Data Strategy

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The Asset the Balance Sheet Cannot See

Every AI conversation arrives at data eventually, and most arrive too late. Data strategy, as this briefing treats it, is the governance and commercial discipline applied to the asset every AI system runs on: the proprietary records, relationships, and knowledge that may now contribute more to enterprise value than the buildings that house them. Global intangible value reached an all-time high of USD 97.6 trillion in 2025 (Brand Finance 2025), yet accounting standards prohibit capitalising internally generated data, so the balance sheet never sees the data, and the stewardship disciplines that follow the balance sheet never reach it.

What goes wrong is rarely ambition. It is altitude. The Boards I meet seldom lack a data strategy on paper; what they lack is stewardship. Data initiatives are delegated to IT as technology projects, ownership fragments across functions, quality degrades undetected, and too often nobody assesses whether the data is fit for the purposes AI is now asking of it. A physical asset treated this way would be condemned. Data, in governance terms, already is.

The position these articles take is that the disciplines already exist. Boards know how to steward assets: named ownership, regular condition assessment, maintenance, and impairment testing. Extending them to data is a governance choice, not an accounting one, and it does not need to wait for the standards to be reformed. The argument then goes a level deeper. Quality tells the organisation whether its information is reliable; structure tells the machine what it means. The definitions encoded in an organisation’s ontologies and knowledge graphs now carry the weight a chart of accounts has always carried, because they shape what the organisation’s AI systems treat as true.

And because proprietary data is the AI input competitors cannot purchase, it is also where the commercial opportunity sits. I have been monetising information assets since I began digitising 271 years of newspaper content in 1998, and the pattern has not changed: identify the assets, structure them, and build value exchange models that reward the organisations that created them. The AI era raises the stakes on both sides of that equation, multiplying what proprietary data is worth and testing whether its owners capture any of it.

Read this briefing and you should be able to make one judgement about your own organisation: whether it is stewarding its data as the asset its AI strategy silently assumes it to be, and if not, which of ownership, quality, structure, or commercial model is the gap the Board should close first.

Then read this

From Stewardship to Structure to Revenue

The core articles are numbered for a reason, and the sequence is the argument. Start with the articles: The Invisible Asset makes the governance case, establishing why data deserves balance-sheet discipline even though the balance sheet cannot show it. Ontologies and Knowledge Graphs then takes the argument a level deeper, from whether data is reliable to whether a machine can reason over it, which is where durable AI advantage is now being decided.

The sequence then turns to the commercial question. The Enterprise Data Advantage sets out how to identify, value, and monetise data assets, drawing on the newspaper digitisation work where I first learned the trade. Creating Sustainable Value in the AI Era closes the sequence with the question the earlier articles raise: once AI systems consume and synthesise proprietary data, how does its owner make sure the value flows back? The supporting pieces in the further reading show the same thinking delivered to live audiences, at a private equity evening and on a conference keynote stage. Alongside them sit the pieces that take the argument to its foundations: the data loop from The Great Remaking, which shows why proprietary operational data compounds and cannot be bought, and the four indicator types that turn data into decision fidelity in the boardroom.

After the articles, Remake holds the mechanisms beneath the thinking, worth a look wherever a named asset is unfamiliar. The questions gather the ones directors put to me most often on this subject, each answered in a paragraph with a route back into the relevant article, and the references list the external research, standards, and legislation the articles cite, if you want the primary evidence.

If you have 15 minutes, read the first article and the questions. If you have an evening, read the core articles in order; they were written independently, but they build to a single position, and the sequence is the shortest route to it.