Data Platform Review

Who this is for

Engineering and product teams where the data stack has grown faster than the team supporting it. Usually a warehouse, a couple of ingestion pipelines, a BI tool, and an analytics-engineering function that’s either heroic or burnt out. The trigger is often a board reporting question that takes two weeks to answer, or an AI initiative that’s blocked because nobody trusts the underlying data.

The problem

Most scale-up data platforms suffer from the same compounding issues: pipelines built by whoever was around, schemas that drift quietly, dashboards that disagree, and an AI roadmap that assumes data quality the platform doesn’t deliver. The fix isn’t a new warehouse — it’s a structural review of where the value is, where the rot is, and what to do about it.

What you get

• Current-state map of ingestion, transformation, warehouse, and consumption layers
• Data quality and lineage assessment against the use cases that matter most
• Cost and consolidation analysis across the data stack
• Operating model recommendations — analytics engineering vs. data engineering vs. data science boundaries
• Roadmap aligned to the business and product strategy, with AI/ML readiness as a first-class consideration
• Tooling recommendations only where there’s a real gap

How it works

6–8 weeks
Weeks 1–2	discovery, stakeholder interviews, technical access
Weeks 3–5	deep-dive across pipelines, quality, cost, and consumption
Weeks 6–8	findings synthesis, roadmap, read-out, handover

Scope and duration depend on the breadth of the stack and the number of business domains in play.