Data Science: Data Products (Definitions)

"Broadly defined, data means events that are captured and made available for analysis. A data source is a consistent record of these events. And a data product translates this record of events into something that can easily be understood." (Richard Galentino; et al, "Data Fluency: Empowering Your Organization with Effective Data Communication", 2014)

"Self-adapting, broadly applicable economic engines that derive their value from data and generate more data by influencing human behavior or by making inferences or predictions upon new data." (Benjamin Bengfort & Jenny Kim, "Data Analytics with Hadoop", 2016)

"Data products are software applications that derive value from data and in turn generate new data." (Rebecca Bilbro et al, "Applied Text Analysis with Python", 2018)

"[...] a product that facilitates an end goal through the use of data." (Ulrika Jägare, "Data Science Strategy For Dummies", 2019)

"Any computer software that uses data as inputs, produces outputs, and provides feedback based on the output to control the environment is referred to as a data product. A data product is generally based on a model developed during data analysis, for example, a recommendation model that inputs user purchase history and recommends a related item that the user is highly likely to buy." (Suresh K Mukhiya; Usman Ahmed, Hands-On Exploratory Data Analysis with Python, 2020)

"A data product is a product or service whose value is derived from using algorithmic methods on data, and which in turn produces data to be used in the same product, or tangential data products." (Statistics.com)

"A data product, in general terms, is any tool or application that processes data and generates results. […] Data products have one primary objective: to manage, organize and make sense of the vast amount of data that organizations collect and generate. It’s the users’ job to put the insights to use that they gain from these data products, take actions and make better decisions based on these insights." (Sisense) [source]

" A data product is digital information that can be purchased." (Techtarget) [source]

"A strategy for monetizing an organization’s data by offering it as a product to other parties." (Izenda) 

"An information product that is derived from observational data through any kind of computation or processing. This includes aggregation, analysis, modelling, or visualization processes." (Fixed-Point Open Ocean Observatories) 

"Data set or data set series that conforms to a data product specification." (ISO 19131)

Data Science: Descriptive Analytics (Definitions)

"The practice of reporting what has happened, analyzing contributing data to determine why it happened, and monitoring new data to determine what is happening now. Also known as reporting and business intelligence." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"If you are using charts and graphs or time series plots to study the demand or the sales patterns, or the trend for the stock market you are using descriptive analytics. Also, calculating statistics from the data such as, the mean, variance, median, or percentiles are all examples of descriptive analytics." (Amar Sahay, "Business Analytics" Vol. I, 2018)

"The simplest form of data analytics, in which historical data is collated and summarized in a user-friendly format, providing an understanding of what has previously happened." (Board International)

"Descriptive analytics is a form of data analytics that looks at data statistically to tell you what happened in the past. It helps a business understand how it is performing by providing context that will aid stakeholders in interpreting information." (Logi Analytics) [source]

"Descriptive analytics is a preliminary stage of data processing that creates a summary of historical data to yield useful information and possibly prepare the data for further analysis." (Techtarget) [source]

Data Science: Regularization (Definitions)

"It is a formal concept based on fuzzy topology that removes geometric anomalies on fuzzy regions." (Markus Schneider, "Fuzzy Spatial Data Types for Spatial Uncertainty Management in Databases", 2008)

"It is any method of preventing overfitting of data by a model and it is used for solving ill-conditioned parameter-estimation problems." (Cecilio Angulo & Luis Gonzalez-Abril, "Support Vector Machines", 2009)

"Optimization of both complexity and performance of a neural network following a linear aggregation or a multi-objective algorithm." (M P Cuéllar et al, "Multi-Objective Training of Neural Networks", 2009)

"Including a term in the error function such that the training process favours networks of moderate size and complexity, that is, networks with small weights and few hidden units. The goal is to avoid overfitting and support generalization." (Frank Padberg, "Counting the Hidden Defects in Software Documents", 2010)

"It refers to the procedure of bringing in additional knowledge to solve an ill-posed problem or to avoid overfitting. This information appears habitually as a penalty term for complexity, such as constraints for smoothness or bounds on the norm." (Vania V Estrela et al, "Total Variation Applications in Computer Vision", 2016)

"This is a general method to avoid overfitting by applying additional constraints to the model that is learned. A common approach is to make sure the model weights are, on average, small in magnitude." (Rayid Ghani & Malte Schierholz, "Machine Learning", 2017)

"Regularization is a method of penalizing complex models to reduce their variance. Specifically, a penalty term is added to the loss function we are trying to minimize [...]" (Chris Albon, "Machine Learning with Python Cookbook", 2018)

"Regularization, generally speaking, is a wide range of ML techniques aimed at reducing overfitting of the models while maintaining theoretical expressive power." (Jonas Teuwen & Nikita Moriakov, "Convolutional neural networks", 2020)

Data Science: Standard Deviation (Definitions)

"A commonly used measure that defines the variation in a data set." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A measure of the variability in a set of data. It is calculated by taking the square root of the variance. Standard deviations are not additive; the variances are." (Clyde M Creveling, "Six Sigma for Technical Processes", 2006)

"The degree of dispersion of a group of scores around the average. If most scores are close to the average, the standard deviation is low. Conversely, if the scores are widely dispersed, the standard deviation is large." (Ruth C Clark, "Building Expertise: Cognitive Methods for Training and Performance Improvement", 2008)

"The measured range of economic volatility that can occur during the course of doing business." (Annetta Cortez & Bob Yehling, "The Complete Idiot's Guide® To Risk Management", 2010)

"A measure of how distributed the values of a probability curve are, relative to the average." (Jon Radoff, "Game On: Energize Your Business with Social Media Games", 2011)

"The amount of dispersal among test scores or other outcome results. A larger standard deviation indicates greater spread among test scores, while a smaller standard deviation indicates greater consistency among scores." (Ruth C Clark & Richard E Mayer, "e-Learning and the Science of Instruction", 2011)

"Describes dispersion about the data set’s mean. You can think of a standard deviation as an average deviation from the mean. See also average; variance." (E C Nelson & Stephen L Nelson, "Excel Data Analysis For Dummies ", 2015)

