Data as a strategic asset: how to put a number on it
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.
Infonomics: the academic foundation
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.
Data Monetization and Valuation
Knowledge check
1. What is the central argument of Infonomics regarding how organizations handle data?
2. Why is the cost approach described as establishing only a 'floor value' for data?
3. A company wants to value a customer dataset based on the €5M in incremental annual revenue that personalization it enables generates. Which valuation approach are they using?
4. Select ALL statements that correctly describe the market approach to data valuation.
Select all the correct answers.
5. Select ALL statements that accurately reflect the Mastercard example as an illustration of data valuation in practice.
Select all the correct answers.
How mastercard puts a number on its data
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.
What goes on the balance sheet?
The honest answer: not much, under current accounting standards.
Under IFRSIFRSThe global accounting rulebook that governs how companies report financial results, used across the EU and 140+ jurisdictions.View full definition → and US GAAPUS GAAPThe standard set of accounting rules companies follow to prepare consistent, comparable financial statements, dominant in US reporting.View full definition →, 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.
What to do, from this lesson
These actions are compiled in the role's Playbook.
- Build honest, finance-validated ROI cases that under-promise and over-deliver
Related articles
Recent articles from the blog that build on this lesson.
- DataThe data flywheel: how compounding data advantage actually worksThe data flywheel is one of those concepts that gets name-dropped in board presentations but rarely explained with enough precision to act on. This article breaks down the mechanics, shows where the compounding logic holds, and tells you where it quietly breaks down.
- DataHow Fanatics quantified its data platform value and got the board to careFanatics built one of the more rigorous internal cases for data platform investment in sports commerce, moving the conversation from infrastructure cost to measurable business output. Here is how they did it, what the numbers looked like, and what CDOs in other industries can take from the approach.
- DataProving data ROI to the board: why the standard playbook is failing CDOsMost CDOs approach board-level ROI conversations with dashboards, cost savings, and revenue attribution models. The problem is not the data they bring; it is the mental model they are using to frame the argument.