Data Mining and Business Intelligence: A Bibliometric Analysis

Data Mining and Business Intelligence: A Bibliometric Analysis

Ana Azevedo (CEOS.PP, ISCAP, Polytechnic of Porto, Portugal)
DOI: 10.4018/978-1-7998-5781-5.ch001
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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.

Key Terms in this Chapter

Analytics: Analytics represents a combination of computational technologies, scientific management techniques, and statistics to solve real-world problems, while considering that organizations have to analyze their data to understand what is happening, what will happen, and how to take the best option.

Big Data: Big data is a buzz word that refers to collections of data that are big considering three Vs: volume, velocity, and variety. Some authors refer two more Vs: veracity and value.

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