🔬Data Science: Artificial Intelligence [AI] (Definitions)

"A computer would deserve to be called intelligent if it could deceive a human into believing that it was human." (Alan Turing, "Computing Machinery and Intelligence", 1950)

"Artificial intelligence is the science of making machines do things that would require intelligence if done by men." (Marvin Minsky, 1968)

"Artificial intelligence comprises methods, tools, and systems for solving problems that normally require the intelligence of humans. The term intelligence is always defined as the ability to learn effectively, to react adaptively, to make proper decisions, to communicate in language or images in a sophisticated way, and to understand." (Nikola K Kasabov, "Foundations of Neural Networks, Fuzzy Systems, and Knowledge Engineering", 1996) 

"AI views the mind as a type of logical symbol processor that works with strings of text or symbols much as a computer works with strings of Os and Is. In practice, AI means expert systems or decision support systems." (Guido Deboeck & Teuvo Kohonen (Eds), "Visual Explorations in Finance with Self-Organizing Maps" 2nd Ed., 2000)

"Software that performs a function previously ascribed only to human beings, such as natural language processing." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"The branch of computer science that is concerned with making computers behave and 'think' like humans." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed., 2011)

"A field of computer science focused on the development of intelligent-acting agents. Often guided by the theory of how humans solve problems. Has a reputation for overpromising. Wryly definable as all computational problems not yet solved." (Gary Miner et al, "Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications", 2012)

"Artificial intelligence is the mimicking of human thought and cognitive processes to solve complex problems automatically. AI uses techniques for writing computer code to represent and manipulate knowledge." (Radian Belu, "Artificial Intelligence Techniques for Solar Energy and Photovoltaic Applications", 2013)

"It is the investigation exploring whether intelligence can be replicated in machines, to perform tasks that humans can successfully carry out." (Hamid R Arabnia et al, "Application of Big Data for National Security", 2015)

"The study of computer systems that model and apply the intelligence of the human mind" (Nell Dale & John Lewis, "Computer Science Illuminated" 6th Ed., 2015)

"Machines that are designed to evaluate and respond to situations in an appropriate manner. Most artificial intelligence machines are computer based and many of them have achieved remarkable levels of performance in specific areas." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"A discipline with the goal to develop technology that solves complex problems with skill and creativity that rivals that of the human brain." (O Sami Saydjari, "Engineering Trustworthy Systems: Get Cybersecurity Design Right the First Time", 2018)

"A machine’s ability to make decisions and perform tasks that simulate human intelligence and behavior." (Kirti R Bhatele et al, "The Role of Artificial Intelligence in Cyber Security", 2019)

"An attempt to recreate a living intellect, especially human intelligence, in a computer environment." (Tolga Ensari et al, "Overview of Machine Learning Approaches for Wireless Communication", 2019)

"The theory governing the development of computer systems that are able to perform tasks which normally require human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." (Nil Goksel & Aras Bozkurt, "Artificial Intelligence in Education: Current Insights and Future Perspectives", 2019)

"Algorithms which make machines learn from experience, adjust to new inputs and perform human-like tasks." (Lejla Banjanović-Mehmedović & Fahrudin Mehmedović, "Intelligent Manufacturing Systems Driven by Artificial Intelligence in Industry 4.0", 2020)

"It is the method of mimicking the human intelligence by the machines." (Shouvik Chakraborty & Kalyani Mali, "An Overview of Biomedical Image Analysis From the Deep Learning Perspective", 2020)

"AI is a simulation of human intelligence through the progress of intelligent machines that think and work like humans carrying out such human activities as speech recognition, problem-solving, learning, and planning." (Hari K Kondaveeti et al, "Deep Learning Applications in Agriculture: The Role of Deep Learning in Smart Agriculture", 2021)

"Artificial intelligence (AI) refers to the ability of machines to have cognitive capabilities similar to humans using advanced algorithms and quality data." (Vijayaraghavan Varadharajan & Akanksha Rajendra Singh, "Building Intelligent Cities: Concepts, Principles, and Technologies", 2021)

"Domain of science that deals with the development of computer systems to perform actions like speech-recognition, decision-making, understanding human’s natural language, etc., like humans." (Shatakshi Singhet al, "A Survey on Intelligence Tools for Data Analytics", 2021)

"It is a set of software and hardware systems with many capabilities such as behaving human-like or numerical logic, motion, speech, and sound perception. In other words, AI makes machines think and percept like humans." (Mehmet A Cifci, "Optimizing WSNs for CPS Using Machine Learning Techniques", 2021)