"Square root of variance. The standard deviation is an index of variability in the distribution of scores." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"the square root of the variance of a sample or distribution. For well-behaved, reasonably symmetric data distributions without long tails, we would expect most of the observations to lie within two sample standard deviations from the sample mean." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

Data Science: Prescriptive Analytics (Definitions)

"The analytics methods that recommend actions with the goal of finding a set of action that is predicted to produce the best possible outcome." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"Prescriptive analytics manipulate large data sets to make recommendations. Decision support that prescribes or recommends an action, rather than a forecast or a summary report." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"Prescriptive analytics involves analyzing the results of the predictive analytics and 'prescribes' the best category to target and minimize or maximize the objective (s). It builds on predictive analytics and often suggests the best course of action leading to best possible solution. It is about optimizing (maximizing or minimizing) an objective function." (Amar Sahay, "Business Analytics" Vol. I, 2018)

"A combination of analytics, math, experiments, simulation, and/or artificial intelligence used to improve the effectiveness of decisions made by humans or by decision logic embedded in applications." (Forrester)

"A type of data analytics in which a combination of previous performance, business models, and logic is used by a machine to suggest the best course of action to achieve a desired outcome." (Board International)

"Prescriptive analytics is a form of data analytics that uses historical data to forecast what will happen in the future and recommend actions you can take to affect those outcomes." (Logi Analytics) [source]

"Prescriptive analytics is the area of business analytics (BA) dedicated to finding the best course of action for a given situation. Prescriptive analytics is related to both descriptive and predictive analytics. While descriptive analytics aims to provide insight into what has happened and predictive analytics helps model and forecast what might happen, prescriptive analytics seeks to determine the best solution or outcome among various choices, given the known parameters." (Techtarget) [source]

Data Science: Regression Analysis (Definitions)

"A set of statistical operations that helps to predict the value of the dependent variable from the values of one or more independent variables." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling 2nd Ed.", 2005)

"A statistical tool that measures the strength of relationship between one or more independent variables with a dependent variable. It builds upon the correlation concepts to develop an empirical, databased model. Correlation describes the X and Y relationship with a single number (the Pearson’s Correlation Coefficient (r)), whereas regression summarizes the relationship with a line - the regression line." (Lynne Hambleton, "Treasure Chest of Six Sigma Growth Methods, Tools, and Best Practices", 2007)

"A statistical procedure for estimating mathematically the average relationship between the dependent variable (e.g., sales) and one or more independent variables (e.g., price and advertising)." (Jae K Shim & Joel G Siegel, "Budgeting Basics and Beyond", 2008)

"Regression analysis is a statistical technique for estimating the relationship between a set of predictors (independent variables) and an outcome variable (dependent variable). Linear least-squares regression, in which the relationship is expressed in a linear form, is the most common type of regression analysis. The mathematical model used in least-squares linear regression is often called the general linear model (GLM)." (Herbert I Weisberg, "Bias and Causation: Models and Judgment for Valid Comparisons", 2010)

"A statistical technique which seeks to find a line which best fits through a set of data as plotted on a graph, seeking to find the cleanest path which deviates the least from any instance within the set." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

[regression] "Using one data set to predict the results of a second." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The statistical process of predicting one or more continuous variables, such as profit or loss, based on other attributes in the dataset." (Microsoft, "SQL Server 2012 Glossary", 2012)

"A family of methods for fitting a line or curve to a dataset, used to simplify or make sense of a number of apparently random data points." (Meta S Brown, "Data Mining For Dummies", 2014)

"An analytic technique where a series of input variables are examined in relation to their corresponding output results in order to develop a mathematical or statistical relationship." (For Dummies, "PMP Certification All-in-One For Dummies" 2nd Ed., 2013)

"A statistical technique for estimating relationships between variables." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

 "Process to statistically estimate the relationship between different attributes." (Sanjiv K Bhatia & Jitender S Deogun, "Data Mining Tools: Association Rules", 2014)

"Plotting pairs of independent and dependent variables in an XY chart and then finding a linear or exponential equation that best describes the plotted data." (E C Nelson & Stephen L Nelson, "Excel Data Analysis For Dummies", 2015)

"A statistical procedure that produces an equation for predicting a variable (the criterion measure) from one or more other variables (the predictor measures)." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"A statistical technique used to estimate the mathematical relationship between a dependent variable, such as quantity demanded, and one or more explanatory variables, such as price and income." (Jeffrey M Perloff & James A Brander, "Managerial Economics and Strategy" 2nd Ed., 2016)

"A statistical process for estimating the relationships between variables, often used to forecast the change in a variable based on changes in other variables. Linear regression is used to analyze continuous variables, and logistic regression is used for discrete variables." (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

"In a machine learning context, regression is the task of assigning scalar value to examples." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"Algorithms used to predict values for new data based on training data fed into the system. Areas where regression in machine learning is used to predict future values include drug response modeling, marketing, real estate and financial forecasting." (Accenture)

"To define the dependency between variables. It assumes a one-way causal effect from one variable to the response of another variable." (Analytics Insight)

Data Science: Data Processing (Definitions)

"The act of turning raw data into meaningful output, generally associated with computers." (Greg Perry, "Sams Teach Yourself Beginning Programming in 24 Hours" 2nd Ed., 2001)

"Any process that converts data into information. The processing is usually assumed to be automated and running on an information system." (Eleutherios A Papathanassiou & Xenia J Mamakou, "Privacy Issues in Public Web Sites", Handbook of Research on Public Information Technology, 2008) 

"Obtaining, recording or holding the data, or carrying out any operation on the data, including organising, adapting or altering it. Retrieval, consultation or use of the data, disclosure of the data, and alignment, combination, blocking, erasure or destruction of the data are all legally classed as processing." (Mark Olive, "SHARE: A European Healthgrid Roadmap", 2009)

"The operation performed on data through capture, transformation, and storage, in order to derive new information according to a given set of rules." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Collection and elaboration of sensing data with the aim to derivate/infer new knowledge from original raw data." (Paolo Bellavista et al, "Crowdsensing in Smart Cities: Technical Challenges, Open Issues, and Emerging Solution Guidelines", 2015)

"The act of data manipulation through integration of mathematical tools, statistics, and computer application to generate information." (Babangida Zubairu, "Security Risks of Biomedical Data Processing in Cloud Computing Environment", 2018)

