Decision-led data products
Twenty to thirty products tied to named decisions, each with an accountable owner.
Capability
Data & platform engineering
The twenty data products that carry your decisions — built, owned, and measured.
Our point of view
We do not start with a platform. We start with the decisions that matter in the next four quarters and build the data products they require, each with an owner, a contract, and a published quality score.
The platform then grows underneath them, pulled by real demand rather than pushed by an architecture ambition, which is the difference between a programme that ships quarterly and one that reports progress annually.
Engineering standards matter, but the discipline that makes data trustworthy is ownership: somebody must be accountable for freshness, quality, and refusal of degrading requests.
What we do inside this capability
Platform and pipelines
Batch, streaming, and feature infrastructure sized to actual workload.
Quality and observability
Published quality scores, freshness SLAs, and automated anomaly detection.
Cost and performance
Storage, compute, and inference cost engineering as a first-class discipline.
Research behind this work
Article
Data foundations: the unglamorous work that makes AI pay
You do not need a perfect data estate. You need the twenty data products that carry your decisions to be trustworthy, owned, and fast.
4 September 2026 · 8 min read
Report
From pilots to P&L: why 70 per cent of AI use cases stall, and what unsticks them
Pilot purgatory is rarely a modelling problem. It is a measurement, ownership, and integration problem — and all three are fixable inside a quarter.
30 September 2026 · 9 min read
Next step
Where would data & platform engineering move the needle for you?
Bring a live decision. We will bring the pattern from comparable organisations — including the failures.