DEVELOPMENT OF BUSINESS INTELLIGENCE FOR SALES ANALYSIS

Abstract

Business Intelligence (BI) has emerged as a transformative technological paradigm enabling organizations to convert raw transactional data into strategic, actionable insights that drive competitive advantage. This paper presents the development and deployment of a Business Intelligence system for sales analysis, designed to enhance decision-making accuracy and operational efficiency within a mid-sized retail enterprise. The system integrates data warehousing, Extract-Transform-Load (ETL) pipelines, Online Analytical Processing (OLAP) cubes, and interactive Power BI dashboards to provide realtime and historical sales performance visibility across product categories, regions, channels, and time dimensions. Primary data were collected through structured interviews with sales managers and BI analysts; secondary data were drawn from enterprise transaction records, industry BI adoption surveys, and peer-reviewed literature. The developed BI system achieved a 34% reduction in manual reporting time, a 27% improvement in sales forecast accuracy, and enabled identification of previously undetected seasonal demand patterns. The study concludes with design recommendations for scalable BI architectures and a roadmap for AI-augmented predictive analytics integration.