Advisory

Confidential executive support for data, AI, governance, and transformation decisions.

The best fit is a serious leadership problem: trusted reporting, regulatory evidence, commercial data maturity, AI governance, or a platform decision with board-level consequences.

CEOs, CFOs, boards, and PE operating partners

Executive data and AI strategy review

A focused review of whether the data estate, governance model, AI agenda, and reporting stack can support the next commercial or regulatory objective.

  • Executive decision map
  • Risk and opportunity heatmap
  • 90-day sequencing plan
  • Board-ready findings pack
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Scale-ups, regulated firms, and transformation leaders

Fractional data and AI leadership

Hands-on senior leadership for organisations that need credible data direction before, during, or after hiring a permanent executive.

  • Operating model design
  • Team and vendor leadership
  • Platform and tooling decisions
  • Governance forum setup
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Banks, insurers, fintechs, and regulated enterprises

Regulatory data confidence programme

A structured path from weak evidence and inconsistent metrics to inspectable controls, stewardship, lineage, and data quality management.

  • BCBS239 and regulatory gap assessment
  • Critical Data Element framework
  • Lineage and metadata plan
  • Audit evidence model
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Commercial leaders, CTOs, CDIOs, and transformation sponsors

Commercial data platform acceleration

A pragmatic build vs buy and delivery plan for data products that support growth, margin, retention, pricing, risk, and operating efficiency.

  • Target architecture
  • Data product roadmap
  • Integration and remediation plan
  • Delivery governance cadence
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02How I work

A structured advisory sequence from diagnosis to embedded ownership.

Every engagement starts with the decision at stake, not the technology. The four stages below are designed to produce a board-readable outcome, not a slide exercise.

01

Diagnose

Inspect the evidence: data estate, governance posture, reporting quality, and where commercial or regulatory decisions are failing.

02

Frame

Separate immediate control risks from strategic architecture choices; translate technical gaps into board-readable consequences.

03

Decide

Agree the sequencing: what must be fixed first, where to invest, which vendor or platform decisions can be deferred without cost.

04

Embed

Deliver a 90-day plan with clear ownership, governance forums, and measurable milestones that survive beyond the engagement.

Engagement shape

The output is a leadership decision system, not a slide exercise.

A typical engagement clarifies the decision context, inspects the evidence, identifies control and commercial gaps, then sequences the work into a short, board-readable plan.

Define the commercial, regulatory, or board decision at stake.

Trace the data, governance, platform, and operating model constraints.

Separate immediate risks from strategic architecture decisions.

Create an executable 30, 60, and 90-day path with clear ownership.

05Working together

Common questions about engagements.

Practical answers to the questions that come up before a first conversation.

How do engagements typically start?

With a brief, confidential conversation about the specific decision or risk at hand. Most mandates begin as a focused diagnostic — a structured review of the data estate, governance posture, or AI agenda — before scope and phasing are agreed. There is no standard onboarding package; the starting point is shaped by the problem.

How do you handle confidentiality and NDAs?

All enquiries and engagements are treated as confidential by default. NDAs are signed before substantive discussions begin, and engagement terms include explicit data handling obligations. Regulated and PE-adjacent contexts, where information barriers and dual-role sensitivities apply, are handled with appropriate care.

What does an advisory engagement look like in practice?

A typical engagement has three stages: a structured discovery phase (interviews, document review, evidence inspection), an analysis and synthesis phase that produces a board-readable findings pack, and a sequenced action plan with clear ownership. Fractional engagements add ongoing leadership — team direction, vendor management, and governance forums — on a retained basis.

Who do you usually work with?

CEOs, CFOs, boards, and PE operating partners who need independent assurance on a data or AI decision. CTOs, CDIOs, and transformation directors commissioning a structured review. Regulated firms needing fractional data leadership during a search or restructure. The common factor is a consequential decision that requires evidence, not opinion.

How are engagements scoped and priced?

Engagements are scoped by objective and deliverable, not by time-and-materials. A fixed-scope diagnostic typically runs four to six weeks. Fractional leadership is structured as a monthly retainer with a defined commitment of days. Pricing is shared directly after an initial qualifying conversation.

Can you work inside regulated or audited environments?

Yes. The majority of the work is in financial services, insurance, and public sector organisations where FCA, PRA, BCBS239, GDPR, and internal audit requirements shape what evidence is acceptable. Deliverables are designed to withstand regulatory and board scrutiny, not just internal review.

Qualified mandate

Bring a live data, AI, governance, or transformation problem.

The contact form is intentionally qualified so the first conversation can focus on substance.

Discuss a live mandate