Data strategy for AI readiness
Data investment starts producing measurable outcomes once the operating model is decided. We define the value thesis, then deliver a foundation platform and lighthouse data products before scale is on the table.
Data spend keeps rising while measurable outcomes stall
Poor data quality is cited by 68% of executives as the main barrier to AI adoption, and only 30% of data initiatives deliver the business value originally promised. In most programmes, platforms are procured before ownership, shared definitions and decision rights exist, so spend accumulates without a single accountable outcome.
We define the vision, the operating model and the value thesis, then deliver a foundation platform and two lighthouse data products into production, so the roadmap rests on delivered evidence rather than intent. The decision in front of you is not which platform to buy, but which two use cases have to work first, and who owns them afterwards.
Discover, define, design, build, scale
The proven approach from the offering, with what each phase exists to settle.
Discover
Baseline: data landscape, maturity, pain points and the value currently trapped in silos. You cannot strategize what you cannot see.
Define
Vision and principles, target operating model, value thesis and the investment case the board will actually fund.
Design
Target architecture (lakehouse or mesh, semantic layer, AI-ready patterns), governance framework and the first data products.
Build & pilot
Foundation platform stood up and two lighthouse data products delivered end to end, because strategy proves itself in production.
Scale
Delivery rhythms embedded, handover to a permanent operating model and value instrumented so the return compounds.
What the engagement includes
Six service lines and three packaged accelerators, each with fixed scope and a defined deliverable. Not for organisations without an executive data sponsor, or with fewer than two use cases ready to fund.
- 01Data maturity assessment
- 02Vision, operating model and value thesis
- 03Target architecture and governance
- 04Lighthouse data products
Data maturity assessment
Benchmarked across strategy, governance, architecture, people and value, so the baseline is comparable and not anecdotal.
Vision, operating model and value thesis
North star aligned to business strategy, target operating model and the investment case behind it.
Target architecture and governance
Lakehouse or mesh topology, semantic layer, AI-ready patterns and the governance framework around them.
Lighthouse data products
Foundation platform plus two data products delivered end to end, to prove value and de-risk the scale phase.
Why data strategies stall, and what this one does differently
Five failure patterns we find before writing a single line of strategy.
Strategy without execution. A consultancy defines the target and a different vendor tries to build it two years later.
Data quality as an afterthought. Quality is treated as a clean-up task, while it is the top barrier to AI adoption.
Foundations retrofitted for AI. The platform is built for reporting and later forced to serve AI workloads.
Value promised, never measured. Only 30% of data initiatives deliver what they promised, and few organizations check.
Nothing lands in the organization. The operating model stays on the slide and the same silos keep operating.
Where this has already been done
A data strategy for a sector that had never had one
A livestock sector organization that needed to turn scattered operational records into something it could actually run decisions on.
We built a platform able to ingest and transform data automatically and on schedule, as the foundation of their data strategy.
A working data foundation feeding decisions in a traditionally analogue sector.
Start with the diagnostic acceleration
Four to six weeks: maturity assessment, value heatmap, AI readiness scan across five dimensions and an executive roadmap with its business case.
Book the readiness diagnostic