We keep hearing it — data is the new oil.
AI has fundamentally altered the economics of information. What once required teams of data scientists and complex infrastructure can now be done through embedded AI systems or simple natural-language interfaces. The cost of processing data has collapsed, while its usefulness has multiplied.
That shift makes data a new kind of financial resource. Companies can now turn their proprietary data into revenue, use it to power AI models, or even leverage it for financing. Entire business models exist purely because they own or control valuable data.
If data is this valuable, CFOs need to treat it as an asset class — to secure it, measure it, and include it in accounting and risk routines just as they would with any other form of capital.
So in this week’s Balanced AI Insights, let’s look at what it actually means to manage data as capital.
TL;DR:
• Data is quickly becoming one of the most valuable assets a company owns — often worth more than its physical infrastructure.
• AI has made data cheaper to process and far easier to monetize, turning it into a true financial resource.
• Companies like American Airlines and Microsoft already treat data as collateral or acquisition value.
• CFOs should start identifying where the most valuable datasets live and track them like capital investments.
• In the subscriber edition: templates, valuation models, and communication tools to help you manage and report data as part of your capital base.
The Case for Treating Data as Capital (and as an Asset)
The comparison to oil fits because, like a natural resource, raw data only gains value once refined. Transaction histories, customer records, and usage logs become strategic capital when they are structured, validated, and connected to decision-making.
In today’s world, data has become one of the most important assets a company owns, often more valuable than its physical infrastructure. It underpins efficiency, forecasting, pricing, and innovation.
Three shifts have made this transformation possible:
Processing has become inexpensive. Cloud platforms and large language models have democratized analytics, allowing finance and operations teams to extract insights directly.
AI has made data contextual. Models can detect patterns, risks, and opportunities instantly across structured and semi-structured data.
Proprietary data now drives differentiation. Internal datasets power forecasting, copilots, and pricing systems that deliver measurable financial impact.
AI does not merely analyze data — it activates it. Every time a model trains on proprietary information, that dataset becomes more refined, accurate, and reusable. It is one of the few assets that appreciate through use rather than depreciate.
Proof in Practice: When Data Becomes a Financial Asset
We already see examples of data being treated like capital in practice:
American Airlines used its frequent-flyer program data to raise nearly $30 billion during the pandemic — more than the company’s market capitalization at the time. That dataset became the collateral that kept the airline running.
Microsoft’s $26 billion acquisition of LinkedIn effectively valued each user at over $60. What Microsoft bought was not software, but access to professional data on more than 430 million members — a dataset that transformed its B2B ecosystem.
According to a Forbes analysis, third-party appraisals of corporate data often value it at two to three times more than the company’s market capitalization.
Columbia Business School research defines data as a financial resource that reduces uncertainty and improves decision-making—both measurable sources of enterprise value.
These examples illustrate how proprietary, well-structured data can hold more tangible financial value than physical assets.
Where to Find the Value in Your Company’s Data
Most organizations already sit on valuable data, but few finance teams have mapped where it actually lives. Start with these categories:
Customer and commercial data. CRM records, loyalty programs, and billing histories hold deep insight into behavior and lifetime value.
Financial and operational data. Transaction logs, supplier terms, and internal performance data feed forecasting and optimization models.
Product and usage data. Application logs, usage analytics, and feedback loops reveal which features or services drive retention and growth.
Ecosystem and partnership data. Vendor integrations, shared APIs, and platform analytics often contain untapped value — and potential governance risk.
Employee and workforce data. Productivity metrics and training data can inform workforce planning and efficiency modeling.
For CFOs, the task isn’t just recognizing these sources — it’s documenting and governing them with the same discipline used for other capital assets.
Accounting Reality: Not Yet Recognized, but Already Critical
Under IAS 38 Intangible Assets, an asset must be identifiable, controlled, and capable of generating future economic benefit. Data meets all three conditions, but measurement and verification remain challenging.
The Institute of Chartered Accountants in England and Wales now calls corporate data “a new type of intangible,” while the CPA Journal notes that many organizations already treat it operationally as an asset — securing, insuring, and monetizing it — even if it isn’t yet capitalized.
Even without formal recognition, finance leaders should begin managing data like capital.
Track data creation and maintenance costs.
Include data assets in board discussions.
Tie governance metrics to data quality and reuse.
CFO Action Plan
In your next finance or audit committee meeting, bring data into the capital conversation.
Identify the datasets that drive the greatest economic impact.
Quantify collection and maintenance costs.
Review contracts for data ownership and usage rights.
Map where proprietary data powers AI or automation.
Ask auditors whether internal valuation or disclosure would strengthen reporting and governance.
This discussion reframes data from an operational resource into a managed financial asset — exactly where it belongs.
As companies accelerate AI adoption, many are realizing that the real challenge isn’t the technology — it’s managing the data behind it. For CFOs, that means two things: knowing exactly where the most valuable data lives and building financial discipline around it.
In the subscriber edition, I go beyond the concept and show how to do it.
You’ll get:
A ready-to-use Data Asset Register template to catalog and track your datasets like capital assets.
Valuation models and examples to help quantify data’s contribution to enterprise value.
A quarterly CFO checklist to turn data oversight into a repeatable financial process.
And board-ready language and slides to communicate data’s value with confidence.
If you’re serious about managing data as part of your capital base — not just talking about it — this is where to start.
Closing Thoughts
AI may be the engine, but data is the fuel.
The finance function now sits at the intersection of both. By treating data as a governed, measurable asset, CFOs ensure that the organization’s most valuable resource is not just used — it’s owned, protected, and compounded.
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Until next Tuesday, keep balancing!
Anna Tiomina
AI-Powered CFO