Statistics: Median (Definitions)

"The middle value in an ordered set of values for which there are an equal number of values." (Jennifer George-Palilonis, "A Practical Guide to Graphics Reporting", 2006)

"The center-most value in an ordered set of values. If the set quantity is even, then the average of the two center-most values." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The median is a statistical measure of variation. It represents the middle measurement when a set of measurements are collected in ascending order: 50% of the measurements are above the median and 50% are below it." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"The middle value in a set of ordered numbers. The median value is determined by choosing the smallest value such that at least half of the values in the set are no greater than the chosen value. If the number of values within the set is odd, the median value corresponds to a single value. If the number of values within the set is even, the median value corresponds to the sum of the two middle values divided by two." (Microsoft, "SQL Server 2012 Glossary", 2012)

"The middle value in a set of values. Half the values fall below the median, and half the values fall above the median. See also average; mode." (E C Nelson & Stephen L Nelson, "Excel Data Analysis For Dummies ", 2015)

"To find the median, list the values of the data set in numerical order and identify which value appears in the middle of the list." (Christopher Donohue et al, "Foundations of Financial Risk: An Overview of Financial Risk and Risk-based Financial Regulation, 2nd Ed", 2015)

"Middle score in a distribution." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

Statistics: Mean (Definitions)

"In a numerical sequence, the number that has an equal number of values before and after it. In the sequence 3, 5, 7, 9, 11, seven is the mean." (Dale Furtwengler, "Ten Minute Guide to Performance Appraisals", 2000)

"The average value of a sample of data that is typically gathered in a matrix experiment." (Clyde M Creveling, "Six Sigma for Technical Processes: An Overview for R Executives, Technical Leaders, and Engineering Managers", 2006)

"The sum of all values in a variable divided by the number of values." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"The average value of a sample of data that is typically gathered in a matrix experiment." (Lynne Hambleton, "Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices", 2007)

"The sum of all values in a variable divided by the number of values." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2007)

"The result of dividing the sum of all values within a set by the count of all values included." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The mean is a statistical measure of central tendency. It is most easily understood as the mathematical average. It is calculated by summing the value of a set of measurements and dividing by the number of measurements taken." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement", 2012)

"To find the mean add up the values in the data set and then divide by the number of values." (Christopher Donohue et al, "Foundations of Financial Risk: An Overview of Financial Risk and Risk-based Financial Regulation" 2nd Ed., 2015)

"Arithmetic averages of scores. The mean is the most commonly used measure of central tendency, but should be computed only for score data." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

Statistics: Moving Average (Definitions)

"A trend-following indicator that works best in a trending environment. Moving averages smooth out price action but operate with a time lag. Any number of moving averages can be employed, with different time spans, to generate buy and sell signals. When only one average is employed, a buy signal is given when the price closes above the average. When two averages are employed, a buy signal is given when the shorter average crosses above the longer average. Technicians use three types: simple, weighted, and exponentially smoothed averages." (Guido Deboeck & Teuvo Kohonen (Eds), "Visual Explorations in Finance with Self-Organizing Maps 2nd Ed.", 2000)

"For a time series, an average that is updated as new information is received. With the moving average, the manager employs the most recent observations to calculate an average, which is used as the forecast for the next period." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

[exponential moving average:] "A moving average of data that gives more weight to the more recent data in the period and less weight to the older data in the period. The formula applies weighting factors which decrease exponentially. The weighting for each older data point decreases exponentially, giving much more importance to recent observations while still not discarding older observations entirely." (SQL Server 2012 Glossary, "Microsoft", 2012)

"An average that’s calculated by using only a specified set of values, such as an average based on just the last three values." (E C Nelson & Stephen L Nelson, "Excel Data Analysis For Dummies ", 2015)

"A mathematical average of data points over a specified period of time. Moving averages are used on financial price charts to show the average price over a selected interval of time. Examples are the SMA(9), SMA(20), SMA(50), or SMA(200) referring to 9-, 20-, 50-, or 200-period simple moving averages. Other types of moving averages also exist, such as an exponential moving average (EMA) and triangular moving averages (TMA). The EMA places more emphasis on the most recent data points. The TMA places more emphasis on the center data points of the specified range, that is, 9, 20, 50, 200, and so on." (Russell A Stultz, "The Option Strategy Desk Reference", 2019)

📊Business Intelligence: Advanced Analytics (Definitions)

"A subset of analytical techniques that, among other things, often uses statistical methods to identify and quantify the influence and significance of relationships between items of interest, groups similar items together, creates predictions, and identifies mathematical optimal or near-optimal answers to business problems." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Algorithms for complex analysis of either structured or unstructured data. It includes sophisticated statistical models, machine learning, neural networks, text analytics, and other advanced data-mining techniques Advanced analytics does not include database query and reporting and OLAP cubes." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A subset of analytical techniques that, among other things, often uses statistical methods to identify and quantify the influence and significant of relationships between items of interest, group similar items together, create predictions, and identify mathematical optimal or near-optimal answers to business problems." (Evan Stubbs, "Big Data, Big Innovation", 2014)

