Lakehouse build for a grocery retailer

The client is a UK grocery retailer whose data was scattered across dozens of operational systems built up over years. For a retailer, decisions from merchandising to supply chain depend on trustworthy, joined-up data.

With no single source of truth, every report was a bespoke effort and meetings routinely stalled arguing over whose numbers were right.

Data lived in 40 disconnected systems, so every merchandising report started as a bespoke extract stitched together by hand. Analysts spent more time assembling data than analysing it.

No two numbers agreed, which meant meetings argued about whose figure was right instead of what to do. The lack of a shared source of truth was a drag on every decision.

The retailer needed a single trustworthy foundation before analytics could move any faster. Fixing the foundation was the prerequisite for everything else.

We built a governed lakehouse on a medallion architecture, refining raw data through clean, curated layers into trustworthy tables. Layering the data by quality is what makes the top-level tables dependable.

A single semantic layer sat on top, so a metric is defined once and means the same thing everywhere. One definition per metric is what ends the whose-number-is-right argument.

We migrated domain by domain, delivering value incrementally rather than waiting for a big-bang platform, and governance and ownership were built in so the foundation stays trustworthy as it grows. Value landed early and the foundation held.