Using Multidimensional Concepts for Detecting Problematic Sub-KPIs in Analysis Systems
Business Intelligence, and more recently Big Data, have been steadily gaining traction in the last decade. As globalization triggers the ability for small and medium enterprises to enter worldwide markets, monitoring business objectives and pinpointing problems has become more important than ever. Previous approaches have tackled the detection of particular problematic instances (commonly called Key Performance Indicators-KPIs), trying to search for the events that are driving companies to be far away from organization’s main goals. One of the key problems is that even though KPIs are positive, they are normally calculated from other sub-KPIs and therefore, it is crucial to find out which is the concrete sub-KPI that is negatively influencing the main KPI. Therefore, in this paper, we focus on a semi-automatic approach for finding the key sub-KPIs that have bad results for the company. This approach is checked on real data that are used to create a report showing potential weaknesses in order to help companies to find out which factors may affect concrete sub-KPIs. Our approach allows us to provide insights for decision makers and help them to determine the underlying problems for achieving a goal and thereby, aiding them with taking corrective actions.
KeywordsKPI Monitoring Business Intelligence Reporting Business analysis
This work has been partially funded by the Spanish Ministry of Economy and Competitiveness (MINECO/FEDER) under the Granted Project SEQUOIA-UA (Management requirements and methodology for Big Data analytics) (TIN2015–63502-C3-3-R). This work has been partially funded by the University of Alicante under a R&D grant for initiation to research (BOUA 28/06/2016).
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