Data warehousing and business intelligence technologies in retail: a comparative analysis of predictive models for performance optimization

Authors

• Varun Venkatesh Dandasi IEEE

Abstract

In today’s fast-paced, competitive marketplace, the retail industry faces unprecedented challenges, necessitating the adoption of advanced technologies to maintain market position and improve operational efficiency. This study examines the transformative impact of data warehousing and business intelligence technologies on retail organizations, examining both financial and non-financial performance outcomes. With nearly 80% of retailers already implementing or planning to adopt data warehousing solutions, according to Ernst & Young research, understanding their return on investment is critical. The research examines key retail metrics including daily customer traffic, average transaction value, promotional discount rates, inventory turnover, employee headcount, and daily sales performance. Despite the growing adoption trend, there is a paucity of empirical studies examining the organizational impact of data warehousing, especially in emerging markets such as Southern Europe and the Middle East, where implementation is still in its infancy. The study uses advanced analytical techniques, including linear regression and random forest regression, to improve retail operations and decision-making processes. Real-time data analytics is emerging as a strategic imperative for retailers looking to understand consumer behavior, improve customer experiences, and drive sustainable growth. This research addresses a critical gap in understanding how data warehousing technologies specifically impact retail sector performance and competitiveness.