HOW TO SELECT MEASURES FOR DECISION SUPPORT SYSTEMS – AN OPTIMIZATION APPROACH INTEGRATING INFORMATIONAL AND ECONOMIC OBJECTIVES

It is still an open issue of designing and adapting (data-driven) decision support systems and data warehouses to determine relevant content and in particular (performance) measures. In fact, some classic approaches to information requirements determination such as Rockart’s critical success factors method help with structuring decision makers’ information requirements and identifying thematically appropriate measures. In many cases, however, it remains unclear which and how many measures should eventually be used. Therefore, an optimization model is presented that integrates informational and economic objectives. The model incorporates (statistic) interdependencies among measures – i. e. the information they provide about one another –, decision makers’ and reporting tools’ ability of coping with information complexity as well as negative economic effects due to measure selection and usage. We show that in general the selection policies of all-or-none or themore-the-better are not reasonable although they are often conducted in business practice. Finally, the model’s application is illustrated by the German business-to-business sales organization of a global electronics and electrical engineering company as example