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The Intelligent Business - an Assessment of Business Intelligence Practices in Large Swedish Organizations

Lunds universitet/Företagsekonomiska institutionen
Publication Date
  • Decision Support
  • Analytics
  • Business Intelligence
  • Best Practices
  • Assessment Framework
  • Business And Economics


The purpose of this master thesis was to develop a new assessment framework, based on the best Business Intelligence (BI) practices proposed by academia, and use this framework for assessing BI practices in large Swedish organizations. Through this assessment, potential areas of improvement regarding the BI practices in large Swedish organizations were identified and analyzed. In order to fulfill the purpose of this thesis, a review of BI literature was performed and semi-structured interviews were conducted at a number of large Swedish organizations. Concurrently, a new assessment framework was constructed, and applied in assessing the BI practices of the organizations studied. The overall research approach was of qualitative nature and included both deductive and inductive elements. The compiled best BI practices proposed by academia resulted in the new Business Intelligence Assessment Framework (BIAF). In brief, the framework included a prioritizing of 13 focus areas, grouped under five categories, which were considered most important for successful BI practices by researchers. The BI practices in large Swedish organizations were mapped using semi-structured interviews, then assessed through the BIAF, and lastly illustrated by BIAF Radar Charts. The findings suggested that large Swedish organizations to a high degree differ in their BI practices, and that they generally are just barely fairly aligned with academia’s best practices. The greatest discrepancies were found in areas regarding internal and external information sources, as well as the demonstration of BI value and benefits. Improvements were suggested regarding integration of more external information, as well as demonstration of more tangible BI benefits. Other areas, in which the discrepancies were large and improvements needed, were the use of unstructured data, data and information quality criteria, as well as continuous BI review and improvement.

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