Thinking Structurally Helps Business Intelligence Design

Abstract

The design of Business Intelligence (BI) systems needs the integration of different enterprise figures: on the one hand, business managers give their information requirements in terms of Key Performance Indicators (KPI). On the other hand, Information Technology (IT) experts provide the technical skill to compute KPI from transactional data. The gap between managerial and technical views of information is one of the main problems in BI systems design. In this paper we tackle the problem from the perspective of mathematical structures of KPI, and discuss the advantages that a semantic representation able to explicitly manage such structures can give in different phases of the design activity. In particular we propose a novel model of ontology for KPI, and show how this model can be exploited to support KPI elicitation and to analyze dependencies among indicators in terms of common components, thus giving the manager a structured overall picture of her requirements, and the IT personnel a valuable support for source selection and data mart design. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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