and rejections to pair people together. Hinge presents this information to the user with a notification at the top of the screen that lets the person know of high potential compatibility with the given profile. Research shows that since launching this “Most Compatible” feature, Hinge been able to guide its users toward people more suited for them. Research shows that people were eight times more likely to swipe right and agree to a “most compatible” recommendation than the alternative without one. This is ultimately resulting in not only more relationships, but relationships of better quality as well. 

OkCupid’s algorithm uses a similar compatibility feature to match its users together. When filling out a profile for this dating app, users can respond to an extensive questionnaire about their personal traits as well as the traits they are looking for in a partner. For example, someone could report that they are very messy and looking for someone moderately messy. OkCupid would then present the user with potential partners who are moderately messy looking for people who are very messy. The algorithm goes one step further than simple response based matching, it ranks the importance of each trait to pair users as well. This approach must be working because OkCupid was the most mentioned dating app in the New York Times wedding section. 

Not all dating apps use this compatibility approach. Tinder, for instance, relies almost completely  on location and images to suggest potential partners to its users. The other aspect to Tinder’s algorithm is based on a desirability factor. In this case, the more “likes” you get will result in people being presented to you who also get a lot of “likes.” It also works in the opposite circumstance where users who don’t receive a lot of “likes” will be presented with people who also don’t receive a lot of “likes.” As a result, 1.6 billion swipes occur daily on Tinder.

A final example of algorithms in dating apps is how Bumble users can now filter preferences beyond personality traits, professions, and appearances. They are able to filter potential partners  by zodiac signs. In many cultures across the globe, astrological signs have been and continue to be used to measure the compatibility of a couple. Bumble’s AI program takes into account user preferences as well as sign compatibility when presenting a potential partner to its user. Matching zodiac signs is another instance of dating app technology working with user data to create the most compatible matches. The extensiveness of Bumble’s algorithm results in over 60% of matches leading to a conversation. See the chart below for the most popular zodiac signs according to a study of 40 million users by Jaumo. 

Conclusion

AI in dating sites goes beyond the individual’s knowledge of their own personality. It gets to know the users better than they know themselves. By monitoring both user input and user behavior, AI in dating applications truly gets to know the most holistic version of the user. It goes beyond the user’s own notion of themself to reveal truths about the type of partner they are  really looking for. The AI in dating apps aims to reconcile a user’s idealized version of a potential partner with the reality of the types of profiles they like. The trajectory of this revolutionizes the way data will continue to be used in AI mechanisms to help humanity achieve results on multiple platforms, even in dating.

As a relatively new role, “data guru” is a challenging job specification to draft for. Organisations are seeking highly-skilled and well-educated individuals to fulfil the position but, the truth is, the data scientist an organisation needs is not a guru, but a colleague.

Most organisations forget that recruiting the right talent is just as much about them as it is about the potential candidates. For example, does the organisation provide an interesting and successful environment for the data scientist to thrive in? Does it create new opportunities and positions for data scientists? Does it support its data scientists and allow them the freedom to work creatively?  

Understanding what data scientists look for is crucial when looking to recruit and retain the right data talent.

So, what makes a data scientist tick?

The fact of the matter is that the attrition rate for data scientists is very high. A recent poll by KDNuggets data scientists revealed that more than one in three expect to stay in their job for three years of less. There are a number of reasons that can lead to a data scientist deciding to hand in their notice, and often these things are in the organisation’s control – the company’s culture and technology available for the data scientists to use.

If the organisation doesn’t provide access to data and the tools necessary for data scientists to do their jobs well, it will lead to frustration. More importantly, these barriers make it difficult for data scientists to achieve their goals and perform to their best level, which understandably results in shorter tenures.  

Moreover, from a cultural perspective, many businesses aren’t quite up to speed with data. This starts with the C-suite: if senior management cannot see the value of a data-driven culture, then it will stifle efforts. A data scientist will soon feel under-appreciated and question the point of their analyses and recommendations if action isn’t being taken by the business.

Even if data is at the heart of the business, the data scientist is often left out of the decision-making process. Not only does this dissociate them from the hard work they have done, but it often leads to their work being misinterpreted, with the full benefits of the analyses being lost on the board.

What will draw a data scientist to work for a business?

1.The right challenge

Data scientists are often drawn to innovation – they want to be a part of it, to evoke it, and to drive it. First and foremost, you will attract data talent by ensuring that your organisation is pushing the boundaries of data analytics and use. Nothing is more engaging than a challenge, and data scientists want to be challenged by your company if they’re going to consider it as a place to work.

2. The right tools

This almost goes without saying. A good comparison is surgeons. You wouldn’t expect a heart surgeon to be able to carry out their job properly or effectively if they didn’t have the right tools or equipment available to them in the operating room. It’s the same for data scientists. Without the right tools in place, data professionals may only be working with partial, fragmented datasets or they may not have access to all the data they need, in order to gain the insights that will help to transform the business.

3. The right level of empowerment

With the right tools in place, people need to be given the space, time and trust to think and work creatively. Taking on-board their insights and actioning their suggestions will go a long way in making a data scientist feel appreciated and included in the company’s success.

4. The right training and development

Innovation is a constant within data analytics – from new tools and developments to learning from others’ methods and implementations. It is important your data scientists are continuously challenged and are learning new skills to keep up with this ever-developing market. Your organisation should open up a dialogue with your data professionals, so that you know what they want, what they are good at, and what they need from you. Only then can you help them develop themselves and grow into an integral role for the business.

Conclusion – It takes two to data science

The hiring process is not a one-way affair – while the organisations must make the decision to hire a data scientist based on their skills and experience, the data scientist must also decide whether the organisation is the right place for them to grow and develop their career.

As soon as organisations start realising this, then they can work on becoming a more attractive and exciting business to work for – providing the right challenges, tools, culture and environment for data scientists to thrive. In doing so, the pool of prospective data professionals that are applying to work for the business will inevitably increase, enabling them to hire the best people and to help the business grow and maintain data science success moving forwards.

Although there are lots of similarities across Software Development  and Data Science , they also have three main differences: processes, tooling and behavior. Find out.

In my previous  article, I  talked about model governance and holistic model management. I received great response, along with some questions about the differences between Data Science and Software Development  workflow. As a response, this piece highlights the key differences in processes, tools and behavior between Data Science and software engineering teams, as well as best practices we’ve learned from years of serving successful model driven enterprises.

Why Understanding the Key Differences Between Data Science  and Software Development Matters

As Data Science  becomes a critical value driver for organizations of all sizes, business leaders who depend on both Data Science  and Software Development teams need to know how the two differ and how they should work together. Although there are lots of similarities across Software Development  and Data Science , they also have three main differences: processes, tooling and behavior. In practice, IT teams are typically responsible for enabling Data Science teams with infrastructure and tools. Because Data Science  looks similar to Software Development (they both involve writing code, right?), many IT leaders with the best intentions approach this problem with misguided assumptions, and ultimately undermine the Data Science teams they are trying to support.

Data Science  != Software Engineering

I. Process

Software engineering has well established methodologies for tracking progress such as agile points and burndown charts. Thus, managers can predict and control the process by using clearly defined metrics. Data Science  is different as research is more exploratory in nature. Data Science projects have goals such as building a model that predicts something, but like a research process, the desired end state isn’t known up front. This means Data Science  projects do not progress linearly through a lifecycle. There isn’t an agreed upon lifecycle definition for Data Science work and each organization uses its own. It would be hard for a research lab to predict the timing of a breakthrough drug discovery. In the same way, the inherent uncertainty of research makes it hard to track progress and predict the completion of Data Science  projects.

The second unique aspect of Data Science  work process is the concept of hit rate, which is the percentage of models actually being deployed and used by the business. Models created by Data Scientists are similar to leads in a sales funnel in the sense that only a portion of them will materialize. A team with 100 percent reliability is probably being too conservative and not taking on enough audacious projects. Alternatively, an unreliable team will rarely have meaningful impact from their projects. Even when a model didn’t get used by the business, it doesn’t mean it’s a waste of work or the model is bad. Like a good research team, Data Science  teams learn from their mistakes and document insights in searchable knowledge management systems. This is very different from Software Development where the intention is to put all the development to use in specific projects.

The third key difference in the model development process is the level of integration with other parts of the organization. Engineering is usually able to operate somewhat independently from other parts of the business. Engineering’s priorities are certainly aligned with other departments, but they generally don’t need to interact with marketing, finance or HR on a daily basis. In fact, the entire discipline of product management exists to help facilitate these conversations and translate needs and requirements. In contrast, a Data Science  team is most effective when it works closely with the business units who will use their models or analyses. Thus, Data Science team needs to organize themselves effectively to enable seamless, frequent cross-organization communication to iterate on model effectiveness. For example, to help business stakeholders collaborate on in-flight Data Science projects, it’s critical that Data Scientists have easy ways of sharing results with business users.

II. Tools and Infrastructure

There is a tremendous amount of innovation in the Data Science  open source ecosystem, including vibrant communities around R and Python, commercial packages like H2O and SAS, and rapidly advancing deep learning tools like TensorFlow that leverage powerful GPUs. Data Scientists should be able to easily test new packages and techniques, without IT bottlenecks or risking destabilizing the systems that their colleagues rely on. They need easy access to different languages so they can choose the right tool for the job. And they shouldn’t have to use different environments or silos when they switch languages. Although it is preferable to allow greater tool flexibility at the experimentation stage, once the project goes into deployment stage, higher technical validation bars and joint efforts with IT become key to success.  

On the infrastructure front, Data Scientists should be able to access large machines, specialized hardware for running experiments or doing exploratory analysis. They need to be able to easily use burst/elastic compute on demand, with minimal DevOps help. The infrastructure demands of Data Science  teams are also very different from those of engineering teams. For a data scientist, memory and CPU can be a bottleneck on their progress because much of their work involves computationally intensive experiments. For example, it can take 30 minutes to write code for an experiment that would take 8 hours to run on a laptop. Furthermore, compute capacity needs aren’t constant over the course of a Data Science  project, with burst compute consumption being the norm rather than the exception. Many Data Science techniques utilize large machines by parallelizing work across cores or loading more data into the memory.

III. Behavior   

With software, there is a notion of a correct answer and prescribed functionality, which means it’s possible to write tests that verify the intended behavior. This doesn’t hold for Data Science  work, because there is no “right” answer, only better or worse ones. Oftentimes, we’ll hear Data Scientists discuss how they are responsible for building a model as a product or making a slew of models that build on each other that impact business strategy. Unlike statistical models which assume that the distribution of data will remain the same, the distribution of data in machine learning are probabilistic, not deterministic. As a result, they drift and need constant feedback from end users. Data Science  managers often act as a bridge to the business lines and are focused on the quality and pace of the output. Evaluating the model and detecting distribution drift enables people to identify when to retrain the model. Rather than writing unit tests like software engineers, Data Scientists inspect outputs, then obtain feedback from business stakeholders to gauge the performance of their models. Effective models need to be constantly retrained to stay relevant as opposed to a “set it and forget it” workflow.

Final Thoughts

In general, there are several good practices for Data Scientists to learn from Software Development , but there are also some key differences to keep top of mind. The rigor and discipline that modern Software Development  has created is great and should be emulated where appropriate, but we must also realize that what Data Scientists build is fundamentally different from software engineers. Software Development and Data Science processes often intersect as software captures much of the data used by Data Scientists as well as serving as the “delivery vehicle” for many models. So the two disciplines, while distinct, should work alongside each other to ultimately drive business value. Understanding the fundamental nature of Data Science  work can set a solid foundation for companies to build value-added Data Science  teams with the support of senior leadership and IT team.

Here is why Data Scientists need to think like a behavioural economist or psychologist when they communicate or story tell their insights. This helps companies to take concrete and bias-free decisions to acquire customers, retain employees and deal with managers within the organization.