"Any operation or set of operations which is performed on personal data or on sets of personal data, whether or not by automated means, such as collection, recording, organisation, structuring, storage, adaptation or alteration, retrieval, consultation, use, disclosure by transmission, dissemination or otherwise making available, alignment or combination, restriction, erasure or destruction." (Yordanka Ivanova, "Data Controller, Processor, or Joint Controller: Towards Reaching GDPR Compliance in a Data- and Technology-Driven World", 2020)

"Data processing is any action performed to turn raw data into useful information." (Xplenty) [source]

"Data processing occurs when data is collected and translated into usable information. […] Data processing starts with data in its raw form and converts it into a more readable format (graphs, documents, etc.), giving it the form and context necessary to be interpreted by computers and utilized by employees throughout an organization." (Talend) [source]

Data Science: Business Analytics (Definitions)

"Meta-data that includes data definitions, report definitions, users, usage statistics, and performance statistics." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Provides models, which are formulas or algorithms and procedures to BI." (Linda Volonino & Efraim Turban, "Information Technology for Management "8th Ed, 2011)

"The process of leveraging all forms of analytics to achieve business outcomes by requiring business relevancy, actionable insight, performance management, and value measurement." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Application of analytical tools to business questions. Business Analytics focuses on developing insights and understanding related to business performance using quantitative and statistical methods. Business Analytics includes Business Intelligence and Reporting." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"BA is a data-driven decision making approach that uses statistical and quantitative analysis, information technology, and management science (mathematical modeling, simulation), along with data mining and fact-based data to measure past business performance to guide an organization in business planning and effective decision making." (Amar Sahay, "Business Analytics" Vol. I, 2018) 

"Use of data and quantitative and qualitative tools and techniques to improve operations and to support business decision ­making. Emphasis on using statistical and management science techniques, including data mining, to develop predictive and prescriptive models." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"Aggregated information on business processes that enables managers to analyze process trends, view performance metrics, and respond to organizational change." (Appian)

"Refers to the skills, technologies, and practices for investigation of past business performance to gain insight and drive business planning. It focuses on developing new insights and understanding of business performance based on data and statistical methods. While business intelligence (BI) focuses on a consistent set of metrics to both measure past performance and guide business planning, business analytics is focused on developing new insights and understanding based on statistical methods and predictive modeling." (Insight Software)

"Business Analytics describes the skills, technologies, statistical methods and data driven approaches used to explore and investigate past business performance to gain new insights that can support business planning." (Accenture)

"Business analytics is comprised of solutions used to build analysis models and simulations to create scenarios, understand realities and predict future states. Business analytics includes data mining, predictive analytics, applied analytics and statistics, and is delivered as an application suitable for a business user." (Gartner)

"Business analytics (BA) is the iterative, methodical exploration of an organization's data, with an emphasis on statistical analysis." (Techtarget) [source] 

"Business Analytics is the process by which businesses use statistical methods and technologies for analyzing historical data in order to gain new insight and improve strategic decision-making." (OmiSci) [source]

"Business analytics is the process of gathering and processing all of your business data, and applying statistical models and iterative methodologies to translate that data into business insights." (Tibco) [source]

"Describes the skills, technologies, statistical methods and data driven approaches used to explore and investigate past business performance to gain new insights that can support business planning. Examples of business analytics tools include data visualization, business intelligence reporting and big data platforms." (Accenture)

Data Science: Structured Data (Definitions)

"Data that has a strict metadata defined, such as a SQL Server table’s column." (Victor Isakov et al, "MCITP Administrator: Microsoft SQL Server 2005 Optimization and Maintenance (70-444) Study Guide", 2007)

"Data that has enforced composition to specified datatypes and relationships and is managed by technology that allows for querying and reporting." (Keith Gordon, "Principles of Data Management", 2007)

"Database data, such as OLTP (Online Transaction Processing System) data, which can be sorted." (David G Hill, "Data Protection: Governance, Risk Management, and Compliance", 2009)

"A collection of records or data that is stored in a computer; records maintained in a database or application." (Robert F Smallwood, "Managing Electronic Records: Methods, Best Practices, and Technologies", 2013)

"Data that has a defined length and format. Examples of structured data include numbers, dates, and groups of words and numbers called strings (for example, a customer’s name, address, and so on)." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"Data that fits cleanly into a predefined structure." (Evan Stubbs, "Big Data, Big Innovation", 2014)

"Data that is described by a data model, for example, business data in a relational database" (Hasso Plattner, "A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases" 2nd Ed., 2014)

"Data that is managed by a database management system" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"In statistics and data mining, any type of data whose values have clearly defined meaning, such as numbers and categories." (Meta S Brown, "Data Mining For Dummies", 2014)

"Data that adheres to a strict definition." (Jason Williamson, "Getting a Big Data Job For Dummies", 2015)

"Data that has a defined length and format. Examples of structured data include numbers, dates, and groups of words and numbers called strings (for example, for a customer’s name, address, and so on)." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"Data that resides in a fixed field within a file or individual record, such as a row & column database." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"Information that sits in a database, file, or spreadsheet. It is generally organized and formatted. In retail, this data can be point-of-sale data, inventory, product hierarchies, or others." (Brittany Bullard, "Style and Statistics", 2016)

"A data field of a definable data type, usually of a specified size or range, that can be easily processed by a computer." (George Tillmann, "Usage-Driven Database Design: From Logical Data Modeling through Physical Schmea Definition", 2017)

"Data that can be stored in a table. Every instance in the table has the same set of attributes. Contrast with unstructured data." (John D Kelleher & Brendan Tierney, "Data science", 2018)

"Data that is identifiable as it is organized in structure like rows and columns. The data resides in fixed fields within a record or file or the data is tagged correctly and can be accurately identified." (Analytics Insight)

"Refers to information with a high degree of organization, meaning that it can be seamlessly included in a relational database and quickly searched by straightforward search engine algorithms and/or other search operations. Structured data examples include dates, numbers, and groups of words and number 'strings'. Machine-generated structured data is on the increase and includes sensor data and financial data." (Accenture)

Data Science: Semi-Structured Data (Definitions)

"Data that has flexible metadata, such as XML." (Marilyn Miller-White et al, "MCITP Administrator: Microsoft® SQL Server™ 2005 Optimization and Maintenance 70-444", 2007)