"Advanced Analytics is the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations. Advanced analytic techniques include those such as data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks. (Gartner)

"Analytic techniques and technologies that apply statistical and/or machine learning algorithms that allow firms to discover, evaluate, and optimize models that reveal and/or predict new insights." (Forrester)

"Advanced analytics describes data analysis that goes beyond simple mathematical calculations such as sums and averages, or filtering and sorting. Advanced analyses use mathematical and statistical formulas and algorithms to generate new information, to recognize patterns, and also to predict outcomes and their respective probabilities." (BI-Survey) [source]

"Advanced analytics is an umbrella term for a group of high-level methods and tools that can help you get more out of your data. The predictive capabilities of advanced analytics can be used to forecast trends, events, and behaviors. This gives organizations the ability to perform advanced statistical models such as 'what-if' calculations, as well as to future-proof various aspects of their operations." (Sisense) [source]

📊Business Intelligence: Report Snapshot (Definitions)

"A SQL Server Reporting Services report that contains data that was queried at a particular point in time and has been stored on the Report Server." (Victor Isakov et al, "MCITP Administrator: Microsoft SQL Server 2005 Optimization and Maintenance (70-444) Study Guide", 2007)

"A report that contains data captured at a specific point in time. Since report snapshots hold datasets instead of queries, report snapshots can be used to limit processing costs by running the snapshot during off-peak times." (Darril Gibson, "MCITP SQL Server 2005 Database Developer All-in-One Exam Guide", 2008)

"A report that contains data captured at a specific point in time. A report snapshot is stored in an intermediate format containing retrieved data rather than a query and rendering definitions." (Jim Joseph et al, "Microsoft® SQL Server™ 2008 Reporting Services Unleashed", 2009)

"A static report that contains data captured at a specific point in time." (Microsoft, "SQL Server 2012 Glossary", 2012)

🎓Knowledge Management: Keeping Current or the Quest to Lifelong Learning for IT Professionals

Introduction

    The pace with which technologies and the business changes becomes faster and faster. If 5-10 years back a vendor needed 3-5 years before coming with a new edition of a product, nowadays each 1-2 years a new edition is released. The release cycles become shorter and shorter, vendors having to keep up with the changing technological trends. Changing trends allow other vendors to enter the market with new products, increasing thus the competition and the need for responsiveness from other vendors. On one side the new tools/editions bring new functionality which mainly address technical and business requirements. On the other side existing tools functionality gets deprecated and superset by other. Knowledge doesn’t resume only to the use of tools, but also in the methodologies, procedures, best practices or processes used to make most of the respective products. Evermore, the value of some tools increases when mixed, flexible infrastructures relying on the right mix of tools working together.

    For an IT person keeping current with the advances in technologies is a major requirement. First of all because knowing modern technologies is a ticket for a good and/or better paid job. Secondly because many organizations try to incorporate in their IT infrastructure modern tools that would allow them increase the ROI and achieve further benefits. Thirdly because, as I’d like to believe, most of the IT professionals are eager to learn new things, keep up with the novelty. Being an adept of the continuous learning philosophy is also a way to keep the brain challenged, other type of challenge than the one we meet in daily tasks.

Knowledge Sources

    Face-to-face or computer-based trainings (CBTs) are the old-fashioned ways of keeping up-to-date with the advances in technologies though paradoxically not all organizations afford to train their IT employees. Despite of affordable CBTs, face-to-face trainings are quite expensive for the average IT person, therefore the IT professional has to reorient himself to other sources of knowledge. Fortunately many important Vendors like Microsoft or IBM provide in one form or another through Knowledge Bases (KB), tutorials, forums, presentations and Blogs a wide range of resources that could be used for learning. Similar resources exist also from similar parties, directly or indirectly interested in growing the knowledge pool.

    Nowadays reading a book or following a course it isn’t anymore a requirement for learning a subject. Blogs, tutorials, articles and other types of similar material can help more. Through their subject-oriented focus, they can bring some clarity in a small unit of time. Often they come with references to further materials, bring fresh perspectives, and are months or even years ahead books or courses. Important professionals in the field can be followed on blogs, Twitter, LinkedIn, You Tube and other social media platforms. Seeing in what topics they are interested in, how they code, what they think, maybe how they think, some even share their expertize ad-hoc when asked, all of this can help an IT professional considerably if he knows how to take advantage of these modern facilities.

    MOOCs start to approach IT topics, and further topics that can become handy for an IT professional. Most of them are free or a small fee is required for some of them, especially if participants’ identity needs to be verified. Such courses are a valuable resource of information. The participant can see how such a course is structured, what topics are approached, and what’s the minimal knowledge base required; the material is almost the same as in a normal university course, and in the end it’s not the piece of paper with the testimonial that’s important, but the change in perspective we obtained by taking the course. In addition the MOOC participant can interact with people with similar hobbies, collaborate with them on projects, and why not, something useful can come out of it. Through MOOCs or direct Vendor initiatives, free or freeware versions of software is available. Sometimes the whole functionality is available for personal use. The professional is therefore no more dependent on the software he can use only at work. New possibilities open for the person who wants to learn.

