What is Analytics

1.

The mining, research, or examination of large-scale datasets to yield evidence of past, present or future behavior. Learn more in: Use of Big Data Analytics by Tax Authorities

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The systematic computational analysis of data or statistics or information resulting from the systematic analysis of data or statistics. Learn more in: Omni-Channel Retailing: Enriching Customers' Shopping Experience

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It is the process of analyzing data belonging to specific domain using analytical tools. Learn more in: Taxonomy Outline of Big Data Analytics Literature

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Information resulting from the systematic analysis of data or statistics. Learn more in: Big Data Analytics in Retail Supply Chain

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Data that a software can provide to help uncover valuable data about your users. Learn more in: Reflective Learning With Video-Based Annotations

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Has emerged as a catch-all term for a variety of different business intelligence (BI) and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain and so on). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen. Learn more in: Big Data Applications in Business

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Analytics is defined as the scientific process of transforming data into insight for making better decisions. Learn more in: Nonprofit Fundraising Transformation through Analytics

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As emerged as a catch-all term for a variety of different business intelligence (BI) and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain, and so on). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen. Whatever the use cases, “analytics” has moved deeper into the business vernacular. Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data. Learn more in: Big Data, Who Are You?

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The discovery and communication of patterns – with significance – in data. Learn more in: The New “ABC” of ICTs (Analytics + Big Data + Cloud Computing): A Complex Trade-Off between IT and CT Costs

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Refers to data-driven analysis using various methods such as data mining and statistical techniques to provide better decision making. Learn more in: Digital Technology Deployment in Multi-National Enterprises

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Has emerged as a catch-all term for a variety of different business intelligence (BI) and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain and so on). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen. Whatever the use cases, “analytics” has moved deeper into the business vernacular. Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data. Learn more in: What the 3Vs Acronym Didn't Put Into Perspective?

12.

The process of transforming data into insight for the purpose of making decisions. Learn more in: Advanced Analytics for Big Data

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The collection and analysis of educational data to inform future instructional decisions. Learn more in: The Next-Generation CBE Architecture: A Learning-Centric Standards-Based Approach

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Is the process of analyzing single or multiple sets of big or small data to discover meaningful pattern in them. Refer to the definition of big-data analytics, business case analytics, and small-data analytics. Learn more in: Business Case Analytics

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Analytics represents a combination of computational technologies, scientific management techniques, and statistics to solve real-world problems, while considering that organizations have to analyze their data to understand what is happening, what will happen, and how to take the best option. Learn more in: Data Mining and Business Intelligence: A Bibliometric Analysis

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Davenport et al. (2007) AU15: The in-text citation "Davenport et al. (2007)" is not in the reference list. Please correct the citation, add the reference to the list, or delete the citation. describe analytics as “the extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions” (p. 7). Learn more in: Empirical Investigation on the Evolution of BI Maturity in Malaysian Organizations

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Software-based algorithms and statistics used to derive meaning from data. Learn more in: Business and Technology Trends in Social CRM

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A multidisciplinary field that explores, infers and communicates patterns in data. Learn more in: Addressing Continuous Auditing Challenges in the Digital Age: A Literature Review

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Emerged as a catch-all term for a variety of different business intelligence (BI)- and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (e.g., sales, service, supply chain and so on). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores, and predicts what scenarios are most likely to happen. Whatever the use cases, “analytics” has moved deeper into the business vernacular. Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data. Learn more in: Investigating the Factors for Predictive Marketing Implementation in Algerian Organizations

20.

The science of discovering and communicating meaningful patterns in data and developing actionable plans. The Analytics paradigm often favors data visualization methods in reporting results and recommendations. Typically, Analytics consists of three board categories: descriptive, predictive and prescriptive. Learn more in: Teaching and Using Analytics in Management Education

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The analysis and evaluation of data for the purposes of diagnosing, predicting, or prescribing actions. Learn more in: Why Is Data So Hard?: Challenges, Solutions, and Future Directions

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Application of data mining and operations research for predictions, performance evaluations, comparisons, and optimal decision making. Learn more in: Data-Driven Simulation-Based Analytics

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Has emerged as a catch-all term for a variety of different business intelligence (BI) and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain and so on). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen. Whatever the use cases, “analytics” has moved deeper into the business vernacular. Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data. Learn more in: Techniques and Methods That Help to Make Big Data the Simplest Recipe for Success

24.

Has emerged as a catch-all term for a variety of different business intelligence (BI) and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as Website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain, and so on). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen. Whatever the use cases, “analytics” has moved deeper into the business vernacular. Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data. Learn more in: Building an Analytics Culture to Boost a Data-Driven Entrepreneur's Business Model

25.

Consumer behavioral data related to their platform usage behaviors such as the number of visits to a platform, time spent on the platform, and actions when using the platform. Using these behavioral data, advertisers and marketers are able to segment target audience to increase their experiences and to improve campaign effectiveness. Learn more in: Optimization Models in Google Ads Campaigns

26.

