Data Mining and Business Intelligence: A Bibliometric Analysis

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Author(s): Ana Azevedo (CEOS.PP, ISCAP, Polytechnic of Porto, Portugal)
Copyright: 2021
Pages: 12
Source title: Integration Challenges for Analytics, Business Intelligence, and Data Mining
Source Author(s)/Editor(s): Ana Azevedo (CEOS.PP, ISCAP, Polytechnic of Porto, Portugal)and Manuel Filipe Santos (Algoritmi Centre, University of Minho, Guimarães, Portugal)
DOI: 10.4018/978-1-7998-5781-5.ch001

Keywords: Business & Management / Business Intelligence / Data Mining and Databases / Engineering Science Reference

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Abstract

From the middle of this second decade of the 21st century, analytics has become commonly associated with the topics business intelligence and data mining. Data mining (DM) is being applied with success in business intelligence (BI) environments and several examples of applications can be found. BI and DM have different roots and, as a consequence, have significantly different characteristics. DM came up from scientific environments; thus, it is not business oriented. DM tools still demand heavy work in order to obtain the intended results. On the contrary, BI is rooted in industry and business. As a result, BI tools are user-friendly. This chapter reflects on these differences from a historical perspective. Starting with a separated historical perspective of each one, analytics, BI, and DM, the author then discusses how they converged when DM is used and integrated in BI environments with success.

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