Please use this identifier to cite or link to this item: https://hdl.handle.net/1959.11/61486
Title: Suboptimal business intelligence implementations: Understanding and addressing the problems
Contributor(s): Boyton, Janelle (author); Ayscough, Peter (author); Kaveri, David (author); Chiong, Raymond  (author)orcid 
Publication Date: 2015
DOI: 10.1108/JSIT-03-2015-0023
Handle Link: https://hdl.handle.net/1959.11/61486
Abstract: 

Purpose – The purpose of this paper is to examine the failures of business intelligence (BI) implementations and to understand why they fail as well as what action can be taken to ensure implementation success.

Design/methodology/approach – The paper is based on a literature review of academic journals and case studies relating to BI, and the success and failure of the implementation of such projects. It focuses on four areas of BI projects to measure success: return on investment, non-concrete measures, project management measures and user satisfaction. The literature provides insights into what factors contribute to the success of a BI implementation and what factors contribute to the failure. Once the failures can be ascertained, a strategic approach to remedying the failure is discussed.

Findings – Implementation failure specifically relating to BI is a rarely discussed topic. This paper provides an understanding of why BI implementations fail and how organisations can ensure, prior to implementing such a solution, the considerations that need to be made to ensure that success is achieved from a technological, organisational and process perspective.

Originality/value – The paper uses empirical evidence from the literature to provide an understanding of why BI implementations fail. The factors contributing to BI failure are examined along with insights into how to succeed with a BI implementation.

Publication Type: Journal Article
Source of Publication: Journal of Systems and Information Technology, 17(3), p. 307-320
Publisher: Emerald Publishing Limited
Place of Publication: United Kingdom
ISSN: 1758-8847
1328-7265
Fields of Research (FoR) 2020: 4602 Artificial intelligence
Peer Reviewed: Yes
HERDC Category Description: C1 Refereed Article in a Scholarly Journal
Appears in Collections:Journal Article
School of Science and Technology

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