Data mining as a technique for knowledge management in business process redesign
Purpose ‐ Business process redesign (BPR) is undertaken to achieve order-of-magnitude improvements over "old" forms of the organization. Practitioners in academia and the business world have developed a number of methodologies to support this competitive restructuring
that forms the current focus of concern, many of which have not been successful. The purpose of this paper is to suggest the use of data mining (DM) as a technique to support the process of redesigning a business by extracting the much needed knowledge hidden in large volumes of data maintained
by the organization through the DM models. Design/methodology/approach ‐ The paper explains how the DM/BPR tool will extract and transfer the much-needed knowledge necessary for implementing the new business. Findings ‐ The process of extracting knowledge hidden
from large volumes of data (DM) has proved very successful in solving many business or scientific problems to achieve competitive advantage. As suggested in the DM/BPR framework, the DM model can be deployed on the massive data collected from past business processes of the organization which
then yields the previously unknown knowledge and trends needed by top managers or decision makers in the organization for effective business process redesigning. Originality/value ‐ The proposed DM/BPR framework transforms the old business into a new prospect-oriented business
organization by carefully re-engineering the old system incorporating the new discovered knowledge which helps the manager to make wise and informed business decisions in the area of accountability, business change management expertise, business process analysis, business model design, business
model implementation and others.