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Big data ethics – Ethics of mass data analytics; Big data maturity model – Aspect of computer science; Big memory – A large amount of random-access memory; Data curation – Organization of collected data; Data defined storage – Marketing term for managing data by combining application, information and storage tiers
Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. [4]
Data analysis focuses on the process of examining past data through business understanding, data understanding, data preparation, modeling and evaluation, and deployment. [8] It is a subset of data analytics, which takes multiple data analysis processes to focus on why an event happened and what may happen in the future based on the previous data.
The difficulty in ensuring data quality is integrating and reconciling data across different systems, and then deciding what subsets of data to make available. [3] Previously, analytics was considered a type of after-the-fact method of forecasting consumer behavior by examining the number of units sold in the last quarter or the last year. This ...
One of his most popular books (coauthored with Jeanne Harris), Competing on Analytics: The New Science of Winning, [2] provides guidelines for basing competitive strategies on the analysis of business data, and highlights several firms that do so. One of his sons, Hayes Davenport, is a television comedy writer and podcaster living in Los ...
Topological data analysis (TDA) is a mathematical framework that uses tools from topology, including algebraic, differential, and geometric topology, to study the shape and structure of data. In summary, data analysis and data science are distinct yet interconnected disciplines within the broader field of data management, analysis, and mathematics.
Augmented Analytics is an approach of data analytics that employs the use of machine learning and natural language processing to automate analysis processes normally done by a specialist or data scientist. [1] The term was introduced in 2017 by Rita Sallam, Cindi Howson, and Carlie Idoine in a Gartner research paper. [1] [2]
Thomas Davenport, professor of information technology and management at Babson College argues that business intelligence should be divided into querying, reporting, Online analytical processing (OLAP), an "alerts" tool, and business analytics. In this definition, business analytics is the subset of BI focusing on statistics, prediction, and ...