Cloud Data Platform Modernization for a Mid-Market Banking Group
We modernized the data infrastructure of a mid-market banking group — migrating from legacy on-premise data warehouses to a cloud-native lakehouse architecture, redesigning the analytics operating model and establishing data governance foundations for regulatory reporting. The programme reduced data processing time by 78% and automated 14 regulatory reports that had previously required manual preparation.
The hidden cost of legacy data infrastructure in regulated banking
The banking group operated on a legacy on-premise data warehouse that had served the organization for over a decade but had become a material constraint on both operational efficiency and regulatory compliance. Regulatory report preparation consumed 3-4 weeks of manual effort each quarter, with data teams spending more time reconciling inconsistencies between source systems than analysing the data itself. The analytics team had a 6-week backlog of business intelligence requests, and the risk function lacked the real-time visibility needed for modern credit risk management. Our assessment quantified these costs explicitly — not just in infrastructure spend, but in analyst time, regulatory risk, delayed business decisions and opportunity cost. The business case for modernization was built on operational savings, regulatory risk reduction and the revenue impact of faster, data-informed decision-making.
Building a data foundation that serves both regulators and the business
The architecture was designed around a cloud-native lakehouse that unified structured banking data and semi-structured operational data into a single, governed platform. The data governance layer was not an afterthought — it was built into the architecture from day one, with automated data lineage tracking that gave the compliance team full auditability from source system to regulatory report. The 14 automated regulatory reports replaced manual processes that had previously required 2 full-time analysts for preparation and reconciliation. The analytics operating model shifted from a centralized IT-owned reporting function to a governed self-service model, with business analysts empowered to build their own dashboards and reports within defined guardrails. Within the first year, the platform reduced data processing time by 78%, eliminated $1.2M in annual infrastructure and manual processing costs, and gave the risk function the near-real-time credit exposure visibility that had been a strategic priority for two years.
Measured outcomes
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