Harnessing Data Integrity: A Study of Master Data Management Best Practices

Abstract

This paper presents a comprehensive study of MDM best practices aimed at optimizing data integrity. The paper begins by highlighting the importance of data integrity in driving organizational success and the challenges posed by fragmented and inconsistent data sources. It then delves into the core principles of MDM, including data governance, data quality management, and data integration, which collectively form the foundation for ensuring data integrity. Furthermore, the paper examines a range of MDM best practices derived from real-world implementations and industry expertise. These best practices encompass strategies for establishing data governance frameworks, implementing data quality controls, and orchestrating seamless data integration processes. Through the analysis of case studies and practical examples, this paper demonstrates how organizations can effectively implement MDM best practices to enhance data integrity and derive maximum value from their data assets. It also discusses the role of emerging technologies such as artificial intelligence and machine learning in augmenting MDM efforts and ensuring ongoing data integrity.