What is Big Data

Large amounts of data generated and stored by an enterprise, often in the areas of operations, transactions, and/or communications.

Any kind of data characterized by high volume, variety and/or velocity, which are difficult to process using traditional database management tools or data processing applications.

Big data is an extremely large set of data that allows computational analysis to reveal particular human behavior, trends, and relationships.

A relatively large amount of data, defined sometimes as over a million rows of data or a certain size of data (such as by terabytes and petabytes).

Big data hold great promises for discovering subtle population patterns and heterogeneities that are not possible with small-scale data.

Is data with at least one of the ten big characteristics, consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market.

Big data is a combination of structured and unstructured data which is abundant in quantity, variety as well as the rate at which it gets generated resulting in a complex data set which cannot be stored, managed and processed by conventional statistical tools.

Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.

Is enormous data set (mostly unstructured data) that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to interactions.

This expression describes the large volume of both structured and unstructured data that is created in an organization or business. Data grow exponentially, making it difficult to manage them. Traditional data management tools are unable to store or process data efficiently, thus new systems and tools are necessary.

High-velocity, valuable, and/or multi-variety data with volumes beyond the ability of commonly used software to capture, manage, and process within a tolerable elapsed time. These Big Data necessitate new forms of processing to deliver high veracity (and low vulnerability) and to enable enhanced decision making, insight, knowledge discovery, and process optimization.

It is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications.

A large volume of structured and unstructured data that inundates an organisation.

This is data characterized by its large volume, wide variety, and high velocity. Volume refers to high quantity of data usually running into terabytes. The data is not only numbers or text, but can include images, videos, etc., making it very varied and the data is generated rapidly which is called its high velocity.

Is the area of knowledge that studies how to treat, analyze, and obtain information from data sets that are too large to be analyzed by traditional systems.

The large-scale aggregation of data from software and wired devices, used for the purposes of analyzing systems or user behavior; it is characterized by its large volume, wide variety, velocity (or speed of transfer), its increased variability, and undetermined veracity.

The data which are not possible to be stored in relational databases at low cost and cannot be analyzed by classical methods, due to its volume and characteristics.

Vast amounts of data generated today characterized by volume, velocity, and variety.

According to some sources ( Boyd & Crawford, 2012 ; Mayer-Schönberger & Cukier, 2013 ) data volumes in the range of petabytes and beyond, which exceed the capacity of most current online storage and processing systems; critical issues related to big data include their volume, velocity, variety, value and veracity. However according to Parks (2014 : 255) data volume does not qualify in itself the “big data”, because the criteria should be relative to the “representativeness” of the specific field analysed.

Data that spans over 3 Vs namely Volume, Variety and Velocity.

Is a concept related to dealing with really high amounts of data. Traditional databases can not store and analyze such big amounts of data.

It is the area of knowledge that studies how to treat, analyze and obtain information from very large data sets.

Is an evolving term that describes any voluminous amount of structured, semi-structured, and unstructured data that has the potential to be mined for information.

Big data also known as mega-data refers to all the digital data produced by the use of new technologies for personal or professional purposes.

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.

Data with at least one of the ten big characteristics consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market.

Collaborative analysis done from structured (formally, predictable and formatted) and unstructured (posts, informal communication, social media usage) data regarding one specific topic.

Data sets created by the convergence of various devices, equipment, and systems and that are too complex for traditional data processing software.

The concept that near-infinite storage and computer processing has permitted the mass collection and storage of information about every aspect of our lives.

Datasets that are large and complex.

In MICE industry, it is a common tool in following and tracking some useful information in regards to reaching the targeted meetings’ location history, subject details, national representatives, local ambassadors, hosting organizations and contact details of sponsors that supported those meetings, events or exhibitions.

Large and complicated data sets that cannot be processed or evaluated using conventional data processing methods are referred to as “big data.” In order to extract insights and value from data, one must employ specialized tools and procedures that can handle its size, diversity, and velocity.

Very large data sets. These are used in higher education in a number of ways. Most relevant to this chapter are the ways they are used to anticipate students’ needs, choices, risks, etc.

Extremely large dataset that are generated in the digital age, which contains hidden useful information that can only be mined with modern computing technologies. Big data are usually unstructured have 5Vs property (volume, variety, velocity, value, and veracity).

Datasets of diverse types and formats with various non-standard properties, such as large volume, enormous complexity, or heterogeneous quality.

Data users leave behind when using different apps connected to the Internet. Collecting these data in real time allows us to identify crucial user characteristics that can be used for customising marketing content and strategies. Marketing is only one possible way to use big data.

Is data with at least one of the ten big characteristics consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market.

The volume of data that is just beyond technology’s capability to store, manage and process efficiently.

Datasets whose size, speed and variety are beyond the ability of typical database software tools to capture, store, administer, and analyze.

Consists of large data sets that can be analyzed via computers to reveal trends, patterns, and associations, made up of five key parts: volume, variety, velocity, veracity, and value.

Extremely large set of data which is used to extract some meaningful information.

A term that defines a large dataset that produces in the size of collected data over time. It refers to the size of dataset that surpasses the capturing, storage, management, and analysis of traditional databases. The term refers to the dataset that has huge, more diverse, and multifaceted structure, accompanies by difficulties of data storage, analysis, and visualization. Big Data are characterized with the commonly known attributes: high-volume, -velocity, and –variety information assets.

Big data is the term used to describe data sets that are too big or complicated for conventional data-processing application software to handle. While data with more complexity may result in a higher false discovery rate, data with more entries give better statistical power.

