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  2. Big data - Wikipedia

    en.wikipedia.org/wiki/Big_data

    Big data "size" is a constantly moving target; as of 2012 ranging from a few dozen terabytes to many zettabytes of data. [26] Big data requires a set of techniques and technologies with new forms of integration to reveal insights from data-sets that are diverse, complex, and of a massive scale. [27]

  3. Big data maturity model - Wikipedia

    en.wikipedia.org/wiki/Big_Data_Maturity_Model

    This maturity model is prescriptive in the sense that the model consists of four distinct phases that each plot a path towards big data maturity. Phases are: Phase 1, undergo big data education; Phase 2, assess big data readiness; Phase 3, pinpoint a killer big data use case; Phase 4, structure a big data proof-of-concept project [11]

  4. Thomas H. Davenport - Wikipedia

    en.wikipedia.org/wiki/Thomas_H._Davenport

    Davenport notes that this “big data” evolution was predominantly driven by Internet firms with capabilities to work with large amounts of data. According to Davenport, we now find ourselves in the third analytics era where companies of any industry are increasingly “competing on analytics”.

  5. Business intelligence - Wikipedia

    en.wikipedia.org/wiki/Business_intelligence

    Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information. [1] Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text ...

  6. Continuous analytics - Wikipedia

    en.wikipedia.org/wiki/Continuous_analytics

    Analytics is the application of mathematics and statistics to big data. Data scientists write analytics programs to look for solutions to business problems, like forecasting demand or setting an optimal price. The continuous approach runs multiple stateless engines which concurrently enrich, aggregate, infer and act on the data.

  7. Industrial big data - Wikipedia

    en.wikipedia.org/wiki/Industrial_Big_Data

    Industrial big data refers to a large amount of diversified time series generated at a high speed by industrial equipment, [1] known as the Internet of things. [2] The term emerged in 2012 along with the concept of "Industry 4.0”, and refers to big data”, popular in information technology marketing, in that data created by industrial equipment might hold more potential business value. [3]

  8. Data technology - Wikipedia

    en.wikipedia.org/wiki/Data_technology

    Data technology sector includes solutions for data management, and products or services that are based on data generated by both human and machines. [1] DataTech is an emerging industry that uses Artificial Intelligence , Big Data analysis and Machine Learning algorithms to improve business activities in various sectors, such as digital ...

  9. Data Analytics Library - Wikipedia

    en.wikipedia.org/wiki/Data_Analytics_Library

    software.intel.com /content /www /us /en /develop /tools /data-analytics-acceleration-library.html oneAPI Data Analytics Library (oneDAL; formerly Intel Data Analytics Acceleration Library or Intel DAAL), is a library of optimized algorithmic building blocks for data analysis stages most commonly associated with solving Big Data problems.