Maximizing the Knowledge Value

    Despite the considerable numbers of knowledge resources, for an IT professional the most important part of his experience comes from hand-on experience acquired on the job. If the knowledge is not rooted in hand-on experience, his knowledge remains purely theoretical, with minimal value. Therefore in order to maximize the value of his learning, an IT professional has to attempt using his knowledge as much and soon as possible in praxis. One way to increase the value of experience is to be involved in projects dealing with new technologies or challenges that would allow a professional to further extend his knowledge base. Sometimes we can choose such projects or gain exposure to the technologies, though other times no such opportunities can be sized or identified.

    Probably an IT professional can use in his daily duties 10-30% of what he learned. This percentage can be however increased by involving himself in other types of personal or collective (open source or work) projects. This would allow exploring the subjects from other perspective. Considering that many projects involve overtime, many professionals have also a rich personal life, it looks difficult to do that, though not impossible.

    Even if not on a regular basis achievable, a professional can allocate 1-3 hours on a weekly basis from his working time for learning something new. It can be something that would help directly or indirectly his organization, though sometimes it pays off to learn technologies that have nothing to do with the actual job. Somebody may argue that the respective hours are not “billable”, are a waste of time and other resources, that the technologies are not available, that there’s lot of due tasks, etc. With a little benevolence and with the right argumentation also such criticism can be silenced. The arguments can be for example based on the fact that a skilled professional can be with time more productive, a small investment in knowledge can have later a bigger benefit for both parties – employee and employer. An older study was showing that when IT professionals was given some freedom to approach personal projects at work, and use some time for their own benefit, the value they bring for an organization increased. There are companies like Google who made from this type of work a philosophy.

    A professional can also allocate 1-3 hours from his free time while commuting or other similar activities. Reading something before going to bed or as relaxation after work can prove to be a good shut-down for the brain from the daily problems. Where there’s interest in learning something new a person will find the time, no matter how busy his schedule is. It’s important however to do that on a regular basis, and with time the hours and knowledge accumulate.

    It’s also important to have a focused effort that will bring some kind of benefit. Learning just for the sake of learning brings little value on investment for a person if it’s not adequately focused. For sure it’s interesting and fun to browse through different topics, it’s even recommended to do so occasionally, though on the long run if a person wants to increase the value of his knowledge, he needs somehow to focus the knowledge within a given direction and apply that knowledge.

    Direction we obtain by choosing a career or learning path, and focusing on the direct or indirect related topics that belong to that path. Focusing on the subjects related to a career path allows us to build our knowledge further on existing knowledge, understanding a topic fully. On the other side focusing on other areas of applicability not directly linked with our professional work can broaden our perspective by looking at one topic from another’s topic perspective. This can be achieved for example by joining the knowledge base of a hobby we have with the one of our professional work. In certain configurations new opportunities for joint growth can be identified.

    The value of knowledge increases primarily when it’s used in day-to-day scenarios (a form of learning by doing). It would be useful for example for a professional to start a project that can bring some kind of benefit. It can be something simple like building a web page or a full website, an application that processes data, a solution based on a mix of technologies, etc. Such a project would allow simulating to some degree day-to-day situations, when the professional is forced to used and question some aspects, to deal with some situations that can’t be found in textbook or other learning material. If such a project can bring a material benefit, the value of knowledge increases even more.

    Another way to integrate the accumulated knowledge is through blogging and problem-solving. Topic or problem-oriented blogging can allow externalizing a person’s knowledge (aka tacit knowledge), putting knowledge in new contexts into a small focused unit of work, doing some research and see how other think about the same topic/problem, getting feedback, correcting or improving some aspects. It’s also a way of documenting the various problems identified while learning or performing a task. Blogging helps a person to improve his writing communication skills, his vocabulary and with a little more effort can be also a visit card for his professional experience.

    Trying to apply new knowledge in hand-on trainings, tutorials or by writing a few lines of code to test functionality and its applicability, same as structuring new learned material into notes in the form of text or knowledge maps (e.g. concept maps, mind maps, causal maps, diagrams, etc.) allow learners to actively learn the new concepts, increasing overall material’s retention. Even if notes and knowledge maps don’t apply the learned material directly, they offer a new way of structuring the content and resources for further enrichment and review. Applied individually, but especially when combined, the different types of active learning help as well maximize the value of knowledge with a minimum of effort.

Conclusion

    The bottom line – given the fast pace with which new technologies enter the market and the business environment evolves, an IT professional has to keep himself up-to-date with nowadays technologies. He has now more means than ever to do that – affordable computer-based training, tutorials, blogs, articles, videos, forums, studies, MOOC and other type of learning material allow IT professionals to approach a wide range of topics. Through active, focused, sustainable and hand-on learning we can maximize the value of knowledge, and in the end depends of each of us how we use the available resources to make most of our learning experience.