"Machines that work and react like humans using computer programs known as algorithms Algorithms must remain current for AI to work properly, so they rely on machine learning to update them with changes in the worldwide economy and society." (Sue Milton, "Data Privacy vs. Data Security", Global Business Leadership Development for the Fourth Industrial Revolution, 2021)

"Science of simulating intelligence in machines and program them to mimic human actions." (Revathi Rajendran et al, "Convergence of AI, ML, and DL for Enabling Smart Intelligence: Artificial Intelligence, Machine Learning, Deep Learning, Internet of Things", 2021)

"The theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages." (Jan Bosch et al, "Engineering AI Systems: A Research Agenda", Artificial Intelligence Paradigms for Smart Cyber-Physical Systems, 2021)

"AI is any set of concepts, applications or technologies that allow a computer to perform tasks that mimic human behavior." (RapidMiner) [source]

"Artificial intelligence (AI) is the simulation of human intelligence processes by machines, especially computer systems. Specific applications of AI include expert systems, natural language processing (NLP), speech recognition and machine vision." (Techtarget) [source]

"A discipline involving research and development of machines that are aware of their surroundings. Most work in A.I. centers on using machine awareness to solve problems or accomplish some task." (KDnuggets)

"An area of computer science which refers to the creation of intelligent machines that can react to scenarios and make decisions as a human would." (Board International)

"A set of sciences, theories and techniques whose purpose is to reproduce by a machine the cognitive abilities of a human being." (Council of Europe) 

"The theory and capabilities that strive to mimic human intelligence through experience and learning." (Forrester)

"Artificial Intelligence (AI) is the broad term used to describe the set of technologies that enable machines to sense, comprehend, act and learn." (Accenture)

"Artificial intelligence (AI) applies advanced analysis and logic-based techniques, including machine learning, to interpret events, support and automate decisions, and take actions." (Gartner)

🔬Data Science: Metamodel (Definitions)

"Model of a model that dictates the rules for creation of modeling mechanisms like the UML" (Bhuvan Unhelkar, "Process Quality Assurance for UML-Based Projects", 2002)

"A description or definition of a well-defined language in the form of a model." (Anneke Kleppe et al, "MDA Explained: The Model Driven Architecture™: Practice and Promise", 2003)

"A model that defines other models. The UML metamodel defines the element types of the UML, such as Classifier." (Craig Larman, "Applying UML and Patterns", 2004)

"A description of a model. A meta model refers to the rules that define the structure a model can have. In other words, a meta model defines the formal structure and elements of a model." (Nicolai M Josuttis, "SOA in Practice", 2007)

"The model of a language used to develop systems. In the case of UML, the definition of UML itself is the metamodel." (Bruce P Douglass, "Real-Time Agility: The Harmony/ESW Method for Real-Time and Embedded Systems Development", 2009)

"A description of a model. A meta-model refers to the rules that define the structure a model can have. In other words, a meta-model defines the formal structure and elements of a model." (David Lyle & John G Schmidt, "Lean Integration", 2010)

"1.Generally, a model that specifies one or more other models. 2.In Meta-data Management, a model of a meta-data system or a data model for a meta-data repository." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Model that describes how and with what the architecture will be described in a structural way (model of the model)." (Gilbert Raymond & Philippe Desfray, "Modeling Enterprise Architecture with TOGAF", 2014)

"When common sets of design decisions can be identified that are not specific to any one domain, they often become systematized in textbooks and in design practices, and may eventually be designed into standard formats and architectures for creating organizing systems. These formally recognized sets of design decisions are known as abstract models or metamodels. Metamodels describe structures commonly found in resource descriptions and other information resources, regardless of the specific domain." (Robert J Glushko, "The Discipline of Organizing: Professional Edition" 4th Ed., 2016)

🔬Data Science: Sensitivity Analysis (Definitions)

"The practice of changing a variable in a financial model or forecast to determine how a change in that variable affects the overall outcome. For example, to consider the way in which a change in price might affect the gross profit in a product forecast, one might vary the price in small increments and recompute the figures to see how gross profit changes." (Steven Haines, "The Product Manager's Desk Reference", 2008)

"Sensitivity analysis is a methodology for assessing whether an empirical effect is a valid causal effect. The basic idea is to simulate the change in the empirical effect that would result under plausible assumptions about the possible impact of the most likely sources of bias." (Herbert I Weisberg, "Bias and Causation: Models and Judgment for Valid Comparisons", 2010)