Picture this scenario: There are two investment firms which have each introduced competing products. Both firms have gathered information about their clients and are trying to group them in order to accurately match these new products with their clients’ needs. There is a high risk of losing clients if the firms don’t offer the right product fit or bother them with non targeted messaging. Furthermore, one of the companies wants to use this data to shake up the current internal processes to add value by being more competitive not only because of the introduction of new products but from reshaping the company as a whole.

Firm A has been using advanced clustering algorithms to identify different customer profiles in an attempt to target new clients to offer these new financial instruments. While the firm has been careful to gather pertinent behaviour profile information on these new clients, they have also been careful to address the needs of the IT department and keep the collection of data from transactions, social media behavior, client needs and life goals, etc. to a minimum and store as little as possible.

Meanwhile, Firm B follows a different approach. It knows the behaviour is messy, and that even a seemingly unimportant bit of data can turn out to be highly explanatory. The firm, therefore, collects as much as they legally can, stores it, and their IT department has also figured out how to deal with the large volume of data. The firm also gathers behavioural, process and performance data at different points in time regarding how old products have evolved and merges them with customer related behavioural data.

While both firms A and B are successful with the new release, the customer churn for firm B is lower as they were able to outline the main focus of customers. For instance, based on the data collected and the analysis, they knew which customers were checking their accounts during the weekend and could accordingly guide their customer service team.

This allowed the firm to have a new product in the market based on the real need of its customers and not just a  “gut feeling”. Additionally, the firm was able to efficiently staff up the customer service department and introduced, among other measures, earning-sharing mechanisms that fostered the openness and dedication of all departments to customers. This improved the customer journey and set the pace to transform and change the behaviour of the different departments that had touch points with the customers.

In this, factual example, there is only one more ingredient to add – customers were able to opt-in from the start knowing who, how and for what their data was used which decreased churn and increased their willingness and engagement with firm B. What we see here is that behaviour matters and when linked with data, it can completely change the rules of the game about engagement, customers and companies. It is important to think carefully about how bits of data might somehow reveal counterintuitive or unexpected aspects of a customer or employees behaviour.

This is a clear example of how data, used to support and to foster change requires a deep understanding of the human aspects that are behind it. Technology and data can make companies act smarter but human sciences and behaviour will differentiate them from the rest making change and transformation easier. If a change of mindset is required to turn a traditional company into a digital one where data-driven decision-making is at the core, data science needs to meet social sciences to think more broadly incorporating not only the economic context of the company but also its human dimension first.

Below are three key behavioural ideas that modern-day data scientists need to take into account:

Actions don ́t always follow the data

Data is supposed to be objective, as it might inform about facts that have taken place. The problem is how we transform data into information and information into knowledge for our decision-making process and actions hereafter. Human brains need to process information to make daily business judgments and decisions and it is at that point in which another kind of bias (and a strong one) comes into play – cognitive bias. This is a human ́s inalienable feature by default which makes us eat less if we sit in front of a mirror, or imitate others behaviour in an elevator when they stare at a certain place without asking.

This is the behavioural side that data scientists must have when they want to provide actionable data insights. They need to think like a behavioural economist or psychologist when they communicate or story tell their insights. Knowing one’s audience is key in this case. They need to jump out of their shoes and not only think of different ways that they might draw conclusions from their data, but they need to understand the minds of the managers who will use these insights as an input in their decision-making process and their eventual actions. This will allow them to coordinate behaviour and data to avoid the possible confirmation biases that managers may often fall prey to.

Good data sources – Strategic data foresight

As in the example mentioned at beginning of this article, data needs to be collected from multiple sources (such as social media, Net Promoter Score surveys etc.) and shouldn’t only focus on present needs. A cost-benefit analysis needs to be carried out to determine and size the data that will be gathered. Are we looking just to sell a new product or to be the best sellers of that new product? The more data we can combine and the further ahead into the future we can look will determine not only our success to provide actionable insights but to establish the need and reliability of the gathering-analysis-decision- action cycle within companies.

Furthermore, from the point in time in which we decide that this kind of analysis will be useful to the time in which we are able to draw conclusions, there is a time lapse. This delay requires us to think in advance about the experimental design, future hypothesis and the small nuances in behavioural data that can make the difference.

As an example, recall the experiment done in Stanford in 1975 in which a group of students had to classify the authenticity of suicide notes. Two groups were formed and after the experiment took place; both groups were assigned a score of either high or low based on their ability to choose the actual notes from the fake notes. Both groups were then told that they performed average as a whole. Despite this, the group that scored high points, still believed that they could accurately pick the actual notes from the fake notes and the group that scored low points believed that they couldn’t accurately pick the actual notes from the fake notes. This indicated that their perception was anchored to their assigned score rather than the actual ‘facts’ presented to them. This is something that data scientists need to consider not only in the next A/B test that they design but also in the way they collect and present data.

Harnessing the power of visual perception

One important part of a data scientist’s everyday job in corporations is to highlight actionable data insights that will drive change. With an array of code-free plug and play solutions and new tools, coding is becoming ‘easier’. The democratization of data science thanks to this, is bringing the power of analytics to more people’s hands and therefore the need of visualizations to convey real insights for less deep data analysts. Again, behaviour plays a big part in this. The need to understand the aspects of visual perception is becoming critical for data analytics and business intelligence professionals who need to design dashboards and tools to translate their findings into a managerial language and bridge the gap between the complex abstract thinking behind data analysis and the potential need for organizational change.

Conclusion

These are just three examples of the way in which behaviour and data are closely intertwined. This trend is here to stay and will grow as sound data analysis is adopted by more companies in their decision-making processes. In the new reality of data science, math/stats, coding and IT are not the only ingredients required to be a successful actor of change as a data scientist. Behaviour from data gathering to visualization are critical tools every data scientist should have in their toolbox. We are definitely at a crossroads between behaviour and data in which those who successfully incorporate the former to their analysis will have an advantage through a better understanding of not only their customers but also of the mindset of organizations.

References:

Zimbardo, Philip. “Stanford Prison Experiment.” Stanford Prison Experiment, 2019, www.prisonexp.org/.

How do you merge the perspectives of the data scientists, entrepreneurs, investors, student and CTO’s under one roof when it comes to data-driven decisions? How do you bring together the likes of Google and IBM with academics and universities to find answers to the most relevant questions in the world of data and technology? How do you combine the tech trends ranging from AI and Blockchain into digital health or data analytics into the field of sports? You would have got some of these answers if you were one of the 2000 enthusiasts to attend Data Natives 2018.

A hundred and thirty speakers shared their views on the most relevant tech trends affecting lives and economies across the globe. Here are a few highlights to make you relive parts of this conference through the perspective of some of our speakers.

Artificial Intelligence can destroy inequality between nations; here is how

Bart De Witte, Chair Faculty of Futur Medicine, Futur.io and Director Digital Health, IBM DACH, started his talk by explaining how social media platforms such as Facebook, Instagram, Snapchat are really good at capturing our attention by providing us with information, services and entertainment. These platforms resell our attention to advertisers and please their shareholders. It is well known that we are not their customers, we are their product.

“After providing you with information, I won’t resell your attention to advertisers to benefit some shareholders, but I hopefully will convince all —or at least many of you—that we as a collective have to resell our attention to humanity, and let us become a product for society, if we want to create a future that is desirable.  In 40 to 50 years from now, we will probably have access to a much broader form of AI that is capable of helping us to prevent, diagnose and treat diseases. We are currently creating the foundation of medical Artificial Intelligence that will permeate every aspect of our lives and health,” he says.

He continued by saying that we are most probably the only generation, that has the opportunity, to build medical Artificial Intelligence, that has the opportunity to further destroy inequality between different nations; progress of equality in one and the same nation and lastly, the real improvement of man.

He adds, “Technology is not a verb —it is capable of doing great things. But it doesn’t want to do great things. It doesn’t want anything. It’s up to us, as a collective to decide how we will develop AI in healthcare. Do we look at it, as a technology that allows us to build the next unicorns or do we look at it as something we as a collective can develop together in an open-sourced way, governed by all of us, following the rules of our societies, to serve both the needs and the wants humanity.”  

The relationship of Data with Machine Learning, Deep Learning and AI systems

How can you have a conference on tech and not hear straight from the horse’s mouth? Yes, we are talking about IBM. Romeo Kienzler, Chief Data Scientist, IBM Watson IoT delivered the Keynote at Data Natives. He says, “If data is the oil for Machine Learning, it is plutonium for Deep Learning and AI systems. The most important matters to address are openness, fairness and robustness. Training a deep learning neural network has become a commodity. So, who owns the most relevant data always wins.”  

Romeo gave Mozilla Commonvoice and DeepSpeech as an example of how to unlock AI. In addition, he emphasized the importance of data protection in order to prevent AI oligopoly. He gave the recent Uber hack as an example and encouraged attendees to encrypt their emails, use secure messengers like Signal or TOX, and of course to not forget their annual donation to the Wikimedia foundation.

Removing model bias against minorities and robustness against adversarial noise attacks was the second major point to address in his talk. Therefore IBM open-sourced all relevant tooling in that space which is all part of IBM One AI – in the cloud, in your data centre, on your desktop or on the IBM PowerAI supercomputer. Finally, he gave an outlook taking recent advancements in robotics, 3D printing and neural network synthesis (AI that creates AI) into account.

Are the drugs we consuming authentic? Blockchain can help you answer

The startups in the unconference room had pitches ranging from topics such as Blockchain in Health Tech to sports tech and “privacy by design” in GDPR.  One of the talks was by Lea Dias, CEO and CoFounder, Protolync who flew from Paris to talk at Data Natives 2018.

With the estimated cost of counterfeit drugs valued at $90 billion and cost of lives estimated at over 700 000, there is a growing imperative to bring traceability, transparency and security to the medication supply chain. Currently, the medication supply chain is flooded with fake drugs, incomplete and falsified information, complicated and complex supply chain logistics and error-prone manual processes, leading to inefficiencies, errors and wastage.

“Blockchain technology ensures seamless transparency and traceability of medications information along global supply chains, thereby improving inventory and stock management. Traceability of medications from manufacturer to customer verifies the authenticity of returned, recalled or missing medication, and provides transparency on consent in clinical trials, hence improving the quality and reliability of clinical trial data,” she says.

In addition, Blockchain technology establishes an immutable hack-resistant permission-based ecosystem, eliminating fraud and allowing information to be shared securely with partners and clients without any fear of information leaks.

The evolving role of AI: What is ROI on AI?

More and more companies are claiming to use AI to improve their processes- but it can be difficult to assess the return on investment for AI-based solutions when no case studies or benchmarks exist. Not to mention, success in AI projects relies on a number of predetermined factors: having the right amount and right type of data, having the right staff to tackle the problem and having a use case where AI is appropriate, for example. Investors, entrepreneurs, intrapreneurs and companies will discuss the current state of ROI on AI and share some learnings.

Claudia Pohlink, Head of Intelligence at Deutsche Telekom Innovation Laboratories gave a perspective straight from the customer’s mind, “For our customers at Deutsche Telekom, AI plays a major role already today to keep the high quality of our network and customer interactions. But before you dream of AI, do your homework. And that means, clean up your data, establish a well-organized data management and take care of data quality.”

But what is the ROI on AI when it comes to venture capital investments.  Stephen Candelmo from Azafran Partners tells us, “In the end, ROI of AI is the same as any business…how are you saving the customer money or how are you helping the customer make more money?  Understand the specific metrics as to how you do that whether through human augmentation for greater efficiency in systems and processes or enhancing sales results through AI…”

Data and Research in Digital Health

Hours of research goes into any disruptive innovation and how could we not highlight the work of researches in the field. Johannes Starlinger, Project Leader Health Data Science & Digital Health Architect mentioned that in the Data Science in Perioperative Care Lab at the Department of Anesthesiology and Operative Intensive Care Medicine (CCM/CVK) at Charité – Universitätsmedizin Berlin, they combine data from a number of clinical sources to do statistical, predictive, and visual analyses.