📊Business Intelligence: Data Analytics (Definitions)

"Business Intelligence procedures and techniques for exploration and analysis of data to discover and identify meaningful information and trends." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Analytics is the systematic analysis of large databases to solve problems and make informed decisions." (John R Schermerhorn Jr, "Management" 12th Ed., 2012)

"Procedures and techniques for exploration and analysis of data to discover and identify new and meaningful information and trends." (Craig S Mullins, "Database Administration", 2012)

"A data-driven process that creates insight. These processes incorporate a wide variety of techniques and may include manual analysis, reporting, predictive models, time-series models, or optimization models." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"A suite of technical solutions that uses mathematical and statistical methods. The solutions are applied to data to generate insight to help organizations understand historical business performance as well as forecast and plan for future decisions." (Jim Davis & Aiman Zeid, "Business Transformation", 2014) 

"Analytics is the discovery and communication of meaningful patterns in data." (Elaine Biech, "ASTD Handbook" 2nd Ed., 2014) 

"The business intelligence and analytics technologies that are grounded mostly in data mining and statistical analysis." (Xiuli He, "Supply Chain Analytics: Challenges and Opportunities", 2014)

"Data analytics refers to qualitative and quantitative techniques and processes used to enhance productivity and business gain." (Piyush K Shukla & Madhuvan Dixit, "Big Data: An Emerging Field of Data Engineering", 2015)

"The act of extracting and communicating meaningful information among the data sets." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015) 

"A broad term that includes quantitative analysis of data and building quantitative models. Analytics is the science of analysis and discovery. Analysis may process data from a data warehouse, may result in building model-driven DSS, or may occur in a special study using statistical or data mining software. In general, analytics refers to quantitative analysis and manipulation of data." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"A scientific and systematic approach to examine raw data in order to draw valid conclusions about them. Data are extracted and structured, and qualitative and quantitative techniques are used to identify and analyze patterns." (Lesley S J Farmer, "Data Analytics for Strategic Management: Getting the Right Data", 2017)

"Techniques used to identify patterns in data sets. Qualitative and quantitative techniques are employed to derive meaning that may be valuable and could result in a positive business gain for an organization." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"The discovery, interpretation, and communication of meaningful patterns in data to inform decision making and improve performance." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"Analytics refers to quantitative and statistical analysis and manipulation of data to derive meaning. Analytics is a broad umbrella term that includes business analytics and data analytics." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"Involves drawing insights from the data including big data. Analytics uses simple to advanced tools depending upon the objectives. Analytics may involve visual display of data (charts and graphs), descriptive statistics, making predictions, forecasting future outcomes, or optimizing business processes." (Amar Sahay, "Business Analytics" Vol. I, 2018)

"Is the science of examining raw data with the purpose of drawing actionable information from it, data analytics is used to allow companies and organization to make better business decisions and in the sciences to verify or disprove existing theories." (Dennis C Guster, "Scalable Data Warehouse Architecture: A Higher Education Case Study", 2018)

"Data analytics is a process that examines, clears, converts and models data to explore useful information, draws conclusions and supports decision making." (A Aylin Tokuç, "Management of Big Data Projects: PMI Approach for Success", 2019)

"A rapidly emerging field of information science arising from the explosion of data generated by many Internet based applications and services. Data analytics embodies a sequential process of descriptive, diagnostic, predictive and prescriptive analytics. Each type has a different purpose and requires different techniques to gain meaningful outcomes. The latter two often employ machine learning to gain valuable insights and directional guidance in decision making, such as in self-driving automobiles." (Darrold L Cordes et al, "Transforming Urban Slums: Pathway to Functionally Intelligent Cities in Developing Countries", 2021)

"Discovery, interpretation, and communication of meaningful patterns in data; and the process of applying those patterns towards effective decision making." (Francisco S Gutierres & Pedro M Gome, "The Integrated Tourism Analysis Platform (ITAP) for Tourism Destination Management", 2021)

"The science of extracting meaningful information continuously with the assistance of specialized system for finding patterns to get feasible solutions." (Selvan C & S  R Balasundaram, "Data Analysis in Context-Based Statistical Modeling in Predictive Analytics", 2021)

"Analytics encompasses the discovery, interpretation, and communication of meaningful patterns in data. It relies on the simultaneous application of statistics, computer programming and operations research to quantify performance and is particularly valuable in areas with large amounts of recorded information. The goal of this exercise is to guide decision-making based on the business context. The analytics flow comprises descriptive, diagnostic, predictive analytics and eventually prescriptive steps." (Accenture)

"Data Analytics describes the end-to-end process by which data is cleaned, inspected and modeled. The objective is to discover useful and actionable information that supports decision-making." (Accenture)

"Data analytics enables organizations to analyze all their data (real-time, historical, unstructured, structured, qualitative) to identify patterns and generate insights to inform and, in some cases, automate decisions, connecting intelligence and action." (Tibco) [source]

"Data analytics is a set of technologies and practices that reveal meaning hidden in raw data." (Xplenty) [source]

"Data and analytics is the management of data for all uses (operational and analytical) and the analysis of data to drive business processes and improve business outcomes through more effective decision making and enhanced customer experiences." (Gartner)

"Data analytics (DA) is the process of examining data sets in order to draw conclusions about the information they contain, increasingly with the aid of specialized systems and software." (Techtarget) [source]

"Data analytics is the process of querying and interrogating data in the pursuit of valuable insight and information." (snowflake) [source]