"Use of quantitative and qualitative information to study changes in results that would occur with changes in various assumptions. Also see best-case and worst-case scenario." (Leslie G Eldenburg & Susan K Wolcott, "Cost Management 2nd Ed", 2011)

"Study of the impact that changes in one or more parts of a model have on other parts or the outcome." (Linda Volonino & Efraim Turban, "Information Technology for Management" 8th Ed, 2011)

"A quantitative risk analysis and modeling technique used to help determine which risks have the most potential impact on the project. It examines the extent to which the uncertainty of each project element affects the objective being examined when all other uncertain elements are held at their baseline values. The typical display of results is in the form of a tornado diagram." (Cynthia Stackpole, "PMP® Certification All-in-One For Dummies®", 2011)

"A form of simulation modeling that focuses specifically on identifying the upper and lower bounds of model outputs given a series of inputs with specific variance." (Evan Stubbs, "Delivering Business Analytics: Practical Guidelines for Best Practice", 2013)

"An analysis used in mathematical modelling, where the sensitivity of model results to variations in a particular variable is studied." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"An analysis technique to determine which individual project risks or other sources of uncertainty have the most potential impact on project outcomes, by correlating variations in project outcomes with variations in elements of a quantitative risk analysis model." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide )", 2017)

"An analysis that involves calculating a decision model multiple times with different inputs so a modeler can analyze the alternative results." (Ciara Heavin & Daniel J Power, "Decision Support, Analytics, and Business Intelligence 3rd Ed.", 2017)

"A technique used to determine how different values of an independent variable will impact a particular dependent variable under a given set of assumptions. It allows an analyst to determine whether a statistical finding will remain consistent under a variety of conditions. |" (Jonathan Ferrar et al, "The Power of People: Learn How Successful Organizations Use Workforce Analytics To Improve Business Performance", 2017)

🔬Data Science: Data Analysis (Definitions)

"Obtaining information from measured or observed data." (Ildiko E  Frank & Roberto Todeschini, "The Data Analysis Handbook", 1994)

"Refers to the process of organizing, summarizing and visualizing data in order to draw conclusions and make decisions." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2006)

"A combination of human activities and computer processes that answer a research question or confirm a research hypotheses. It answers the question from data files, using empirical methods such as correlation, t-test, content analysis, or Mill’s method of agreement." (Jens Mende, "Data Flow Diagram Use to Plan Empirical Research Projects", 2009)

"The study and presentation of data to create information and knowledge." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"Process of applying statistical techniques to evaluate data." (Sally-Anne Pitt, "Internal Audit Quality", 2014)

"Research phase in which data gathered from observing participants are analysed, usually with statistical procedures." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"Data analysis is the process of creating meaning from data. […] Data analysis is the process of creating information from data through the creation of data models and mathematics to find patterns." (Michael Heydt, "Learning Pandas" 2nd Ed, 2017)

"Data analysis is the process of organizing, cleaning, transforming, and modeling data to obtain useful information and ultimately, new knowledge." (John R. Hubbard, Java Data Analysis, 2017)

"Techniques used to organize, assess, and evaluate data and information." (Project Management Institute, "A Guide to the Project Management Body of Knowledge (PMBOK® Guide )", 2017)

"This is a class of statistical methods that make it possible to process a very large volume of data and identify the most interesting aspects of its structure. Some methods help to extract relations between different sets of data, and thus, draw statistical information that makes it possible to describe the most important information contained in the data in the most succinct manner possible. Other techniques make it possible to group data in order to identify its common denominators clearly, and thereby understand them better." (Soraya Sedkaoui, "Big Data Analytics for Entrepreneurial Success", 2019)

"The process and techniques for transforming and evaluating information using qualitative or quantitative tools to discover findings or inform conclusions." (Tiffany J Cresswell-Yeager & Raymond J Bandlow, "Transformation of the Dissertation: From an End-of-Program Destination to a Program-Embedded Process", 2020)

"Data Analysis is a process of gathering and extracting information from the data already present in different ways and order to study the pattern occurs." (Kirti R Bhatele, "Data Analysis on Global Stratification", 2020)

"A data lifecycle stage that involves the techniques that produce synthesized knowledge from organized information. A process of inspecting, cleaning, transforming, and modeling data with the goal of highlighting useful information suggesting conclusions, and supporting decision making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, in different business, science, and social science domains." (CODATA)

