Problem

A large financial services organization lacked a centralized data catalog and had no enterprise‑wide visibility into its technical metadata, business metadata, business glossary, data governance policies, standards, or data lineage. Without a cataloging platform, teams relied on tribal knowledge, manual documentation, and inconsistent definitions.

During the implementation of Alation as a SaaS solution, the organization also discovered significant gaps in foundational metadata: no authoritative inventory of data assets, unclear ownership and stewardship assignments, inconsistent lifecycle statuses, and missing connection details. These gaps surfaced as the team attempted to onboard data assets into the catalog.

Approach

• Implemented the Alation Data Catalog as a SaaS platform to establish a single system of record for metadata, glossary terms, governance standards, and lineage.
• Conducted an enterprise‑wide discovery effort to identify all data assets, their locations, ownership, stewardship roles, lifecycle status (active/retired/in development), and connection details.
• Developed a standardized onboarding model for connecting data assets:
  • For platform‑hosted assets, collected server and connection details to enable direct metadata ingestion.
  • For legacy or third‑party assets, created virtual data source connections to ensure representation in the catalog.
• Partnered with Business Unit Data Owners, Business Data Stewards, and Technical Data Stewards to validate asset information and establish clear accountability.
• Connected Alation to cloud, on‑prem, and third‑party systems, enabling automated ingestion of technical metadata.
• Worked with Business Units to enrich technical metadata with business metadata, ensuring definitions, descriptions, classifications, and ownership were consistently applied.
• Leveraged Alation’s AI capabilities to accelerate business metadata population, generate glossary terms, and recommend term‑to‑asset mappings.
• Designed a repeatable lineage model, using Databricks Unity Catalog on the Azure Data Lake to automate lineage extraction and support impact analysis.

Outcome

• Delivered the organization’s first enterprise‑wide data catalog, providing centralized visibility into technical metadata, business metadata, glossary terms, governance standards, and lineage.
• Established a complete inventory of data assets, including ownership, stewardship, lifecycle status, and connection details.
• Enabled automated metadata ingestion across platforms, improving transparency into schemas, tables, columns, and data flows.
• Strengthened governance maturity by defining and operationalizing clear data ownership and stewardship roles across Business Units.
• Accelerated metadata enrichment through AI‑assisted business metadata population and automated glossary term mapping.
• Created a scalable lineage model that reduced manual documentation, improved impact analysis, and supported regulatory reporting.
• Positioned the organization to adopt active metadata practices, improving data discovery, trust, and cross‑team collaboration.