The term that represents data sets that are extremely large to handle through traditional methods. Big data represents information that has such a high volume, velocity, variety, variability, veracity and complexity that require specific mechanisms in order to produce value from it.

Is extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Big data is a huge amount of data that able to capture, link, collect, store, analized, and organize to provide value for the users.

Data that is so large and complex that it cannot be processed using traditional data processing tools or applications.

It refers to large volume of data and has characteristics such as volume, variety and veracity etc.

It is the form of the data that has been analyzed, classified, and transformed into a meaningful and processable form.

Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Large or complex datasets that are too difficult to easily analyze with traditional processes including the utilization of predictive analysis for decision making.

Is an extremely large amount and varied data sets that may be analyzed computationally to unearth valuable information patterns, trends, relations, and associations, especially relating to human treatments, behaviour and interactions that can help institutions make informed decisions.

Refers to the massive amount of data generated and collected through various digital sources and platforms.

As with the concept of “artificial intelligence”, there is a great variety of definitions that different authors have proposed for the concept of Big Data, a term whose use is relatively recent. A simple way to define it is as large sets of data, structured or unstructured, that can come from different sources and in different formats and that have the potential to be analyzed by computer techniques to extract information that is difficult to obtain directly.

Big data refers to the very large and diverse sets of structured, semi-structured and unstructured digital data from different sources that accumulate and grow very rapidly on continuous basis. Big data technologies enable enhanced decision making in various types of businesses.

Very large sets of data that can only be stored, used, and analyzed with the help of technological tools and methods.

Data sets that are too large and complex to handle with traditional information technology tools.

A term that describes the large, diverse set of information.

Enormous amount of unstructured, heterogeneous and distributed data that is constantly changing over time and depending on the surrounding context. Big data can be seen as both a resource and a process, both of which are linked to the different interactions occurring on the web.

Big data is a discipline that deals with methods for analysing, methodically extracting information from, or otherwise dealing with data volumes that are too massive or complicated for typical data-processing application software to handle.

High-velocity, valuable, and/or multi-variety data with volumes beyond the ability of commonly-used software to capture, manage, and process within a tolerable elapsed time. These Big Data necessitate new forms of processing to deliver high veracity (and low vulnerability) and to enable enhanced decision making, insight, knowledge discovery, and process optimization.

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.”

The term is used for large and/or complex sets of structured or unstructured data that cannot be processed in traditional ways. As a concept, big data is defined around the four V’s: volume (scale of data), velocity (analysis of streaming data), variety (different forms of data), and veracity (uncertainty of data).

Large and complicated datasets that are difficult to handle, process, or analyze with conventional data processing methods are referred to as “big data. These datasets are characterized by their volume, velocity, variety, and often, their veracity.

Very large sets of data that can only be stored, used, and analyzed with the help of technological tools and methods.

The size, speed and sorts of data that exceed an organization’s traditional database capacity to access, integrate, store, and analyze for accurate and timely decision making.

A buzzword for huge amounts of data.

Is defined as the processing of massive amounts of data that collect over time that are difficult to analyze and handle using common database management tools. The data are very unstructured and analyzed for marketing trends in business as well as in the fields of manufacturing, medicine, and science.

An extremely large and complex data set of structured and unstructured data that cannot be processed using traditional data processing applications.

Broad term for data sets so large or complex 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. Accuracy in big data may lead to more confident decision making.

Big data frequently refers to data sets with sizes away from the ability of frequently used software tools to confine, curate, administer, and process data contained by a supportable elapsed instance. Big data philosophy encompasses shapeless, semi-structured and prepared data, however the main focal point is on unstructured data.

The broad spectrum of data that have appeared throughout the development of the global information society.

Is a term referring to an extremely large collection of data, typically in the petabyte range or larger. The data is only considered “big” if it taxes current state-of-the-art computing capabilities.

Extremely large data sets that may be analysed computationally to reveal patterns, trends, and associations, especially relating to human behaviour and interactions.

Data set that is extremely large in size and mostly unstructured in nature.

Big data is defined as a large amount of data sets that cannot be analyzed and managed with traditional data processing tools.

Data with at least one of the ten big characteristics, consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market.

Big data is a field that deals with methods for analyzing and methodically extracting information from, or otherwise dealing with data volumes that are too large or complicated for typical data-processing application software to handle.

Literally means a gigantic volume of data. Therefore, it is usually defined through five Vs: volume, velocity, variety, value, and veracity. Volume comes from the growing increase in data with which the Web is fed today. Velocity refers to the increased scalability of the computer that allows for faster processing of data. Variety accounts for the heterogeneity of data types. Value implies the concern with the reliability of the data and, finally, Veracity corresponds to the distinction that must be established between false and true data.

Datasets characterized by Volume, Velocity, Variety, Variability, Veracity, Visualization, and Value.

It refers to the possibility of analyzing and systematically extracting information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

It is a popular term used to describe the exponential growth and availability of data, both structured and unstructured.

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.

It refers to the possibility of analyzing and systematically extracting information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

Term used to describe the set of technologies and methods of analysis of massive data. The term indicates the ability to analyze and relate a huge amount of data to discover the links between different phenomena and formulate predictions.

Large amount of data, in many different formats, that flows rapidly in real time. This data should undergo some sort of analysis in order to extract useful information or knowledge.

A large quantity of data engendered by interconnected Internet of things in real time.

Large and complex data sets which cannot be adequately processed using traditional data processing software or techniques. New techniques of statistics and machine learning have been developed to deal with big data.

