Ideas from our practitioners.

Twenty hires, twelve successes, eight regrets. The profile that actually works for the first serious data leader at a mid-market organisation.

Snowflake, Databricks, Fabric, BigQuery, self-hosted — a practical comparison for mid-market buyers, and the questions that decide the choice more than features do.

Why most data strategy documents are artifacts of procurement rather than strategy, and what a strategy that actually changes decisions looks like.

Why a data consultancy ships custom silicon and firmware. The pattern we use for edge-AI and sensor-rich environments, and when you should actually build your own hardware.

The manufacturing data maturity curve in 2026 — OEE, quality, predictive maintenance, and the specific programme structure that works for 50–500M turnover manufacturers.

Beyond the "AI will do the accounts" narrative — what a mid-tier firm actually needs to do with data to remain competitive in 2026.

From eight Lloyd's-and-company-market engagements, the AI patterns with reproducible impact — and the ones that keep appearing in decks and failing in production.

What a defensible damp-and-mould data practice looks like — signal sources, triangulation, model design, stewardship, and the honest limits of what a model can do.

From twenty housing association engagements, the questions that consistently land at board level. Damp and mould, EPC, asset condition, and the RSH tenant-satisfaction measures.

RAG demos are cheap. RAG at scale is not. A breakdown of where the cost actually goes, and the levers that matter.

No migration. No re-platforming. A structured three-week review of AWS, Azure or GCP spend, with specific levers and a ranked savings plan.

The checklist we run on every AI vendor our clients consider. Security, data-handling, commercial terms, exit. Updated for the 2026 vendor landscape.

The difference between an LLM demo and a production LLM system is the evaluation harness. Here is how to build one that catches real regressions.

Beyond the hype — where agent orchestration genuinely moves the needle, and the three places enterprises consistently overreach.

If "revenue" means three different things across three dashboards, you have a metric-definition problem, not a BI tool problem. Here is how the semantic layer fixes it.

Most BI estates are mostly dead. How to find the live ones, retire the rest, and rebuild the top seventy on a semantic layer that does not drift.

A practical guide for council DPOs and public-sector data leads — the DPIA patterns we use to get AI use cases live, auditable and defensible.

Stop aiming for FAANG-grade governance. A pragmatic, 90-day framework that makes mid-market organisations auditable without freezing the business.

Why enterprise MDM projects fail, what to do instead, and the lightweight pattern we run for clients between 200 and 5,000 staff.

Lessons from three SSIS-to-Snowflake migrations, including the one that nearly failed. A pragmatic, dated, sequenced plan — not a vendor pitch.

The vocabulary has drifted; the decisions have not. A practitioner's guide to choosing between the three patterns that dominate enterprise data in 2026.

How to migrate from a 15-year-old on-premise warehouse to a modern lakehouse while every dashboard stays live and every regulator stays quiet.

If you cannot state the outcome in pounds, you are not ready to choose a platform. Here is the template we use on every engagement.