Modernizing Enterprise Data Governance with AI-Powered Lineage Intelligence, End-to-End Data Traceability, and Automated Metadata Management in SAP Datasphere
DOI:
https://doi.org/10.71238/snnst.v2i02.197Abstract
Trust becomes difficult to sustain when organizations cannot explain data origins, transformations, or consumption. This study explores the modernization of enterprise data governance through AI-powered lineage intelligence, end-to-end traceability, and automated metadata management in SAP Datasphere. The research problem concerns disconnected metadata, opaque data movement, labor-intensive impact assessment, and inconsistent accountability across systems. A mixed-method design combines measures of lineage coverage, metadata completeness, dependency-detection accuracy, and governance response time with qualitative evidence from data stewards, architects, analysts, and compliance specialists. Results show that intelligent lineage discovery clarifies data relationships, exposes hidden dependencies, shortens impact analysis, and improves readiness for audits and regulatory reviews. A unified governance framework is introduced to connect metadata harvesting, semantic modeling, lineage visualization, policy monitoring, stewardship workflows, and human verification. This framework advances practice by linking technical data flows with business definitions, ownership responsibilities, and governance controls. Practical implications include stronger data quality, reliable analytical assets, responsible AI adoption, and faster decision-making. Academic significance arises from positioning lineage intelligence as a component of governance maturity. The study concludes that SAP Datasphere can support transparent and accountable data ecosystems when automated intelligence operates alongside human stewardship, offering a scalable governance direction for regulated industries and data-intensive enterprises.
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