Gartner defines Big data as high-volume, high-velocity, and/or high-variety information assets that require new forms of processing to enable enhanced decision making, insight discovery and process optimization (The importance of Big Data: A Definition. Douglas L).

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.

The conventional techniques to store data are relational databases and sequential data processing. These techniques are efficient to handle and process limited data size. However, in today's scenario, there is very huge data been generated, through these conventional mechanisms, whereas fail to handle huge volumes of data. As a solution to this problem, big data mechanisms and tools are effective to handle these huge volumes of data sets.

The big data is data that contains greater variety, arriving in increasing volumes and with more velocity.

Data that contains greater variety, arriving in increasing volumes and with high velocity.

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.

A data set this is too large for “standard” software programs, and is generally used to identify large trends, and predict behaviors and outcomes.

Large data sets that are analysed using a computer to reveal patterns, trends, and relations pertaining to human behaviour and interactions.

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.”

Traditional technologies of data processing, have difficulties to handle in tolerable time a large dataset.

An exponentially increasing volume of heterogeneous data which is differentiated from traditional data based on its volume, variety, veracity, velocity and value.

Big data refers to large amounts of heterogeneous data that cannot be easily processed using conventional database techniques. Big data characteristics include volume, variety, and velocity of data. This data is generated by many sources including smart sensors, smartwatches, wristbands, medical equipment/devices, tablets, mobile phones, web usage records, social media, and simulation.

Refers to large data sets. The definition of the term may also include the specific tools for their processing and structured or unstructured analysis that facilitate business decision making by revealing new patterns, associations, and trends.

Big data is large, complex, and diverse sets of data that keep on growing exponentially over time. Traditional data management systems are not compatible with storing, process, and analysing big data due to their large volume and complexity.

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).

Unconventionally large data streams from many sources to a bunch of (not necessarily known) recipients.

Big data – a simple word turned into a technical jargon now-a-days to many common people because of its extensive use in the computing world with both its implicit and explicit meanings. The connotation of the term does not mean only the size of the raw facts the concerned people have to deal with, it also gives an impression about the scope and importance of those facts, budget, tools, technologies, and expertise to process those unstructured facts, and an apprehension of the utility of the information to be derived from those raw facts.

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.”

Very large sets of data that are produced by people using the internet, and that can only be stored, understood, and used with the help of special tools and methods.

A collection of data sets which are too large and complex to process using traditional methods or techniques.

A large-scale data collection that greatly exceeds the capabilities of traditional database software tools in terms of acquisition, storage, management, and analysis.

Big data usually includes data sets with sizes beyond the ability of commonly-used software tools to capture, curate, manage, and process the data within a tolerable elapsed time.

Data sets or combinations of datasets whose size (volume), complexity (variability), and speed of growth (velocity) make it difficult to capture, manage, process, or analyze them using conventional technologies and tools, such as relational databases and conventional statistics or visualization packages, within the time necessary to make them useful.

The term that represents data sets that are extremely large to handle through traditional methods. Big data represents information that has such a high volume, velocity, variety, variability, veracity and complexity that require specific mechanisms to produce real value from it in a timely way.

It is the technology that uses solutions based on algorithms that capture and cross-data organized, which allows the storage and retrieval of data of various formats at a high scale.

A term for the amount of data that has emerged in recent years and refers to data sets of such a size that they are difficult to process with traditional methods. (sw. big data)

Digital data whose massive scale and complex, unstructured content defy analysis with conventional tools, but which can increasingly be put to useful purposes through emerging data processing technologies.

The use of very large and complex data sets for business analytics. Big Data sets are usually defined by their size (exceeding the capability of common statistical software and metods), complexity (as they can contain data of multiple types, including numbers, text, image, etc.), velocity (as these sets can be collected and analyzed in a continuous fashion, e.g. in case of data capturing the patterns of use of an application by clients).

A set of data whose size makes it difficult for it to be recorded, managed and analysed by using conventional tools.

Large collections of either structured, semi-structured, or unstructured data that is processed by computer applications.

Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Voluminous amount of structured, semi-structured and unstructured data that has potential to be mined for information.

It refers to the emerging technologies that are designed to extract value from data having four Vs characteristics; volume, variety, velocity and veracity. Big data is set to affect the future network traffic and hence the network architectures.

Big data is a term that refers to massive, various amounts of data that are accumulating at an exponential rate.

The whole of the processable form of high-volume data obtained from different sources.

Is a recurring term when talking about technology and digitalization, its involvement in many areas of life begins to be relevant. Big Data refers to the management and analysis of huge volumes of data, which due to the magnitude of the information, exceed the capacity of data processing and should be treated in a special way.

Is data with at least one of the ten big characteristics consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market.

Is a field that deals with analysis, information extraction and visualization from very large data sets that are too complex to be processed by traditional systems.

Big data usually includes data sets with sizes beyond the ability of commonly-used software tools to capture, curate, manage, and process the data within a tolerable elapsed time.

Extensive amount of data which cannot be gathered, saved, or converted by traditional instruments such as word processor within a given time span.

Sum of the digital data gathered.

data which exceeds the processing capabilities of traditional database systems because of greater volume, velocity and variety of the data but which also has the potential to become a source of great value for the organization collecting it.

The term is used for massive amounts of increasing data, structured or unstructured data, which can be processed and analyzed through various techniques/algorithms. As a concept, big data is defined around the five V’s: volume (scale of data), velocity (analysis of streaming data), variety (different forms of data), veracity (uncertainty of data), and visibility (accessed from the disparate geographic location). But in the last years, another V is added: value.