“While this includes a large amount of structured data continuously recorded in our intensive care units, much important data recorded in clinical settings today is contained in free form text and not readily accessible to analytical processing,” he says.

He adds that for English language clinical text, tools, terminologies, and corpora are available to automate the extraction of structured data. For most other languages, including German, such tools and resources are rare and everyone has to build their own data extraction pipelines and tools sets from scratch – if they can get legal access to sufficiently sized corpora of clinical text at all. In fact, one of the most pertinent hurdles is the (un)availability of clinical texts to the community knowledgeable in building text mining systems. To overcome this hurdle and to fuel development in clinical natural language processing, we have to find ways to engage patients in safely sharing their medical documents (and data) with researchers within and outside the healthcare system itself.

Data Privacy and Data Ethics

You cannot end a conference on Big Data without talking about privacy, which was a major topic of discussion throughout the panels. Martin Lopatka, Senior Staff Data Scientist/Applied Statistician at Mozilla questions, “How do we perform research into web browsing activity in a privacy-respectful manner? We ask permission. We acknowledge and identify the biases of an opt-in data collection model and we supplement these shortcomings with non-personally identifiable data where possible. We try hard to have a human-readable privacy policy and get real informed consent by default. We believe in transparency around every study launched on this platform; as such, both the code and the results are discussed in the open.”

Dennis Grishin, CoFounder, Nebula Genomics says that when people talk about how a data-driven solution is going to have an impact, it’s usually about how to make data accessible, analyze it, use the gained insight etc: “However, with genomics, we are facing the unique challenge that the data that we want does not exist yet. The main challenge that we are facing is the data generation. How can we incentivize more people to sequence their genomes? What are the obstacles that deter them? How can we solve these issues? These are the challenges that we are trying to solve at Nebula Genomics. We believe that by addressing privacy concerns that many people have and shifting the sequencing costs to biopharma companies we can accelerate the beginning of a genomics age that is going to transform medical research and healthcare.”

Let us leave you with a fun question asked by Cassie Kozyrkov, Chief Decision Scientist at Google–  “What spaceship would you choose? One that never flew but you thoroughly tested it, or one where you have no idea how the thing flies, but it successfully flew many times before?” Cassie would pick the one she tested, as crafted testing is the basis of trust.

Elena Poughia, Managing Director at Dataconomy Media and Head Curator at Data Natives conference captures the essence of all the excitement we went through, “Data Natives exceeded our expectations this year. More attendees than anticipated eager to stay till the very last talk on the second day to learn. Hands-on nerdy content by unapologetically honest and sincere practitioners. It is apparent that Data Natives is more than a conference. Its a community of tech disruptors.”

Perhaps even moreso than even big data or blockchain, AI is fast becoming the buzzword on everyone’s lips. Machine learning has been a promising field for years, but with the astonishing success of deep learning techniques, we’re rapidly being propelled into an automated future. But can AI withstand the hype? What’s standing between us and successful large-scale adoption of ML and AI techniques?

Ahead of Data Natives 2018 on the 22nd & 23rd November, I talked to four key speakers in the AI space about the future of AI, and what transformations AI will (and won’t!) bring about.

On What the Public Needs to Know About AI

“AI needs data. If you have enough data, an AI can learn anything. This is stated by the universal function approximator theorem. Even a single hidden layer neural network can represent any mathematical function. Those functions are highly sought-after, since the allow us to control and predict nearly everything, ranging from a detailed psychological profile of an internet user to a complete model of the physical world. The individuals and corporations that own these models have power in their hands similar to a nuclear bomb. So let’s create clean energy from AI.”

Romeo Kienzler, Chief Data Scientist, IBM Watson IoT; read our full interview with Romeo here.

On AI’s Ability to Enhance Human Life

“AI is going to fundamentally change how we operate in our work lives, and our private lives. I see strong parallels to how personal computers revolutionized these spaces, freeing us from manual, repetitive tasks and allowing us to focus on more creative and strategy-intensive topics. AI will do this to an even greater degree, because it can actually support us in extremely difficult tasks, enhancing our most human features, intelligence and creativity. AI will become an omnipresent personal assistant in every aspect of our lives, maybe even developing real personalities. But this is maybe 10 years out still.”

Dr. Heiko Schmidle, Lead Data Scientist, DCMN

On Busting Through the AI Hype

“As most professionals know, there are two types of AI – the general AI and the narrow AI. Over time, it will probably be hard to draw a clear line between the two, but the today’s so called “narrow AI” is, in my view, just well-trained algorithms and, therefore, it should be actually referred to as such. Having said that, the term “AI” will certainly continue circulating around with respect to such algorithms at the very least for the marketing purpose. Nevetherless, I believe that the trained algorithms will become better and better exponentially. So, there are certainly many exciting innovations to be expected to emerge within just a couple of years. I, for one, am particularly curious about the merge of the machine learning and blockchain technologies, which can already be observed today. There are still not many companies that combine these two areas, but the potential of this, so to speak, collaboration is exciting and quite promising.”

Igor Drobiazko, CTO, elastic.io

On Combining Data Streams for Stronger Insights

“More data sets will be analyzed together for better insight. You’ll see bundles combining web, social, and Internet of Things (IoT) data in various combinations to see how different ideas from various areas of the online world weave together and influence one another. We’ll see, for example, how social media usage directly influences reliance on particular integrated IoT devices and vice versa. Eventually, the divide between different genres of data will erode and we’ll deal with data as a single entity.”

Justin Wyman, VP, Socialgist

Romeo, Heiko, Igor and Justin will all be speaking at Data Natives– the data-driven conference of the future, hosted in Dataconomy’s hometown of Berlin. On the 22nd & 23rd November, 110 speakers and 1,600 attendees will come together to explore the tech of tomorrow, including AI, big data, blockchain, and more. As well as two days of inspiring talks, Data Natives will also bring informative workshops, satellite events, art installations and food to our data-driven community, promising an immersive experience in the tech of tomorrow.

The pace of life and industry is accelerating at an unprecedented rate. Interconnected tech, inconceivably fast data processing capabilities and sophisticated methods of using this data all mean that we’re living in fast-forward. The Data Natives Conference 2018 will be exploring life at an accelerated pace, and what rapid innovation means for cutting-edge tech (blockchain, big data analytics, AI) across industries.

From governments to genomic projects, the quickening of life, work and research impacts every industry- and Data Natives 2018 offers two intense days of workshops, panels and talks to explore this impact. With more than 100 speakers presenting over 48 hours, the breadth of expertise at DN18 is vast; luckily, we’re here to help you curate your conference experience. The Data Natives content team have selected six talks that perfectly encapsulate this year’s topic and focus- trust us, these are six presentations you can’t afford to miss!

1. A 21st Century Paradox: Could Tech Be the Answer to Climate Change?

Climate change is one of the greatest concerns of our lifetime- and many are wondering if technology holds the answer to decelerating the impending climate disaster. Dr. Patrick Vetter of Supper und Supper will be presenting one use case which demonstrates the tangible benefits of ecotech: “Wind Turbine Segmentation in Satellite Images Using Deep Learning”. In layman’s terms, Dr. Vetter will be sharing the details of his project to optimise wind turbine placement using deep learning and analysis on “wind energy potential”. Exploring the potential of rapidly accelerating data technologies to curb the rapid acceleration of climate change, Dr. Vetter’s talk is definitely one to watch.

2. Cutting Through Propaganda: Government Policy Priorities in Practice

Any citizen of a democracy knows that there’s usually a huge gulf between the promises made in government officials’ election manifestos and what actually becomes policy. Cutting through the propaganda, is it possible to find a quantitative measure of the government’s priorities (and how they shift) over time? American Enterprise Institute Research Fellow Weifeng Zhong has been working on just such a measure: the Policy Change Index (PCI). Running machine learning algorithms on the People’s Daily, the official newspaper of the Communist Party of China, Zhong has found a way to infer significant shifts in policy direction. The PCI currently spans the past 60+ years of Chinese history- through the Great Leap Forward, the Cultural Revolution, and the economic reform program- and can now also make short-term predictions about China’s future policy directions. Zhong will be allowing us to glimpse under the hood of the PCI at Data Natives 2018, as well as sharing some of the more remarkable findings with us.

3. Blockchain: Beyond a Buzzword

Over our four editions of Data Natives, we’ve seen blockchain emerge from a promising but niche sphere into a full-blown game-changing technology. However, blockchain and decentralised computing are still shrouded in hype, and have a long way to go to garner full consumer trust. That’s where Elke Kunde, Solution Architect, Blockchain Technical Focalpoint DACH at IBM Deutschland, comes in. Her talk on “Blockchain in Practice” at Data Natives 2018 aims to demystify blockchain, slash through the hype, and enlighten the audience about how IBM clients are already using decentralised computing in their tech projects. This talk is a must-see for anyone who’s excited by the promise of blockchain, but still unclear on how exactly decentralisation can change the tech game- and their business- forever.

4. Using Machine Learning to Predict (and Hopefully Prevent) Crime

Predictive policing has been a hot topic for many years- and the technical methods behind it have become more sophisticated than ever before. Du Phan, a Data Scientist at Dataiku, will walk DN18 attendees through one particularly sophisticated model, which uses a variety of techniques including PostGIS, spatial mapping, time-series analyses, dimensionality reduction, and machine learning. As well as discussing how to visualise and model the multi-dimensional dataset, Phan will also discuss the ethical principles behind predictive policing- and what we can do to prevent crime rather than predict it.

5. Putting a Price on Personal Data

Data privacy and the price of personal data have been hot topics for years, coming to a boil with events such as the Cambridge Analytica scandal. Even Angela Merkel has declared that putting a price on personal data is “essential to ensure a fair world”- but how do we put a price on data, and how can this be enforced? Jennifer Lyn Morone- the artist who registered herself as a corporation and sold dossiers of her personal data in an art gallery- will discuss her perspective on these issues in a closing keynote for Data Natives which will bring the ethics of data science into focus.

Data Natives will take place on the 22nd and 23rd November at Kuhlhaus Berlin. For tickets and more information, please visit datanatives.io. 

The cart before the horse? What are we talking about:

You have the perfect scenario in front of you. You or your team just learnt about an exciting way to analyse data, found a new data-source that looks very promising, or were impressed by a new data visualisation tool. Understandably, you feel like you should put together a data-science project and show your organisation how you are exploring the frontiers of what is possible. Not only that but once you deliver the project you will make available to the organisation a completely new set of data-driven insights. What could go wrong?

Surely, those decision-makers in your organisation that always complain about the quality, quantity and availability of data will jump at this opportunity, adopt your project results, and move closer to true evidence-based management. Unfortunately, more often than not, after investing hard work and significant resources, the new and shiny data analytics solution is not taken up as expected. If this story sounds familiar to you, it is because it is a very common sight. Vendors, consultants and a long list of articles put the focus on the strengths and untapped potential of new analytics, data visualisations and the increasing volume and quality of data. All this creates excitement and an urge to act that is increased by the fear of missing out (FOMO). However, among all the rush to act we miss clarity about the actual problem that we are trying to solve.

Feeling the urge to act is not a bad thing, we need it in order to experiment and be agile. The problem is when we systematically put “the cart before the horse” in data-science projects. In our eagerness to try out new tools and data, we forget about what should be our real driver: helping our stakeholders to solve very concrete challenges by providing the best possible data-driven decision-support. This article is a call to go back to basics, to re-examine the drivers of our projects. My main aim here is to provide a few helpful tips to increase the chances of success and long-term adoption of data-science projects. For this, I use a simple set of guidelines that have the objective of re-calibrating our focus towards fundamental project drivers during the early stages of design and planning.

Back to basics: the driving challenge should always be in front of our cart

Before continuing, a note of caution: following the advice below will definitely slow you down at the start. This is a good thing. A data-science project can easily span multiple months, affect a large number of people and end up embedded within expensive systems filled with complex networks with hard to untangle dependencies. For this reason, it is best to front-load the conceptual and system level design work as much as possible, so that we can avoid hard-to-reverse mistakes later.

