Data-Driven Business Intelligence in Energy Distribution: Analytics and Environment-Focused Approaches

Abstract

This research examines data-driven business intelligence (BI) in energy distribution, concentrating on analytics and environmental methods to improve efficiency and sustainability. The main goals are to explore how BI frameworks can integrate environmental metrics like greenhouse gas emissions, energy loss, and resource efficiency and how predictive analytics, AI, and edge computing can optimize energy distribution systems. The review uses secondary data from academic literature, case studies, and industry reports. Results show that energy distributors may make sustainable choices by integrating environmental parameters into BI frameworks, although data integration, real-time processing, and cybersecurity remain issues. To address these issues, AI, machine learning, and blockchain can improve data processing, grid management, and transparency. The research also recommends governmental interventions to standardize data standards, reinforce cybersecurity frameworks, and create data science and AI workforces. These policy consequences are essential for promoting BI technology adoption and guaranteeing efficient, environmentally friendly energy distribution networks. This research shows that data-driven BI may make energy distribution more sustainable and resilient, meeting global sustainability targets.