International Journal of Science, Engineering and TechnologyAuthors: Associate Professor Edward Parker, Associate Professor Richard Phillips, Associate Professor Steven Wallace, Chaitanya Srinivas, Yashwanth kumar
Abstract: The rapid growth of enterprise data across cloud platforms, databases, applications, data warehouses, and analytical environments has created significant challenges in managing metadata and maintaining accurate, discoverable, and trustworthy data catalogs. Traditional data cataloging approaches often depend on manual metadata collection, static documentation, and fragmented governance processes, resulting in incomplete metadata, inconsistent classifications, limited data lineage visibility, and difficulties in identifying suitable enterprise data assets. This paper proposes a Unified Framework for Metadata Automation and Intelligent Enterprise Data Cataloging that integrates automated metadata discovery, metadata extraction, semantic enrichment, data classification, lineage analysis, quality assessment, and governance controls into a unified cataloging architecture. The framework employs intelligent automation techniques to continuously collect metadata from heterogeneous enterprise data sources and enrich catalog entries using contextual, structural, and business-level information. It further incorporates machine learning and rule-based approaches to identify sensitive data, recommend classifications, detect metadata inconsistencies, establish relationships among data assets, and improve data discovery. A governance layer provides mechanisms for metadata ownership, stewardship, policy enforcement, access control, compliance monitoring, and auditability. The proposed framework also supports continuous metadata synchronization to ensure that catalog information remains aligned with evolving enterprise data environments. An evidence-mapping approach is used to organize existing research and identify key capabilities, challenges, and research gaps associated with intelligent data cataloging and metadata automation. The framework provides organizations with a scalable and governance-oriented approach for improving data discoverability, metadata quality, lineage transparency, and overall enterprise data management effectiveness.
