Metadata & Lineage Automation for Enterprise-Scale Governance

A reference framework for automating metadata management and end-to-end data lineage using Collibra and Informatica to deliver transparency, regulatory confidence, and trusted analytics across complex enterprise environments.

Executive Summary

This case study presents a reference metadata and lineage automation framework informed by practitioner-led delivery experience associated with SentinelX Digital. The framework illustrates how large, distributed enterprises can automate metadata management and end-to-end lineage using platforms such as Collibra and Informatica to strengthen governance, transparency, and regulatory confidence at scale.

The reference model demonstrates how dynamic metadata synchronization, automated lineage capture, and cross-domain visibility can support trusted analytics and compliance-ready data environments across complex technology and telecom ecosystems.

Challenge

Enterprises operating distributed data landscapes often face persistent challenges in data discovery, traceability, and quality management. Multiple business units may maintain isolated metadata repositories, inconsistent naming conventions, and limited lineage visibility.

This fragmentation increases audit effort, slows regulatory reporting, and undermines confidence in analytics outputs. To support business agility and regulatory alignment — including requirements associated with BCBS 239 and ISO 27001 — comparable organizations required a centralized, automated metadata governance operating model capable of scaling across domains and platforms.

Reference Metadata & Lineage Automation Framework

This case study outlines a metadata-led governance and automation framework informed by delivery patterns observed across large enterprise programs.

The reference architecture integrates governance workflows and metadata ingestion capabilities to enable real-time synchronization, lineage visualization, and stewardship automation. Typical framework components include:

Automated metadata scanning across core data warehouses, data lakes, and BI platforms
End-to-end lineage visualization mapping source-to-consumption data flows
Metadata quality rules and validation thresholds to support accuracy assurance
Role-based stewardship models with embedded review, approval, and accountability workflows

These components are commonly implemented using REST-based integrations and rule-driven synchronization to support scalability and operational resilience.

Outcomes & Impact

Comparable enterprise programs applying this framework have demonstrated:

~60% reduction in manual metadata collection effort
Improved audit readiness and lineage traceability across multiple enterprise domains
Increased confidence in data quality among business, analytics, and compliance users
A sustainable metadata governance operating model aligned with regulatory and business KPIs

Disclaimer

This anonymized case study illustrates reference methodologies and operating models informed by practitioner-led engagements associated with SentinelX Digital.
All metrics are indicative of outcomes typically observed in comparable enterprise metadata and lineage automation programs.
Client identities, delivery responsibilities, and implementation specifics have been anonymized and generalized to preserve confidentiality.