"Understanding Business Intelligence Implementation Failure from Technology, Organization, and Process Perspectives"

• Published in: IEEE engineering management review Vol. 52; no. 1; pp. 1 - 27
• Main Authors: Williams, Randy A., Duman, Gazi Murat, Kongar, Elif, Tenney, Dan
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.02.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Analytic hierarchy process; Analytic Hierarchy Process (AHP); Analytical models; and Process (TOP) model; BI Implementation; Business Analytics; Business intelligence; Business Intelligence (BI); Decision making; Failure; Hierarchies; Information management; Information technology; Intelligence (information); Investment; Organizational; Organizations; Standards organizations; System implementation; Technical
• ISSN: 0360-8581; 1937-4178
• DOI: 10.1109/EMR.2023.3331247

Business Intelligence (BI) systems are a suite of technologies enabling rapid decision-making in modern business environments of rapidly changing market dynamics and exploding data volumes. BI implementation is intended to enable enterprises to become data-driven, delivering actionable insights based on factual synthesis of up-to-the-minute information. Contrary to its vastly increasing importance and investment, research suggests a majority of BI implementations fail to achieve successful results [1]. Relevant studies fall short of explaining failures across various deployment sizes and their overall impact. In an effort to address the gap, this study attempts to assess drivers of failed BI implementation across scenarios using expert opinion of practitioners. Using the Technology, Organization, and Process framework, the analysis provides a ranking of failure drivers under three deployment scenarios: enterprise-wide, departmental or business unit level, and small team or individual-sized deployments. Practitioners cannot assume that a one-size-fits-all model for explaining BI implementation failure is appropriate. To create a more holistic evaluation framework, the Analytical Hierarchy Process (AHP) is adopted to provide evaluation of significance for each perspective and criterion under alternate scenarios. The findings will enable decision-makers to make more informed investment decisions, providing significant savings while contributing to literature via a customizable data-driven model.