A MULTICRITERIA APPROACH TO THE SELECTION OF BUSINESS INTELLIGENCE TOOLS

Abstract

This article describes the application of Multi-attribute Utility Theory (MAUT) to select a Business Intelligence (BI) tool. To accomplish this goal, we developed a case study of a BI project in the Brazilian branch of an insurance and financial products multinational company. The weights of the MAUT utility function were defined using the Swing Weighting technique. The analysis of collected data revealed the benefits and limitations of adopting a formal decision support procedure, such as the MAUT method. Overall, it was observed that the application of MAUT contributed greatly to increase the objectivity and transparency in decision making. In addition, the method was fairly intuitive and easy to master by the decision agents. It also helped to focus the interactions of these agents, reducing conflicts and guiding the analysis. The main limitations observed were the restrictions on gathering information about the problem, the composition of the team responsible for selecting the tool, the a priori exclusion of some alternatives, and rationality and quantification assumptions that are inherent to MAUT.