USE OF BIG DATA TECHNOLOGIES IN ENTERPRISE PRICING UNDER THE TRANSITION TO A CIRCULAR ECONOMY

Abstract

The article examines contemporary approaches to the use of Big Data technologies in pricing processes amid the digitalization of the economy and intensifying global competition. It is substantiated that within the framework of the circular economy, big data analytics facilitates the development of flexible pricing strategies focused on efficient resource utilization, product life-cycle extension, and waste reduction.

The study systematizes key data sources and processing methods, including predictive analytics and algorithmic models that support the transition to dynamic pricing. Practical cases of Big Data implementation in retail, transportation, e-commerce, and the financial sector are analyzed, demonstrating how such solutions enhance market transparency and promote sustainable business models.

The research identifies the main advantages of big data adoption, such as improved managerial decision-making accuracy, increased operational efficiency, and strengthened enterprise competitiveness. At the same time, it outlines critical challenges related to ethical considerations, data privacy, and maintaining high data quality standards. The findings can serve as a theoretical and methodological basis for developing strategic pricing decisions that integrate digital tools with the principles of the circular economy.