The board-level practice for turning a fragmented data estate into a governed, commercially exploitable asset — built around the decisions your executives already need to make, not the dashboards they already ignore.
The tension
Warehouses overflow with records that cannot be reconciled. Analytics teams ship reports that are immediately questioned. AI initiatives stall because the training data cannot be trusted. The problem is almost never a shortage of data.
It is a shortage of data strategy — the executive discipline that connects governance, architecture, and commercial intelligence into one operating model the C-suite can actually defend in a board paper.
The maturity ladder
We begin every engagement by locating you on this ladder — not to grade maturity, but to sequence the next three moves that will earn their investment case.
A central warehouse exists. Pipelines run. But the semantic layer is unclear and self-service is a rumour.
Tell-tale signals
The practice
Each is an entry point. Most engagements start with one and expand — because once a governance contract is live, the BI and AI programmes it enables become the natural next move.
Anatomy of a data decision
Break any link and the decision becomes opinion. Our operating model hardens all five — then measures the latency between them.
A market, operational, or customer event.
e.g. churn spike in mid-market cohort
The system of record that captured it.
CRM · billing · product telemetry
Transformation into a trusted asset.
governed dbt model + tests
A single, certified definition.
Net Revenue Retention — semantic layer
The executive action it informs.
Retention investment + pricing move
The 4-A methodology
A sequenced programme that refuses to build advanced capabilities on fragile foundations — and refuses to let foundations languish in isolation from commercial value.
Data maturity diagnostic across six governance dimensions, benchmarked to DAMA-DMBOK. Outputs: scored heatmap, risk register, value hypothesis.
Deliverables
Target-state architecture: lakehouse vs mesh, semantic layer, data contracts, catalogue, and MDM. Technology selection defended with TCO.
Deliverables
Ownership domains, quality dashboards, and the first wave of certified metrics. Shadow BI retired. Executive KPI frame goes live.
Deliverables
AI readiness, feature stores, MLOps, and self-service literacy. Data becomes a product — with a roadmap, SLAs, and a commercial business case.
Deliverables
Reference stack
We implement on whatever fits the estate — but we defend the architectural pattern, not the logo. These are the platforms we most often land on.
Operational & event data into the lakehouse.
The governed lakehouse or warehouse.
Contracted models with tests & lineage.
Semantic layer → BI → embedded analytics.
Conversations we have
“Can I defend my forecast with this data?”
Our answer
We retire the seven 'adjusted' versions of ARR living in side-spreadsheets, replace them with a governed, auditable definition on the semantic layer, and connect it straight to the board pack. Forecast variance typically drops 30–50% in the first quarter.
“How do we make governance survive without me chasing it?”
Our answer
Data contracts, domain ownership, and a council with delegated authority — plus quality dashboards that surface breaches to the owner automatically. Governance stops being a committee meeting and starts being a production system.
“Can this intelligence actually inform our next market entry?”
Our answer
Primary interviews, third-party market sizing, AI-driven signal monitoring of competitor moves and hiring patterns — synthesised into an investment committee paper, not a 60-slide trend deck.
Field note
“Within ninety days the executive team stopped debating the numbers in the board pack. That alone paid the programme back — the architectural work that followed was upside.”
What moved
The math
Average annual cost of poor data quality per enterprise
Source · Gartner
Of organisations lack trusted data for critical decisions
Source · Forrester
Higher customer acquisition rate for data-driven organisations
Source · McKinsey
Model accuracy lift measured across our programmes since 2016
Source · Internal, 64 engagements
Start the brief
We scope every data-strategy engagement with a short, written diagnostic — free, unobligated, and built around what your board is actually asking of your data.