A Pragmatic Framework for Data-Driven Decision-Making Process in the Energy Sector: Insights from a Wind Farm Case Study

Konstantinos Konstas, Panos T. Chountalas (), Eleni A. Didaskalou and Dimitrios A. Georgakellos
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Konstantinos Konstas: School of Social Sciences, Hellenic Open University, 18 Aristotelous St., 26335 Patras, Greece
Panos T. Chountalas: Department of Business Administration, University of Piraeus, 80 Karaoli & Dimitriou St., 18534 Piraeus, Greece
Eleni A. Didaskalou: Department of Business Administration, University of Piraeus, 80 Karaoli & Dimitriou St., 18534 Piraeus, Greece
Dimitrios A. Georgakellos: Department of Business Administration, University of Piraeus, 80 Karaoli & Dimitriou St., 18534 Piraeus, Greece

Energies, 2023, vol. 16, issue 17, 1-26

Abstract: In an era of big data, organizations increasingly aim to adopt data-driven decision-making processes to enhance their performance. This paper investigates the data-driven decision-making process by developing a framework tailored for application in the energy sector. The proposed framework integrates interdisciplinary approaches to comprehensively address the “data, information, knowledge” triad, applying it to both operational and maintenance decision-making. Designed to be managerially focused rather than technically oriented, the framework aims to engage all employees, including those without technical backgrounds, enabling them to effectively contribute to the decision-making process from their respective roles. To demonstrate the practical application of the proposed framework, this paper presents a case study of an energy organization managing a wind farm project, which implemented the framework to improve its decision-making process. The case study examines how the organization identified its objectives and information needs, formulated key performance questions for each stakeholder, explicitly defined and measured the key performance indicators, employed data collection and organization methods, managed the progression from data to information to knowledge, and transformed the acquired knowledge into informed decisions. By adopting this pragmatic framework, energy organizations are anticipated to solve problems, predict trends, and discover new opportunities, thereby enhancing their efficiency and predictability.

Keywords: decision-making; data mining; energy sector; renewable energy sources; wind farm; framework; case study (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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