"'Text' documents, such as e-mail, word processing, presentations, and spreadsheets, whose content can be searched." (David G Hill, "Data Protection: Governance, Risk Management, and Compliance", 2009)

"Data that, although unstructured, still has some degree of structure. A good example is e-mail: Even though it is predominantly text, it has logical blocks with different purposes." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Data that have already been processed to some extent." (Carlos Coronel & Steven Morris, "Database Systems: Design, Implementation, & Management" 11th Ed., 2014)

"A structured data type that does not have a formal definition, like a document. It has tags or other markers to enforce a hierarchy of records within a particular object, but may be different from another object." (Jason Williamson, Getting a Big Data Job For Dummies, 2015)

"Semi-structured data has some structures that are often manifested in images and data from sensors." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"a form a structured data that does not have a formal structure like structured data. It does however have tags or other markers to enforce hierarchy of records." (Analytics Insight)

Data Science: Big Data (Definitions)

"Big Data: when the size and performance requirements for data management become significant design and decision factors for implementing a data management and analysis system. For some organizations, facing hundreds of gigabytes of data for the first time may trigger a need to reconsider data management options. For others, it may take tens or hundreds of terabytes before data size becomes a significant consideration." (Jimmy Guterman, 2009)

"A buzzword for the challenges of and approaches to working with data sets that are too big to manage with traditional tools, such as relational databases. So called NoSQL databases, clustered data processing tools like MapReduce, and other tools are used to gather, store, and analyze such data sets." (Dean Wampler, "Functional Programming for Java Developers", 2011)

"Big data: techniques and technologies that make handling data at extreme scale economical." (Brian Hopkins, "Big Data, Brewer, And A Couple Of Webinars", 2011) [source]

"Big Data is data whose scale, distribution, diversity, and/or timeliness require the use of new technical architectures and analytics to enable insights that unlock new sources of business value." (McKinsey & Co., "Big Data: The Next Frontier for Innovation, Competition, and Productivity", 2011)

"Data volumes that are exceptionally large, normally greater than 100 Terabyte and more commonly refer to the Petabyte and Exabyte range. Big data has begun to be used when discussing Data Warehousing and analytic solutions where the volume of data poses specific challenges that are unique to very large volumes of data including: data loading, modeling, cleansing, and analytics, and are often solved using massively parallel processing, or parallel processing and distributed data solutions." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Big data is data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the strictures of your database architectures. To gain value from this data, you must choose an alternative way to process it." (Edd Wilder-James, "What is big data?", 2012) [source]

"A collection of data whose very size, rate of accumulation, or increased complexity makes it difficult to analyze and comprehend in a timely and accurate manner." (Kenneth A Shaw, "Integrated Management of Processes and Information", 2013)

"A colloquial term referring to exceedingly large datasets that are otherwise unwieldy to deal with in a reasonable amount of time in the absence of specialized tools. They are different from normal data in terms of volume, velocity, and variety and typically require unique approaches for capture, processing, analysis, search, and visualization." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Big data is the term increasingly used to describe the process of applying serious computing power – the latest in machine learning and artificial intelligence – to seriously massive and often highly complex sets of information." (Microsoft, 2013) [source]

"Big data is what happened when the cost of storing information became less than the cost of making the decision to throw it away." (Tim O’Reilly, [email correspondence, 2013)

"The capability to manage a huge volume of disparate data, at the right speed and within the right time frame, to allow real-time analysis and reaction. Big data is typically broken down by three characteristics, including volume (how much data), velocity (how fast that data is processed), and variety (the various types of data)." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A colloquial term referring to datasets that are otherwise unwieldy to deal with in a reasonable amount of time in the absence of specialized tools. Common characteristics include large amounts of data (volume), different types of data (variety), and ever-increasing speed of generation (velocity). They typically require unique approaches for capture, processing, analysis, search, and visualization." (Evan Stubbs, "Big Data, Big Innovation", 2014)

"An extremely large database which generally defies standard methods of analysis." (Owen P. Hall Jr., "Teaching and Using Analytics in Management Education", 2014)

"Datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze." (Xiuli He et al, Supply Chain Analytics: Challenges and Opportunities, 2014)

"More data than can be processed by today's database systems, or acutely high volume, velocity, and variety of information assets that demand IG to manage and leverage for decision-making insights and cost management." (Robert F Smallwood, "Information Governance: Concepts, Strategies, and Best Practices", 2014)

"The term that refers to data that has one or more of the following dimensions, known as the four Vs: Volume, Variety, Velocity, and Veracity." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"A collection of models, techniques and algorithms that aim at representing, managing, querying and mining large-scale amounts of data (mainly semi-structured data) in distributed environments (e.g., Clouds)." (Alfredo Cuzzocrea & Mohamed M Gaber, "Data Science and Distributed Intelligence", 2015)

"A process to deliver decision-making insights. The process uses people and technology to quickly analyze large amounts of data of different types (traditional table structured data and unstructured data, such as pictures, video, email, and Tweets) from a variety of sources to produce a stream of actionable knowledge." (James R Kalyvas & Michael R Overly, "Big Data: A Businessand Legal Guide", 2015)

"A relative term referring to data that is difficult to process with conventional technology due to extreme values in one or more of three attributes: volume (how much data must be processed), variety (the complexity of the data to be processed) and velocity (the speed at which data is produced or at which it arrives for processing). As data management technologies improve, the threshold for what is considered big data rises. For example, a terabyte of slow-moving simple data was once considered big data, but today that is easily managed. In the future, a yottabyte data set may be manipulated on desktop, but for now it would be considered big data as it requires extraordinary measures to process." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"Big data is a discipline that deals with processing, storing, and analyzing heterogeneous (structured/semistructured/unstructured) large data sets that cannot be handled by traditional information management technologies that have been used to process structured data. Gartner defined big data based on the three Vs: volume, velocity, and variety." (Saumya Chaki, "Enterprise Information Management in Practice", 2015)

"Records that are so large (terabytes and exabytes) and diverse (from sensors to social media data) that they require new, powerful technologies for storage, management, analysis and visualization." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)

