Understanding Business Intelligence Implementation Failure From Technology, Organization, and Process Perspectives

Abstract

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 the 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 . Relevant studies fall short of explaining failures across various deployment sizes and their overall impact. In an effort to address the gap, this article attempts to assess the drivers of failed BI implementation across scenarios using the 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 is adopted to provide the 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.