Exceedingly large volumes of data to be analyzed.

is a huge set of collected data. These data may contain images, sound, and text information.

A large volume of structured, semi-structured, and unstructured data that come from different sources with the difficulties of storage and analysis.

Data with at least one of the ten big characteristics consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market.

High-velocity, valuable, and/or multi-variety data with volumes beyond the ability of commonly-used software to capture, manage, and process within a tolerable elapsed time. These Big data necessitate new forms of processing to deliver high veracity (& low vulnerability) and to enable enhanced decision making, insight, knowledge discovery, and process optimization.

Is extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Big data is a vast collection of information that continues to grow exponentially over time. It's a data set that's so large and complex that traditional data management tools can't store or analyze it efficiently. The difference between big data and regular data is that big data is substantially greater.

An extensive set of data that, due to its volume, velocity, and variety, requires advanced technologies and analytical methods for processing and extracting valuable information.

Datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.

Refers to data sets that are too big to be handled by regular storage and processing infrastructures.

Big and complex data sets that necessitates advanced processing software and hardware.

High-velocity, high-value, and/or high-variety data with volumes beyond the ability of commonly-used software to capture, manage, and process within a tolerable elapsed time. These Big data necessitate new forms of processing to deliver high veracity (& low vulnerability) and to enable enhanced decision making, insight, knowledge discovery, and process optimization.

Big Data is mainly defined by its 3Vs fundamental characteristics. The 3Vs include Velocity (data are growing and changing in a rapid way), Variety (data come in different and multiple formats) and Volume (huge amount of data is generated every second).

Big data is data that contains huge, variable and growing data.

A dataset that is larger than a typical dataset that requires unique tools, techniques, and technologies to store, manage, and analyze data.

Very large and complex datasets that cannot be manipulated by traditional data processing methods.

A term presenting to datasets that own big amounts of data (volume), various data (variety), and rising speed of generation (velocity).

It is a popular term used to describe the exponential growth and availability of data, both structured and unstructured.

The use of extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, that can be used to describe human interactions which can be used to assess learning, evaluate marketing behavior, determine the effects of a policy and other forms of assessment.

Data generated in the large volume, diverse, complex, longitudinal, and/or distributed form.

Big data is defined as a large amount of data sets that cannot be analyzed and managed with traditional data processing tools.

It is a term used in the field of Information Technology that difines a large volume of data. It can be related to the analisys and interpretation of a variety of data. It is related as the instrument used in Data science.

Indicates the huge volume of data sets used computationally to expose beneficial patterns or trends.

Very large sets of data that can only be stored, used, and analyzed with the help of technological tools and methods.

Is extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Big data refers to large volumes of structured and unstructured data generated at a high velocity. It encompasses data with various formats and from diverse sources, and its analysis can reveal patterns, trends, and insights that can inform decision-making.

Primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing techniques.

Large amounts of data generated and stored by an enterprise, often in the areas of operations, transactions, and/or communications.

Data obtained from variety of sources and demonstrate the characteristics of high volume, high velocity, and high veracity that can be used in creating value by using analytical methods.

Big data refers to the situation when the dataset exhibits several characteristics, such as high volume, high variety, and high data processing velocity ( Choi et al., 2018 ).

Big data, obtained automatically by advanced Web technology, digital sensors, and so forth, refers to data so large and complex that they create significant challenges for data management and analysis tools.

Is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

Large volumes of structured, semi-structured and unstructured data from one or more sources, created and analyzed through innovative forms of information processing to develop insights on patterns, especially on human behaviour and corporate interactions, resulting in better decisions, strategic business decisions, and process adaptation. Characterized by high volume, high velocity and/or high variety.

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.”

An extremely large database which generally defies standard methods of analysis.

Big Data includes datasets that are too large and complex to manipulate or interrogate with standard methods or tools. It is characterized by ‘‘three Vs’’: Volume, Velocity and Variety ( Cao et al., 2015 ).

Big data has a broad concept that includes large, comprehensive and always available data, which is nourished by various data sources such as information-sensing mobile devices, software logs, digital images and videos, GPS signals and wireless communication networks or so on.

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.

Big data can generally be characterized by a set of V’s: volume, variety, velocity, value, and veracity.

Is a set of data that enables the coordination of the services offered through the Internet, organizing and classifying customer data.

is huge data sets that may be analysed computationally to discover patterns, trends, and associations, especially relating to human behaviour and interactions.

Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Technologies for collecting, processing, and storing significant arrays of heterogeneous information; are the basis for the development of machine learning and artificial intelligence algorithms, solving analytical problems and optimizing business processes.

Big data refers to enormous and complex datasets that exceed the capacity of typical data.

It is a large, more complex data set, especially from new data sources data that contains greater variety, arriving in increasing volumes and with more velocity. Big data can be used to address business problems that cannot be dealt with by simple data collection.

A term for data sets that is so large or complex that traditional data processing applications are inadequate to deal with them. Challenges include analysis, capture, data curation, search, sharing, storage, transfer, visualization, querying, updating and information privacy. The term “big data” often refers simply to the use of predictive analytics, user behavior analytics, or certain other advanced data analytics methods that extract value from data, and seldom to a particular size of data set. Accuracy in big data may lead to more confident decision-making, and better decisions can result in greater operational efficiency, cost reduction and reduced risk.

Unstructured date with five characteristics: volume, velocity, variety, veracity, and value.

Huge amounts of unstructured data (e-mail messages, internet traffic, etc.) and structural data (e.g., data bases) the analysis of which is not possible by means of traditional analytical tools.