"Term used to describe the exponential growth, variety, and availability of data, both structured and unstructured." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"A broad term for large and complex data sets that traditional data processing applications are inadequate. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, and information privacy. The term often refers simply to the use of predictive analytics or other certain advanced methods to extract value from data, and seldom to a particular size of data set." (Suren Behari, "Data Science and Big Data Analytics in Financial Services: A Case Study", 2016)

"A combination of facts and artifacts drawn from a myriad of sources and stored without regard to rational or normalized disciplines or structures." (Gregory Lampshire, "The Data and Analytics Playbook", 2016)

"A term that describes a large dataset that grows in size over time. It refers to the size of dataset that exceeds the capturing, storage, management, and analysis of traditional databases. The term refers to the dataset that has large, more varied, and complex structure, accompanies by difficulties of data storage, analysis, and visualization. Big Data are characterized with their high-volume, -velocity and –variety information assets." (Kenneth C C Yang & Yowei Kang, "Real-Time Bidding Advertising: Challenges and Opportunities for Advertising Curriculum, Research, and Practice", 2016)

"Big data is a blanket term for any collection of data sets so large or complex that it becomes difficult to process them using traditional data management techniques such as, for example, the RDBMS (relational database management systems)." (Davy Cielen et al, "Introducing Data Science", 2016)

"For digital resources, inexpensive storage and high bandwidth have largely eliminated capacity as a constraint for organizing systems, with an exception for big data, which is defined as a collection of data that is too big to be managed by typical database software and hardware architectures." (Robert J Glushko, "The Discipline of Organizing: Professional Edition, 4th Ed", 2016)

"Large sets of data that are leveraged to make better business decisions. Retail data can be sales, product inventory, e-mail offers, customer information, competitor pricing, product descriptions, social media, and much more." (Brittany Bullard, "Style and Statistics", 2016)

"A term used to describe large sets of structured and unstructured data. Data sets are continually increasing in size and may grow too large for traditional storage and retrieval. Data may be captured and analyzed as it is created and then stored in files." (Daniel J Power & Ciara Heavin, "Decision Support, Analytics, and Business Intelligence" 3rd Ed., 2017)

"Datasets of structured and unstructured information that are so large and complex that they cannot be adequately processed and analyzed with traditional data tools and applications. |" (Jonathan Ferrar et al, "The Power of People", 2017)

"Big data are often defined in terms of the three Vs: the extreme volume of data, the variety of the data types, and the velocity at which the data must be processed." (John D Kelleher & Brendan Tierney, "Data science", 2018)

"Very large data volumes that are complex and varied, and often collected and must be analyzed in real time." (Daniel J. Power & Ciara Heavin, "Data-Based Decision Making and Digital Transformation", 2018)

"A generic term that designates the massive volume of data that is generated by the increasing use of digital tools and information systems. The term big data is used when the amount of data that an organization has to manage reaches a critical volume that requires new technological approaches in terms of storage, processing, and usage. Volume, velocity, and variety are usually the three criteria used to qualify a database as 'big data'." (Soraya Sedkaoui, "Big Data Analytics for Entrepreneurial Success", 2019)

"Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation." (Thomas Ochs & Ute A Riemann, "IT Strategy Follows Digitalization", 2019)

"The capability to manage a huge volume of disparate data, at the right speed and within the right time frame, to allow real time analysis and reaction." (K Hariharanath, "BIG Data: An Enabler in Developing Business Models in Cloud Computing Environments", 2019)

"A term used to refer to the massive datasets generated in the digital age. Both the volume and speed at which data are generated is far greater than in the past and requires powerful computing technologies." (Osman Kandara & Eugene Kennedy, "Educational Data Mining: A Guide for Educational Researchers", 2020)

"Refers to data sets that are so voluminous and complex that traditional data processing application software is inadequate to deal with them." (James O Odia & Osaheni T Akpata, "Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection", 2021)

"The evolving term that describes a large volume of structured, semi-structured and unstructured data that has the potential to be mined for information and used in machine learning projects and other advanced analytics applications." (Nenad Stefanovic, "Big Data Analytics in Supply Chain Management", 2021)

"The term 'big data' is related to gathering and storing extra-large volume of structured, semi-structured and unstructured data with high Velocity and Variability to be used in advanced analytics applications." (Ahmad M Kabil, Integrating Big Data Technology Into Organizational Decision Support Systems, 2021)

"A collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications." (Board International) 

"A collection of data so large that it cannot be stored, transmitted or processed by traditional means." (Open Data Handbook) 

"an accumulation of data that is too large and complex for processing by traditional database management tools" (Merriam-Webster)

"Extremely large data sets that may be analyzed to reveal patterns and trends and that are typically too complex to be dealt with using traditional processing techniques." (Solutions Review)

"is a term for very large and complex datasets that exceed the ability of traditional data processing applications to deal with them. Big data technologies include data virtualization, data integration tools, and search and knowledge discovery tools." (Accenture)

"The practices and technology that close the gap between the data available and the ability to turn that data into business insight." (Forrester)

"Big data is a term applied to data sets whose size or type is beyond the ability of traditional relational databases to capture, manage and process the data with low latency. Big data has one or more of the following characteristics: high volume, high velocity or high variety." (IBM) [source]

"Big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But it’s not the amount of data that’s important. It’s what organizations do with the data that matters. Big data can be analyzed for insights that lead to better decisions and strategic business moves." (SAS) [source]

"Big data is a combination of structured, semistructured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications." (Techtarget)

"Big data is a term used for large data sets that include structured, semi-structured, and unstructured data." (Xplenty) [source]

"Big data is high-volume, high-velocity and/or high-variety information assets that demand cost-effective, innovative forms of information processing that enable enhanced insight, decision making, and process automation." (Gartner)

"Big data is the catch-all term used to describe gathering, analyzing, and storing massive amounts of digital information to improve operations." (Talend) [source]

"Big data refers to the 21st-century phenomenon of exponential growth of business data, and the challenges that come with it, including holistic collection, storage, management, and analysis of all the data that a business owns or uses." (Informatica) [source]

Data Science: Unstructured Data (Definitions)

