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Data Quality for Data Mining in Business Intelligence Applications: Current State and Research Directions

Data Quality for Data Mining in Business Intelligence Applications: Current State and Research Directions
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Author(s): Arun Thotapalli Sundararaman (Accenture, India)
Copyright: 2015
Pages: 26
Source title: Integration of Data Mining in Business Intelligence Systems
Source Author(s)/Editor(s): Ana Azevedo (Algoritmi R&D Center/University of Minho, Portugal & Polytechnic Institute of Porto/ISCAP, Portugal)and Manuel Filipe Santos (Algoritmi R&D Center/University of Minho, Portugal)
DOI: 10.4018/978-1-4666-6477-7.ch003

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Abstract

Data Quality (DQ) in data mining refers to the quality of the patterns or results of the models built using mining algorithms. DQ for data mining in Business Intelligence (BI) applications should be aligned with the objectives of the BI application. Objective measures, training/modeling approaches, and subjective measures are three major approaches that exist to measure DQ for data mining. However, there is no agreement yet on definitions or measurements or interpretations of DQ for data mining. Defining the factors of DQ for data mining and their measurement for a BI System has been one of the major challenges for researchers as well as practitioners. This chapter provides an overview of existing research in the area of DQ definition and measurement for data mining for BI, analyzes the gaps therein, besides reviewing proposed solutions and providing a direction for future research and practice in this area.

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