Optimizing Data Governance: Enhancing Quality through AI-Integrated Master Data Management Across Industries

Abstract

In the era of data-driven decision-making, the integrity and consistency of master data are paramount for organizational efficiency and analytical accuracy. This study explores the transformative potential of integrating Artificial Intelligence (AI) with Master Data Management (MDM) to enhance data quality and streamline data governance processes across various industries, including finance, retail, and manufacturing. By leveraging AI technologies, MDM systems can significantly improve data accuracy, consistency, and governance efficiency, addressing critical challenges such as data silos, redundancy, and inconsistency. This research investigates the methods and technologies employed to achieve this integration, evaluating their effectiveness and impact on organizational data management practices. Additionally, the study identifies key challenges and opportunities associated with AI-driven MDM, including data privacy, security, and ethical considerations. The findings provide a comprehensive understanding of how AI-enabled MDM can optimize data governance, preparing organizations to navigate future trends and regulatory landscapes in the evolving data ecosystem. This research contributes to the growing body of knowledge on data governance and offers practical insights for organizations seeking to enhance their data management capabilities through AI integration.