"Data that does not neatly fit into a tabular structure with well-defined and bounded definitions. Examples of unstructured data are e-mail messages and video streams. Many customer databases contain comment fields where customer service reps put in additional notes about customers." (Jill Dyché & Evan Levy, "Customer Data Integration: Reaching a Single Version of the Truth", 2006)

"Computerised information which does not have a data structure that is easily readable by a machine, including audio, video and unstructured text such as the body of a word-processed document - effectively this is the same as multimedia data." (Keith Gordon, "Principles of Data Management", 2007)

"Data that has no metadata, such as text files." (Victor Isakov et al, "MCITP Administrator: Microsoft SQL Server 2005 Optimization and Maintenance (70-444) Study Guide", 2007)

"Natively bitmapped data, such as video, audio, pictures, and MRI scans, that can be sensed either visually, audibly, or both." (David G Hill, "Data Protection: Governance, Risk Management, and Compliance", 2009)

"Data that does not fit into a structured data model or does not fit well into relational tables. Common examples include binary information such as video or audio and free-text information." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"Data that does not follow a specified data format. Unstructured data can be text, video, images, and so on." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"Unstructured data has no real structure, such as the data in an email and a memo. Interestingly, estimates have 85% of all business information as unstructured data. There are now many products coming on the market that can put some structure into unstructured data so that it can be categorized or organized hierarchically." (Michael M David & Lee Fesperman, "Advanced SQL Dynamic Data Modeling and Hierarchical Processing", 2013)

"Data that exist in their original (raw) state; that is in the format in which they were collected." (Carlos Coronel & Steven Morris, "Database Systems: Design, Implementation, & Management  Ed. 11", 2014)

"Data whose logical organization is not apparent to the computer" (Daniel Linstedt & W H Inmon, "Data Architecture: A Primer for the Data Scientist", 2014)

"Information (typically stored digitally) that either does not have a predefined data model or is not organized in a predefined manner. Most unstructured data is created by humans and includes email, documents, text messages, tweets, blogs, and more." (Brenda L Dietrich et al, "Analytics Across the Enterprise", 2014)

"Text, audio, video, and other types of complex data that won’t easily fit into a conventional relational database. Unstructured data isn’t as simple as the numbers and short strings that most data analysts use." (Meta S Brown, "Data Mining For Dummies", 2014)

"Data that cannot fit cleanly into a predefined structure." (Evan Stubbs, "Big Data, Big Innovation", 2014)

"Data without data model or that a computer program cannot easily use (in the sense of understanding its content). Examples are word processing documents or electronic mail" (Hasso Plattner, "A Course in In-Memory Data Management: The Inner Mechanics of In-Memory Databases" 2nd Ed., 2014)

"Data (generally text-based) which is not presented in a structured form such as a database, ontology, table, etc. Newspaper articles, government reports, blogs, and e-mails are all examples of unstructured data." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"Data that doesn’t fit into a fixed and strict definition. Things like sound files, images, text, and web pages can be considered unstructured data." (Jason Williamson, "Getting a Big Data Job For Dummies", 2015)

"Information that does not follow a specified data format. Unstructured data can be text, video, images, and such." (Judith S Hurwitz, "Cognitive Computing and Big Data Analytics", 2015)

"Data that does not have a specific format. It can be customer reviews, tweets, pictures, or even hashtags." (Brittany Bullard, "Style and Statistics", 2016)

"A type of data where each instance in the data set may have its own internal structure; that is, the structure is not necessarily the same in every instance. For example, text data are often unstructured and require a sequence of operations to be applied to them in order to extract a structured representation for each instance." (John D Kelleher & Brendan Tierney, "Data science", 2018)

Data Science: Models (Definitions)

"A model is essentially a calculating engine designed to produce some output for a given input." (Richard C Lewontin, "Models, Mathematics and Metaphors", Synthese, Vol. 15, No. 2, 1963)

"A model is an abstract description of the real world. It is a simple representation of more complex forms, processes and functions of physical phenomena and ideas." (Moshe F Rubinstein & Iris R Firstenberg, "Patterns of Problem Solving", 1975)

"A model is an attempt to represent some segment of reality and explain, in a simplified manner, the way the segment operates." (E Frank Harrison, "The managerial decision-making process" , 1975)

"A model is a representation containing the essential structure of some object or event in the real world." (David W Stockburger, "Introductory Statistics", 1996)

"A model is a deliberately simplified representation of a much more complicated situation." (Robert M Solow, "How Did Economics Get That Way and What Way Did It Get?", Daedalus Vol. 126 (1), 1997)

"Models are synthetic sets of rules, pictures, and algorithms providing us with useful representations of the world of our perceptions and of their patterns." (Burton G Malkiel, "A Random Walk Down Wall Street", 1999)

"A model is an imitation of reality" (Ian T Cameron & Katalin M Hangos, "Process Modelling and Model Analysis", 2001)

"Models are replicas or representations of particular aspects and segments of the real world" (Paulraj Ponniah, "Data Modeling Fundamentals", 2007)

"A model is a simplification of reality." (Alexey Voinov, "Systems Science and Modeling for Ecological Economics", 2008)

"a model is a representation of reality intended for some definite purpose." (Michael Pidd, "Tools for Thinking" 3rd Ed., 2009)

"A model is a representation of some subject matter." (Alec Sharp & Patrick McDermott, "Workflow Modeling" 2nd Ed, 2009)

"An abstract representation of how something is built (or is to be built), or how something works (or is observed as working)." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"A model is a simplified representation of a system. It can be conceptual, verbal, diagrammatic, physical, or formal (mathematical)." (Hiroki Sayama, "Introduction to the Modeling and Analysis of Complex Systems", 2015)

"A formal set of relationships that can be manipulated to test assumptions. A simulation that tests the number of units that can be processed each hour under a set of conditions is an example of a model. Models do not need to be graphical." (Appian)

"Model is simply a representation or simulation of some real-world phenomenon." (Accenture)

Data Science: Data (Definitions)

"Facts and figures used in computer programs." (Greg Perry, "Sams Teach Yourself Beginning Programming in 24 Hours" 2nd Ed., 2001)

"A representation of facts, concepts, or instructions suitable to permit communication, interpretation, or processing by humans or by automatic means. (2) Used as a synonym for documentation in U.S. government procurement regulations." (Richard D Stutzke, "Estimating Software-Intensive Systems: Projects, Products, and Processes", 2005)

