Automated Prediction of Relevant Key Performance Indicators for Organizations

Abstract

Organizations utilize Key Performance Indicators (KPIs) to monitor whether they attain their goals. For this, software vendors offer predefined KPIs in their enterprise software. However, the predefined KPIs will not be relevant for all organizations due to the varying needs of them. Therefore, software vendors spend significant efforts on offering relevant KPIs. That relevance determination process is time-consuming and costly. We show that the relevance of KPIs may be tied to the specific properties of organizations, e.g., domain and size. In this context, we present our novel approach for the automated prediction of which KPIs are relevant for organizations. We implemented our approach and evaluated its prediction quality in an industrial setting.