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Data, Analytics & AI
Data Strategy & Consulting
Operating models for the data-rich enterprise.
Capability audits, target-state architectures, and sequenced investment theses — engineered around the operating KPIs your business actually runs on.
The case
Most data strategies
fail in execution, not analysis.
The deck is rarely the problem. Investment cases get approved. Architectures get drawn. What stalls is the seam between strategy and delivery — the operating model, the funding rhythm, the accountability for outcomes — that turns a thesis into a compounding capability.
Our work is built around that seam. We anchor every recommendation to a measurable operating KPI, sequence it for the funding cycle you actually live with, and stay close enough to delivery that the strategy survives contact with reality.
A data strategy that can't be executed in the next two quarters is a literature review.
What's in scope
From a clean-eyed read
to a fundable thesis.
Capability & maturity audit
An honest read on data maturity, technical debt, vendor lock-in, talent posture, and operating-model readiness — benchmarked against peers in your sector.
Target-state architecture
A reference architecture for data, analytics, and AI engineered for compounding value — not the most-recommended-vendor poster of the quarter.
Investment thesis & roadmap
Sequenced multi-quarter roadmap with quantified business cases and dependency-aware funding asks — the version your CFO will actually approve.
Operating model & governance
Federated stewardship, a data-product framework, and KPIs that travel with the data — the connective tissue between strategy and shipped outcomes.
Talent & org design
Org shapes, role definitions, and hiring runways for the data and AI org you'll need in 18 months — not the one you copied from a slide deck.
Build vs. buy decisions
Vendor evaluation, proof-of-value design, and the contracts and exit clauses that protect optionality through a five-year horizon.
How we engage
Four phases
from thesis to traction.
Our consulting work is built to land delivery, not produce a deck. Each phase ends in an artefact your teams use the next quarter.
Listen
Weeks 1–2
Working sessions with the people who actually run the business. We collect the questions, frustrations, and unspoken priorities that don't make it into RFPs.
Diagnose
Weeks 3–6
Capability audit, data and platform inventory, peer benchmarking, and a quantified read on where the leverage actually sits.
Frame
Weeks 6–10
Target-state architecture, operating model, and a 24-month roadmap with funded waves — defended against your CFO and your platform leads.
Hand off (or stay)
Week 10+
We transition to delivery — either to your teams, to ours, or a hybrid pod. Either way, we stay close enough to defend the thesis through the first wave.
Where this applies
Most enterprises
have a strategy gap, not a tooling gap.
We've delivered consulting work across these sectors. The framework adapts — vertical context never replaces it. If your sector isn't here, the operating-model gaps probably still are.
- Banking & Capital Markets
- Insurance & Reinsurance
- Healthcare Providers
- Healthcare Payers
- Pharma & Life Sciences
- Manufacturing
- Energy & Utilities
- Retail & Luxury
- Hospitality & Travel
- Telecom & Media
- Logistics & Mobility
- Public Sector & Sovereign
- Higher Education
- Professional Services
Common questions
Before we begin,
what teams ask.
How is this different from a Big-4 strategy engagement?
We are operators, not auditors. Our consulting team has shipped data platforms and AI systems in production. The thesis is grounded in what we've actually built, and we stay close enough to delivery to defend it.
Do you require us to use specific vendors or platforms?
No. The reference architecture is platform-aware but vendor-neutral. We've delivered work on every major cloud and lakehouse stack, and we'll choose against your team and risk model — not a partnership tier.
What does the engagement cost and how long does it run?
Most diagnostics run 8–12 weeks at a fixed scope. The longer roadmap and operating-model work typically extends into a 16–24 week engagement, with a transition to delivery after that. We share commercials only after a scoping call.
Will you implement, or only advise?
Either. Most clients ask us to stay through the first delivery wave so the thesis survives contact with reality. We can also hand off cleanly to your teams or a third party — whichever protects velocity.
Related in Data, Analytics & AI
Adjacent capabilities
in this practice.
Data Lakehouse & Warehouse
Lakehouse on Databricks, Snowflake, or BigQuery — engineered for governed scale and downstream AI.
Advanced AI & Analytics
Forecasting, segmentation, and anomaly detection wired into operational workflows.
SAP Data & Analytics
Datasphere, BTP, and SAP Analytics Cloud — engineered into a federated data fabric across SAP and non-SAP.
Start the conversation
A 60-minute call
is usually enough to scope.
We'll listen first, frame the problem with you, and propose a starting point that pays back inside two quarters — not eighteen months.