"A recording of facts, concepts, or instructions on a storage medium for communication, retrieval, and processing by automatic means and presentation as information that is understandable by human beings." (William H Inmon, "Building the Data Warehouse", 2005)

"An atomic element of information. Represented as bits within mass storage devices, memory, and pprocessors." (Tom Petrocelli, "Data Protection and Information Lifecycle Management", 2005)

"Information documented by a language system representing facts, text, graphics, bitmapped images, sound, and analog or digital live-video segments. Data is the raw material of a system supplied by data producers and is used by information consumers to create information." (Sharon Allen & Evan Terry, "Beginning Relational Data Modeling" 2nd Ed., 2005)

"A term applied to organized information." (Gavin Powell, "Beginning Database Design", 2006)

"Numeric information or facts collected through surveys or polls, measurements or observations that need to be effectively organized for decision making." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"Raw, unrelated numbers or entries, e.g., in a database; raw forms of transactional representations." (Martin J Eppler, "Managing Information Quality" 2nd Ed., 2006)

"Data is a representation of facts, concepts or instructions in a formalized manner suitable for communication, interpretation or processing by humans or automatic means." (S. Sumathi & S. Esakkirajan, "Fundamentals of Relational Database Management Systems", 2007)

"Numeric information or facts collected through surveys or polls, measurements or observations that need to be effectively organized for decision making." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2007)

"Hub A common approach for a technical implementation of a service-oriented MDM solution. Data Hubs store and manage some data attributes and the metadata containing the location of data attributes in external systems in order to create a single physical or federated trusted source of information about customers, products, and so on." (Alex Berson & Lawrence Dubov, "Master Data Management and Data Governance", 2010)

"Raw facts, that is, facts that have not yet been processed to reveal their meaning to the end user." (Carlos Coronel et al, "Database Systems: Design, Implementation, and Management" 9th Ed., 2011)

"Facts represented as text, numbers, graphics, images, sound, or video (with no additional defining context); the raw material used to create information." (Craig S Mullins, "Database Administration: The Complete Guide to DBA Practices and Procedures 2nd Ed", 2012)

"Data are abstract representations of selected characteristics of real-world objects, events, and concepts, expressed and understood through explicitly definable conventions related to their meaning, collection, and storage. We also use the term data to refer to pieces of information, electronically captured, stored (usually in databases), and capable of being shared and used for a range of organizational purposes."(Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement ", 2012)

"Data are abstract representations of selected characteristics of real-world objects, events, and concepts, expressed and understood through explicitly definable conventions related to their meaning, collection, and storage. We also use the term data to refer to pieces of information, electronically captured, stored (usually in databases), and capable of being shared and used for a range of organizational purposes." (Laura Sebastian-Coleman, "Measuring Data Quality for Ongoing Improvement", 2013)

"A collection of values assigned to base measures, derived measures and/or indicators." (David Sutton, "Information Risk Management: A practitioner’s guide", 2014)

"Raw facts, that is, facts that have not yet been processed to reveal their meaning to the end user." (Carlos Coronel & Steven Morris, "Database Systems: Design, Implementation, & Management"  11th Ed., 2014)

"A formalized (meaning suitable for further processing, interpretation and communication) representation of business objects or transactions." (Boris Otto & Hubert Österle, "Corporate Data Quality", 2015)

"Data is a collection of one or more pieces if information." (Robert J Glushko, "The Discipline of Organizing: Professional Edition, 4th Ed", 2016)

"Facts about events, objects, and associations. Example: data about a sale would include date, amount, and method of payment." (Gregory Lampshire, "The Data and Analytics Playbook", 2016)

"Discrete, unorganized, unprocessed measurements or raw observations." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide )", 2017)

"Any values from an application that can be transformed into facts and eventually information.." (Piethein Strengholt, "Data Management at Scale", 2020)

"A set of collected facts. There are two basic kinds of numerical data: measured or variable data … and counted or attribute data." (ASQ)

"A representation of information as stored or transmitted." (NISTIR 4734)

"A representation of information, including digital and non-digital formats." (NIST Privacy Framework Version 1.0)

"A variable-length string of zero or more (eight-bit) bytes." (NIST SP 800-56B Rev. 2)

"Any piece of information suitable for use in a computer." (NISTIR 7693)

"(1) Anything observed in the documentation or operation of software that deviates from expectations based on previously verified software products or reference documents.(2) A representation of facts, concepts, or instructions in a manner suitable for communication, interpretation, or processing by humans or by automatic means." (IEEE 610.5-1990)

"Data may be thought of as unprocessed atomic statements of fact. It very often refers to systematic collections of numerical information in tables of numbers such as spreadsheets or databases. When data is structured and presented so as to be useful and relevant for a particular purpose, it becomes information available for human apprehension. See also knowledge." (Open Data Handbook)

"Distinct pieces of digital information that have been formatted in a specific way." (NIST SP 800-86)

"Information in a specific representation, usually as a sequence of symbols that have meaning." (CNSSI 4009-2015 IETF RFC 4949 Ver 2)

"Pieces of information from which “understandable information” is derived." (NIST SP 800-88 Rev. 1)

“re-interpretable representation of information in a formalized manner suitable for communication, interpretation, or processing” (ISO 11179)

Data Science: Data Science (Definitions)

"A set of quantitative and qualitative methods that support and guide the extraction of information and knowledge from data to solve relevant problems and predict outcomes." (Xiuli He et al, "Supply Chain Analytics: Challenges and Opportunities", 2014)

"A collection of models, techniques and algorithms that focus on the issues of gathering, pre-processing, and making sense-out of large repositories of data, which are seen as 'data products'." (Alfredo Cuzzocrea & Mohamed M Gaber, "Data Science and Distributed Intelligence", 2015)

"Data science involves using methods to analyze massive amounts of data and extract the knowledge it contains. […] Data science is an evolutionary extension of statistics capable of dealing with the massive amounts of data produced today. It adds methods from computer science to the repertoire of statistics." (Davy Cielen et al, "Introducing Data Science", 2016)

"The workflows and processes involved in the creation and development of data products." (Benjamin Bengfort & Jenny Kim, "Data Analytics with Hadoop", 2016)

