Governed data is what makes AI trustworthy, not just fast.
Helping teams improve data governance, prepare operational data for AI, and deploy applied AI where reliability matters most shaped by a decade of engineering and big data expertise.
Four pillars, one governed foundation.
AI is only as safe, useful, and adoptable as the data foundation underneath it. Each engagement draws on these capabilities to build that foundation and put it to work.
Build a data-fluent organisation
Teams that trust, question, and use data confidently — not just receive dashboards.
- Data literacy programmes & microsites
- Self-serve analytics enablement
- Stakeholder training
- Data culture design
Guardrails that hold up under scrutiny
Risk-aware, auditable AI adoption — built for regulated, high-consequence environments.
- AI risk & impact assessments
- Governance frameworks & access controls
- Model & data drift monitoring
- Audit-ready documentation
Agentic workflows on governed data
Automating real operational decisions — not just chat interfaces.
- Agent architecture on trusted data layers
- Anomaly detection & decision-support agents
- Human-in-the-loop workflow design
- Integration with existing BI & ops tooling
The foundation AI initiatives need to scale
Assessing and building the architecture, quality, and governance behind every AI use case.
- Data & cloud architecture assessment
- Data quality & lineage foundations
- Roadmap from raw data to AI-ready platform
- Vendor & platform selection support
Assess, govern, enable, scale.
The same sequence, whether it's a two-week readiness assessment or a multi-year technical lead role.
Understand the data
Audit sources, quality, architecture, and governance maturity against the use case.
Put guardrails in place
Establish ownership, lineage, quality controls, and safety boundaries first.
Build literacy & trust
Train teams and ship self-serve tools that make the governed data usable.
Deploy AI on solid ground
Roll out agents and AI capability on a foundation already governed and adopted.
AI grounded in the data that drives your business.
Microsoft Certified Data Engineer with 10+ years of combined industry and academic experience in Big Data, Analytics, and AI. I specialise in using data to drive innovation, efficiency, and strategic decision-making in the renewable energy sector.
As Data & AI Technical Lead for the Renewables Centre of Excellence, I deliver high-value data products, AI solutions, and insight capabilities that turn complex engineering and operational data into trusted, governed intelligence. I work as both project manager and data SME, across cloud platforms, Big Data engineering, data architecture, data governance, and AI-ready data foundations.
I contribute to the wider data community through CDOXUK, Network Group, PyData, and DataNext Europe, and mentor with The Data Lab. I'm also a qualified First-Aider, a British Council GREAT Scholar, and a recipient of the King's Award for Voluntary Service.
Community & recognition
- Mentor, The Data Lab
- Contributor, CDOXUK & Network Group
- Speaker, PyData & DataNext Europe
- British Council GREAT Scholar
- King's Award for Voluntary Service
Credentials
Request a service.
Tell me about your organisation, the data challenge you're facing, and which of the four pillars is the priority. Submitting opens your email client with everything pre-filled, addressed straight to me.