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Without sufficient investment in expertise for big data veracity, the volume and variety of data can produce costs and risks that exceed an organization's capacity to create and capture value from big data. [5] Current usage of the term big data tends to refer to the use of predictive analytics, user behavior analytics, or certain other ...
Journal of Big Data is a scientific journal that publishes open-access original research on big data.Published by SpringerOpen since 2014, it examines data capture and storage; search, sharing, and analytics; big data technologies; data visualization; architectures for massively parallel processing; data mining tools and techniques; machine learning algorithms for big data; cloud computing ...
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]
Analytics is the systematic computational analysis of data or statistics. [1] It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data science. [2] Analytics also entails applying data patterns toward effective decision-making.
However, data has staged a comeback with the popularisation of the term big data, which refers to the collection and analyses of massive sets of data. While big data is a recent phenomenon, the requirement for data to aid decision-making traces back to the early 1970s with the emergence of decision support systems (DSS).
[5] [6] Mashey was one of the founders of the Standard Performance Evaluation Corporation (SPEC) benchmarking group, was an ACM National Lecturer for four years, has been guest editor for IEEE Micro , and one of the long-time organizers of the Hot Chips conferences. [ 5 ]
Data mining is the process of extracting and finding patterns in massive data sets involving methods at the intersection of machine learning, statistics, and database systems. [1] Data mining is an interdisciplinary subfield of computer science and statistics with an overall goal of extracting information (with intelligent methods) from a data ...
Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. [1] Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science ...