The main building blocks of the guideline are shown in figure 1 below. Overall, the idea is to start the process from left to right, drawing first from problem-driven approaches with a focus first on the “why”, and the “what” and only afterwards on the “how”. Once the first left to right is iteration is complete, we can move towards data-driven approaches, where the possibilities of data and tools become more prominent parts of our design and implementation decisions.

Figure 1: Overview of the problem-driven guidelines for data-science projects, including its key building blocks and relations

The proposed guideline is structured into six modular but interconnected building blocks that can be summarised as:

1. The project’s guiding challenge, which should act as the objective by which we ultimately evaluate success
2. The key questions that we want to answer with this project
3. The indicators (they can be often expressed in the form algorithms) that will help us find a data-driven answer to the posed questions
4. The data visualisation solutions that will help us communicate the indicators to change-agents / decision-makers / stakeholders
5. The analytics and data infrastructure necessary to produce and deploy the indicators and visualisations
6. The data that we will use to feed our indicators and visualisations

We should strive to clarify each of the six building blocks and their interfaces as early as possible in the project.  As we obtain new information during the course of the design and implementation of the project, we should be open to how such information affects the overall alignment and project scope.

In general, changes in the first building blocks have more cascading consequences that change further down the list. For this reason, we should put special attention to the definition of the overarching challenge and the key questions early on during the design process.

In contrast, conventional data-science approaches to model the logical steps in a project typically start the other way around. A case in point is the “data collection => data analysis => interpretation” model, where only at the end of the pipeline we get to understand the potential usefulness and value that the project delivers.

Applying this guideline in practice

Each data-science project is different and there is no silver bullet or one-size-fits-all solution. However, asking and collectively answering the right questions early on can be very helpful. Answering these questions contributes to making sure everyone is on the same page and exposes dangerous hidden assumptions, which might be either plain wrong or might not be shared among our key stakeholders. In this context, for the crucial first four building blocks we can identify a few important issues that should be discussed before starting any implementation work:

1) Challenge

• Challenge description: First, spend some time crafting a precise and clear formulation of the challenge that is compelling and shared across stakeholders. The challenge formulation should be such that we can come back at the end of the project and easily determine if the project was able or not to contribute to solving the challenge.
• Identification of main stakeholders: List the main challenge stakeholders and briefly describe their roles. The list might include employees from different departments, clients, providers, regulators, etc.
• Description of the “pain” that justifies investing in solving this challenge: Start by specifying the current situation (including currently used tools, methods, processes etc.) and their limitations. Then describe the desired situation (described as the ideal solution to the challenge)
• Expected net-total value of solving this challenge in $: Assuming you are able to reach an “ideal” solution, make an effort to quantify in monetary terms the value that the organisation can capture by solving this challenge. This should be expressed as the incremental value of going from the current situation to the desired situation without considering development costs. The objective of this is to provide some context for the development budget and the maximum total effort that can be justified to solve the challenge.
• List your assumptions: make explicit what is behind your assessment of the desired situation and the calculation of the expected incremental value of moving to the desired situation

2) Questions

• Description of each question: Here is where we define each of the key questions, whose answers are needed inputs to tackle the identified challenge.
  The questions should be described in ways that are answerable using data-driven algorithms. Typical questions can contain one or more of the following data-dimensions:
  
  – Where (geographical/place)
  – When (time)
  – What (object/entity)
  – Who (subject)
  – How (process)
  
  Example question: Who are the top organisations that I should first approach to develop a component for product “Y” and where are they located?  
• Goal of each question: Is the question descriptive, predictive or prescriptive? what is the description, prediction or prescription that the question is looking for?
• Ranking of the questions: Rank the questions according to their overall importance to the project, so that if necessary they can be prioritised.

3) Indicators

• Description of each indicator: The indicators are the algorithmic solutions to the posed questions. Although at an early stage we might not be able to define a fully fledged algorithm, we can express them at a higher level of abstraction, indicating the type of algorithmic solutions that are most helpful and achievable. For example, two indicators are:
  
  – Collaboration algorithm that provides a ranked list of potential collaborators for company [X] given relational, technological and geographical proximity
  
  – Capability mapping algorithm that identifies main technology clusters in a given industry based on the co-occurrence of key R&D-related terms

4) Data visualisations

• Define the target data visualisations: before writing any line of code or using any data, those that should benefit from the deliverables that the data-science project will produce can provide critical information about the data-visualisation formats that would be most useful for them.
  
  A simple but powerful approach is to ask those target users to produce sketches with the types of visual representations that they think would be the best means to communicate the results that the indicators in point 2 should produce.
  
  Other important features to consider when defining the data visualisations include simplicity, familiarity, intuitiveness, and fit with the question dimensions.

• Characteristic of each data visualisation: Characteristics of the data-visualisation solution include:
  
  – The degree of required interactivity
  – The number of dimensions that should display simultaneously

• Purpose of each data visualisation: The purpose of a visualisation can be:

– Exploration: provide free means to dig into the data and analyse relations without pre-defining specific insights or questions

– Narrative: the data visualisation is crafted to deliver a predefined message in a way that is convincing for the target user and aims at providing strong data-driven arguments

– Synthesis: The main focus of the visualisation is to integrate multiple angles of an often complex data-set condensing key features of the data in an intuitive and accessible format

– Analysis: the visualisation helps to divide an often large and complex data-set into smaller pieces, features or dimensions that can be treated separately

Wrap-up

Data science projects suffer from a tendency to over-emphasise the analytics, visualisations, data and infrastructure elements of the project too early on their design process. This translates in spending too little time in the early stages working on a joint definition of the challenge with project stakeholders, identifying the right questions, and on understanding the type of indicators and visualisations that are necessary and usable to answer those questions.

The guideline introduced in this article sought to share learning points derived from working with a new framework that helps to guide the early stages of data-driven projects and make more explicit the interdependencies between design decisions. The framework integrates elements from design and systems thinking as well as practical project experience.

The main building blocks of this framework were developed during work produced for EURITO (Grant Agreement n° 770420). EURITO is a European Union’s Horizon 2020 research and innovation framework project that seeks to build “Relevant, Inclusive, Timely, Trusted, and Open Research Innovation Indicators” leveraging new data sources and advanced analytics.

For more information about this and related topics you can visit www.parraguezr.net and www.eurito.eu

For more on how to make good questions I recommend the book “A More Beautiful Question: The Power of Inquiry to Spark Breakthrough Ideas” and the book “The Design Thinking Playbook: Mindful Digital Transformation of Teams, Products, Services, Businesses and Ecosystems” for ideas about how to include design thinking ideas in data science projects.

Pedro Parraguez will be speaking at Data Natives 2018– the data-driven conference of the future, hosted in Dataconomy’s hometown of Berlin. On the 22nd & 23rd November, 110 speakers and 1,600 attendees will come together to explore the tech of tomorrow. As well as two days of inspiring talks, Data Natives will also bring informative workshops, satellite events, art installations and food to our data-driven community, promising an immersive experience in the tech of tomorrow.

Business dynamics are evolving with every passing second. There is no doubt that the competition in today’s business world is much more intense than it was a decade ago. Companies are fighting to hold on to any advantages.

Digitalization and the introduction of machine learning into day-to-day business processes have created a prominent structural shift in the last decade. The algorithms have continuously improved and developed.

Every idea that has completely transformed our lives was initially met with criticism. Acceptance is always followed by skepticism, and only when the idea becomes reality does the mainstream truly accept it. At first, data integration, data visualization and data analytics were no different.

Incorporating data structures into business processes to reach a valuable conclusion is not a new practice. The methods, however, have continuously improved. Initially, such data was only available to the government, where they used it to make defense strategies. Ever heard of Enigma?

In the modern day, continuous development and improvement in data structures, along with the introduction of open source cloud-based platforms, has made it possible for everyone to access data. The commercialization of data has minimized public criticism and skepticism.

Companies now realize that data is knowledge and knowledge is power. Data is probably the most important asset a company owns. Businesses go to great lengths to obtain more information, improve the processes of data analytics and protect that data from potential theft. This is because nearly anything about a business can be revealed by crunching the right data.

It is impossible to reap the maximum benefit from data integration without incorporating the right kind of data structure. The foundation of a data-driven organization is laid on four pillars. It becomes increasingly difficult for any organization to thrive if it lacks any of the following features.

Here are the four key elements of a comprehensive data management system:

• Hybrid data management
• Unified governance
• Data science and machine learning
• Data analytics and visualization

Hybrid data management refers to the accessibility and repeated usage of the data. The primary step for incorporating a data-driven structure in your organization is to ensure that the data is available. Then you proceed by bringing all the departments within the business on board. The primary data structure unifies all the individual departments in a company and streamlines the flow of information between those departments.

If there is a communication gap between the departments, it will hinder the flow of information. Mismanagement of communication will result in chaos and havoc instead of increasing the efficiency of business operations.

Initially, strict rules and regulations governed data and restricted people from accessing it. The new form of data governance makes data accessible, but it also ensures security and protection. You can learn more about the new European Union General Data Protection Regulation (GDPR) law and unified data governance over here in Rob Thomas’ GDPR session.

The other two aspects of data management are concerned with data engineering. A spreadsheet full of numbers is of no use if it cannot be tailored to deduce some useful insights about business operations. This requires analytical skills to filter out irrelevant information. There are various visualization technologies that make it possible and easier for people to handle and comprehend data.

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With its unlimited size, an Enterprise Knowledge Graph contains all of an organization’s data — structured, unstructured, internal or external — presented as trillions of interlinked facts made available in any combination, on-demand to approved users.  The Enterprise Knowledge Graph enables organizations to take advantage of in-memory computing at cloud-scale to bring immediate access and analysis to everyone. These tools support intuitive, interactive, coherent and transparent query generation for all users—from laymen business users to IT veterans.

Fortified by advanced query and analytics mechanisms which traverse organization-wide data for real-time responses, Enterprise Knowledge Graphs hasten time-to-value to equip the business with a newfound command of its data. Simply by using the graph to achieve business objectives, IT leadership inevitably transforms its organizations with these strategic benefits reinforcing a data-driven culture:

• Data becomes understandable in business terms. Too often, the meaning of data is obscured by data storage definitions and terms meaningful to only a handful of technical and back office personnel.
• Citizen data scientists abound. Empowered by an understanding of data’s meaning due to its business terminology and the Enterprise Knowledge Graph’s interactive querying horsepower, business users become “citizen” data scientists accessing and deploying data at will.
• Future technologies and applications are guaranteed. Due to the machine-readable nature of data in an Enterprise Knowledge Graph, it’s amenable to whichever forms of artificial intelligence and machine learning become the most valuable tomorrow.
• Digital transformation is accelerated. An Enterprise Knowledge Graph provides a high-resolution, “digital twin” of all data that pushes digital transformation to the forefront of organizations.

The transcendent nature of these effects produces a profound impact upon organizations. The ensuing sense of empowerment increases trust in data and its processing so that data begins to feel like a true differentiator. In turn, its users—those who rely on data to do their jobs—take a greater sense of ownership as organizations become data-intensive.

The top four strategic differentiators of an Enterprise Knowledge Graph are:

1. Understanding the Data

An Enterprise Knowledge Graph is business-user accessible because it renders data’s meaning within the language of the business. The graph captures every value, data point, fact and relationship, which is markedly different than non-scaling graph approaches. The technologies attending these graphs automatically create human readable models of the data and their metadata, which aids in the understanding of how data relates to business terminology. Those technologies effectively construct a ‘mind map’ of the way data applies to organizational objectives. Data relationships are clearly defined and modeled with constructs identical to how they affect business processes, without the arcane coding of dated repositories or data sources. This approach allows the data to ‘speak’ the language of the business without the unnecessary technological intermediaries required to define relationships with other commonly used technologies reliant on tables, joins, and machine languages.

