🔬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]