Abstract

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.

In this position paper we discuss the importance of Green IT as a new research field that investigates

all the environmental and energy issues related to IT and information systems in general. In particular

we focus on the energy consumption of software applications, which is amplified by all the above IT

layers in a data center and thus is worth a greater attention. By adopting a top-down approach, we

address the problem from a logical perspective and try to identify the original cause that leads to

energy consumption, i.e. the elaboration of information. We propose a research roadmap to identify a

set of software complexity and quality metrics that can be used to estimate energy consumption and to

compare specific software applications

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