"is the process of inspecting, cleansing, transforming, and modeling data to discover useful information, and support decision-making. The many different types of data analysis include data mining, a predictive technique used for modeling and knowledge discovery, and business intelligence, which relies on aggregation and focuses on business information." (Accenture)

"This discipline is the little brother of data science. Data analysis is focused more on answering questions about the present and the past. It uses less complex statistics and generally tries to identify patterns that can improve an organization." (KDnuggets)

"Data Analysis is the process of inspecting, cleansing, transforming, and modeling data to discover useful information, and support decision-making. The many different types of data analysis include data mining, a predictive technique used for modeling and knowledge discovery, and business intelligence, which relies on aggregation and focuses on business information." (Accenture)

🔬Data Science: Exploratory Data Analysis (Definitions)

"Exploratory data analysis (EDA) is a collection of techniques that reveal (or search for) structure in a data set before calculating any probabilistic model. Its purpose is to obtain information about the data distribution (univariate or multivariate), about the presence of outliers and clusters, to disclose relationships and correlations between objects and/or variables." (Ildiko E  Frank & Roberto Todeschini, "The Data Analysis Handbook", 1994)

"Processes and methods for exploring patterns and trends in the data that are not known prior to the analysis. It makes heavy use of graphs, tables, and statistics." (Glenn J Myatt, "Making Sense of Data: A Practical Guide to Exploratory Data Analysis and Data Mining", 2007)

"The process of analyzing data to suggest hypotheses using statistical tools, which can then be tested." (DAMA International, "The DAMA Dictionary of Data Management", 2011)

"In statistics, exploratory data analysis is an approach to analyzing datasets to summarize their main characteristics, often with visual methods." (Keith Holdaway, "Harness Oil and Gas Big Data with Analytics", 2014)

"Process in which data patterns guide the analysis or suggest revisions to the preliminary data analysis plan." (K  N Krishnaswamy et al, "Management Research Methodology: Integration of Principles, Methods and Techniques", 2016)

"Exploratory Data Analysis is about taking a dataset and extracting the most important information from it, in such a way that it is possible to get an idea of what the data looks like." (Richard M Reese et al, Java: Data Science Made Easy, 2017)

🔬Data Science: MapReduce (Definitions)

"A data processing and aggregation paradigm consisting of a 'map' phase that selects data and a 'reduce' phase that transforms the data. In MongoDB, you can run arbitrary aggregations over data using map-reduce." (MongoDb, "Glossary", 2008)

"A divide-and-conquer strategy for processing large data sets in parallel. In the 'map' phase, the data sets are subdivided. The desired computation is performed on each subset. The 'reduce' phase combines the results of the subset calculations into a final result. MapReduce frameworks handle the details of managing the operations and the nodes they run on, including restarting operations that fail for some reason. The user of the framework only has to write the algorithms for mapping and reducing the data sets and computing with the subsets." (Dean Wampler & Alex Payne, "Programming Scala", 2009)

"A method by which computationally intensive problems can be processed on multiple computers in parallel. The method can be divided into a mapping step and a reducing step. In the mapping step, a master computer divides a problem into smaller problems that are distributed to other computers. In the reducing step, the master computer collects the output from the other computers. Although MapReduce is intended for Big Data resources, holding petabytes of data, most Big Data problems do not require MapReduce." (Jules H Berman, "Principles of Big Data: Preparing, Sharing, and Analyzing Complex Information", 2013)

"An early Big Data (before this term became popular) programming solution originally developed by Google for parallel processing using very large data sets distributed across a number of computing and storage systems. A Hadoop implementation of MapReduce is now available." (Kenneth A Shaw, "Integrated Management of Processes and Information", 2013)

"Designed by Google as a way of efficiently executing a set of functions against a large amount of data in batch mode. The 'map' component distributes the programming problem or tasks across a large number of systems and handles the placement of the tasks in a way that balances the load and manages recovery from failures. After the distributed computation is completed, another function called 'reduce' aggregates all the elements back together to provide a result." (Marcia Kaufman et al, "Big Data For Dummies", 2013)

"A programming model consisting of two logical steps - Map and Reduce - for processing massively parallelizable problems across extremely large datasets using a large cluster of commodity computers." (Haoliang Wang et al, "Accessing Big Data in the Cloud Using Mobile Devices", Handbook of Research on Cloud Infrastructures for Big Data Analytics, 2014)