2. Army of Citizen Data Scientists

The Enterprise Knowledge Graph’s advanced querying capabilities exploit the improved understanding of data’s meaning to drastically reshape the business. By enabling simple conversations in which users ask and immediately answer questions of their data, it fulfills a task traditionally assigned to data scientists—getting the business data-driven insight. Thus, the Enterprise Knowledge Graph obliterate the business’s dependence on IT, placing data at the fingertips of those who actually use it, and simultaneously increasing user knowledge and workplace effectiveness. The automatically-generated query process turns business users into citizen data scientists responsible for procuring the data and analytics results themselves. These intuitive methods improve the enterprise’s workforce by making laymen users data-savvy. The result is an increased ability to meet business objectives coupled with an enhanced trust in data’s impact on the enterprise.

3. Future Preparation

Perhaps the most enduring effect of an Enterprise Knowledge Graph is its penchant for future-proofing the enterprise. In a tenuous world in which advancements in AI and machine learning occur daily, Enterprise Knowledge Graph users are assured of the capacity for deploying whichever technique becomes most viable due to the fundamental properties of these repositories. The technologies underpinning the Enterprise Knowledge Graph are machine readable and readily adapt to all forms of machine learning, Natural Language Processing, and other AI manifestations. Users are not required to decide on a technological option today which might become obsolete in the future. Investments in an Enterprise Knowledge Graph yield recurring returns by preparing the enterprise for whichever form of AI becomes most useful. These platforms are ideal sources for feeding emerging machine learning algorithms or, even better, for leveraging machine learning’s output to enhance data and their relationships within the Enterprise Knowledge Graph itself.

4. Accelerating Digital Transformation

The Enterprise Knowledge Graph spurs digital transformation, a vital necessity for contemporary IT processes. The foundational component of these repositories is a high-resolution, digital facsimile of all data found throughout an organization. This digital ‘twin’ encompasses all data points and connects these data with open standards focused on clarifying the data points and relationships between data elements. By fundamentally understanding the way all data relates throughout the enterprise, the Enterprise Knowledge Graph offers an added dimension of contextualization which informs everything from initial data discovery attempts to analytics. Whereas other methods of connecting data rely on hybrid replication approaches involving basic metadata and relationship information, the Enterprise Knowledge Graph’s digital twin includes comprehensive relationship understanding, metadata, and data assets. The encompassing nature of this platform is attributed to its scale, which suitably accommodates these facets of data with governance and security measures for lasting value.

The adoption rates of Enterprise Knowledge Graphs are directly related to their means of solving an expanding assortment of data-centered problems. Simply by using them, organizations attain gains which increase their capacity to monetize data. Deploying these platforms correlates to greater business understanding of what data means, how they interrelate, and their relationship to various business problems. Autonomous query tools enable casual, conversational interactions with data, which are fortified by an improved propensity for digital transformation. These platforms also future-proof the enterprise for impending data management developments.

By increasing business involvement with the fundamentals required for using data, the Enterprise Knowledge Graph effectively propagates the sort of culture required to consistently profit from data.

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Image Credit: Lukas Masuch

I recently read an article (paywall) in the WSJ about Paul Allen’s Vulcan initiative to curb illegal fishing. It’s insightful and sheds light on Big Data techniques to address societal problems. After thinking on the story, it struck me that it could be used as a pedagogical tool to synthesize data science with domain knowledge. To me, this stands as the biggest limitation of what I refer to as ‘data science thinking’– letting technical skills drive the analysis, only later incorporating domain understanding.

This post somewhat reads like a case note from business school and the idea is to get data scientists, product managers and engineers talking earlier on in the process. I’ve laid it out to provide sufficient context around illegal fishing and how one might develop models to answer the key business question: can illegal fishing be combatted through novel approaches?

Next I reframe the issue by considering how additional data can help narrow uncertainty and offer a fresh perspective on the problem. Finally, I seek to reconcile a science-driven approach with one that incorporates more domain thinking. I suggest the reader starts with the article (if you don’t have a sub, try Googling the article title and you might find it):

Context

Why do we care about illegal fishing and poaching? It raises multiple economic and environmental concerns:

• Global seafood consumption per capita is at an all time high
• Illegal fishing contributes to rapid depletion of fish stock
• The UN’s Sustainable Development Goals make many nations treaty-bound
• Around 20% of the global seafood catch is illegal, which hasdisproportionate impact on coastal and less diversified economies
• Loss of jobs, taxable revenue, lax safety standards
• Violence and international disputes

Monitoring/policing/enforcing illegal fishing activities is difficult for a variety of reasons:

• No Registry: No unique vessel ownership IDs, leading to reflagging, renaming and other tricks to mask ship identity/activity (oddly the International Maritime Organization requires persistent IDs for other types seafaring vessels and is dragging its heels/anchor when it comes to fishing vessels)
• Size: The oceans are very large and there is no magic technology to track assets
• Rogue Actors: Non-signatories to illegal fishing regimes may harbor bad actors
• Compliance: Enforcement activities are underfunded, lack centralization, training, etc…

So, the overarching business issue is how to use data to best stop illegal fishing?

From the article:

Australian government scientists and Vulcan Inc., Mr. Allen’s private company, have developed a notification system that alerts authorities when suspected pirate vessels from West Africa arrive at ports on remote Pacific islands and South America.

The system, announced Sunday U.S. time, relies on anticollision transponders installed on nearly all oceangoing craft as a requirement under maritime law. These devices are detectable by satellite.

A statistical model helps identify vessels whose transponders have been intentionally shut off. Other data identifies fishing boats that are loitering in risk areas, such as near national maritime boundaries.

The article references “anticollision transponders,” which is the AIS, used by maritime traffic to monitor/track all passenger ships and most cargo. Then there’s the bit about “statistical models” which I suppose is some flavor of machine learning to estimate when a transponder is turned off and a vessel is engaged in nefarious activity. How the “notification system alerts authorities when suspected pirate vessels…arrive at ports” if AIS is not active is unclear. Likely predicting movement in some way.

“Other data identifies fishing boats that are loitering in risk areas” is vague but maybe of immense value– does this mean other vessels visually identifying a target, a super large-scale sort of geofence, satellite imagery or something else?

Is this information sufficient to know where and when illegal fishing occurs? With what level of confidence? And how can we test our predictions? And what about ‘the last mile’ of relaying this to local authorities– does that happen in real time or is there a (say) week lag, further impeding enforcement? To proceed we need to better understand what we do not know, clarify what we do know and make some informed assumptions about how ocean fishing works.

What We Don’t Know

The answers below are clearly knowable, but arriving at the questions is the hard/interesting part. The relevant unknowns I identified are:

• How often is AIS relayed? Is it standard to have continuous broadcast or every x hours? Is the interval such that it will provide an area of uncertainty that is too vast to send maritime police to intercept?
• How extensive is AIS coverage? Just because an illegally fishing vessel turns off his transponder doesn’t mean anybody will know. Knowing which swaths of the earth are covered and how frequently they are refreshed by satellites is crucial info. To reiterate, the earth is a big place.
• Are AIS messages authentic/legitimate? AIS message types include a fair amount of metadata, some of which could be spoofed/incorrect in the hope of confusing enforcement regimes.
• Why would AIS be inactive? Was the transponder turned off because of a technical issue (loss of power), inadvertent (not knowing you unplugged the radio) or something else (pirates)? While a ‘dark ship’ may not indicate nefarious activity, a broadcasting ship does not imply full compliance.
• How Vulcan’s initiative fit with Leo’s World Fishing Watch or Pew’sProject Eyes On The Sea. From what I understand, all rely on AIS data but focus on different regions. I’d hope they collaborate on their different approaches but who knows.

Understanding Inputs

First, let’s look at (data) inputs. Below is a sample AIS message that has been formatted in JSON from source. The details don’t matter; essentially lng/lat is broadcast periodically with a bunch of attributes.

 {
    "day": 14, 
    "fix_type": 1, 
    "hour": 11, 
    "id": 4, 
    "minute": 33, 
    "mmsi": 2320717, 
    "month": 3, 
    "position_accuracy": 0, 
    "raim": false, 
    "repeat_indicator": 3, 
    "second": 30, 
    "slot_offset": 2250, 
    "slot_timeout": 0, 
    "sync_state": 0, 
    "transmission_ctl": 0, 
    "x": -5.782454967498779, 
    "y": 57.842193603515625, 
    "year": 2012
 }

I imagine looking at AIS data on a screen is what one would expect: you’ll see a blip/ship of whatever icon you choose, with vectors displaying bearing and speed. When the transponder is turned off, the blip/ship disappears. This graphic shows this (obvious) concept, but it illustrates a limitation of visualization/user interface– capturing temporal changes can be much more difficult if the user is not technical.

Now it’s time to think like somebody in the ocean fishing business. Why would you (willfully) turn off your transponder? What could induce the transponder to stop broadcasting without human intervention? How about unwillingly turning off the transponder?

Thinking about feature engineering, we can begin to get a sense of what patterns of activity might be indications of fishing– maybe a reduction in speed and/or irregular course changes? Of course there might be shallow/dangerous areas that require slower speeds, but we also have to consider geographic target areas, ie, where the fish are. This could also play into weather.

Does illegal fishing happen with multiple ships? Maybe a large trawler rendezvous with smaller fishing vessels to transfer to a larger cargo hold? Now we also need to understand interactions of multiple ships in proximity considering transponder status, movement and location. It is helpful to think of activities performed by vessels that might be indicative of illegal behavior.

So far we have developed a fairly complex model, and we have more work to do! There are multiple factors to consider:

• AIS status
• Vessel movement patterns
• Geographic location
• Ships in proximity
• Weather/environmental conditions

This gets even more complicated when thinking about probabilities as we lack a robust ground truth– an indisputable source to help train our model. Without it we will have models with an unknown level of confidence.

So after all this (and a lot of technical work), we may not have enough varied data to meaningfully impact illegal fishing. Sigh. However, there still might be value in a rule-based system that encodes domain knowledge as that will help all future parties (it could also create adverse behavior to game the system, as described above).

From AIS data alone our analysis would require boiling the ocean to arrive at a manageable solution set that would still be challenging, at best, to test. The good news is is this was done in a relatively short timeframe using one brain.

But Wait, There’s More Data Out There

Enter a second independent data source that could help increase our confidence identifying a bad actor. We’ll use some flavor of remote sensing which permits us to observe ship location. It is unclear if we can track ships with imagery alone (see below). Cloud cover and other environmental events might limit what we can infer or see. As with AIS, the important questions to ask about this data type:

• Revisit Rate: are there enough satellite passes to track ships?
• Resolution: Can a human or machine identify the entity or is the picture too coarse?
• Sensor type: A bit technical, but are optics used or another instrument? This can help when environmental conditions are not favorable (SAR, for example, doesn’t see clouds).

We’ve already defined a set of rule-based activities that indicate illegal fishing behaviours. The maritime industry would be a great source of knowledge and some common-sense ideas could also be considered.

Now let’s revisit our conceptual model using both data types– ship broadcast AIS and remote-sensed imagery. The beauty of this approach is that the sources are not correlated, meaning a change in one does not impact the other. With this independence of measurement, we can use one source to validate the other. For this example, what if every time an AIS transponder went dark we could light up the target vessel using imagery, allowing us to track it using a different data source?

The convergence and interplay of these two data sources are what allow us to derive signal— confidence with the ability to act. The approach is well-used in quant hedge funds but applicability to non-financial markets is vast.

To get to this point we sought to make explicit what we didn’t know and made (we hope) reasonable assumptions. By walking through the analysis it became clear that uncertainty was reduced by an order of magnitude when introducing the second data source.

Conclusion

Like many things, this may seem obvious, but hopefully only in hindsight. I wrote this case as a way to explain how domain-specific thinking can bolster data science. It is an emergent skill dominant in hedge funds with the rise of the quantalist. It’s not a poke at data scientists, but rather a gap in how they can best collaborate with product management and business strategy. Simply put, don’t spend time on high cost activities until it makes sense to do so, as represented below. This chart is a sort of conceptual Bayesian inference at its most simplistic.

