Selection and Optimization Model of Key Performance Indicators

Abstract

Big data innovations and development brings opportunities to leverage the impacts of performance metrics on a strategic plan. The purpose of this research work is to study the selection of the performance metrics based on the preferences, and find out what metrics are prioritized in a current strategic plan and how to translate big data insights into meaningful managerial actions. The authors prioritized group commitments and commitment of KPIs in the case study of universities in Mongolia, using the AHP approach for multi-criteria decision makings. Hardcopy questionnaire and interview sessions were used for collecting data to analyse the prioritization results. Based on the prioritization results of strategies, we find that decision-makers strongly prefer strategies with the goals and this preference causes the imbalance of metrics on the dashboard. The prioritization of metrics is highly influenced by the data availability of management reporting. Prioritization of performance metrics are data-driven and bring bottom-up impacts on the dynamics of the strategic plan. The conflicted interests of stakeholders, internal policy, and the excessive amount of metrics make strategic decision makings difficult. The application of AHP in the case study contributes to improving the speed of decision-making processes, providing scientific results on KPIs prioritization and selection, and providing an approach to assess the differences between what strategic planners think and what they execute and prioritize on a practical level. The prioritization of performance metrics is the sustainable approach that can be extended to other strategic decision makings as well, for example prioritizing business model elements or business processes. This research work proves that the multi-criteria decision-making approach AHP can be used to prioritize and identify strategic focus based on selections of the performance metrics. The research also demonstrates the importance of combining bottom-up data-driven analytics with top-down strategy-making processes in big data developments.