Reinventing Data Trust: A Metadata-Driven Framework For Lineage, Quality, And Compliance

Abstract

As enterprise data ecosystems grow more complex across cloud, on-premises, and real-time platforms, organizations face increasing challenges in maintaining trust, transparency, and governance in their analytical environments. This paper presents an expanded and unified framework that integrates metadata management, data lineage, and data quality as foundational pillars of modern data warehouse architecture. Metadata provides the semantic and structural context for data assets, while lineage delivers end-to-end traceability across ingestion, transformation, and consumption layers. Data quality ensures the accuracy, completeness, and reliability of information flowing through analytical pipelines. By combining these disciplines into a lineage-driven, metadata-centric governance model, organizations achieve significant improvements in audit readiness, impact analysis, operational efficiency, and analytical confidence. The study synthesizes existing literature, evaluates industry tool capabilities, and examines real-world implementations using platforms such as Ab Initio Metadata Hub, Collibra, and Informatica EDC. Results show substantial gains in metadata consistency, lineage transparency, and data quality accuracy, demonstrating that the integrated approach enables self-governing, intelligent data ecosystems and forms a scalable foundation for next-generation data warehouse modernization.