Big data is a general term used to describe the voluminous amount of unstructured and semi-structured data a company creates, data that would take too much time and cost to load into a relational Database for analysis.

Big data can be defined as larger, more complex data sets arising from new data sources, and the techniques to analyze and extract meaning from it.

Huge amount of unstructured data, generated and retrieved digitally.

It is a mass of data that is constantly flowing through various channels at a great speed and in a wide variety of structures and is difficult to process with traditional database systems.

Massive amount of structured or unstructured data generated from social networks and Google searches.

A collection of data that has become so large and complex that it is difficult to process using traditional database and software applications.

Big data is a combination of structured, semi structured 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.

It is a research field studying methods and algorithms for too large and complex data sets extraction, processing, and analysis.

Huge amount of information sets that can be computationally analyzed to disclose patterns, trends, and associations, particularly with respect to human behavior and related issues.

It is considered an area of knowledge that studies how to obtain, process, and analyze data and information considering a large magnitude of structured and unstructured data.

Large volumes of structured and unstructured data whether homogeneous or heterogeneous data. In general, it is not the amount of data that matters the most but what organizations intend to do with the data. In this sense, the goal of Big Data analysis is to provide some insights that may lead to better decisions and strategic business support.

Big Data may be defined as a collection of huge data that becomes challenging to process through available conventional data processing tools and applications. It involves high-volume, high-velocity and high-variety of information resources that require economical, productive and new methods of information processing for improved and enhanced decision making.

Large and complex data sets that traditional data processing software cannot handle. Big data is characterized by high volume, velocity, and variety, and it requires specialized tools for analysis, storage, and processing.

Technologies for collecting, processing, and storing significant arrays of heterogeneous information; are the basis for the development of machine learning and artificial intelligence algorithms, solving analytical problems and optimizing business processes.

Dataset with a very large number of units.

The broad spectrum of data that have appeared throughout the development of the global information society.

A means of identifying structured and unstructured data of large volumes and considerable diversity which is hard to process using traditional methods, including structured data, media, and random objects.

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 multifaceted data structure, accompanies by difficulties of data storage, analysis, and visualization. Big Data are characterized with their high volume, velocity, and variety information assets.

Broadly defined as information whose size is beyond the ability of typical data and information software tools to capture, store, manage, and analyze it.

Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate.

Data that is very large to be analyzed using traditional models and tools. Different forms exists including structured data such as address or age and unstructured data such as e-mails or records. The biggest sources of big data are social media networks and platforms.

Data with at least one of the ten big characteristics consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market.

A type of structured and un-structured data that is characterized by its big volume usually measured in Terabytes, Exabytes or Petabytes and requires specific hardware and software tools for processing.

refers to massive complex structured and unstructured data sets that are rapidly generated and transmitted from a wide variety of sources. Will play a key role in the future of fast-moving industry like fashion (Kim & Lee, 2018).

Relatively large data-sets that originate from diverse sources and that are not analyzable through standard methods and tools.

Is data sets that cannot be captured, managed and processed by general computers within an acceptable scope (Chen, Mao and Liu, 2014; Song and Liu, 2017).

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.

Huge and complex quantities of data sets that can be analyzed to reveal trends, patterns and behaviors. The resulting insights can be used for better decision making or for the development of new products/services.

It is a field of study by which it analyses the various systematic extracted information to take a decision.

Data integrated from multiple sources. This could be in structured, unstructured, or semi-structured format. Its characteristics are velocity, large volume, and variety.

Data that exceeds the processing capacity of conventional database systems.

Large amounts of structured and unstructured data that is too complex to be handled by standard data-processing software.

Is an area of knowledge that studies how to treat, analyze and obtain information from very large data sets.

Massive amounts of information which is difficult to store, manage, and analyze.

Using sophisticated analytical tools, big data analytics is the study of huge and varied data sets.

Refers to large volumes of data, dealing with the computational techniques to manage them.

The datasets that are of large size and have a greater complexity.

The high number and complexity of the data records that are processed and stored by electronic applications.

Technology provides farmers with granular data on rainfall patterns, water cycles, fertilizer requirements, and more. It enables them to make smart decisions (such as what crops to plant for better profitability and when to harvest), ultimately improving farm yields.

A big data set is one that is too large or complicated for standard data processing techniques.

A high-volume, high-velocity and/or high-variety collection of data that can be mined for information and used in machine learning projects, predictive modeling, and other advanced analytics applications.

Volumes of data that are too large and too complex to be processed by traditional data-processing application software.

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.”

Very large data sets which can be analyzed computationally to find patterns, trends, and associations, based on human behavior and interactions.

Big data is a collection of data that is larger in volume,has variety and cannot be processed by traditional tools.

Large complex data sets that can be analyzed on a computer to inform patterns, trends, and associations, especially relating to human behavior and interactions.

Big Data refers to extremely large and complex sets of data that cannot be easily managed, processed, or analyzed using traditional data processing tools. It involves the collection, storage, and analysis of massive amounts of structured and unstructured data to reveal patterns, trends, and insights (OpenAI, 2023 AU19: The in-text citation "OpenAI, 2023" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. AU20: The in-text citation "OpenAI, 2023" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. ).

An analysis technique that systematically extracts information from structured or unstructured data by means of advanced algorithms and supercomputing competence.

It is a phenomenon that involves a set of tools, technologies and techniques that aim to process huge amounts of data of all types in order to exploit the information contained and obtain some kind of advantage.

The set of methodologies and technologies that allow the capture, management and exploitation of considerable amounts of data within a tolerable period of time.