"Data analytics is the pursuit of extracting meaning from raw data using specialized computer systems. These systems transform, organize, and model the data to draw conclusions and identify patterns." (Informatica) [source]

"Data analytics refers to the use of processes and technology to combine and examine datasets, identify meaningful patterns, correlations, and trends in them, and most importantly, extract valuable insights." (Qlik) [source]

"The discovery, interpretation, and communication of meaningful patterns in data. They are essentially the backbone of any data-driven decision making." (Insight Software)

"The process and techniques for the exploration and analysis of business data to discover and identify new and meaningful information and trends that allow for analysis to take place."(Information Management)

📊Business Intelligence: Text Analytics (Definitions)

"A technique whereby software employs linguistics and pattern detection techniques to impute some larger meaning to the words in a document. Entity extraction and document categorization are two emerging types of text analytics." (Mike Moran & Bill Hunt , "Search Engine Marketing, Inc", 2005)

"Transforms unstructured text into structured 'text data' that can then be searched, mined, or discovered." (Linda Volonino & Efraim Turban, "Information Technology for Management 8th Ed", 2011)

"The process of analyzing unstructured text, extracting relevant information, and transforming it into structured information that can be leveraged in various ways." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"Refers generally to the process of deriving patterns and trends from unstructured content such as notes, reports, and comments." (Jim Davis & Aiman Zeid, "Business Transformation: A Roadmap for Maximizing Organizational Insights", 2014)

"The practice of analyzing unstructured data." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"Text analytics a variety of computer-based techniques designed to deriving information from text sources." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"the process of analyzing unstructured text, extracting relevant information, and transforming it into structured information that can be leveraged in various ways." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"The process of deriving insights from large volumes of text, typically through the use of specialized software to identify patterns, trends, and sentiment. " (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

[AI-based text analytics:] "Machine-learning and rules-based analytics technology that mines semistructured and unstructured text data sources and extracts structured information (such as keywords, concepts, entities, topics, sentiment, emotion, and intent) to analyze the findings for correlations, trends, outliers, patterns, and anomalies." (Forrester)

"A subset of natural language processing (NLP) technologies that identifies structures and patterns in text and transforms them into actionable insights to drive better business outcomes." (Forrester)

"Text analytics is the process of deriving information from text sources. It is used for several purposes, such as: summarization (trying to find the key content across a larger body of information or a single document), sentiment analysis (what is the nature of commentary on an issue), explicative (what is driving that commentary), investigative (what are the particular cases of a specific issue) and classification (what subject or what key content pieces does the text talk about)." (Gartner) 

📊Business Intelligence: Operational Intelligence (Definitions)

"A business intelligence solution where all the data reflects its most current state in real-time." (Martin Oberhofer et al, "The Art of Enterprise Information Architecture", 2010)

"Operational BI provides time-sensitive, relevant information to operations managers and frontline, customer-facing employees to support daily work processes. These data-driven DSS differ from other DSS in terms of purpose, targeted users, data latency, data detail, and availability." (Ciara Heavin & Daniel J Power, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"Operational Intelligence is the application of data analysis techniques to data that is generated or collected in real-time through an organization's IT infrastructure. The purpose of Operational Intelligence is to gather data from throughout the IT system, analyze it in real-time (as it is created or collected), and present it to IT operators in a simplified format that enables them to take rapid action and make decisions based on the results." (Sumo Logic) [source]

📊Business Intelligence: Data Storytelling (Definitions)

"A narrative way of describing a scenario, product idea, or strategy intended to provide a real-world context to promote decision making and better understanding." (Steven Haines, "The Product Manager's Desk Reference", 2008)

[storytelling:] "A method of communicating and sharing ideas, experiences and knowledge in a specific context." (Darren Dalcher, "Making Sense of IS Failures", Encyclopedia of Information Science and Technology 2nd Ed., 2009)

"A method of explaining a series of events through narrative." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"using a combination of data facts and a qualitative 'story' that provides effective communication of a business message." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"Data storytelling can be defined as a structured approach for communicating data insights using narrative elements and explanatory visuals." (Brent Dykes, "Effective Data Storytelling: How to Drive Change with Data, Narrative and Visuals", 2019)

[storytelling:] "The social and cultural activity of sharing stories, with great application to journalism." (Georgios Vassis et al, "Review and Evaluation of Systems Supporting Data Journalism", 2021)

"Data storytelling forms a compelling narrative by putting data in context to show the challenges, insights and solutions of a specific business problem. It normally highlights a series of changes or trends over time through linked visualizations that combine to tell a story." (Sisense) [source]

"Data storytelling is a method of visually presenting data to make it more understandable and easy to digest. Visualizations such as charts and graphs guide users toward a conclusion about their data and empower them to make a decision based on that conclusion." (Logi Analytics) [source]

"Data storytelling is a methodology for communicating information, tailored to a specific audience, with a compelling narrative. It is the last ten feet of your data analysis and arguably the most important aspect." (Nugit) [source]

"Data storytelling is the practice of building a narrative around a set of data and its accompanying visualizations to help convey the meaning of that data in a powerful and compelling fashion." (TDWI)

