Year: 2025
Type: article
Abstract: In recent years, cloud computing has emerged as a transformative force for various industries, particularly in the energy sector. This manuscript explores cloud migration strategies for electrical utilities, focusing on Azure's capabilities in facilitating low-latency data handling essential for rapid decision-making. With the increasing demand for real-time data processing and analytics, electrical utilities are facing mounting pressures to modernize their IT infrastructures. This research highlights the significance of adopting effective cloud migration strategies to enhance operational efficiency, reduce costs, and improve data management. The study employs a qualitative approach, analyzing case studies of electrical utilities that have successfully migrated to the cloud using Azure. Key findings indicate that strategic cloud migration not only reduces operational latency but also enhances data analytics capabilities, enabling utilities to make informed decisions more swiftly. Moreover, the research underscores the financial benefits associated with cloud migration, including cost savings on infrastructure maintenance and improved operational efficiency. The findings are illustrated through various tables that summarize the cloud migration strategies adopted by different utilities, performance metrics before and after migration, and a cost-benefit analysis. The results demonstrate significant improvements in latency, data processing speeds, and overall decision-making efficiency post-migration. The manuscript concludes by emphasizing the necessity of tailored cloud migration strategies for electrical utilities and the pivotal role of Azure in facilitating this transition. This research contributes valuable insights for industry stakeholders considering cloud adoption, ultimately aiming to optimize data handling and decision-making processes in the electrical utility sector.
Author: Dr Abhishek Jain
Institution: Uttaranchal University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414829348
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