What I write about
Data
My work on what organisations know, how confidently they know it, and what they can do with that knowledge.
My take
I learned the value of structure in 1998, when metadata made our newspaper archives findable and the relationships around them made the same information commercially valuable. The content had not changed. What changed was what we knew about it, how we could connect it, and what those connections allowed us to do.
I don’t think data becomes an asset simply because an organisation has accumulated a lot of it. It becomes one when its condition is known, its meaning is explicit, its ownership is clear, and it can be put to work. That is why my writing moves from quality, stewardship, and monetisation into ontologies and knowledge graphs: the structural layer that lets people and machines work from the same account of what the organisation knows.
The question I keep coming back to is what the organisation officially knows, and whether it can show how it knows it. That reaches from the quality of a customer record to the definitions inside an ontology, the path through a knowledge graph, and the evidence behind an AI-supported decision. The work gathered here follows that chain from stewardship and structure to the decisions, products, and value built on top.
Latest writing (12)
Ontologies and Knowledge Graphs: Why Structure is the Next Data Frontier
Quality tells the organisation whether data is reliable. Structure tells the machine what it means, and structure is where durable AI advantage is now decided.
Maximum Fidelity: How Four Indicator Types Strengthen Board Decisions
Four indicator types give boards progressively higher decision fidelity: lagging, leading, predictive, and reasoned. Together they represent the most accountable governance instrument available.
The Great Remaking: The Questions Boards Should Be Asking About Their AI Position
Pilot counts and budget lines cannot tell a Board whether work is being remade. These questions, built on the data, talent and process loops, can.
The Great Remaking: Why Fast Following Does Not Work When the Gap Compounds
Every previous technology wave rewarded fast followers. The Great Remaking does not: the advantage is operational accumulation that cannot be bought and compounds with time.
MCP Explained: The Agent Infrastructure Standard Boards Need to Understand
An agent that sees only the public internet is an expensive search engine. MCP connects agents to the proprietary systems that constitute advantage.
The Invisible Asset: Why Boards Should Govern Data Like It's on the Balance Sheet
Boards steward physical assets with condition checks and ownership, and govern data as if it did not exist. The gap is governance, not technology.
From Print to Web to AI: Creating Sustainable Value in the AI Era
AI answer engines are rewriting how value flows through information, as the web did to print. Value exchange now has to be designed, not assumed.
Implementing Decision Analytics: A Practical Guide for Boards
Decision analytics at Board level works when staged: a parallel input before a replacement, human judgement in the loop while the record builds.
Transforming the Board: Using Decision Analytics for Strategic Advantage
Decision analytics moves the Board from backward-looking metrics to predictive indicators that model possible futures, and changes how directors exercise judgement.
Demystifying data monetisation: Insights for private equity portfolio companies
Data turns into value when a portfolio company knows what it holds and how to use it: my Chief Wine Officer talk for private equity.
The enterprise data advantage: Turning information assets into strategic value
Data becomes an asset when its structure, meaning, and relationships are known. A 1998 newspaper archive taught me how information turns into value.
Unlocking your data with AI: Insights from Monday.com Elevate
From my Monday.com Elevate keynote: the misconceptions about AI adoption, and how to put the data an organisation already has to work.
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The ideas that underpin my writing
Ideas I’ve named and matured writing about Data: what each one means, and where it started.
Chart of Entities
The governed set of core entity definitions that, like a chart of accounts, determines what every AI system in the organisation treats as true.
Data Loop
A compounding cycle in which AI-integrated workflows generate higher-quality, better-structured operational data that feeds back into an organisation's AI systems, improving their performance and, in turn, producing still better data over time.
Decision Analytics
AI applied to Board decision-making that models what could happen, evaluates responses, and quantifies outcomes across scenarios, integrating internal metrics with external signals rather than merely reporting historical performance.
Board Briefings
For directors who want to go deeper, my Board Briefings bring the related writing, evidence, and practical questions together in one place.
Data Strategy
Governing and monetising the asset every AI system runs on
AI Business Cases
Investment cases that survive contact with the Board
AI Accountability
Who answers when the machine decides
AI Governance
Governing AI the Board cannot fully see, without strangling it
The Board in the Machine
Signals
My early reads on technologies and ideas that are changing how organisations structure, connect, and use what they know, while the picture is still forming.
Knowledge Graphs
Knowledge graphs make relationships and provenance explicit, helping enterprise AI produce answers that are more consistent, traceable, and easier to govern.
Automated Reasoning
Formal logic and mathematical proof, industrialised. For some precisely specified, high-consequence controls, testing is no longer the strongest assurance available.
Agentic AI
Generative models are being given goals, tools, and the authority to act. The Board question is where to transfer agency, and under what limits.
Questions Boards ask about Data
How do we turn what the organisation knows into strategic value?
The useful starting point is not the data itself but the question it can answer, who values that answer, and how the organisation can deliver it repeatedly. Value may be direct, through a product, licence, or data service, or indirect, through better decisions and AI grounded in proprietary operational data. Ownership, quality, and a clear route from information to outcome are what turn the asset into value.
Is this data asset still fit for the purposes now being asked of it?
Treat fitness for purpose as a current judgement, not a property the asset acquired when it was created. A customer dataset assembled for reporting may now support personalisation, AI training, or automated decisions, each of which asks more of the data. A credible answer names the owner, the uses now being made of the asset, the relevant measures of completeness, accuracy, and timeliness, and when those measures were last reviewed against those uses.
Which of these data assets, if improved, would unlock the most strategic value?
Assess each asset against two dimensions: the strategic value it could support and its current quality. High-value, high-quality assets should be protected and put to work, while high-value assets in poor condition are the priority for improvement. Lower-value assets still need proportionate governance, but not the same investment. The point is to direct effort towards the few improvements that unlock decisions, products, or capabilities the organisation could not otherwise pursue.
Who owns the chart of entities?
The answer will vary by organisation, but it needs to name one accountable owner for the definitions and relationships on which reporting, regulation, and AI depend. The Chief Data Officer is often the natural home, although the remit may need to extend beyond conventional data management into structural governance. What matters is that ownership sits high enough to resolve competing definitions and that changes to core entities are visible to the Board where they affect revenue, risk, regulatory exposure, or customer trust.
What does the organisation’s AI strategy assume about structure?
An AI strategy that assumes only clean, accessible data is incomplete. Models also need explicit meaning and relationships if the organisation expects them to reason across customers, contracts, products, suppliers, and policies rather than merely retrieve and summarise. The useful test is whether the strategy names the structural work, such as shared definitions, ontologies, and knowledge graphs, who owns it, and which decisions depend on it. If it does not, the data plan may support access without supporting reasoning the organisation can trace and defend.