Abstract
Key Performance Indicators (KPIs) operationalize ambiguous enterprise goals into quantified variables with clear thresholds. Their usefulness has been established in multiple domains yet it remains a difficult and error-prone task to find suitable KPIs for a given strategic goal. A careful analysis of the literature on both strategic modeling, planning and management reveals that this difficulty is due to a number of factors. Firstly, there is a general lack of adequate conceptualizations that capture the subtle yet important differences between performance and result indicators. Secondly, there is a lack of integration between modelling and data analysis techniques that interleaves analysis with the modeling process. In order to tackle these deficiencies, we propose an approach for selecting explicitly KPIs and Key Result Indicators (KRIs). Our approach is comprised of (i) a novel modeling language that exploits the essential elements of indicators, covering KPIs, KRIs and measures, (ii) a data mining-based analysis technique for providing data-driven information about the elements in the model, thereby enabling domain experts to validate the KPIs selected, and (iii) an iterative process that guides the discovery and definition of indicators. In order to validate our approach, we apply our proposal to a real case study on water management.
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Acknowledgments
This work has been partially supported by the European Research Council (ERC) through advanced grant 267856, titled “Lucretius: Foundations for Software Evolution” (04/2011/2016) http://www.lucretius.eu. Alejandro Maté is funded by the Generalitat Valenciana (APOSTD/2014/064). 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).
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Maté, A., Trujillo, J., Mylopoulos, J. (2016). Key Performance Indicator Elicitation and Selection Through Conceptual Modelling. In: Comyn-Wattiau, I., Tanaka, K., Song, IY., Yamamoto, S., Saeki, M. (eds) Conceptual Modeling. ER 2016. Lecture Notes in Computer Science(), vol 9974. Springer, Cham. https://doi.org/10.1007/978-3-319-46397-1_6
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DOI: https://doi.org/10.1007/978-3-319-46397-1_6
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