📊Business Intelligence: Big Data Analytics (Definitions)

"Big Data Analytics is the process of examining large amounts of data of a variety of types (big data) to uncover hidden patterns, unknown correlations and other useful information using advanced analytic techniques." (Pethuru Raj, "Big Data Analytics Demystified", 2014)

"Big data/analytics is defined as the capability of processing extremely large data sets to identify patterns of relationships (correlation, causality) among data to be used in detecting market trends, consumer behaviour and preferences." (James O Odia & Osaheni T Akpata, "Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection", 2021)

"Big data analytics is the process of examining large and varied data sets of big data to uncover information including hidden patterns and unknown correlations that can help organizations make better business decisions." (Ahmad M Kabil, Integrating Big Data Technology Into Organizational Decision Support Systems, 2021)

"Big data analytics is the use of advanced techniques to analyze, process and examine big data to uncover hidden patterns, trends and relations in order to assist management decision making." (Steven C S Hui et al, Enhancing Online Repurchase Intention via Application of Big Data Analytics in E-Commerce, 2021)

"Big Data Analytics refers to the intricate process of analyzing vast datasets to uncover hidden patterns, correlations, and customer behaviors from various sources like videos, social networks, and sensors."  (ICT Express, 2023)

"Big data analytics describes the process of uncovering trends, patterns, and correlations in large amounts of raw data to help make data-informed decisions. These processes use familiar statistical analysis techniques - like clustering and regression - and apply them to more extensive datasets with the help of newer tools." (Tableau) [source[

"Big Data Analytics examines large and diverse datasets (i.e. big data) to identify patterns, trends, correlations, and other information that lead to insights organizations can harness in support of better decision-making. Big Data Analytics is the science and engineering of problem solving where the nature, size, and shape of the data renders traditional analytics tools difficult or even impossible to use." (Accenture)

"Big data analytics is the process of evaluating that digital information into useful business intelligence." (Talend) [source]

"Big data analytics refers to the methods, tools, and applications used to collect, process, and derive insights from varied, high-volume, high-velocity data sets." (Microsoft) [source]

"Big data analytics refers to the systematic processing and analysis of large amounts of data and complex data sets, known as big data, to extract valuable insights. Big data analytics allows for the uncovering of trends, patterns and correlations in large amounts of raw data to help analysts make data-informed decisions." (IBM) [source]

📊Business Intelligence: Performance Indicator [PI] (Definitions)

"The measurement of the execution of activities. A performance indicator is often compared to recommended practices. It is a quantifiable target for achieving the adopted key performance factors. Metric is the unit of measure, and measure is a specific observation when tracking performance. The terms performance indicator, metric, and measure are often used interchangeably." (Paul C Dinsmore et al, "Enterprise Project Governance", 2012)

"A quantitative or qualitative measure to determine progress." (Fran Ackermann et al, "Visual Strategy: Strategy Mapping for Public and Nonprofit Organizations", 2014)

"A high-level metric of effectiveness and/or efficiency used to guide and control progressive development, e.g. Defect Detection Percentage (DDP) for testing [CMMI]." (Standard Glossary, "ISTQB", 2015)

"Quantifiable metrics used to measure the success of activities undertaken to reach strategic goals." (Gina Abudi & Brandon Toropov, "The Complete Idiot's Guide to Best Practices for Small Business", 2011)

📊Business Intelligence: Predictive Analytics (Definitions)

"Includes a variety of statistical and data mining techniques to analyze historical and current data to make predictions about the future." (Paulraj Ponniah, "Data Warehousing Fundamentals for IT Professionals", 2010)

"An area of statistical analysis that deals with extracting information from data and using it to predict future trends and behavior patterns." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The branch of data mining that focuses on forecasting trends (e.g., regression analysis) and estimating probabilities of future events. Business analytics, as it is also called, provides the models, which are formulas or algorithms, and procedures to BI." (Linda Volonino & Efraim Turban, "Information Technology for Management 8th Ed", 2011)

"A statistical or data-mining solution consisting of algorithms and techniques that can be used on both structured and unstructured data (together or individually) to determine future outcomes. It can be deployed for prediction, optimization, forecasting, simulation, and many other uses" (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A methodology for forecasting futures events and trends using a variety of technologies including statistics and artificial intelligence." (Owen P. Hall Jr., "Teaching and Using Analytics in Management Education", 2014)

"A set of data–driven tools and methods to study a system behavior over time and to predict the future outcomes." (Shokoufeh Mirzaei, "Defining a Business-Driven Optimization Problem", 2014) 

"An advanced form of analytics that uses business information to find patterns and predict future outcomes and trends; determining credit scores by looking at a customer’s credit history and other data is a typical use for predictive analytics." (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"Analytic methods used to make predictions. The practice of using mathematical modeling to predict outcomes." (Meta S Brown, "Data Mining For Dummies", 2014)

"Predictive analytics requires new methods and technologies by an organization to mine data to discover trends/patterns and test large numbers variables for unexpected insight." (Avnish Rastogi, "New Payment Models and Big Data Analytics", 2014)

