Advancing Enterprise Data Governance and Data Quality Management through Comprehensive Metadata-Centric Frameworks for Modern Data Ecosystems
DOI:
https://doi.org/10.21590/ijtmh.03.03.03Keywords:
Enterprise data governance, metadata management, metadata driven governance, data quality management, enterprise data architecture, data stewardship, data lineage management, data catalog frameworks, master data management, data lifecycle management, governance policy enforcement, enterprise data integration, information governance frameworks, data compliance management, metadata repositories, data standardization, data transparency, organizational data trust, data management frameworks, scalable data governance, enterprise data ecosystems.Abstract
Enterprise organizations increasingly depend on large scale data ecosystems to support analytics, decision making, and regulatory compliance. However, fragmented data ownership, inconsistent metadata practices, and limited visibility into data lineage often weaken governance effectiveness and reduce trust in enterprise data assets. This study examines how metadata centric frameworks can strengthen enterprise data governance and improve data quality management across modern data environments. The research investigates governance challenges within complex data architectures and proposes a structured framework that integrates metadata management, policy enforcement, and stewardship practices to enhance organizational data control and transparency. A mixed methodological approach was adopted that combines qualitative analysis of enterprise governance practices with quantitative evaluation of governance outcomes across multiple data management scenarios. The proposed framework introduces mechanisms for metadata standardization, automated policy alignment, and traceable lineage mapping, enabling improved monitoring of data quality, ownership accountability, and compliance adherence. Findings demonstrate that metadata driven governance models significantly enhance data consistency, operational transparency, and cross domain data integration while reducing governance complexity in distributed data platforms. The study contributes a conceptual and architectural foundation that bridges academic perspectives on data governance with practical enterprise implementation strategies. The results highlight the strategic role of metadata as an enabling layer for scalable governance, sustainable data quality management, and organizational data trust. The research offers important implications for both industry practitioners and academic researchers seeking to design resilient governance models that support evolving enterprise data ecosystems.
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