Master Data Management Strategies for Improving Data Quality and Accuracy: A Comprehensive Framework for Enterprise Excellence

Abstract

This article presents a strategic framework for implementing master data management excellence within enterprise organizations, with particular emphasis on manufacturing and distribution environments. The article explores the critical role of master data as the foundational information asset that transcends departmental boundaries and serves as the authoritative source for enterprise-wide business processes. Through systematic analysis of governance frameworks, data cleansing techniques, and quality assurance methodologies, this article establishes six guiding principles that form the foundation of enterprise data strategy maturity: governing organization, process standardization, data quality, data as an asset, data source rationalization, and single source of truth. The article reveals that organizations with mature master data management practices achieve substantially higher operational efficiency and improved customer satisfaction scores compared to their counterparts with fragmented data architectures. The article emphasizes the journey-based nature of master data maturity evolution, proposing a progressive development approach that builds capabilities incrementally while delivering measurable business value at each maturity stage. The article provides actionable recommendations for implementing sustainable governance frameworks, establishing comprehensive data stewardship programs, and integrating advanced data cleansing techniques within broader master data management ecosystems. The findings demonstrate that successful master data management requires holistic approaches that integrate governance frameworks, technology infrastructure, and organizational change management initiatives to create lasting competitive advantages through enhanced data capabilities.