How AI is tranforming Data Catalogs

From Static IT-centric repositories to Dynamic business-centric platforms

Evolution of Data Catalogs

Traditional data catalogs served as metadata documentation tools, offering basic information such as schema definitions, table structures, and data locations. While useful, these catalogs were static, labor-intensive, and disconnected from the broader data lifecycle. Challenges included:

• Manual Metadata Management: Metadata entry and updates required significant manual effort.
• Limited Context: Catalogs lacked insights into data quality, usage patterns, or lineage.
• Siloed Operations: Traditional catalogs operated independently, offering little integration with analytics, ETL pipelines, or governance systems.

Data catalog tools have evolved from static, IT-focused repositories to dynamic, AI-driven platforms that enable discovery, governance, and collaboration in real-time. Modern catalogs are a cornerstone of the modern data stack, providing the foundation for self-service analytics, data governance, and operational efficiency in complex, cloud-first environments. Data catalogs have evolved across the following dimensions

• Proliferation of Data Sources: Modern catalogs support diverse sources like cloud data lakes, streaming platforms, and SaaS applications.
• Shift to Cloud: The rise of cloud-native architectures has driven demand for catalogs that integrate…