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As enterprises race to adopt AI, most still overlook the asset that underpins it all: their data. AI strategist Doug Laney argues that until organisations value information with the same financial discipline they apply to physical assets, true monetization will remain out of reach
For decades, companies have called data their “most valuable asset.” But as AI strategy expert Doug Laney points out, many enterprises still treat it as an expense.
“Data doesn’t appear anywhere on the balance sheet,” Laney says. “And when you don’t measure an asset, you don’t manage it. When you don’t manage it, you certainly don’t monetize it.”
Laney argues that the next wave of competitive advantage will come not from bigger data stacks or faster models, but from a new alliance between the CIO and the CFO. Only when organizations can value their data like any other asset, he says, will they begin to unlock its true potential.
The Missing Line on the Balance Sheet
Laney’s earlier book, Infonomics, helped popularise the idea that information has measurable economic value. He and his colleagues have since taken that philosophy into practice by helping companies build what he calls “supplemental balance sheets” for data.
“We work with CFOs to develop models that quantify the cost basis of data and its contribution to revenue, efficiency, or risk reduction,” he explains. “Once you’ve got both the numerator and the denominator, you can start managing data like a financial asset.”
The approach is deceptively simple.
First, identify and catalogue the organization’s major data assets. Then estimate their cost to acquire, store, and maintain.
Next, model their measurable contribution to key value streams, things like sales uplift, expense savings, or risk mitigation. The result is a ledger that gives data a price tag. And that price tag changes the conversation: executives can now weigh investments in data governance, quality, or analytics infrastructure with the same financial discipline they apply to factories or fleets.
A Case Study in Value Recognition
Laney shares one engagement with a large healthcare company that wanted to prove the worth of its internal data-management group, a function often seen purely as overhead.
Working with the CIO and CFO teams, his consultants created a model to calculate the return on assets for specific datasets. By using proxies for missing inputs—such as estimated allocations of sales and marketing costs to certain data assets—they were able to determine that one dataset alone was worth roughly $38 million.
“Once the CFO’s office saw those numbers, the tone changed,” Laney recalls. “Data management wasn’t a cost centre anymore; it was a profit enabler.”
The exercise did more than shift perception. It provided a tangible framework for deciding which datasets to invest in, which to retire, and how to justify new data-governance budgets in language finance leaders understood.
When Data Becomes Collateral
If accounting principles haven’t yet caught up to the idea of data as an asset, markets increasingly have. Laney cites a striking example from the early months of the pandemic: facing grounded fleets and evaporating revenue, United Airlines and American Airlines needed to raise capital fast.
Their planes and gates were leased and useless as collateral. Their customer-loyalty data, however, was not. “They went to the banks and said, ‘Can we borrow against our loyalty programmes?’” Laney says. “Those programs were valued at between $20 and $30 billion, two to three times the value of the airlines themselves.”
It’s a dramatic demonstration of how undervalued data can be on paper versus in practice. And it’s part of a growing trend. Firms like Gulp Data, for instance, now help digital-native companies collateralize their datasets for loans, using comparable market data from brokers to estimate fair value.
The CFO as Chief Data Valuer
The broader implication, Laney says, is that data strategy is no longer solely an IT discipline. Technology leaders can catalogue, govern, and secure data, but determining its economic worth requires financial rigour and executive sponsorship.
“The CFO should be the organization’s chief data valuer,” Laney argues. “They already understand depreciation, amortization, and risk. Those same principles apply to information assets.”
For CIOs, that means reframing the conversation in financial terms: What is the ROI on a data-quality initiative? How much value leakage occurs when key datasets remain siloed or ungoverned? When these questions are expressed in currency rather than code, they resonate across the boardroom.
Why Measurement Drives Imagination
Ironically, Laney believes that putting a dollar value on data can liberate creativity, not constrain it. Once teams see the margin between the cost of data and the value it generates, they begin to look for new ways to expand that margin, be that through smarter use cases, data sharing, or external monetization.
“It’s a margin topic,” he says. “When you can measure both cost and contribution, innovation follows naturally. “You can’t have a data-driven business if you can’t express the value of your data in business terms.”
Join us at CDAO Financial Services in February to hear more insights on this and many more crucial topics affecting leaders in data, analytics, and AI.
