4 to 6 week diagnostic
Review the data estate, ownership, platform footprint, value opportunities, AI readiness and operating model gaps.
Board-ready view of what is working, what is blocked and what should happen next.Executive Data & AI Leader / Fractional CDO / Board & PE Adviser
CDO-level Data and AI leadership for trusted data, practical AI and real adoption.
I help PE-backed, scale-up and enterprise leadership teams turn fragmented data, reporting and platforms into trusted foundations the business can use, the kind of foundations that make AI actually work. I am an engineer by foundation, with deep experience in enterprise data, AI adoption and operating model change, and I can stand a credible capability up fast.
Experience: Virgin Media O2, Salesforce and MuleSoft.
Proof it pays off: enabled circa £500M in business value across data-led
initiatives, with at least £23M directly attributed since July 2024 on a conservative
basis.
Public proof: Tableau, Google Cloud Next 2026, Interface Magazine and
British Data Awards 2026.
Availability: Taking selected fractional, interim and advisory
conversations.
Watch the 3-minute story
EVP, Data Democratisation
Enterprise Data & AI Transformation Remit at VMO2
Enterprise Data & AI Transformation Remit at VMO2
Which conversation are we having?
Fractional or interim leadership
For companies that need senior cover, a reset, or CDO-level judgement before or during a
permanent hire.
Diagnostic or 90-day reset
For teams that want a fast, board-ready read of the data estate, AI readiness and value
opportunities, then a plan they can act on.
PE and board advisory
For sponsors and leadership teams making data, AI, platform or role-design decisions where
the stakes are material.
01 / Engagement Shapes
Three practical ways to bring senior data and AI judgement into the business.
The work is usually part strategy, part operating model, part technology judgement and part adoption. It can take the shape of a diagnostic, a 90-day reset, a fractional or interim brief, or a focused advisory engagement.
90-day reset or interim cover
Set the governance, product model, delivery rhythm, adoption plan and senior decision path so the team can move.
Clear operating rhythm, priority roadmap and decisions that unblock execution.Board, PE and role-design advisory
Help leadership teams make better decisions on CDO/CDAO role design, AI readiness, platform simplification and value creation.
A safer brief, better sponsorship and clearer decision path.Engagements are usually shaped around a clear problem, sponsor and time horizon: a 4 to 6 week diagnostic, a 90-day reset, interim cover, or a focused advisory rhythm. Exact structure depends on the scope and decision path.
02 / Case Proof
Proof that the work moves beyond strategy slides.
The strongest pattern in my work is turning fragmented systems, unclear ownership and low-trust data into something the business can use.
Customer Trust Indicator, data turned into a top business measure
Problem: the business needed a real measure of how well it serves customers, beyond NPS and internal revenue numbers.
Intervention: I co-led the measurement behind VMO2's Customer Trust Indicator and my team built the Experience Trust Indicator on the enterprise data foundations we had created, turning hundreds of signals into a trusted score.
Result: proof that a trusted-data foundation can carry one of the company's most important measures, the kind of measurable, board-level outcome a PE-backed or founder business wants from its data.
VMO2 Data Democratisation
Problem: fragmented reporting, weak ownership, manual workarounds and inconsistent definitions.
Intervention: operating model, governed business layers, 250+ certified data products, self-service, support and culture, led with a 300+ extended data delivery organisation across employees, contractors and vendor support, including Tableau to the Techs for 2,000+ field technicians and Martian Frontier as a data-culture mechanism.
Result: the programme launched 300+ Tableau dashboards, enabled 11,000+ colleagues to self-serve trusted data, and delivered measurable business value with at least £23m directly attributed since July 2024 on a conservative basis, part of circa £500M enabled across data-led initiatives including £100M+ in network investment optimisation.
Gemini Enterprise and data agents
Problem: business teams needed a trusted and governed way to start using AI with data.
Intervention: I set the framework and roadmap and managed the Google partnership, governance, privacy and adoption while my engineering team built, driving adoption of Gemini Enterprise, conversational analytics and data agents on governed data. Consolidated nine BI and data platforms into a modernised stack along the way.
Result: 2,500 of 5,000 Gemini Enterprise licences deployed, with data agents and conversational analytics live on governed data, and £13M+ in annual platform savings from consolidation. This is the data foundation that makes AI work, not a frontier-model build.
If this sounds close to the problem you are trying to solve, it is worth a conversation.
Discuss a role or engagementOperating Model Transformation
Moving data from fragmented, low-trust platforms toward governed business layers, Data Mesh patterns, self-service adoption and commercial value creation. The transformation lens I use is MASS: Mindset, Abilities, Systems and Structure.
Business ownershipDomains, definitions,
value cases and senior decision rhythms.
