When we first engaged with a major pension fund managing over $200 billion in assets, their data was scattered across dozens of on-premise databases. Each investment team had built their own independent solutions. There was no enterprise visibility, no single source of truth, and no path to the analytics-driven strategies the fund wanted to pursue.
This is not an unusual story. In our experience, it’s the norm across financial services.
The Starting Point
Most large financial institutions share the same data challenges:
- Decades of accumulated systems — each built for a specific team or function
- Duplicated efforts — multiple teams maintaining identical skill sets across siloed environments
- No data catalog — teams don’t know what data exists across the organization
- Manual reconciliation — significant effort spent just keeping different systems in sync
The Path Forward
The transformation we led followed a deliberate sequence:
- Audit and catalog — Map every data source, owner, and downstream consumer
- Design the target architecture — Cloud-native, with security, governance, and access patterns built in from day one
- Migrate incrementally — Move data sources one at a time, validating quality at each step
- Enable self-service — Build API-based access and searchable catalogs so teams can find and use data independently
- Create a sandbox — Give teams a safe environment to experiment with new datasets and technologies
The Results
Investment teams went from not knowing what data was available to having hundreds of datasets accessible through a searchable catalog. New features deployed in hours instead of months. And critically, the organization now had the unified data foundation required to pursue AI and machine learning initiatives.
The Lesson
You can’t skip the data modernization step. Every successful AI deployment we’ve seen — in financial services and beyond — is built on a clean, accessible, well-governed data foundation.
Ready to modernize your data infrastructure? Start the conversation.