Authors: Mark Henderson, Paul Richardson, Brian Wallace, Chaitanya Srinivas, Yashwanth kumar
Abstract: Enterprise organizations increasingly depend on complex data ecosystems that include databases, data warehouses, data lakes, cloud platforms, applications, and distributed data pipelines, making accurate and up-to-date data documentation and metadata management a significant challenge. Traditional documentation practices often rely on manual processes, predefined templates, spreadsheets, and fragmented institutional knowledge, resulting in documentation gaps, inconsistent metadata, limited data traceability, and increased maintenance effort. This research proposes a Generative AI-Driven Framework for Automated En-terprise Data Documentation and Metadata Management that leverages Generative Artificial Intelligence and Large Language Models (LLMs) to automate the extraction, generation, enrichment, classification, validation, and maintenance of enterprise metadata and documentation. The proposed framework integrates automated schema analysis, metadata extraction, data profil-ing, semantic interpretation, data lineage analysis, natural-language documentation generation, and metadata validation within a unified architecture. Generative AI analyzes structured and semi-structured enterprise data assets to generate contextual descrip-tions of tables, columns, business entities, relationships, transformations, and lineage information. A governance layer incorpo-rates metadata standards, validation rules, access controls, quality checks, and human-in-the-loop review mechanisms to im-prove the accuracy, consistency, and reliability of AI-generated documentation. The framework further supports continuous metadata synchronization by integrating with enterprise data pipelines and data cataloging platforms. The proposed approach aims to reduce manual documentation effort, improve metadata completeness and consistency, enhance data discoverability, strengthen data lineage and governance, and support scalable management of enterprise data assets. The effectiveness of the framework can be evaluated using metrics such as metadata completeness, documentation accuracy, semantic consistency, generation quality, processing time, and reduction in manual effort. Overall, the framework demonstrates the potential of Gen-erative AI to transform conventional enterprise data documentation into an intelligent, automated, scalable, and continuously maintained metadata management capability.
