Case Study
Sainsbury's Bank Datalabs: cross-sector data platform for marketing and risk intelligence
Stakes
Sainsbury's Bank had spent the prior years separating its data estate from Lloyds and onto AWS. The next problem was harder: turning that estate into something analysts, marketers and risk modellers could actually work in. Datasets were spread across the bank and adjacent partner sources, governance was inconsistent, and the cost of any new analytical question was measured in weeks of cross-team coordination. The brief was to build a production-grade data platform that integrated those datasets cleanly, supported ML/AI workloads end-to-end, and gave Datalabs analysts a single governed surface to work from.
Constraints
- Highly regulated environment: every pipeline accountable to PRA, FCA, ICO and bank-internal risk standards
- Cross-sector dataset integration: banking, customer behaviour and partner signals, each with its own contract and lineage
- Production-grade Data Science programme required from day one - not a research notebook estate
- Operating inside Accenture's lead transformation programme alongside the bank's internal teams
- Audit trail and model governance required for any output influencing customer-facing decisions
Approach
Serverless data platform on AWS
We built the platform fully serverless on AWS so the bank carried no idle compute cost, scaling automatically with the analytical load. Ingestion pipelines normalised heterogeneous data sources into a governed lake, with strong identity, lineage and access controls applied at landing.
ML/AI pipelines treated as products
Models for marketing analytics, financial forecasting and risk/fraud signal detection were delivered as versioned, monitored, automatically deployed pipelines. Training data, feature definitions, model artefacts and evaluation results were each first-class artefacts with full lineage back to source.
A working surface for Datalabs analysts
The platform was designed for the analysts, not just the engineers. Cross-sector datasets were exposed through a consistent governed interface so a marketing analyst, a risk modeller and a forecasting team could all start from the same trustworthy substrate without re-deriving it each time.
Governance and audit by default
Encryption, least-privilege IAM, automated compliance checks and full audit lineage were baked into the platform from the foundation. Decommissioning of legacy components (mortgages middleware, ETL handover for Faster Payments tokenisation) was sequenced so nothing went out before its replacement was production-grade and audited.
Deliverables
- Production serverless data platform on AWS, integrating cross-sector datasets behind a single governed surface
- ML/AI pipelines for marketing analytics, financial modelling, risk and fraud intelligence, and operational forecasting
- Production-grade Data Science delivery programme with model governance and automated deployment
- Decommissioning of legacy mortgages components and middleware ETL handover for Faster Payments tokenisation
- Audit, lineage and access controls aligned to bank-internal and regulator-facing governance standards
- Operating model and runbooks handed over to the in-bank Datalabs team
Outcome
Sainsbury's Bank Datalabs ran on a single, governed, audit-ready data foundation with ML/AI pipelines feeding marketing intelligence, risk and fraud modelling, and operational forecasting. Decisions that previously took cross-team coordination to even pose now started from a trustworthy shared substrate. The platform set the operating standard for how production-grade data and AI should be delivered inside a regulated bank.