While the server-based Business Intelligence (BI) has been in service for a long time, the question emerged whether the existing Computing Resources are available in an appropriate capacity with respect to RAM, CPU, memory, for the fluctuated unexpected workload of BI systems. In the era of globally growing businesses and data, the pressure on the BI systems has increased. BI developers are encouraged to speed up the progress of collecting, processing, and storing the data in order to deliver the required knowledge at the predefined time to the IT decision makers. For achieving the quality of delivery, BI applications should be monitored daily for taking actions in case of troubles. How-ever, the monitoring itself takes time, especially when there are many BI applications with each having an own detector system. Therefore, the value of analysis loses its meaning due to the information distraction.

As a solution to this problem, this thesis is attempting to build a dashboard cockpit system that comprises all of the available information on the behavioural BI activities and the utilization of Computing Resources by BI system landscape. Moreover, BI dash-board has beneficial aspects like supporting the decision makers with historical information for performing predictions as well as unifying the information into one source. The latter provides with the possibility of predicting the future needs and behavioural changes in connection to the external and internal factors.