"The practice of using statistics and data mining to analyze current and historical information to make predictions about what will happen in the future. Predictive modeling, the fitting of some data to some model, is a step in predictive analytics. Typically, predictive analytics also includes applying a model to additional data." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"Predictive analytics and modeling are statistical and analytical tools that examine and capture the complex relationships and underlying patterns among variables in the existing data in efforts to predict the future organizational performances, risks, trends, and behavior patterns". (Sema A Kalaian & Rafa M Kasim, "Predictive Analytics", 2015)

"A technique used in many business areas to enable organizations and companies to make more informed business discussions by making inference from analyzing patterns and relationships in consumer behavior data. A term refers to the procedure and technique to enable researchers or businesses to extra information from existing datasets to identify consumer behavioral patterns and insights to predict future trends and outcomes." (Kenneth C C Yang & Yowei Kang, "Real-Time Bidding Advertising: Challenges and Opportunities for Advertising Curriculum, Research, and Practice", 2016)

"A branch of advanced analytics that is used to make forecasts about future events." (Jonathan Ferrar et al, "The Power of People", 2017)

"A general term for using simple and complex models to predict what will happen, to support decision making. A process of using a quantitative model and current real-time or historical data to generate a score that is predictive of future behavior. Statistical analysis of historical data identifies a predictive model to support a specific decision task." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"General term for using simple and complex ­models to support anticipatory decision making. Often a process of using a ­quantitative model and current real-time or historical data to generate a score that is predictive of future behavior." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"[...] predictive analytics is about predicting the future outcomes. It also involves forecasting demand, sales, and profits for a company. The commonly used techniques for predictive analytics are different types of regression and forecasting models. Some advanced techniques are data mining, machine learning, neural networks, and advanced statistical models." (Amar Sahay, "Business Analytics" Vol. I, 2018)

"Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior." (Thomas Ochs & Ute A Riemann, "IT Strategy Follows Digitalization", 2018)

"A statistical or data mining solution consisting of algorithms and techniques that can be used for both structured and unstructured data to determine future outcomes." (K Hariharanath, "BIG Data: An Enabler in Developing Business Models in Cloud Computing Environments", 2019)

"Predictive analytics is the branch of data mining concerned with the prediction of future probabilities and trends. The central element of predictive analytics is the predictor, a variable that can be measured for an individual or other entity to predict future behavior." (Thomas Ochs & Ute A Riemann, "IT Strategy Follows Digitalization", 2019)

"Predictive analytics represent any solution that supports the identification of meaningful patterns and correlations among variables in complex, structured, unstructured, historical, and potential future data sets for the purposes of predicting events and assessing the attractiveness of various courses of action." (Satyadhyan Chickerur et al, "Forecasting the Demand of Agricultural Crops/Commodity Using Business Intelligence Framework", 2019)

"A process for analyzing data in a manner that seeks to predict a likely future scenario or outcome. It can be used to improve decision making, mitigate risk, improve operations, and identify best practices." (Mike Gregory & Cynthia Roberts, "Maturing an Information Technology Privacy Program: Assessment, Improvement, and Change Leadership", 2020)

"It is a statistical process for denoting the average relationship between two or more factors with the involvement of dependent and independent variables." (Selvan C & S R Balasundaram, "Data Analysis in Context-Based Statistical Modeling in Predictive Analytics", 2021)

"A type of data analytics which identifies trends in historical datasets and uses those trends to forecast future performance, such as predicted sales revenue or demand." (Board International)

"[...] describes the practice of using historical data to predict future outcomes. It combines mathematical models (or 'predictive algorithms') with historical data to calculate the likelihood (or degree to which) something will happen." (Accenture)

"Techniques, tools, and technologies that use data to find models - models that can anticipate outcomes with a significant probability of accuracy." (Forrester)

"the practice of extracting information from existing data sets in order to determine patterns and predict future outcomes and trends. Applied to business, predictive models and analysis are used to analyze current data and historical facts in order to better understand customers, products and partners and to identify potential risks and opportunities for a company." (KDnuggets)

"Predictive analytics is a form of advanced analytics that uses both new and historical data to forecast activity, behavior and trends. It involves applying statistical analysis techniques, analytical queries and automated machine learning algorithms to data sets to create predictive models that place a numerical value - or score - on the likelihood of a particular event happening." (Techtarget) [source]

"Predictive analytics is a set of methods and technologies that can be used to analyze current and historical data with the goal of making predictions about future events. Predictive analytics includes a wide variety of mathematical modeling and computer science techniques with the common goal of using past events to indicate the probability or likelihood of a future event." (Sumo Logic) [source]

"Predictive analytics is a sub-division of advanced analytics and focuses on the identification of future events and values with their respective probabilities." (BI Survey) [source]

"Predictive analytics is an area of data mining that is related to the overall prediction of future probabilities and trends. It uses historical data, machine learning, and AI to predict what will happen in the future." (Logi Analytics) [source]

"Predictive Analytics is the practice of employing statistics and modeling techniques to extract information from current and historical datasets in order to predict potential future outcomes and trends." (OmiSci) [source]

"Predictive analytics is the umbrella term for analyzing patterns found in data to predict future behavior or results. It includes techniques and algorithms found in statistics, machine learning, artificial intelligence, and data mining." (TDWI)