Abstracting this specific example, I am interested in better understanding statistical theory and applying it to real world/actionable opportunities. This is written from a guy who doesn’t know how to install an R package, so be under no illusion that I have mysterious training. It’s a matter of deconstructing questions until they are manageable, then leveraging unique insight. That alone can help increase (technical and emotional) confidence while efficiently using resources. The basic approach is one encapsulated in superforecasting.

If you liked this post, you might enjoy my others on related topics. I’d appreciate knowing what you think about this post, good and bad. Thanks to Adam Smith, who unknowingly got me started on this post, Nathan Gould and Tyler Bell for giving valuable feedback!

This article originally appeared on Post-employment

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Data innovation and technology are a much discussed but rarely successfully implemented in large financial services firms.  Despite $480 Billion spent globally in 2016 on financial services IT, the pace of financial innovation from incumbents lags behind FinTech which received a comparatively puny $17 Billion in investment in 2016.  What lies behind the discrepancy?

We provide a unique vantage point, having pushed for enterprise-wide innovation from inside Credit Suisse and having worked closely with a dozen major financial institutions to develop and train their big data and innovation talent at The Data Incubator.  Drawing on that experience, we have identified four consistent obstacles to adoption of data and innovation.  These obstacles are: organizational structure, constrained budgets, data talent gap, and legacy cash-cow businesses.

Organizational structure

Large bank’s organizational structures block digital innovation, which demands a re-imagined value chain or further, true platforms that cut across traditional functional and hierarchical divisions.  Functionally, a typical bank organization consists of IT, usually a cost center, a product/solution manufacturing department, and client-facing or sales units. In an digital born company, these divisions do not exist and the firm leverages  a single automated platform that operates seamlessly across these activities.

It’s not just divisions but bank hierarchies that can impede innovation. Visionaries at the top may see the threat but be too distant from the day-to-day to adequately address it.  Millennial employees at the bottom are eager but not institutionally powerful enough, and the largest population, the middle management layer, defend their hard-won positions, fearing job losses to automation.

Constrained IT budgets

We often hear the argument that banks’ multi-hundred million dollar IT budgets allow them to outspend FinTech upstarts operating with limited resources.  But observers fail to note that keeping legacy systems alive and compliant with regulations consumes 80-90% of those bank budgets.  This is particularly for banks that run on disparate systems, loosely glued together, built through multiple acquisitions (which is the majority). Despite large budgets, banks actually have very limited resources for IT innovation.

They also operate on a timescale incompatible with contemporary technological developments.  Many still rely on a sequential (non-iterative) waterfall model for software design designed in the same era as their computer systems.  Meanwhile nimble FinTech upstarts use agile development processes that center on iterative feedback, cloud platforms, open-source code, mobile-first approaches deploying a structurally cheaper technology cost curve to reach customers.

Data Talent Gap

Companies (especially legacy ones with tonnes of historical databases) often speak about data as a strategic asset.  But even more important than having lots of data is the capacity to derive actionable analytics from it.  Often, data sharing is inhibited by competing departments looking to protect their database turf or an overly-strict division of responsibilities where IT needs to pull data from the system before analysts can analyze it.  These problems are compounded by non-standardized database systems that are a legacy of multiple acquisitions.  In contrast, Fintech startups employ a “permissions on by default” philosophy that democratizes and breaks down barriers to data access.  They invest in standardizing their data systems and hiring and training the best data scientists.  These jack-of-all trade analysts are capable of handling both data extraction and analysis and embedded across the company, building a self-service culture that cuts out delays and potential sources of error in the analytics pipeline.

Legacy cash-cow businesses

Banks are reluctant to cannibalize existing high margin businesses threatened by automation. Digital businesses reconfigure value chains, and operate at 1/10 to 1/100 the cost. Prices for many financial intermediation services are falling broadly, and digital automation and programmable APIs are facilitating interoperability and adding fuel to the fire. Banks realize this, and will milk cash cows as long as they can rather than accelerate the transition to the disintermediated digital world. Further, they may even miss the strategic implications of automation by co-opting digital solutions to defend established businesses. For example, using a robo-advisor to distribute proprietary ETFs defeats the very purpose of the robo-advisor and defers the inevitable unbundling of mutual fund and ETF structures that are no longer needed in a digital world that can assemble optimal portfolios with fractional shares tailored to a client’s unique risk profile.

How financial firms can innovate

To overcome many of these barriers, companies need to stop viewing data and innovation as cost-center functions and start viewing them as potentially business transformative capabilities. We’ve helped clients at large firms successfully implement “Innovation Groups” or “BIg Data Centers of Excellence.”  Regardless of the name, these departments spearhead efforts to drive innovation and data literacy throughout the company, working with key stakeholders to defining internal best practices, hosting firm-wide trainings to increase data literacy and data culture, and sponsoring internal accelerators to identify and promote innovative ideas originating from throughout the company.  When new innovative projects are identified, they often need to be put into a new division to free them from traditional org-chart bureaucracies and legacy cash cow businesses.

This risks involved can be daunting, especially given the substantial investment required to see any new initiative through.  Managers can mitigate the risks by borrowing a page from the VC handbook: making scaled, strategic bets, looking for quick wins and doubling down on early successes while being disciplined about shutting down unsuccessful projects.  Today’s financial-services incumbents face a true innovator’s dilemma and they must make the hard decisions necessary to innovate, sometimes at the expense of their own businesses, or face extinction in a rapidly innovating environment.

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Every year, data science experts, practitioners, and enthusiasts are known to flock to a variety of exciting and informative conferences around the world.  At these conferences, experts and luminaries in the field of data science will congregate to share experiences and ideas and inspire their colleagues in the industry. There are many interesting conferences scheduled for this summer, and these are our top picks.  

The Australia Sports Analytics Conference – Melbourne

The Australia Sports Analytics Conference in Melbourne will take place on the 4th of August. It will offer over 30 presentations with 3 key tracks that attendees will be able to choose from, with the 3 tracks being Sports Teams/Leagues, Brands/Fans/Engagement, and Sports Technology/Data Science. Ultimately attendees will have many options for finding presentations of interest revolving around analytics in sports.

TDWI 2017 – Anaheim

AUGUST 6—11, 2017

The TDWI Anaheim conference is one of the leading events for analytics, big data, and science training. It takes place from August 6th to August 11th at the Disneyland Hotel in Anaheim, California.

KDD 2017

AUGUST 13 – 17, 2017

KDD 2017 is a premier interdisciplinary conference bringing together researchers and practitioners from data science, data mining, knowledge discovery, large-scale data analytics, and big data.

SSTD 2017 – Washington D.C.

AUGUST 21-23, 2017

The 15th International Symposium on Spatial and Temporal Databases will convene in Washington D.C. from August 21st to the 23rd at George Mason University. It will discuss the newest developments in spatial and temporal databases and related technologies.

Big Data and Analytics Innovation Summit – Shanghai

SEPTEMBER 6-7, 2017

The Big Data and Analytics Innovation Summit in Shanghai will take place on the 6th and 7th of September and will cover the latest innovations and strategies in the business applications of big data and analytics. It will also feature talks from data and analytics experts from companies such as Adidas, Alibaba, and Chery Jaguar Landrover.

However, if you couldn’t get enough Data Science after these summer conferences, we look forward to meeting you in Berlin for the third annual Data Natives

Data Natives – Berlin

NOVEMBER 16-17, 2017

Data is part of our new cultural identity, transforming the way we communicate, learn and interact. Data Natives is the meeting point for industry experts, entrepreneurs, tech and business professionals to inspire each other and disrupt the status quo.

The third edition of Data Natives will welcome over 2,000 attendees and more than 80 speakers across the spectrum of the most exciting of technology today

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In recent years’ evidence has been mounting that points to a crisis in the reproducible results of scientific research. Reviews of papers in the fields of psychology and cancer biology found that only 40% and 10%, respectively, of the results, could be reproduced.

Nature published the results of a survey of researchers in 2016 that reported:

• 52% of researchers think there is a significant reproducibility crisis
• 70% of scientists have tried but failed to reproduce another scientist’s experiments

In 2013, a team of researchers published a paper describing ten rules for reproducible computational research. These rules, if followed, should lead to more replicable results.

All data science is research. Just because it’s not published in an academic paper doesn’t alter the fact that we are attempting to draw insights from a jumbled mass of data. Hence, the ten rules in the paper should be of interest to any data scientist doing internal analyses.

Rule #1—For every result, keep track of how it was produced

It’s important to know the provenance of your results. Knowing how you went from the raw data to the conclusion allows you to:

• defend the results
• update the results if errors are found
• reproduce the results when data is updated
• submit your results for audit

If you use a programming language (R, Python, Julia, F#, etc) to script your analyses then the path taken should be clear—as long as you avoid any manual steps. Using “point and click” tools (such as Excel) makes it harder to track your steps as you’d need to describe a set of manual activities—which are difficult to both document and re-enact.

Rule #2—Avoid manual data manipulation steps

There may be a temptation to open data files in an editor and manually clean up a couple of formatting errors or remove an outlier. Also, modern operating systems make it easy to cut and paste been applications. However, the temptation to short-cut your scripting should be resisted. Manual data manipulation is hidden manipulation.

Rule #3—Archive the exact versions of all external programs used

Ideally, you would set up a virtual machine with all the software used to run your scripts. This allows you to snapshot your analysis ecosystem—making replication of your results trivial.

However, this is not always realistic. For example, if you are using a cloud service, or running your analyses on a big data cluster, it can be hard to circumscribe your entire environment for archiving. Also, the use of commercial tools might make it difficult to share such an environment with others.

At the very least you need to document the edition and version of all the software used—including the operating system. Minor changes to software can impact results.

Rule #4—Version control all custom scripts

A version control system, such as Git, should be used to track versions of your scripts. You should tag (snapshot) multiple scripts and reference that tag in any results you produce. If you then decide to change your scripts later, as you surely will, it will be possible to go back in time and obtain the exact scripts that were used to produce a given result.

Rule #5—Record all intermediate results, when possible in standardized formats

If you’ve adhered to Rule #1 it should be possible to recreate any results from the raw data. However, while this might be theoretically possibly, it may be practically limiting. Problems may include:

• lack of resources to run results from scratch (e.g. if considerable cluster computing resources were used)
• lack of licenses for some of the tools, if commercial tools were used
• insufficient technical ability to use some of the tools

In these cases, it can be useful to start from a derived data set that is a few steps downstream from the raw data. Keeping these intermediate datasets (in CSV format, for example), provides more options to build on the analysis and can make it easier to identify where a problematic result when wrong—as there’s no need to redo everything.

Rule #6—For analyses that include randomness, note underlying random seeds

One thing that data scientists often fail to do is set the seed values for their analysis. This makes it impossible to exactly recreate machine learning studies. Many machine learning algorithms include a stochastic element and, while robust results might be statistically reproducible, there is nothing to compare with the warm glow of matching the exact numbers produced by someone else.

If you are using scripts and source code control your seed values can be set in your scripts.

Rule #7—Always store raw data behind plots

If you use a scripting/programming language your charts will often be automatically generated. However, if you are using a tool like Excel to draw your charts, make sure you save the underlying data. This allows the chart to be reproduced, but also allows a more detailed review of the data behind it.

Rule #8—Generate hierarchical analysis output, allowing layers of increasing detail to be inspected

As data scientists, our job is to summarize the data in some form. That is what drawing insights from data involves.

However, summarizing is also an easy way to misuse data so it’s important that interested parties can break out the summary into the individual data points. For each summary result, link to the data used to calculate the summary.

Rule #9—Connect textual statements to underlying results

At the end of the day, the results of data analysis are presented as words. And words are imprecise. The link between conclusions and the analysis can sometimes be difficult to pin down. As the report is often the most influential part of a study it’s essential that it can be linked back to the results and, because of Rule #1, all the way back to the raw data.