"The discipline of analysis that helps relate data to the events and processes that produce and consume it for different reasons." (Gregory Lampshire, "The Data and Analytics Playbook", 2016)

"The extraction of knowledge from large volumes of unstructured data which is a continuation of the field data mining and predictive analytics, also known as knowledge discovery and data mining (KDD)." (Suren Behari, "Data Science and Big Data Analytics in Financial Services: A Case Study", 2016)

"A knowledge acquisition from data through scientific method that comprises systematic observation, experiment, measurement, formulation, and hypotheses testing with the aim of discovering new ideas and concepts." (Babangida Zubairu, "Security Risks of Biomedical Data Processing in Cloud Computing Environment", 2018)

"Data science is a collection of techniques used to extract value from data. It has become an essential tool for any organization that collects, stores, and processes data as part of its operations. Data science techniques rely on finding useful patterns, connections, and relationships within data. Being a buzzword, there is a wide variety of definitions and criteria for what constitutes data science. Data science is also commonly referred to as knowledge discovery, machine learning, predictive analytics, and data mining. However, each term has a slightly different connotation depending on the context." (Vijay Kotu & Bala Deshpande, "Data Science" 2nd Ed., 2018)

"A field that builds on and synthesizes a number of relevant disciplines and bodies of knowledge, including statistics, informatics, computing, communication, management, and sociology to translate data into information, knowledge, insight, and intelligence for improving innovation, productivity, and decision making." (Zhaohao Sun, "Intelligent Big Data Analytics: A Managerial Perspective", 2019)

"Data science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from data in various forms, both structured and unstructured similar to data mining." (K Hariharanath, "BIG Data: An Enabler in Developing Business Models in Cloud Computing Environments", 2019)

"Is a broad field that refers to the collective processes, theories, concepts, tools and technologies that enable the review, analysis, and extraction of valuable knowledge and information from raw data. It is geared toward helping individuals and organizations make better decisions from stored, consumed and managed data." (Maryna Nehrey & Taras Hnot, "Data Science Tools Application for Business Processes Modelling in Aviation", 2019)

"It is a new discipline that combines elements of mathematics, statistics, computer science, and data visualization. The objective is to extract information from data sources. In this sense, data science is devoted to database exploration and analysis. This discipline has recently received much attention due to the growing interest in big data." (Soraya Sedkaoui, "Big Data Analytics for Entrepreneurial Success", 2019)

"the study and application of techniques for deriving insights from data, including constructing algorithms for prediction. Traditional statistical science forms part of data science, which also includes a strong element of coding and data management." (David Spiegelhalter, "The Art of Statistics: Learning from Data", 2019)

"A relatively new term applied to an interdisciplinary field of study focused on methods for collecting, maintaining, processing, analyzing and presenting results from large datasets." (Osman Kandara & Eugene Kennedy, "Educational Data Mining: A Guide for Educational Researchers", 2020)

"Data Science is the branch of science that uses technologies to predict the upcoming nature of different things such as a market or weather conditions. It shows a wide usage in today’s world." (Kirti R Bhatele, "Data Analysis on Global Stratification", 2020)

"Data science is a methodical form of integrating statistics, algorithms, scientific methods, models and visualization methods for interpretation of outcomes in organizational problem solving and fact based decision making." (Tanushri Banerjee & Arindam Banerjee, "Designing a Business Analytics Culture in Organizations in India", 2021)

"Data science is a multi-disciplinary field that follows scientific approaches, methods, and processes to extract knowledge and insights from structured, semi-structured and unstructured data." (Ahmad M Kabil, Integrating Big Data Technology Into Organizational Decision Support Systems, 2021)

Data Science is an inter-disciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights." (R Suganya et al, "A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics: Healthcare Applications", 2021)

"Data Science is the science and art of using computational methods to identify and discover influential patterns in data." (M Govindarajan, "Introduction to Data Science", 2021)

"Data science is the study of data. It involves developing methods of recording, storing, and analyzing data to effectively extract useful information. The goal of data science is to gain insights and knowledge from any type of data - both structured and unstructured." (Pankaj Pathak, "A Survey on Tools for Data Analytics and Data Science", 2021)

"It is a science of multiple disciplines used for exploring knowledge from data using complex scientific algorithms and methods." (Vandana Kalra et al, "Machine Learning and Its Application in Monitoring Diabetes Mellitus", 2021)

"The concept that utilizes scientific and software methods, IT infrastructure, processes, and software systems in order to gather, process, analyze and deliver useful information, knowledge and insights from various data sources." (Nenad Stefanovic, "Big Data Analytics in Supply Chain Management", 2021)

"This is an evolving field that deals with the gathering, preparation, exploration, visualization, organisation, and storage of large groups of data and the extraction of valuable information from large volumes of data that may exist in an unorganised or unstructured form." (James O Odia & Osaheni T Akpata, "Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection", 2021)

"A field of study involving the processes and systems used to extract insights from data in all of its forms. The profession is seen as a continuation of the other data analysis fields, such as statistics." (Solutions Review)

"The discipline of using data and advanced statistics to make predictions. Data science is also focused on creating understanding among messy and disparate data. The “what” a scientist is tackling will differ greatly by employer." (KDnuggets)

"Unites statistical systems and processes with computer and information science to mine insights with structured and/or unstructured data analytics." (Accenture)

"Data science is a multidisciplinary approach to finding, extracting, and surfacing patterns in data through a fusion of analytical methods, domain expertise, and technology. This approach generally includes the fields of data mining, forecasting, machine learning, predictive analytics, statistics, and text analytics." (Tibco) [source]

"Data science is an interdisciplinary field that combines social sciences, advanced statistics, and computer engineering skills to acquire, store, organize, and analyze information across a variety of sources." (TDWI)

"Data science is the multidisciplinary field that focuses on finding actionable information in large, raw or structured data sets to identify patterns and uncover other insights. The field primarily seeks to discover answers for areas that are unknown and unexpected." (Sisense) [source]

"Data science is the practical application of advanced analytics, statistics, machine learning, and the associated activities involved in those areas in a business context, like data preparation for example." (RapidMiner) [source]

"Data Science unites statistical systems and processes with computer and information science to mine insights with structured and/or unstructured data analytics." (Accenture)