Data Mesh patternsDomain ownership,
federated governance and reusable products where scale needs it.
Trusted foundationsBusiness layers,
governance, quality, privacy and access controls.
Reusable productsCertified data products,
Tableau, Atlan, dbt and support patterns.
MASS adoptionMindset, Abilities, Systems and
Structure working together.
Adoption systemTraining, office hours,
communications, communities and data culture.
AI readinessData agents, safe adoption,
vendor management and risk controls.
Value trackingCommercial outcomes, usage,
adoption and delivery accountability.
03 / Selected Public Evidence
A few public signals, not a long list of claims.
These links are useful because they show the work from outside the CV: customer stories, interviews, speaking profiles and coverage from the data and cloud ecosystem.
Enterprise Case Study
Read the Tableau VMO2 case
study
Virgin Media O2 and Tableau customer story
The public Tableau customer story says Virgin Media O2 helped prevent £250m worth of fraud and blocked 92m+ malicious texts.
Executive Feature
Read the Interface
Magazine feature
Interface Magazine feature
Executive feature on data democratisation at Virgin Media O2, framed as a cultural movement rather than a reporting programme.
Google Cloud Next Speaker
See the Google Cloud Next
speaker profile
Google Cloud Next 2026 speaker profile
Public speaker profile connected to enterprise AI, modernisation and human-centred transformation.
Ecosystem Insights
Read the Atlan VMO2 data
context case
Atlan, Gartner and ReGovern coverage
Public ecosystem references around data context, governance and enterprise adoption at Virgin Media O2.
Executive Podcast
Listen to the C-Suite
Podcast episode
C-Suite Podcast
Conversation from Google Cloud Next on agentic AI, modernisation and human-centred transformation.
Industry Awards
See the British Data Awards
result
British Data Awards 2026, Data Team of the Year
Winner, Data Team of the Year (20+ people), plus finalist for Data Transformation of the Year. Judged by independent panel across the UK data and analytics industry.
Flagship Speaker
See the Women in Data Flagship 2026 post
Women in Data Flagship, main stage two years running
Main-stage speaker at the Women in Data Flagship in 2025 with "CTRL + ALT + INCLUDE: Resetting Bias in Data & AI" and in 2026 with "From Data Curious to Data Confident", on building data confidence and trusted data and AI.
The 3-minute story
A short version of the career throughline: engineering foundations, enterprise data scale, AI adoption, and why this work matters now.
If the embedded video is blocked on your network, open the video directly. In three minutes, it covers the career throughline from engineering and SaaS platforms to enterprise data leadership, practical AI adoption and the kind of work I am now focused on.
Engineering foundation
Computer Systems Engineer, graduated top of the generation, with early depth in Java,
API architecture and enterprise platforms.
Enterprise transformation
Operating model work across Salesforce, MuleSoft, Peñoles and Virgin Media O2 to
enable governed business adoption.
Trusted data and AI adoption
Building the trusted data foundations that make AI work, then helping teams adopt data
agents and AI-enabled tools safely with the right controls, training and support.
04 / How I Work
Clear scope, senior access and enough time to make it practical.
This page is useful for fractional, interim and advisory conversations where the problem is important, the sponsorship is senior, and the work needs to become practical quickly.
Best-fit situations
- PE portfolio companies needing data and AI maturity ahead of exit or post-acquisition
- Founders and scale-ups building a data operating model before a permanent CDO hire
- Mid-market companies with fragmented data and weak ownership
- Companies needing interim CDO cover during a permanent hire
- Boards and ExCos that need clear data and AI advisory
- Teams that want measurable value from data, not just dashboards
Good-fit engagements
- Fractional or interim CDO / CDAO role
- Data and AI strategy reset or diagnostic
- Data Mesh operating model design and adoption
- AI adoption and data-agent readiness
- Governance, trust and data-product operating model
- PE value creation or pre-exit data maturity acceleration
Working rhythm
Typically 1 to 3 days per week per client. UK-based. Remote-first as the default, with travel for board sessions, key workshops or leadership alignment where it genuinely adds value. A diagnostic or strategy sprint usually takes 4 to 6 weeks: clear assessment, board-ready readout and prioritised roadmap.
I do my best work where data and AI leadership comes with real scope: a business that wants judgement, a better operating model, genuine adoption and accountable progress, not just dashboards. If you are not sure whether your situation fits, share it anyway. I would rather hear it and give you an honest view than have you hold it back. And if it is not right for me, there is a good chance I know someone strong who it is right for.
Start a conversation.
The most useful first note is simple: the role, search or business problem, where the current data or AI work is stuck, and what would need to be true in 90 days for the conversation to be worth it.