Home // International Journal On Advances in Intelligent Systems, volume 10, numbers 1 and 2, 2017 // View article
Authors:
Karin Hartl
Olaf Jacob
Keywords: Information Systems; Data Mining; Association Rule Discovery; Cluster Analysis; Business Intelligence
Abstract:
This paper investigates the opportunities of Data Mining applications for Information System research. Data Mining is a data driven statistical approach for knowledge discovery. Hypotheses and models do not have to be developed at the beginning of the research, which allows the detection of new and otherwise undiscovered patterns in a given dataset. Consequently, current challenges in Information System research can be investigated from a different angle. The Data Mining results may provide additional, surprising and detailed insights to an Information System problem. To prove these assumptions, this study applies Association Rule Discovery and cluster analysis to a questionnaire based data set. This data set has been collected to investigate the relationship between Business Intelligence and Corporate Performance Management. Even though this relationship has been explored at several stages in the Information System research literature, the results are often short on detail. This paper explores, if Data Mining methods can provide additional information to the subject. Both of the applied Data Mining methods provide promising results. Association rules and clusters have been identified, providing a different view on the connection between Business Intelligence and Corporate Performance Management. Therefore, Data Mining techniques offer an option to reuse questionnaire-based data and to gain new insights in Information System research.
Pages: 59 to 70
Copyright: Copyright (c) to authors, 2017. Used with permission.
Publication date: June 30, 2017
Published in: journal
ISSN: 1942-2679