Data Strategy & Consulting
Data strategies fail when they only describe what to build. The expensive part of most estates is what is being kept alive: the warehouse nobody trusts, the four hundred reports of which sixty are opened, the extract that feeds a process that ended in 2019.
We produce strategies that are actionable in both directions — what to build, what to consolidate, and what to decommission, with the sequence and the dependencies made explicit.
Platforms we typically deliver this on
Scope of the service
Current-state assessment
Source systems, integration patterns, storage, consumption and the undocumented dependencies that block every modernisation attempt.
Target-state architecture
A reference architecture appropriate to your scale and constraints — warehouse, lakehouse or hybrid, with an honest cost model.
Data operating model
Ownership, stewardship, funding and the intake process for new data requests. Usually the part that determines whether the architecture holds.
Rationalisation plan
Which systems, marts and reports to consolidate or retire, with usage evidence behind each recommendation.
Modernisation roadmap
A phased plan with dependencies, decision gates and cost by phase.
Platform selection
Independent evaluation against your workloads, residency requirements and existing commitments.
Where this is used
Representative engagements, described at the level our clients permit. Sector and shape are accurate; identifying detail is withheld.
Consolidating fragmented departmental reporting
Nine departments maintained overlapping data marts with conflicting definitions of the same measures. We produced a consolidation plan with a shared semantic layer and a phased decommissioning schedule.
Outcome — One agreed definition per measure, and a plan to retire the duplicates.
Setting direction before a platform commitment
An authority was evaluating platforms without an agreed target state. We documented current state, defined the target and produced selection criteria tied to actual workloads and residency rules.
Outcome — Selection driven by requirements rather than vendor demonstrations.
Establishing data ownership across faculties
Faculty-level autonomy had produced incompatible student and research data. We designed an operating model with federated ownership under central standards.
Outcome — Faculty autonomy preserved without giving up institution-wide reporting.
Before you get in touch
How long does a data strategy take?
Six to ten weeks for most organisations. Larger federated estates take longer, mostly because of stakeholder availability.
Will you recommend a specific platform?
Yes, with the reasoning and the trade-offs written out. We hold partnerships with several platform vendors and disclose them; the recommendation follows the workload.
What if we already have a strategy?
We are often asked to review one. That is a shorter engagement focused on feasibility, sequencing and cost realism.
Often engaged alongside this
Data Platform Modernisation
Lakehouse and warehouse builds designed for parallel running, so the business keeps its numbers while the foundation is replaced underneath.
Read moreData & AnalyticsData Governance & Quality
Catalogues that are current, quality rules that run automatically, and ownership that maps to how your organisation is genuinely structured.
Read moreFixed-scope engagementAI Readiness Assessment
A fixed-fee assessment with a fixed deliverable. You get a data inventory, a scored use-case shortlist, a governance gap analysis and a costed roadmap. No open-ended discovery.
Read moreStart with three weeks and a straight answer
The AI Readiness Assessment is fixed in scope, fixed in price and produces four deliverables you own — whether or not you continue with us.