Data analytics in energy corporations: Conceptual framework for strategic business outcomes
1 Independent Researcher, UK.
2 Niger Delta Power Holding Company, Akure, Nigeria.
3 Hult International Business School, Dubai, New York.
⁴ FieldCore Canada, part of GE Vernova, Canada.
Review Article
World Journal of Advanced Research and Reviews, 2024, 21(03), 952–963
Publication history:
Received on 28 January 2024; revised on 07 March 2024; accepted on 09 March 2024
Abstract:
Data analytics has emerged as a pivotal tool for energy corporations to navigate the complexities of the modern market landscape, optimize operations, and drive strategic decision-making. This abstract presents a conceptual framework delineating the role of data analytics in fostering strategic business outcomes within energy corporations. At its core, the framework emphasizes the integration of advanced data analytics methodologies with the unique operational dynamics of the energy sector. It begins by delineating the data sources available to energy corporations, ranging from sensor data in oil and gas exploration to customer consumption patterns in utilities. These diverse data streams form the foundation upon which analytics-driven insights are built. Central to the framework is the notion of actionable intelligence derived from data analytics. By harnessing advanced analytics techniques such as machine learning, predictive modeling, and optimization algorithms, energy corporations can extract meaningful insights from vast and disparate datasets. These insights facilitate informed decision-making across various business functions, including asset management, supply chain optimization, demand forecasting, and risk mitigation. Furthermore, the framework underscores the importance of leveraging real-time data analytics capabilities to enhance operational agility and responsiveness. In a rapidly evolving energy landscape characterized by fluctuating market dynamics and regulatory changes, the ability to extract actionable insights in real-time confers a competitive advantage. Moreover, the framework advocates for a holistic approach to data analytics integration, encompassing not only technological infrastructure but also organizational culture and capabilities. Effective implementation of data analytics initiatives necessitates alignment with strategic objectives, executive sponsorship, and the cultivation of a data-driven mindset throughout the organization. Ultimately, the conceptual framework presented herein serves as a roadmap for energy corporations seeking to harness the transformative potential of data analytics to drive strategic business outcomes. By embracing data-driven decision-making, these organizations can enhance operational efficiency, optimize resource allocation, mitigate risks, and capitalize on emerging market opportunities in an increasingly data-centric environment.
Keywords:
Data analytics; Energy corporations; Strategic business outcomes; Conceptual framework; Operational efficiency; Predictive Maintenance
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Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0