Data Mining for Business Process Reengineering
Ted E. Lee (The University of Memphis, USA), Robert Otondo (The University of Memphis, USA), Bonn-Oh Kim (Seattle University, USA), Pattarawan Prasarnphanich (The University of Memphis, USA) and Ernest L. Nichols Jr. (The University of Memphis, USA)
Copyright: © 2003
Transitioning from a mining to meaning perspective in organization data mining can be a crucial step in the successful application of data mining technologies. The purpose of this paper is to examine more fully the implications of that shift. The use of data mining technology was part of our cycle time study of the Poplar County Criminal Justice System (a fictitious name). In this paper we will report on the use of data mining in the Poplar County Criminal Justice System (PCCJS) study in an attempt to speed up their case handling processes. Marketing and finance researchers are more involved with “simple” (i.e., direct) relationships, whereas BPR researchers are more concerned with long chains of interacting processes. This difference appears in the tools these researchers use: marketing and finance researchers are more interested in set-theoretic problems, BPR researchers, in graph-theoretic problems. Yet data mining technologies incorporate graph-theoretic algorithms. Consequently, they should be able to support hypothesis generation in BPR activities. We were able to come up with relevant and meaningful hypotheses for BPR in the PCCJS system by using data mining technology, specifically sequential pattern analysis: “Which areas we should look into in order to speed up the case handling process?” This valuable outcome would have not been possible without data mining technology, considering the large volume of data on hand. It is hoped that this study will contribute to broadening the scope of applicability of data mining technology.