"Algorithm that is used to split massive data sets among many commodity hardware pieces in an effort to reduce computing time." (Billie Anderson & J Michael Hardin, "Harnessing the Power of Big Data Analytics", Encyclopedia of Business Analytics and Optimization, 2014)

"MapReduce is a parallel programming model proposed by Google and is used to distribute computing on clusters of computers for processing large data sets." (Jyotsna T Wassan, "Emergence of NoSQL Platforms for Big Data Needs", Encyclopedia of Business Analytics and Optimization, 2014)

"A concept which is an abstraction of the primitives ‘map’ and ‘reduce’. Most of the computations are carried by applying a ‘map’ operation to each global record in order to generate key/value pairs and then apply the reduce operation in order to combine the derived data appropriately." (P S Shivalkar & B K Tripathy, "Rough Set Based Green Cloud Computing in Emerging Markets", Encyclopedia of Information Science and Technology 3rd Ed., 2015) 

"A programming model that uses a divide and conquer method to speed-up processing large datasets, with a special focus on semi-structured data." (Alfredo Cuzzocrea & Mohamed M Gaber, "Data Science and Distributed Intelligence", Encyclopedia of Information Science and Technology 3rd Ed., 2015) 

"MapReduce is a programming model for general-purpose parallelization of data-intensive processing. MapReduce divides the processing into two phases: a mapping phase, in which data is broken up into chunks that can be processed by separate threads - potentially running on separate machines; and a reduce phase, which combines the output from the mappers into the final result." (Guy Harrison, "Next Generation Databases: NoSQL, NewSQL, and Big Data", 2015)

"MapReduce is a technological framework for processing parallelize-able problems across huge data sets using a large number of computers (nodes). […] MapReduce consists of two major steps: 'Map' and 'Reduce'. They are similar to the original Fork and Join operations in distributed systems, but they can consider a large number of computers that can be constructed based on the Internet cloud. In the Map-step, the master computer (a node) first divides the input into smaller sub-problems and then distributes them to worker computers (worker nodes). A worker node may also be a sub-master node to distribute the sub-problem into even smaller problems that will form a multi-level structure of a task tree. The worker node can solve the sub-problem and report the results back to its upper level master node. In the Reduce-step, the master node will collect the results from the worker nodes and then combine the answers in an output (solution) of the original problem." (Li M Chen et al, "Mathematical Problems in Data Science: Theoretical and Practical Methods", 2015)

"A programming model which process massive amounts of unstructured data in parallel and distributed cluster of processors." (Fatma Mohamed et al, "Data Streams Processing Techniques Data Streams Processing Techniques", Handbook of Research on Machine Learning Innovations and Trends, 2017)

"A data processing framework of Hadoop which provides data intensive computation of large data sets by dividing tasks across several machines and finally combining the result." (Rupali Ahuja, "Hadoop Framework for Handling Big Data Needs", Handbook of Research on Big Data Storage and Visualization Techniques, 2018)

"A high-level programming model, which uses the “map” and “reduce” functions, for processing high volumes of data." (Carson K.-S. Leung, "Big Data Analysis and Mining", Encyclopedia of Information Science and Technology 4th Ed., 2018)

"Is a computational paradigm for processing massive datasets in parallel if the computation fits a three-step pattern: map, shard and reduce. The map process is a parallel one. Each process executes on a different part of data and produces (key, value) pairs. The shard process collects the generated pairs, sorts and partitions them. Each partition is assigned to a different reduce process which produces a single result." (Venkat Gudivada et al, "Database Systems for Big Data Storage and Retrieval", Handbook of Research on Big Data Storage and Visualization Techniques, 2018)

"Is a programming model or algorithm for the processing of data using a parallel programming implementation and was originally used for academic purposes associated with parallel programming techniques. (Soraya Sedkaoui, "Understanding Data Analytics Is Good but Knowing How to Use It Is Better!", Big Data Analytics for Entrepreneurial Success, 2019)

"MapReduce is a style of programming based on functional programming that was the basis of Hadoop." (Alex Thomas, "Natural Language Processing with Spark NLP", 2020)

"Is a specific programming model, which as such represents a new approach to solving the problem of processing large amounts of differently structured data. It consists of two functions - Map (sorting and filtering data) and Reduce (summarizing intermediate results), and it is executed in parallel and distributed." (Savo Stupar et al, "Importance of Applying Big Data Concept in Marketing Decision Making", Handbook of Research on Applied AI for International Business and Marketing Applications, 2021)

"A software framework for processing vast amounts of data." (Analytics Insight)

🔬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: 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: 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: 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)