📊Business Intelligence: Measurement (Definitions)

[process measurement] "The set of definitions, methods, and activities used to take measurements of a process and its resulting products for the purpose of characterizing and understanding the process." (Sandy Shrum et al, "CMMI: Guidelines for Process Integration and Product Improvement, Second Edition", 2006)

"Measurement is understood as a continuous process during which process metrics are defined and measurement data are collected, analyzed, and evaluated. The objective is to understand, control, and optimize processes, for instance, to improve project control, reduce development effort and cost, or to improve on work products." (Lars Dittmann et al, "Automotive SPICE in Practice", 2008)

[process measurement] "An evaluation of the performance of a system process.  A measurement from the system process is compared to determine whether it is below the 'Minimum value' or above the 'Maximum value' of the success criterion for that system process. If so, it is the source of a system event type that is the trigger of another system process to correct the situation." (David C Hay, "Data Model Patterns: A Metadata Map", 2010)

"Systematically determining or estimating dimension, quantity, and capacity in order to assign value." (Joan C Dessinger, "Fundamentals of Performance Improvement." 3rd Ed, 2012)

"The process of measurement is the act of ascertaining the size, amount, or degree of something. Measurements are the results of the process of measuring." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"The process of determining the monetary amounts at which the elements of the financial statements are to be recognised and carried in the balance sheet [statement of financial position] and income statement [statement of comprehensive income]." (Project Management Institute, "The Standard for Program Management  3rd Ed..", 2013)

"(1) An instance of a measurement (a 'data point'). (2) The activity or process of making a measurement; for example, mapping empirical values to numbers or symbols of a measurement scale." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"The process of assigning a number or category to an entity to describe an attribute of that entity." (ISO 14598)

📊Business Intelligence: Measures (Definitions)

"A quantitative, numerical column in a fact table. Measures typically represent the values that are analyzed. See also dimension." (Microsoft Corporation, "SQL Server 7.0 System Administration Training Kit", 1999)

"A metric is a measurable or quantitative value." (Microsoft Corporation, "Microsoft SQL Server 7.0 Data Warehouse Training Kit", 2000)

"A measure is a dimensional modeling term that refers to values, usually numeric, that measure some aspect of the business. Measures reside in fact tables. The dimensional terms measure and attribute, taken together, are equivalent to the relational modeling use of the term attribute." (Claudia Imhoff et al, "Mastering Data Warehouse Design", 2003)

"(1) A mapping from empirical properties to quantities in a formal mathematical model called a measurement scale. (2) To obtain a measurement." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"In Dimensional modeling, a specific data item that describes a fact or aggregation of facts. Measures are implemented as metric facts." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)

"A summarizable numerical value used to monitor business activity; it is also known as a fact. " (Reed Jacobsen & Stacia Misner, "Microsoft SQL Server 2005 Analysis Services Step by Step", 2006)

"A column of quantifiable data mapped to a dimension within a cube. Measures are often used to provide access to aggregations of data (such as annual sales of a product or a store), while also giving the ability to drill down into the details (such as quarterly or monthly sales)." (Robert D. Schneider and Darril Gibson, "Microsoft SQL Server 2008 All-In-One Desk Reference For Dummies", 2008)

[business measure:] "Business performance metric captured by an operational system and represented as a physical or computed fact in a dimensional model." (Ralph Kimball, "The Data Warehouse Lifecycle Toolkit", 2008)

"A set of usually numeric values from a fact table that is aggregated in a cube across all dimensions." (Jim Joseph et al, Microsoft® SQL Server 2008 Reporting Services Unleashed, 2009)

[business measures:] "The complete set of facts, base and derived, that are defined and made available for reporting and analysis." (Laura Reeves, "A Manager's Guide to Data Warehousing", 2009)

"A quantitative performance indicator or success factor that can be traced on an ongoing basis to determine successful operation and progress toward objectives and goals." (David Lyle & John G. Schmidt, "Lean Integration", 2010)

"1.Loosely used, a metric. 2.In data modeling, a quantified characteristic; the unit used to quantify the dimensions, capacity, or amount of something." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Value assigned (noun) or the process of assigning a value (verb) to an object through calculation, appraisal, estimation, or some other method." (Leslie G Eldenburg & Susan K. Wolcott, "Cost Management" 2nd Ed., 2011)

"In a cube, a set of values that are usually numeric and are based on a column in the fact table of the cube. Measures are the central values that are aggregated and analyzed." (Microsoft, "SQL Server 2012 Glossary", 2012)

"The act of identifying what to measure as well as actually collecting the measures that would help an organization understand if the process is operating within acceptable limits." (Project Management Institute, "Organizational Project Management Maturity Model (OPM3®)" 3rd Ed., 2013)

"Metrics such as count, maximum, minimum, sum, or average that are used in a fact table. Measures can be calculated with an SQL expression or mapped directly to a numeric value in a column." (Sybase, "Open Server Server-Library/C Reference Manual", 2019)

"The number or category assigned to an attribute of an entity by making a measurement. (ISO 14598)