Authors: Kevin Walker, Brian Scott, Steven Baker, Aaron Rivera, Chaitanya Srinivas, Sai Nishil
Abstract: The rapid growth of enterprise data assets has increased the need for intelligent data catalog systems that enable efficient data discovery, governance, quality management, and analytical decision-making. Traditional data catalogs often face challenges related to fragmented metadata, inconsistent data definitions, limited visibility across data ecosystems, and manual maintenance processes. This research explores how automated metadata integration and advanced analytics can enhance data catalog intelligence by creating a unified, dynamic, and context-aware framework for managing enterprise data assets. The study examines the role of metadata automation in capturing, classifying, enriching, and synchronizing metadata from diverse data sources, including databases, cloud platforms, data warehouses, business applications, and analytical systems. By leveraging artificial intelligence, machine learning, and metadata analytics, intelligent data catalogs can automatically identify data relationships, assess data quality, improve lineage tracking, and provide actionable insights for business users and data stewards. The research further investigates architectural components, implementation strategies, governance considerations, and scalability challenges associated with intelligent metadata-driven ecosystems. The findings demonstrate that automated metadata integration significantly improves data accessibility, operational efficiency, regulatory compliance, and decision support capabilities while reducing manual effort and governance complexity. As organizations continue to embrace data-driven transformation, intelligent data catalogs powered by automated metadata integration and analytics emerge as essential platforms for maximizing the value, usability, and strategic impact of enterprise data resources.
DOI: http://doi.org/10.5281/zenodo.21186870