This can be achieved by adding footnotes to the text that reference files or URLs containing the specific data that led to the observation in the report. If you can’t make this link you probably haven’t documented all the steps sufficiently.

Rule #10—Provide public access to scripts, runs, and results

In commercial settings, it may not be appropriate to provide public access to all the data. However, it makes sense to provide access to others in your organization. Cloud-based source code control systems, such as Bitbucket and GitHub, allow the creation of private repositories that can be accessed by any authorized colleagues.

Many eyes improve the quality of analysis, so the more you can share, the better your analyses are likely to be.

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The late data visionary Hans Rosling mesmerised the world with his work, contributing to a more informed society. Rosling used global health data to paint a stunning picture of how our world is a better place now than it was in the past, bringing hope through data.

Now more than ever, data are collected from every aspect of our lives. From social media and advertising to artificial intelligence and automated systems, understanding and parsing information have become highly valuable skills. But we often overlook the importance of knowing how to communicate data to peers and to the public in an effective, meaningful way.

The first tools that come to mind in considering how to best communicate data – especially statistics – are graphs and scatter plots. These simple visuals help us understand elementary causes and consequences, trends and so on. They are invaluable and have an important role in disseminating knowledge.

Data visualisation can take many other forms, just as data itself can be interpreted in many different ways. It can be used to highlight important achievements, as Bill and Melinda Gates have shown with their annual letters in which their main results and aspirations are creatively displayed.

Everyone has the potential to better explore data sets and provide more thorough, yet simple, representations of facts. But how can do we do this when faced with daunting levels of complex data?

A world of too many features

We can start by breaking the data down. Any data set consists of two main elements: samples and features. The former correspond to individual elements in a group; the latter are the characteristics they share.

Anyone interested in presenting information about a given data set should focus on analysing the relationship between features in that set. This is the key to understanding which factors are most affecting sales, for example, or which elements are responsible for an advertising campaign’s success.

When only a few features are present, data visualisation is straightforward. For instance, the relationship between two features is best understood using a simple scatter plot or bar graph. While not that exciting, these formats can give all the information that system has to offer.

Data visualisation really comes into play when we seek to analyse a large number of features simultaneously. Imagine you are at a live concert. Consciously or unconsciously, you’re simultaneously taking into account different aspects of it (stagecraft and sound quality, for instance, or melody and lyrics), to decide whether the show is good or not.

This approach, which we use to categorise elements in different groups, is called a classification strategy. And while humans can unconsciously handle many different classification tasks, we might not really be conscious of the features being considered or realise which ones are the most important

Now let’s say you try to rank dozens of concerts from best to worst. That’s more complex. In fact, your task is twofold, as you must first classify a show as good or bad and then put similar concerts together.

Finding the most relevant features

Data visualisation tools enable us to bunch different samples (in this case, concerts) into similar groups and present the differences between them.

Clearly, some features are more important in deciding whether a show is good or not. You might feel an inept singer is more likely to affect concert quality than, say, poor lighting. Figuring out which features impact a given outcome is a good starting point for visualising data.

Imagine that we could transpose live shows onto a huge landscape, one that is generated by the features we were previously considering (sound for instance, or lyrics). In this new terrain, great gigs are played on mountains and poor ones in valleys. We can initially translate this landscape into a two-dimensional map representing a general split between good and bad.

We can then go even further and reshape that map to specify which regions are rocking in “Awesome Guitar Solo Mountain” or belong in “Cringe Valley”.

From a technical standpoint, this approach is broadly called dimensionality reduction, where a given data set with too many features (dimensions) can be reduced into a map where only relevant, meaningful information is represented. While a programming background is advantageous, several accessible resources, tutorials and straightforward approaches can help you capitalise on this great tool in a short period of time.

Network analysis and the pursuit of similarity

Finding similarity between samples is another good starting point. Network analysis is a well-known technique that relies on establishing connections between samples (also called nodes). Strong connections between samples indicate a high level of similarity between features.

Once these connections are established, the network rearranges itself so that samples with like characteristics stick together. While before we were considering only the most relevant features of each live show and using that as reference, now all features are assessed simultaneously – similarity is more broadly defined.

The amount of information that can be visualised with networks is akin to dimensionality reduction, but the feature assessment aspect is now different. Whereas previously samples would be grouped based on a few specific marking features, in this tool samples that share many features stick together. That leaves it up to users to choose their approach based on their goals.

Venturing into network analysis is easier than undertaking dimensionality reduction, since usually a high level of programming skills is not required. Widely available user-friendly software and tutorials allow people new to data visualisation to explore several aspects of network science.

The world of data visualisation is vast and it goes way beyond what has been introduced here, but those who actually reap its benefits, garnering new insights and becoming agents of positive and efficient change, are few. In an age of overwhelming information, knowing how to communicate data can make a difference – and it can help keep data’s relevance in check.

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This article was originally published on The Conversation. Read the original article.

Image: KamiPhuc/Flickr, CC BY-SA

Many process manufacturing owner-operators in this next phase of a digital shift have engaged in technology pilots to explore options for reducing costs, meeting regulatory compliance, and/or increasing overall equipment effectiveness (OEE).

Despite this transformation, the adoption of advanced analytics tools still presents certain challenges. The extensive and complicated tooling landscape can be daunting, and many end users lack fundamental understanding of process data analytics. Combined with a lack of awareness of the practical benefits that analytics offer, this leaves many engineers stuck in day-to-day tasks, using spreadsheets and basic trend analysis tools for the bulk of their daily analysis.

In this article we discuss the need for improved analytics awareness for the modern process engineer. We also explore key considerations in creating such awareness and the capabilities that state-of-the-art self-service analytics tools offer for process performance optimization.

Connected IIoT and Data

Today factories are producing more data than ever, forming an Industrial Internet of Things (IIoT) that enables smart factories where data can be visualized from the highest level to the smallest detail. The key to this digital revolution is the network of connected sensors, actors and machines in a plant generating trillions of samples per year.

This digital revolution offers unprecedented opportunities for improving efficiency and real-time process management – but it also presents new challenges that require innovative solutions and a new way of thinking.

Technology has evolved rapidly in response to the scale of data generated, with systems for business intelligence and data lakes now an essential part of operational excellence. However, for many engineers little has changed. They use the same systems and experience few benefits from the digital transformation taking place in their plants as they are unable to directly access the insights this new data provides.

Complexities in Analytics Options

Engineers now face a complex landscape populated with a variety of analytics tools, all of which promise to make sense of the newly available data, including tools from traditional historians and MES (manufacturing execution system) vendors, generic big data systems such as Hadoop and independent analytics applications. These tools address a variety of business needs, but are not necessarily designed to meet the specific needs of engineers in the process industry.

The sheer number of business systems leads to issues with integration and increased reliance on IT and big data experts. The corporate analytics vision is often based on one big data lake for all data, and proof of concepts are launched to store finance data, marketing data, quality data and limited amounts of production data in such lakes. However, companies frequently struggle to fit in the massive time series data from processes in these exercises.

In response, many organizations create central analytics teams to address the most critical process questions affecting profitability. Data scientists create advanced algorithms and data models to combine data from multiple sources and deliver insights to optimize production processes. These analytics experts lead the way in translating time series data into actionable information.

While the insights gained from analytics teams are essential, this approach alone is insufficient to enable engineers to leverage analytics in their daily tasks. Engineers are time-poor, with little room to learn new tools; they are more concerned with meeting the immediate needs of the plant than the promise of new and perhaps unproven technologies. They may be skeptical that they will gain practical benefits from investing time in the analytics system(s). If past analytics projects have failed to meet their expectations, there may also be frustration and disappointment. With the pressing need to ensure optimal processes, it is natural that they will revert to their current systems and tools as proven ways to get the job done.

Educating Users to Build the Perfect Beast

Just as technology has evolved to create connected plants, so engineers must be empowered to manage these factories. This is a critical shift in business culture as the entire organization must be educated and made aware of the potential of analytics as it applies to their role.

Instead of relying solely on a central analytics team that owns all the analytics expertise, subject matter experts such as process engineers should be empowered to answer their own day-to-day questions. Not only will this spread the benefits to the engineers involved in process management, it will also free the data scientists to focus on the most critical business issues.

Enabling engineers does not mean asking them to become data scientists – it means providing them with access to the benefits of process data analytics. Process engineers will not (easily) become data scientists because the education background is different (computer science versus chemical engineering). However, they can become analytics aware and enabled.

By bringing engineers closer in their understanding of analytics, they can solve more day-to-day questions independently and enhance their own effectiveness. They will in turn provide their organizations with new insights based on their specific expertise in engineering. This delivers value to the owner-operator at all levels of the organization and leverages (human) resources more efficiently.

To bring an organization to this modern approach requires the addition of a self-service analytics platform tailored to the subject matter expert users’ needs and the education of users.

Self-service analytics tools are designed with end users in mind. They incorporate robust algorithms and familiar interfaces to maximize ease of use without requiring in-depth knowledge of data science. No model selection, training and validation are required; instead users can directly query information from their own process historians and get one-click results. Immediate access to answers encourages adoption of the analytics tool as the value is proven instantly: precious time is saved and previously hidden opportunities for improvement are unlocked.

This self-service approach to analytics results in heightened efficiency and greater comfort with use of analytics information for the engineers, allows data scientists to focus on the questions most critical to the entire organization, and delivers enhanced profitability for owner-operators.

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Financial markets offer countless ways of making (or losing) money. A key distinction among them is the investment horizon, which can range from fractions of a second to years. Walnut Algorithms and Global Systematic Investors are new investment management firms representing the high-frequency and low-frequency sides, respectively. I sat down to talk with their founders about investing, data, and the challenges of starting up. Part 1, my interview with Guillaume Vidal, co-founder and CEO of Walnut Algorithms ran last week. Below is my talk with Dr. Bernd Hanke, co-founder and co-Chief Investment Officer of Global Systematic Investors.

What is the origin of Global Systematic Investors?

Bernd Hanke: It came from all of our backgrounds. I did a PhD in finance and then worked for two systematic asset managers. In other words, managers who use systematic factors in order to do individual stock selection, quantitatively rather than using human judgment. Obviously, human judgment goes into the model when you select factors to forecast stock returns, but once you’ve built your model, the human element is reduced to a necessary minimum in order to try to remain disciplined. So that was my background. Both of my partners used to be in a portfolio management role at Dimensional Fund Advisors and one of them has always been very research-oriented. They both come from the same mindset, the same type of background, which is using systematic factors in order to forecast asset returns, in our case, stock returns.  

How has your strategy evolved over time and how do you expect it to evolve in the future?

BH:  We’ve worked on the strategy for quite some time, building the model, selecting the factors, working on the portfolio construction, on basically how you capture the systematic factors in an optimal, risk-controlled manner that is robust and makes intuitive sense. We developed the model over several years and we will keep enhancing the model as we continue to do more research. We are not making large changes frequently, but we gradually improve the model all the time, as new academic research becomes available, as we try to enhance some of these academic ideas, and as we do our own research.

There is a commonly held view that in today’s markets, investment strategies are increasingly short-lived, and so they stop working quickly. You don’t share this view?

BH:  We are using a very low frequency model, so the factors we are using have a fairly long payoff horizon. I think when you talk about factors having a relatively short half-life in terms of usability, that is mostly true for higher frequency factors. If you back-test them, they sometimes look like there’s almost no risk associated, just a very high return, and then obviously as soon as people find out about these factors that almost look too good to be true, the effects can go away very quickly. Instead, we are looking at longer-term factors with a payoff horizon of several months or sometimes even a year. We recognize that there’s risk associated with these factors, but they have been shown to be working over long periods of time. In the US you can go back to the 1920’s studying these factors because the data is available. In other regions, there’s less data, but you have consistent findings. So as long as you are prepared to bear the risk and you diversify across these long-term factors, they can be exploited over long periods of time.

What kind of long-term factors are we talking about?

