Data Cataloging with Automated Updates Using Generative AI: Transforming Enterprise Metadata Management

This article examines the transformative impact of Generative Artificial Intelligence on enterprise data cataloging processes. Data cataloging represents a critical component of modern data management strategies, with traditional approaches facing significant challenges in maintaining accurate documentation across rapidly evolving environments. The integration of Large Language Models (LLMs) introduces unprecedented capabilities for autonomous metadata generation, schema evolution tracking, and semantic relationship inference. These AI-driven systems continuously analyze data structures, automatically document changes, and identify implicit connections between datasets without human intervention. The architectural framework combines sophisticated ingestion mechanisms, semantic analysis engines, and validation frameworks that integrate with existing commercial and open-source catalog platforms. Implementation follows a graduated approach that balances technical considerations with organizational change management. The resulting capabilities deliver substantial business value through improved data discovery, accelerated compliance processes, and enhanced governance frameworks while transforming human roles from documentation creation to strategic oversight and contextual enrichment.