Abstract

In the era of data-driven decision-making, high-impact programs rely on robust data governance frameworks to ensure quality, consistency, and compliance. However, enterprises face persistent challenges such as data redundancy, fragmented data systems, and non-compliance risks, which impede operational efficiency and strategic insights. This study presents a comprehensive Data Governance Framework designed to address these challenges, specifically tailored to high-impact programs operating at scale. The proposed framework integrates advanced strategies for reducing data redundancy, enhancing data quality, and ensuring regulatory compliance. Central to the framework is the application of machine learning algorithms for duplicate detection and data standardization, enabling automated identification and resolution of redundant entries across disparate systems. Additionally, the framework emphasizes the use of metadata management and data lineage tracking to provide transparency and maintain the integrity of enterprise datasets. Through centralized data stewardship roles and cross-functional governance committees, organizations can foster collaboration and accountability in maintaining data quality and compliance. This study also underscores the importance of scalable data governance policies that align with dynamic regulatory requirements and enterprise growth. Key elements include adaptive data classification protocols, real-time monitoring of data processes, and the deployment of privacy-enhancing technologies to safeguard sensitive information. The framework's effectiveness is demonstrated through case studies, showcasing significant reductions in duplicate records, streamlined data workflows, and improved compliance metrics. By refining existing governance models, this framework provides actionable insights for organizations aiming to enhance data quality at scale, reduce redundancies, and ensure compliance in complex enterprise environments.