Our process is based on a value and a diversification component.  When people hear “value”, they usually think about a book-to-price ratio. That’s probably the most well-known value factor. Thousands of academics have found that the value effect exists and it does persist over time. It has its drawdowns, of course, the tech bubble being one of them, and value actually worked very poorly, but then value came back strongly after the tech bubble had burst. We’ve broadened the definition of value. We also use cash flow and earnings-related factors, and we are using a factor related to net cash distributions that firms make to shareholders.

We are also using a diversification factor. We are targeting a portfolio that is more diversified across company sizes and across sectors than a market weighted index.

And the advantage of being more diversified is lower volatility?

BH:  Not necessarily. Stock-level diversification actually increases volatility because you’re capturing a size effect. You’re investing in smaller companies than a market-weighted index would. But smaller companies are more risky than larger companies. So if you tilt more towards smaller stocks you actually increase the risk, but you also increase returns. On the sector side, the picture is quite different. By diversifying more across sectors than the market-weighted index does, you get both lower risk and higher returns.   

Does the fact that your factors are longer-term and riskier mean that it could take you longer to convince an outside observer that your strategy is working?

BH:  Yeah, that’s true. That’s one of the luxuries that high frequency funds have given that their factors have such a short payoff horizon. They only need relatively short periods of live performance in order to demonstrate that the model works, whereas someone who uses a lower frequency model needs a longer period to evaluate those factors.

So what are the benefits of going with such a slow-trading strategy compared to a fast-trading strategy?

BH:  One big advantage is of course that these long-term factors have a much higher capacity in terms of assets that you are able to manage with these factors. It is more robust, in the sense that even if liquidity decreased and transaction costs increased, it wouldn’t really hurt the performance of that fund very much because the turnover is so low. Whereas for high-turnover, short-term strategies, transaction costs and liquidity are obviously key, and even slight changes in the liquidity environment of the market can completely destroy the performance of these strategies. Another advantage related to that is that with lower frequency factors you can also go into small capitalization stocks more. You can tilt more towards small cap because you’re not incurring much turnover even though small cap is more costly to trade. And in small cap there are often more return opportunities than in large cap, presumably because small cap stocks are less efficiently priced than large cap stocks.  

Once you settled on your investment strategy, was it obvious to you how you would monetize it, that you would go for the fund structure that you have today?

BH:  The fund we have now is a UCITS fund. We were looking at different legal structures that one could have. It also depends a little bit on who you want to approach as a client or who you might be in contact with as a potential client. If you’re talking to a very large client for example, they might not even want a fund. They might want a separate account or they may have an existing account already and then they appoint you as the portfolio manager for that account. So then the client basically determines the structure of the fund. If it’s a commingled fund as ours, then there are a couple of options available. Some are probably more appealing to just UK investors and some are more global in nature. The UCITS structure is fairly global in nature. It tends to work for most investors except for US investors who have their own structures that differ from UCITS.

What would be your advice to people who think they have a successful investment strategy and are thinking about setting up their own fund?

BH: Well, my advice would be, find an investor first. Ideally, a mix of investors. So if one investor backs out, then you have someone else to put in. That’s obviously easier said than done. But I think that this is quite important.  

How dependent is your strategy on getting timely and accurate data?

BH: For us, timeliness is not as crucial as for high frequency strategies. Obviously, we want to have the latest information as soon as possible, but if there was a day or perhaps even a week delay in some information coming in, it wouldn’t kill our strategy.  

But data accuracy is very important. Current data that we get is usually quite accurate. The same cannot necessarily be said about the historical data that we use in back tests. In the US, data is fairly clean, but not for some other countries. All of the major data vendors claim that there is no survivorship bias in the data. But it’s hard to check, and accuracy is often somewhat questionable for some of the non-US data sources in particular. We’re not managing any emerging markets funds, but even in developed markets going back, there tend to be many problems even for standard data types such as market data and accounting data.

And the data sources that you are using now are mostly standard accounting data?

BH:  Yes. There are some adjustments that we could make and that we would like to make. For example, one fairly obvious adjustment would be to use more sector-specific data. If you are just thinking about a simple value factor which some people measure as book-to-price, it’s basically looking at the accounting value of a company relative to the market value of the company. You could call the accounting value the intrinsic value of the company. You could measure that differently for different industries. For example, if you think about the oil and gas industry, you might want to look at the reserves that these companies have in the ground rather than just using a standard book value. For metals and mining companies, you could do something similar. Other industries also use other sector-specific data items that could be relevant for investors. Most accounting data sources now incorporate quite a lot of sector-specific data items. One issue is that the history is usually not very long. So if you want to run a long back test using sector-specific data, that is usually not feasible because that type of data has typically only been collected over the last few years.

What role do you see for data science and data scientists in investment management now and going forward?

BH: Right now there is a huge demand for data scientists. That, however, is mostly in the hedge fund area. It is much less for long-only funds. We are managing a long-only fund. There are some quantitative asset managers, that manage both long-only funds and hedge funds, and they might be using a similar investment process for both. So these managers may hire data scientists even to work on the long-only portfolios, but it’s mostly systematic hedge funds and it’s mostly the higher frequency hedge funds. Different people refer to “high frequency” in very different ways, but what I would call “high frequency” would be factors with a payoff horizon of at most a couple of days, maybe even intraday factors. So those types of hedge funds seem to be the ones hiring the most data scientists at the moment.  Also, new service providers keep popping up that employ data scientists and they then sell services to hedge funds, such as trading strategies or new types of data sets.

How valuable are these non-standard or “alternative” data sources?

BH:  The data is there and we now have the computational power to exploit it. So I think it will become more useful, but it’s a gradual process. Everybody talks about big data, but I think right now only a small minority of funds have successfully employed non-standard or unstructured data sources (commonly labeled “Big Data”) in their strategies in a meaningful manner. For some types of non-standard data, I think there there’s an obvious case for using it. For example, credit card payment data can help you see whether there are particular trends that some companies might be benefitting from in the future, or looking at the structure of the sales and trying to use that in forecasting, and so on. And there are other data types where it’s probably more doubtful whether the data is useful or not. There is some tendency at the moment, I think, to be over-enthusiastic in the industry about new data without necessarily thinking carefully enough about formulating the right questions to investigate using the data and doing thoughtful data analysis.

Where do you see investing heading, in terms of passive versus active strategies?

BH:  One trend is away from traditional active. Most institutional investors have come to the conclusion that traditional fundamental active long-only managers have underperformed. So, many institutional investors have moved to passive for their long-only allocation, or if not passive, then to what is often referred to as “semi-passive” or “smart beta” strategies. These are mostly one-factor strategies, where the assets, often in an ETF, are managed according to one factor such as a value factor. For example, fundamental indexing uses a value factor composite and that is the only factor. There are other strategies, such as minimum risk and momentum. Everything that is not a market weighted strategy is active, strictly speaking, but often investors refer to strategies that use fixed rules that are made publicly available to every investor as semi-passive.

And then at the other end of the spectrum, you have hedge funds, and it used to be the case that systematic or quantitative fund managers, both long-only as well as long/short managers, mostly used similar factors. That became very apparent in August 2007 during the “quant liquidity crunch”. Basically what happened was that most quantitative investors were betting on the same or very similar factors, and once more and more quant investors had to liquidate their positions, that caused the factors to move against them in an extreme manner. So most quant factors had huge drawdowns at the beginning of August 2007. Then after 2007-2008, hedge funds attempted to move away from these standard factors to more proprietary factors as well as to non-standard data sources, and at the same time more and more data became available. I think systematic strategies used by many hedge funds now are actually more different than they used to be in 2007. However, the opposite might be true for many smart beta strategies. So, hedge funds are often trying to limit their portfolios’ exposures to standard factors used by the smart beta industry. Whether they are able to do this successfully remains to be seen. If there is going to be another quant crisis, that might be the acid test.

So that’s been a fairly significant change over the last 10 years.  If you had a crystal ball, what would be your prediction of how things will be different 10 years from now?

BH:  One prediction I would make is that smart beta is not going to remain as simplistic as it often is at the moment. Most likely, it will be developed into something that we had before 2007 in quant strategies. People will probably combine fairly well-known smart beta factors like value, momentum, low risk into multi-factor strategies rather than offering them separately for each factor and so that then investors have to combine the strategies themselves to diversify across factors. It is more efficient if the investment manager combines factors at the portfolio level because these factors, to the extent that they have low correlation, often partially offset each other. This means that trades based on different factors can be netted against each other and this saves trading costs. That is happening to some degree already. Several managers have started offering multi-factor smart beta portfolios.

On the hedge fund side, I think the prediction is going to be more difficult. It remains to be seen how successful artificial intelligence and machine learning strategies turn out to be, and it also remains to be seen to what extent new data types are exploitable in terms of predicting subsequent stock returns and risk. My suspicion is that there are going to be many disappointments. Some new data types will be worthwhile but many probably won’t be. Similarly for machine learning and artificial intelligence. It is likely that only a small subset of today’s tools turn out to be useful.   

Do you see fintech companies making headway in investment management, either as asset managers or as suppliers to the industry?

BH:  Oh, definitely, on all sides. Robo-advisors being one of the big ones, I guess, that could change a lot how the asset management industry operates. And it’s in all areas, also other service providers, portfolio analytics providers and so on. There’s a lot of development in this area currently, which is probably a good thing. In terms of data vendors, for example, there is still a strong oligopoly consisting of Thomson Reuters, FactSet, Bloomberg and S&P who sometimes charge inflated prices for their data. And the data often isn’t particularly clean. Even worse are some of the index providers like MSCI, FTSE and S&P. They are offering very simple data at exorbitant prices. They are not really charging clients for the data. Instead they are charging them for usage of their brand name, for example, for the right to use the MSCI name in their marketing material. Now there are more and more fintech companies that are offering the same service, except for the brand name, at much lower cost to the client.

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Image: Michael Dunn, CC BY 2.0

Categorical data is a kind of data which has a predefined set of values. Taking “Child”, “Adult” or “Senior” instead of keeping the age of a person to be a number is one such example of using age as categorical. However, before using categorical data, one must know about various forms of categorical data.

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First of all, categorical data may or may not be defined in an order. To say that the size of a box is small, medium or large means that there is an order defined as small<medium<large. The same does not hold for, say, sports equipment, which could also be categorial data, but differentiated by names like dumbbell, grippers or gloves; that is, you can order the items on any basis. Those which can be ordered are known as “ordinal” while those where there is no such ordering are “nominal” in nature.

Many a time, an analyst changes the data from numerical to categorical to make things easier. Besides using “Adult”, “Child” or “Senior” class instead of age as a number, there can also be special cases such as using “regular item” or “accessory” for equipment. In many problems, the output is also categorical. Whether a customer will churn or not, whether a person will buy a product or not, whether an item is profitable etc. All problems where the output is categorical are known as classification problems. R provides various ways to transform and handle categorical data.

A simple way to transform data into classes is by using the split and cut functions available in R or the cut2 function in Hmisc library.

Let’s use the iris dataset to categorize data. This dataset is available in R and can be called by using ‘attach’ function. The dataset consists of 150 observations over 5 features – Sepal Length, Sepal Width, Petal Length, Petal Width and species.

attach(iris) #Call the iris dataset

x=iris #store a copy of the dataset into x

#using the split function
list1=split(x, cut(x$Sepal.Length, 3)) #This will create a list of 3 split on the basis of sepal.length
summary(list1) #View the class ranges for list1
Length Class         Mode
(4.3,5.5] 6          data.frame list
(5.5,6.7] 6          data.frame list
(6.7,7.9] 6          data.frame list
#using Hmisc library
library(Hmisc)
list2=split(x, cut2(x$Sepal.Length, g=3)) #This will also create a similar list but with left boundary included
summary(list2) #View the class ranges for list2
Length Class          Mode
[4.3,5.5) 6          data.frame list
[5.5,6.4) 6          data.frame list
[6.4,7.9] 6          data.frame list