Refreshing the data strategy and architecture of a top-20 US regional bank
A regional bank with an established data platform asked us to refresh its data strategy for analytics and AI. We benchmarked its maturity against peers and designed the target architecture and operating model the bank adopted as its data roadmap.
- Client
- A top-20 US regional bank
- Industry
- Banking
- Offering
- AI-Ready Data
The challenge
- Business analytics groups struggled to find, access and put data to work across disparate solutions.
- Self-serve and governance were inconsistent, and quality gaps reduced trust in the data.
- Data operations needed more automation and a faster path from idea to production.
What we did
- Refined the data strategy with forward-looking capabilities and built a maturity model benchmarked against peer institutions.
- Simplified data discovery and access through federated, governed self-serve.
- Built security, governance and quality in through a central catalogue, policies and rules.
- Standardized tooling with templates and recipes, taking an automation-first approach across the platform.
- Delivered a target-state architecture and operating model.
Results
The bank adopted the target-state architecture and operating model as its go-forward data roadmap. Standardized tooling and reusable recipes reduced tool sprawl, and the plan moves the bank’s data maturity score from 2.3 to 3.3 over two years on the peer-benchmarked model.
Our work
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