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Using Fuzzy Logic for Optimizing Business Intelligence Success in Multiple Investment Combinations
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Author(s): Mandana Farzaneh (Sharif University of Technology, Iran), Iman Raeesi Vanani (Department of Information Technology Management, University of Tehran, Iran)and Babak Sohrabi (Department of Information Technology Management, University of Tehran, Iran)
Copyright: 2015
Pages: 13
Source title:
Encyclopedia of Information Science and Technology, Third Edition
Source Author(s)/Editor(s): Mehdi Khosrow-Pour, D.B.A. (Information Resources Management Association, USA)
DOI: 10.4018/978-1-4666-5888-2.ch091
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Abstract
Business intelligence (BI) has become a critical foundation of competition for organizations over the last several years and has a significant role in sustaining competitive advantage. However there are many problems in BI investment, which are derived from the existence of a large amount of criteria involved in its success which are not all tangible. According to the large amount of complicated quantitative and qualitative factors to be considered in the candidate BI projects and because of the vagueness of them, fuzzy logic is employed to optimize BI investment in businesses. In this regards a fuzzy inference system (FIS) is designed to specifically help in overcoming the complexity of evaluation the business intelligence investment. Proposed FIS is based on a knowledge-driven approach. The main objective is to present to readers a concise deep insight of critical success factors of BI investment model based on FIS.
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