Financial institutions constantly feel pressure to improve data transparency, stay compliant, and implement AI governance. However, many are still grappling with fragmented data landscapes, poor data lineage tracking and compliance gaps.
In this webinar, Ortecha’s Managing Partner and Head of North America, Mark McQueen, joined Viktor Godaly, Head of Group Data Governance and Data Control at Danske Bank and Christian Bremeau, CEO at Meta Integration, to discuss how financial institutions can master data lineage for risk, compliance and AI governance.
Mark shared first-hand experience from the post-2008 financial crisis era, reflecting on the limitations of manual lineage efforts and the importance of automation, business-IT alignment and treating lineage as a core risk control. The discussion also underscored the role of data lineage in AI governance, emphasising explainability, trust and the importance of design observability alongside data observability.
5 Key Takeaways:
1. Data Lineage Must Be Use-Case Driven
Avoid the “boil the ocean” approach. Focus on priority business and regulatory use cases to demonstrate early value and build momentum. Start from where data is consumed (e.g. reports) and trace backward.
2. Technical Lineage Alone Is Not Enough
While automation tools can capture technical lineage, aligning this with business metadata and ownership is critical. Two user views, technical and business, must be supported from a single, trusted source of truth.
3. Lineage is a Regulatory Imperative
Frameworks like BCBS 239, GDPR and IFRS 9 implicitly or explicitly demand transparent data flows. Regulators want evidence that firms are in control of their data, not just lineage diagrams.
4. Design Observability Complements Data Observability
Lineage should capture not only what happened to the data, but what was supposed to happen – linking standards, policies and roles to real-world execution enables compliance evidence by dashboard.
5. Modernisation and AI Governance Depend on Lineage
Enterprise data lineage is a foundation for infrastructure transformation and AI model governance. It supports explainability, accountability, and ensures trustworthy data input and outcomes.
“Treat your data lineage as a risk control. Link it to your control libraries, critical data elements and compliance dashboards. This is how you move from theory to embedded practice.”
– Mark
Thank you to the webinar host, A-Team Insight and sponsor, Meta Integration, for inviting Ortecha to join the discussion.
Watch the webinar on-demand here:
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