Extremely large data sets that may be analyzed to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

A field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

A term used to describe large volumes of data of both structured and unstructured data type. Gartner defines it as data with three dimensions of Volume, Velocity and Variety.

Data that are being generated by the environment, namely, by nature, technology and humans. Sometimes these data exist as a side effect of some process, for example, the vehicles' positions on the roads are generated automatically as a side effect of the traffic.

Mainly was used as a term to define a large or complex volume of structured, semi-structured and unstructured data that can be mined for information and used in machine learning projects and other advanced analytics applications.

Extremely large data sets that can be analyzed in order to reveal patterns, trends and associations.

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.

This refers to large amounts of data from different sources; it is made up of all kinds of data (e.g., photos, music, text, figures and numerals, voice recording, videos, and a jumble of electronic data), which needs ordering in order to convey meaning to end-users.

Under the GDPR, personal data is defined as any information relating to an identified or identifiable natural person. This definition includes a wide range of data types that can be collected and processed, including those that fall under the umbrella of big data. Big data generally refers to extremely large and complex datasets that are often collected, analyzed, and processed using advanced technology and algorithms. While there is no specific legal definition of big data, the GDPR applies to the processing of personal data regardless of whether it is part of a big data set or not. The GDPR imposes obligations on organizations that process personal data, such as obtaining consent, ensuring transparency, providing data subject rights, implementing appropriate security measures, and complying with the principles of data protection. In summary, while there is no explicit definition of big data in EU law, the GDPR and other data protection regulations apply to the processing of personal data, including when it is part of big data sets.

Term employed to describe large data sets, it is commonly used to describe analytical methods to obtain value from data. The proper use of Big Data may improve the decision making process.

Big data is an “umbrella term” that describes data processing approaches in situations where enormous and/or unstructured datasets cannot be efficiently manipulated via traditional handling techniques (e.g., relational databases).

A gargantuan dataset having the feature of high volume, variety, velocity, value, variability, and veracity which is captured through technologies like Optical Character Readers (OCRs), Smart Cards, Biometrics, Voice Recognition, Magnetic strips, etc.

Large or complex datasets that too difficult to easily analyze with traditional processes including the utilization of predictive analysis for decision making.

Extremely large volume of data - both structured and unstructured - that inundates a business on a day-to-day basis and may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Big data can be referred to as data characterized by high levels of volume (amount of data), variety (number of types), and velocity (speed).

The generation of data has started to outpace the processing capabilities of the typical technology tools.

Large and complex datasets that require advanced data processing technology in order to be analyzed.

The large amount of data that is being generated and needs to be processed by new digital tools and technologies.

A large volume of data that may be analyzed computationally and systematically to reveal characteristics, patterns, trends, and associations in various fields.

Complex multi-dimensional digital data sets.

Big data is a combination of structured, semi structured, 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.

The gigantic digital databases, held, acquired, and used by governments, companies or other organizations, and later analyzed through the use of algorithms.

Large volume of data that inundates a business on a day-to-day basis.

The current phrase used to describe the changes in the accumulation of data over the past decade; the distinguishing factors of which are volume (2.5 Exabyte’s per day), velocity (speed at which data is created), and variety (images, texts, videos, etc.).

Unstructured data that does not fit into traditional tables found on the Internet—e.g. photos, videos and blog posts, among others.

Large-scale data, often obtained from online environments.

Large volumes of structured, semi-structured and unstructured data from one or more sources, created and analyzed through innovative forms of information processing to develop insights on patterns, especially on human behaviour and corporate interactions, resulting in better decisions, strategic business decisions, and process adaptation. Characterized by high volume, high velocity and/or high variety.

A term used to describe large volume of structured and unstructured data generated through multiple devices and platforms that require non-conventional handling and analytical tools to ascertain patterns and trends that are useful for effective decision making.

An exponentially increasing volume of heterogeneous data which is differentiated from traditional data based on its volume, variety, veracity, velocity, and value.

Large amounts of data generated and stored by an enterprise, often in the areas of operations, transactions, and/or communications.

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.”

Bigdata is a term used to describe a collection of data that is huge in size and yet growing exponentially with time.

Data that exceeds that processing capacity of conventional database systems.

Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

The high number and complexity of the data records that are processed and stored by electronic applications.

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 is data with a huge size; a term used to describe a collection of data that is huge in volume and yet growing exponentially with time. In short such data is so large and complex that none of the traditional data management tools are able to store it or process it efficiently. In other words, it is a massive volume of both structured and unstructured data that is so large it is difficult to process using traditional database and software techniques. In most enterprise scenarios the volume of data is too big or it moves too fast or it exceeds current processing capacity.

Big data means max. Volume of data with characteristics like volume, variety and veracity, etc.

Big data is a buzz word that refers to collections of data that are big considering three Vs: volume, velocity, and variety. Some authors refer two more Vs: veracity and value.

A concept mainly characterized by the volume, velocity and variety of data being generated.

Datasets that are (1) extremely large, (2) generated by many different sensors and devices, and (3) collected and transmitted quickly and frequently.

Computationally analysed sets of data that is disclose complex associations, trends, and patterns for human interactions.

Various data types are brought together by recording large amounts of data, obtaining meaningful results. Big data analytics provides a potential competitive advantage that radically changes social, cultural, political, and economic relations.

Large, dynamic and unstructured collections of data often distributed and streamed.

Is data with at least one of the ten big characteristics consisting of big volume, big velocity, big variety, big veracity, big intelligence, big analytics, big infrastructure, big service, big value, and big market.

