Most organizations treat data like their infrastructure: necessary, depreciating, and best ignored on the balance sheet.
That's wrong, and increasingly, regulators, investors, and acquirers know it.
Douglas Laney, former Gartner analyst and the man who coined "Big Data" in 2001, spent years developing a rigorous framework for treating data as a financial asset. He called it Infonomics.
The core thesis: data meets the definition of an economic asset (it has value, it can be traded, it can generate revenue) but organizations don't manage it like one. They don't measure it, they don't report it, and they don't make economic decisions about it.
Laney's three valuation approaches mapmapUsing software to automate repetitive marketing tasks and campaigns, enabling personalisation at scale across channels like email, web, and social.View full definition → to established asset valuation frameworks:
Cost approach: What would it cost to recreate this data from scratch? This is a floor value, most data is worth more than its reproduction cost, but this establishes a minimum. Your customer behavioral dataset took five years to accumulate. The cost to recreate it isn't just the storage, it's the five years of customer interactions.
Market approach: What would someone pay for this data? Reference market transactions: data marketplace prices, data licensing deals, comparable acquisitions. Increasingly possible as data markets mature, Snowflake Marketplace, AWS Data Exchange, and data brokers like Acxiom provide reference price points.
Income approach: What net present valuenet present valueNet Present Value is the sum of an investment's future cash flows discounted to today, minus the initial outlay. A positive NPV signals value creation.View full definition → of future cash flows does this data enable? The most rigorous but most forward-looking. If your customer behavioral dataset enables personalization driving €5M in incremental annual revenue, the income-based value is the discounted present value of those future revenue streams.
Knowledge check
1. Who coined the term 'Big Data' in 2001 and later developed the Infonomics framework?
2. How much annual revenue does Mastercard's Insights division generate from selling anonymized transaction data?
3. Which platform is an example of a marketplace that provides reference price points for data assets?
4. Select ALL valuation approaches Laney identified within the Infonomics framework:
Sélectionnez toutes les réponses correctes.
5. Select ALL correct statements about the core thesis of Infonomics:
Sélectionnez toutes les réponses correctes.
Mastercard's data business is one of the most transparent examples of data asset valuation in practice.
Their Mastercard Insights division sells anonymized, aggregated transaction data to retailers, governments, and investors. This business generates over $2B in annual revenue, from data collected as a byproduct of processing payments.
When Mastercard presents to investors, they explicitly separate their "network revenues" (payment processing) from their "other revenues" (data services). The data business trades at a premium multiple because it has higher margins and is viewed as a genuine strategic asset.
This is data asset valuation at scale.
The honest answer: not much, under current accounting standards.
Under IFRS and US GAAP, internally developed data is generally not capitalized as an intangible asset. This is beginning to change, the IASB and FASB are actively discussing how to treat data assets, but for most organizations, data doesn't appear on the balance sheet.
However, data appears implicitly in acquisitions. When Microsoft paid $26.2B for LinkedIn, they were buying data on 430M+ professionals, assets that didn't appear on LinkedIn's balance sheet. The premium over book value was largely payment for data and network effects.
For CDOs: build the internal case for data asset value even if it doesn't hit the P&L directly. The CFO who understands what your customer dataset is worth in a data marketplace will protect your data budget differently than the CFO who sees data as an IT cost center.