Analytics involves analysis of the data stream through systematic processing to infer and interpret the information contained in it for data pattern recognition and decision making. Learn more in: Fog-Cloud Collaboration for Real-Time Streaming Applications: FCC for RTSAs

27.

Analytics is the process of statistics in which the data revelation, perception, and communication of meaningful patterns in data is obtained. Learn more in: Big Data Analytics: Educational Data Classification Using Hadoop-Inspired MapReduce Framework

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The discovery, interpretation and communication of meaningful patterns in data. Learn more in: A Framework for Profiling Prospective Students in Higher Education

29.

Has emerged as a catch-all term for a variety of different business intelligence (BI) and application-related initiatives. For some, it is the process of analyzing information from a particular domain, such as website analytics. For others, it is applying the breadth of BI capabilities to a specific content area (for example, sales, service, supply chain and so on). In particular, BI vendors use the “analytics” moniker to differentiate their products from the competition. Increasingly, “analytics” is used to describe statistical and mathematical data analysis that clusters, segments, scores and predicts what scenarios are most likely to happen. Whatever the use cases, “analytics” has moved deeper into the business vernacular. Analytics has garnered a burgeoning interest from business and IT professionals looking to exploit huge mounds of internally generated and externally available data. Learn more in: First of All, Understand Data Analytics Context and Changes

30.

A method to do systematic analysis of data/information. Learn more in: A Model for Evaluating HR Analytics Critical Success Factors in Industry 5.0

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The discovery and transmission of patterns in data that are meaningful based on the context of data. Learn more in: Querying of Time Series for Big Data Analytics

32.

The systematic computational analysis of data or statistics. Learn more in: Information Science in the Analytics of Healthcare Data

33.

The primary technology that facilitates decision making, along with other components, such as search and reporting. The key platform capabilities of Analytics are Data collection, large volume Data Storage, Data synchronization, Data analysis and Reporting. Learn more in: Big Data Predictive and Prescriptive Analytics

34.

The field of data analysis. Analytics often involves studying past historical data to research potential trends, to analyze the effects of certain decisions or events, or to evaluate the performance of a given tool or scenario. The goal of analytics is to improve the business by gaining knowledge which can be used to make improvements or changes. Learn more in: The Role of Business Analytics in Performance Management

35.

the extensive use of data, statistical and quantitative analysis, exploratory and predictive models, and fact-based management to drive decisions and actions. Learn more in: A Framework for Using Analytics to Make Decisions

36.

In the IoT, analytics can be defined as the results achieved by analyzing data obtained through various intelligent devices. Learn more in: Internet of Things in Tourism: A Proposal of the Information System for Cappadocia Hot-Air Ballooning

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The extensive use of data, statistical and quantitative analysis, explanatory and predictive models, and fact-based management to drive decisions and actions. Learn more in: Analytics for Nonprofits

38.

An umbrella term for data analysis applications. It is the use of “rocket science” algorithms (e.g., machine learning, neural networks) to analyze data. Learn more in: BI and Analytics for Effective Disaster Recovery Management Lessons From the Bayou

39.

Intensive use of structured and unstructured customer data whose quantitative and qualitative statistical exploitation, predictive and explanatory models allow to improve the decision-making process of companies and the creation of competitive advantages and generation of value in organizations. Learn more in: Studying Customer Experience and Retention Using Applied Data Science and Artificial Intelligence

40.

Analytics is the processing of data, through IT, of a quantitative and qualitative type to help an organization better understand its businesses and markets (knowledge discovery) and to make timely business decisions. Learn more in: Data Analytics: Challenges and Opportunities for the Family Business

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The systematic analysis of data (thyroid dataset). Learn more in: A Literature Review on Thyroid Hormonal Problems in Women Using Data Science and Analytics: Healthcare Applications

42.

Systematic analysis of data or statistics. Learn more in: Data Science in Economics and Business: Roots and Applications

43.

Big Data analytics is the process of finding meaningful patterns or to get insight on data with extensive use of statistical and mathematical techniques. Learn more in: Big Data Computing Strategies

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Consumer behavioral data related to their platform usage behaviors such as the number of visits to a platform, time spent on the platform, and actions when using the platform. Using these behavioral data, advertisers and marketers are able to segment target audience to increase their experiences and to improve campaign effectiveness. Learn more in: What Do Facebook Users Feel About Facebook Advertising?: Using an Experience Sampling Method (ESM) to Explore Their Digital Advertising Experiences

45.

realizes information as competitive advantage. It is the process of analyzing single set or multiple sets of data to discover meaningful patterns in them. It facilitates the creation of the right solutions and the asking and answering of why to find the root causes in the chain of problems. Learn more in: Small-Data Analytical Culture Analytics in ERP

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A data driven, manual analysis and optimization models including process that creates insight. Learn more in: Intelligent Techniques for Analysis of Big Data About Healthcare and Medical Records

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The use of large data sets and statistical analysis to answer questions and support decision making. Learn more in: Valuing Social Responsibility in the Era of Data Analytics: A Process Model for Effective Practice