This refers to extremely large data sets that are analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Although there is no consensus on how much should be considered Big Data, it is possible to define it as the point at which the computing capabilities of a given piece of equipment are exceeded.

Data that is collectively too large and complex to be analyzed with traditional data mining techniques.

Huge Quantity of Data generated by Smart Devices.

Big data is any data that is difficult to handle, and cannot be managed by the standard data processing software. The three popular terms associated with Big Data are Volume, Velocity and Variety known as the three V’s.

Any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications.

Large data sets that cannot be processed and analysed using traditional processing systems.

Refers to the large volume of data – both structured and unstructured – that a business deals with on a day-to-day basis.

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.

The massive amount of data which is identified by four characteristics including high volume, velocity, variety, and veracity.

Vastly large data sets that cannot be analyzed through traditional data processing techniques are collectively known as Big Data. Organizations in possession of Big Data (majorly business organizations) are attempting to capture and analyze even more samples in order to make sound decisions based on human behavior.

Extremely large and highly complex data sets that can be analyzed computationally contextually which describes approximately contemporary to human interaction based on oneself emotion.

There are large, complex, and unprocessed datasets that cannot be processed by traditional application but can offer knowledge and value if properly analyzed.

A collection of huge amounts of real time data in very high volume, variety, and velocity in nature that cannot be managed effectively with traditional database management systems.

The challenge of Big Data was initially posed by the aspects of increasing data volumes, data that needs to be rapidly handled and the increasing complexity of data structures in data processing.

A large amount of data that can be utilized to find patterns related to a given outcome or output.

Stored data sets that can be manipulated and analysed to develop new interventions.

Datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze.

A computational domain for analysis and systematical extraction of information from too large/complex data sets, which cannot be already processed by the traditional application software (Big data, n.d.), useful also in media analysis and prediction.

Are interesting high-velocity, high-value, and/or high-variety data with volumes beyond the ability of commonly-used software to capture, manage, and process within a tolerable elapsed time. These Big Data necessitate new forms of processing to deliver high veracity (& low vulnerability) and to enable enhanced decision making, insight, knowledge discovery, and process optimization.

A business management term that refers to applications and technologies that are used to gather, provide access to, and analyze data and information about their company operations.

A term commonly used to describe vast and complex volumes of unstructured and structured dataset generated and conveyed from different sources.

A term used to describe large-scale data sets that include multiple kinds of demographic, behavioral, or performance data. Sometimes used to describe the unique kinds of data analysis that can be performed on these data sets.

Is a popular term used to describe the exponential growth and availability of data, both structured and unstructured.

Big data is a general term used to describe the voluminous amount of unstructured and semi-structured data a company creates, data that would take too much time and cost to load into a relational Database for analysis.

It is the collection of such a huge amount of data (both structured and unstructured) with much complexity that traditional data management tool cannot store or process it efficiently.

Data that is received in high volume and it can be stored in databases, and it comes at a high speed with various data formats.

Big data is a combination of structured, semi structured, 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.

Big data is a collection of data sets so large and complex that it becomes difficult to process using traditional analytics algorithms. The challenges include capture, creation, storage, search, sharing, analysis, and visualization. The 4 V’s of big data are Volume (large volumes of data), Velocity (speed of data generation), Variety (structured, unstructured), and Veracity (trust and integrity).

Datasets that are in large volume and high variety.

Data featuring high volume, high variety, high velocity, high veracity, and high value.

Refers to the growth in the volume of structured and unstructured data, the speed at which it is created and collected, and the scope of how many data points are covered. Big data often comes from multiple sources and arrives in multiple formats.

Data that exceeds the capacity of commonly used hardware and software tools to capture, store, and analyze within a tolerable elapsed time is considered big data. The three main characteristics of big data are volume, variety, and velocity.

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.

Area of knowledge that studies how to treat, analyze and obtain information from very large data sets.

Refers to data sets that are so voluminous and complex that traditional data processing application software is inadequate to deal with them.

The term “big data” pertains to data sets that are characterized by their vast size and intricate nature, posing challenges for processing and analysis by conventional data processing techniques. The primary attributes associated with big data encompass: The generation of data from diverse sources has resulted in a substantial volume. In the realm of big data, data sets are typically characterized by their substantial size, sometimes measured in terabytes and petabytes. Diverse Range - Big data encompasses a diverse array of data sources and formats. The data may be organized in a structured format, such as databases, or in an unstructured format, encompassing emails, images, videos, and similar content. The velocity of data generation and processing in the context of big data is characterized by a high rate of speed. Streaming data plays a key role within the realm of big data. The veracity of data can be compromised by inconsistencies, noise, and irregularities. The task of maintaining the high quality and integrity of data presents significant challenges. The primary attribute of big data is in its capacity to reveal insights that contribute to company value, impact, and improved decision-making capabilities. The extraction of value from intricate data is of paramount importance.

A voluminous amount of structured, semi-structured, and unstructured data from various sources around us, such us Internet, sensors, nature, and society.

Area of knowledge that studies how to treat, analyze, and obtain information from data sets that are too large to be analyzed by traditional systems.

A huge and moving set of data of various varieties, structure, and format, produced by various sources, at various frequencies that must be processed quickly using specialized tools, other than common database management software.

A term that describes large amount of data that could be structured, unstructured or real time and is difficult to be handled by traditional systems.It is identified by major 3 V’s: Volume, Variety and Velocity.

This term emerged to solve the treatment and storage massive data that has specific defined features as speed, volume and variety.

An increasingly popular term that refers to the large amount of digital data that is being accumulated about individuals, where the data is voluminous, collected at great velocity and extremely variegated in detail offering a nearly precise identity narrative of the individual.

