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Data Science for Business Analytics and Business Intelligence

Data Science for Business Analytics and Business Intelligence
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Author(s): Matthias Lederer (International School of Management (ISM), Munich, Germany)and Patrick Schmid (International School of Management (ISM), Munich, Germany)
Copyright: 2021
Pages: 14
Source title: Encyclopedia of Organizational Knowledge, Administration, and Technology
Source Author(s)/Editor(s): Mehdi Khosrow-Pour D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-7998-3473-1.ch037

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Abstract

Data science as the interdisciplinary collection of methods and techniques to support businesses is becoming more and more popular. This article begins with definitions and shows how systematically competitive advantages can be built up on the basis of digital data. Essential sources and types of data-driven knowledge are introduced. Then a classification of approaches of data science concepts is explained. A distinction is made between Business Analytics and Business Intelligence as different levels of analytical skills. The paper goes into depth with these concepts and presents concrete techniques, algorithms, and application scenarios. Thus, the contribution introduces State of the Art approaches to analysis, control, monitoring but also to advanced approaches such as prediction, simulation, and optimization.

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