Comparison of the methods and a model for the evaluation of the readiness in implementing business intelligence projects: a hybrid approach

Farrokhi, Vahid
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In recent years, Business Intelligence (BI) systems have consistently been rated as one of the highest priorities of Information Systems (IS) and of business leaders. BI allows firms to apply information to support their processes and decisions by combining its capabilities in both organizational and technical issues. A significant portion of companies’ IT budgets is being spent on BI and related technologies. In spite of these investments, the risk of failure in implementing is high and only 24% of BI implementations are identified as being very successful. Hence, the evaluation of BI readiness is vital because it serves two important goals. First, it reveals gap areas where a company is not ready to proceed with its BI efforts, so by identifying BI readiness gaps, wasting time and resources can be avoided. Second, the evaluation points out what we need to close the gaps and implement BI with a high possibility of success. This dissertation presents an overview of BI and the necessities for the evaluation of the readiness, and a comparative analysis of the evaluation methods and identifying and ranking the right methods which can be applied in building a model to assess the readiness of organizations. There are many Multiple Criteria Decision Making (MCDM) methods and other further methods which can be applied for building a model of evaluation but each of them has its own advantages and disadvantages. By combining and integrating these methods with each other and also with various other methods, we can avoid the disadvantages and improve the model of evaluation. We also examine the MCDM methods in the other unrelated area to show their applicability in order to confirm the validity of our approach in applying these methods for the comparison of the techniques and methods. In addition, we provide important and critical success factors and classify them into two main categories; organizational and technical. Finally, we show the process of building the hybrid model by using Interpretive Structural Modeling (ISM) and Graph Theory and Matrix Approach (GTMA) and examine it in a real company as a case study.
Business Intelligence, MCDM methods, Critical Success Factors