Are large data sets that are so big and complex that conventional computers are unable to process the data at all or not in real time. Additionally, big data refers to the use of large volumes of data to make business decisions or to predict customer choices in real time using customer data.

Data that contains greater variety, arriving in increasing volumes and with more velocity.

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.

Big data is a term that is used to describe data that is high volume, high velocity, and/or high variety; requires new technologies and techniques to capture, store, and analyse it; and is used to enhance decision making, provide insight and discovery, and support and optimize processes.

Is data that exceeds that processing capacity of conventional database systems.

A term used to refer to the study and applications of data sets that are too complex for traditional data-processing application software to adequately deal with.

The capacity to use technologies which can work with huge volume of different types of data and in real time basis.

Big data refers to exceptionally large and complex datasets that cannot be easily managed, processed, or analyzed with traditional data processing tools and techniques.

Large amounts of information on a topic that are sometimes difficult to manage. It can be structured or unstructured. This term is often associated with Data Bases or Deep Learning.

Big data is a combination of structured, semi-structured, 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.

Big data is a combination of structured, semi structured 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.

Is a term for data sets that are so large or complex that traditional data processing applications are inadequate.

Referred to as Exabyte and beyond, has enlarged the technological capacity to store, manage, process, understand, and visualise the volume of data.

An extremely large dataset that cannot be analysed by traditional methods, and require distributed, highly paralelizable computing to extract the information.

Big data refers to data sets that are too large or complex to be dealt with by traditional data-processing application software. Data with many fields offer greater statistical power, while data with higher complexity may lead to a higher false discovery rate.

A rea of knowledge that studies how to treat, analyze and obtain information from data sets that are too large to be analyzed by traditional systems.

A large amount of information about an organization (both structured and unstructured) that cannot be analysed using traditional computing methods.

Big data refers to large amount of transactional data which could be computationally retrieved and analsyed to understand hidden trends, patterns and various attribute relationships.

Big and versatile data sets, that are hard to analyze using standard analytical methods. Analysis of big data enables to identify new information and gain knowledge by finding relationships between variables not visible before.

A numerous amount of information available on the internet, which can be utilized to figure out the problem in various fields.

Is an extremely large amount and varied data sets that may be analyzed computationally to reveal valuable information like patterns, trends, relations, and associations, especially relating to human treatments and behaviour and interactions that can help institutions make informed decisions.

Big Data refers to huge and complicated data sets that are too massive and varied to be handled using typical data processing methods, necessitating the use of specialised tools and methodologies for analysis.

Big data refers to the immense and complex raw data accumulating over time due to the internet and technology use.

The size, speed and sorts of data that exceed an organization’s traditional database capacity to access, integrate, store, and analyze for accurate and timely decision making.

Data sets that are too large and cannot sit on a single computer or storage device.

large databases of behavioural tracking as well as the tools required to handle these data. Ever-increasing databases and electronic recordings of individual activities and corporate and societal repositories. Cell phones, social media conversations, web browsing, GPS, CCTV cameras and use of credit/debit cards constantly leave traces of the individual.

It reflects extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

The large datasets that are enabled by IT systems which support, capture, and disseminate these data.

Big data is a broad term for data sets so large or complex that traditional data processing applications are inadequate. There is not a common definition of big data, and big data are generally characterized by some properties such as volume, velocity, variety, and so on.

It refers to a massive volume of structured or unstructured data that is difficult to be processed by traditional data management tools.

The term used for the 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.

The data with frequency of generation of the size petabytes / terabytes per second and is so huge in volume and not fit to be stored in the existing traditional relational database storage.

Very large data sets generated predominantly by human interaction with digital systems and by digital systems interaction with the environment.

Set of analytical techniques and methods for large volumes of data with the final obtaining of computing solutions.

Big data is a term for data sets that are so large or complex that traditional data processing application software is inadequate to deal with them. The main characteristics of these datasets are volume, variety, and velocity.

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.

Data that is too large to be stored using conventional data storage capabilities. Big data also includes non-traditional types of data such as images, text, weblog searches, and social media data.

Big data is term for massive datasets having large, more varied complex structure with the difficulties of storing, analyzing and visualizing for further processes or results.

A term for data sets that are so large or complex that traditional data processing applications are inadequate.

Any data set that is too difficult to be handled by traditional database systems due to its size and complexity.

Extremely large volume of data—both structured and unstructured—that inundates a business on a day-to-day basis and may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

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.”

The term for collections of data that are too large and complex to process using on-hand database management tools or traditional data processing applications.

Big data refers to exceptionally large and complex datasets that cannot be easily managed, processed, or analyzed with traditional data processing tools and techniques.

Big data is a term for dataset that are huge and complex to process by traditional data processing applications.

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.

Extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations, especially relating to human behavior and interactions.

Large amounts of data generated and stored by an enterprise, often in the areas of operations, transactions, and/or communications.

Term used in information technology to refer to the processing of a large set of data that needs to be processed and stored. The initial concept was based on 3 Vs (speed, volume, and variety) and then some more Vs were gradually added.

Huge amount of structured (text fields such as name and phone number) and unstructured (e.g., video, images, gestures, etc.) data in sizes that exceed the capacity of traditional tools to manage them satisfactorily.

Refers to the 3Vs (i.e., Volume, Velocity, Variety) of data which is captured for actionable insights.

The act of gathering and analyzing massive data sets from conventional and digital sources in order to uncover trends and patterns that may be utilized in decision-making is known as big data .