You know data, right? Right?
Data as an Asset: A Data Boardroom Primer
Welcome to the first of a new series of primers from The Data Boardroom. These deliver sharp foundational lessons, designed for aspiring data leaders (or indeed anyone with an interest in the strategy of data).
Each primer tackles one key topic. Think of it as your knowledge baseline - a jumping off point if you want to dig deeper.
You work with data, but do you understand it?
Most data professionals have spent years mastering how to work with data. The pipelines, the platforms, the governance frameworks, the models.
You are no doubt technically adept. Well done. It helps 😉.
But, let me ask you a question. Despite all your technical knowledge, do you really understand data?
Do you understand the raw material, itself?
Not technically. Economically. How it behaves. What makes it valuable. What makes it dangerous. Why it defies the logic that governs every other asset class your organisation manages.
If you’re not sure how to answer this question - then read on.
In this primer I want you to think like an asset manager. Someone who manages an asset vital to their organisation’s mission. There is method behind my madness, because it is this mindset that turns data professionals from backroom technicians into commercial operators.
Few data professionals spend much time thinking about the fundamental economic behaviour of data as raw material. Somehow, they pursue their craft without studying the substance. Like a carpenter who's never thought carefully about how different woods behave,they still build useful things. But they'll make avoidable mistakes and miss what's possible.
This mindset matters. Because if you aspire to advise your business on data investment, data risk, or data strategy without a clear model of how data actually behaves commercially,then you’ll struggle.
It is my firm belief that data people who master asset thinking, are not only better at their jobs, they become indispensable and credible economic advisors to their organisations.
As I said - ‘asset managers.’ People who know to exploit the base characteristics of data to the advantage of their business.
If you’ve ever wondered what sets successful data leaders apart, it is that they understand the lessons you are about to read.
📏 Lesson 1: You can’t manage what you can’t measure
Most asset classes have a natural unit that allows for comparison. This gives leaders a common language for investment decisions, performance management, and value comparison.
Data is different. You can count rows, gigabytes, tables, or data sources, but none of those tell you anything meaningful about value or quality. Two datasets of identical size can differ by orders of magnitude in what they’re worth to the business. The absence of a natural unit of comparison is the reason data investment decisions so often default to gut feel, technical preference, or whoever shouts loudest.
This is the foundational economic challenge of data. Data’s measurement framework is its value-in-use, in your context. Building a measurement framework for your data assets, based on the value they unlock for your organisation is arguably the most important thing a commercially-savvy data leader can do.
(Side-note: if you want to know more about valuing data assets then contact me. It is what we do at Anmut😎).
Lesson 2: The asset that survives every use
Physical assets deplete. Fuel burns. Machinery wears. Inventory shrinks. Data doesn’t follow that logic. The same dataset can be used simultaneously by finance, operations, and marketing - without any of them consuming it.
Most data leaders know this in the abstract. Fewer apply it as a commercial discipline. If a physical asset sat idle 80% of the time, the business would act. Your data estate almost certainly has significant latent value sitting unused, and the marginal cost of an additional use case is often close to zero. The best data leaders know that before you buy more tools or look for more data, it is best to sweat what you already have.
💎 Lesson 3: Context compounds value
A single customer transaction tells you very little. A year of transactions reveals patterns. Five years across a million customers lets you predict behaviour, price risk, and personalise at scale. Data compounds. Value increases non-linearly as volume and variety grow.
Commercially literate data leaders know that data investment decisions evaluated in isolation are routinely wrong. The value of a new data source depends heavily on what you already hold. If you’re building business cases dataset by dataset, you’re missing the portfolio effect (data is worth more combined) — and almost certainly undervaluing your asset.
Data valuation is needed at the asset level and the use case portfolio level (which typically depends on a mix of assets)
🪴 Lesson 4: The asset that makes more of itself
Unlike almost any other asset, data is generative. Models trained on data produce predictions, which produce new data. Customer interactions generate behavioural signals. Operational sensors feed back into product development. The asset reproduces itself.
The best organisations deliberately engineer feedback loops that compound value over time. As a data leader, you need to consider where in the business data generates more data. These are often your highest-leverage investments. The places to seek more value.
🏅 Lesson 5: The competitive value of unique data
Most data investment conversations focus on what data enables today - the decisions it supports, the processes it improves, the models it trains. This is operational value.
But there’s a second kind of value that rarely appears in business cases: positional value. The advantage that comes from having data that others don’t, or from having held it long enough that the history itself becomes the value.
Longitudinal data (years of customer behaviour, infrastructure performance, environmental readings) is extraordinarily difficult to replicate once the window has passed. A competitor who starts collecting today cannot buy their way back to where you are. This asymmetry has real strategic value, and in most organisations it’s invisible because nobody is accounting for it.
Data leaders who understand this can argue for investment in data collection not just on the basis of current use cases, but on the basis of future optionality. The dataset you’re building today may be most valuable five years from now, in a context you can’t yet fully specify. This can be a legitimate and important investment argument in a forward-looking business.
🏚️ Lesson 6: Competitive advantage can erode
Data that confers competitive advantage today may be a commodity tomorrow. When a signal becomes widely available (through open data initiatives, market aggregators, regulatory disclosure, or the maturation of a sector) the advantage it once provided vanishes. Undifferentiated data loses its value.
This dynamic is poorly understood in most data strategies. Leaders evaluate data investments on quality and relevance, without asking the harder question: How long before this is table stakes rather than advantage? The answer should shape how aggressively you move, how much you invest, and whether your strategic position is more fragile than it appears.
Part of your job as a data leader is not just identifying valuable data - it’s staying aware of the erosion of that value as the competitive landscape shifts.
📅 Lesson 7: Every dataset has an expiry date
Data deteriorates. Customer addresses change. Market conditions shift. Behavioural patterns evolve. A dataset that was decision-quality eighteen months ago may now be misleading the business.
Decisions made on stale data carry a real economic cost: the wrong customer targeted, the wrong risk priced, the wrong market pursued. Maintenance cost is part of data asset economics, and it belongs in your investment numbers. If it isn’t, your business cases will understate the true cost of ownership.
📚Lesson 8: Off the books, but not the table
Data doesn’t appear as an asset in conventional accounting. The investment in creating, acquiring, and maintaining it flows through as cost. The value it generates may appear as revenue (or productivity or risk managed), but without any line connecting the two. This makes data one of the most significant off-balance-sheet value drivers in the modern economy, and one of the least well-articulated.
Your board, your investors, and any future acquirer are forming judgements about your data capability without a proper framework to evaluate it. Data leaders who can close that gap — who can articulate the economic value of the data estate in business terms, not technical ones — have a material advantage in the conversations that shape strategic direction.
So, there you go, eight quick ‘data asset’ lessons that every leader needs to know. There are many more, but if you want to raise your game - remember, it is time to start thinking like an asset manager.
Until next time,
James
Got a question or have an idea you want me to explore? Let me know, and I may write an article just for you.