Deakin Research Online This is the published version: Yeoh, William and Koronios, Andy 2010, Critical success factors for business intelligence systems , Journal of computer information systems , vol. 50, no. 3, Spring, pp. 23-32. Available from Deakin Research Online: Reproduced with the kind permission of the copyright owner. Copyright: 2010, International Association for Computer Information Systems.
Spring 2010 Journal of Computer Information Systems 23 CRITICAL SUCCESS FACTORS FOR BUSINESS INTELLIGENCE SYSTEMS WILLIAM YEOH ANDY KORONIOS University of South Australia University of South Australia SA 5095 Australia SA 5095 Australia Received: June 11, 2009 Revised: August 6, 2009 Accepted: September 14, 2009 The implementation of a business intelligence (BI) system is a complex undertaking requiring considerable resources. Yet there is a limited authoritative set of critical success factors (CSFs) for management reference because the BI market has been driven mainly by the IT industry and vendors. This research seeks to bridge the gap that exists between academia and practitioners by investigating the CSFs influencing BI systems success. The study followed a two-stage qualitative approach. Firstly, the authors utilised the Delphi method to conduct three rounds of studies. The study develops a CSFs framework crucial for BI systems implementation. Next, the framework and the associated CSFs are delineated through a series of case studies. The empirical findings substantiate the construct and applicability of the framework. More significantly, the research further reveals that those organisations which address the CSFs from a business orientation approach will be more likely to achieve better results. Keywords: Business intelligence system, Critical success factors, Delphi method, Case study INTRODUCTION Recently Business Intelligence (BI) applications have been dominating the technology priority list of many CIOs [11, 12]. According to Reinschmidt and Francoise , a BI system is “an integrated set of tools, technologies and programmed products that are used to collect, integrate, analyse and make data available”. Stated simply, the main tasks of a BI system include “intelligent exploration, integration, aggregation and a multidimensional analysis of data originating from various information resources” . Implicit in this definition, data is treated as a highly valuable corporate resource, and transformed from quantity to quality . As a result, massive data from many different sources of a large enterprise can be integrated into a coherent body to provide ‘360 degrees’ view of its business [5, 27]. Hence, meaningful information can be delivered at the right time, at the right location, and in the right form [5, 20] to assist individuals, departments, divisions or even larger units to facilitate improved decision- making .
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