The Hidden Price of Data
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- Authors: LAURA VELDKAMP
- Published: November 17, 2025
Summary
- Data accumulates as a by-product of modern life: every search, click, or phone-enabled walk leaves information that firms can use.
- Data functions as an ambient, invisible asset that firms exploit to improve algorithms and generate revenue, yet its explicit price is hidden from consumers.
- Consumers engage in a "dual transaction": they pay the observable monetary price for goods and services and simultaneously "sell" transaction data; the observable price is the net of these two exchanges.
- Turning data into a countable, priced asset is necessary for consumers to become "savvy suppliers" who can demand fair value.
Economic logic and market incentives
- If a profit-maximizing firm values customer data, it has an incentive to encourage more transactions because more transactions yield more data.
- Firms must discount goods and services to generate more sales and thereby obtain more data; discounts are driven by the firm’s desire for data, not by fairness.
- Bundling the sale of goods with the implicit sale of data prevents consumers from observing the data "discount," leaving them unable to learn market prices for their data.
Price bundling and consumer impact
- Bundling (requiring simultaneous purchase of a product and sale of transaction data) hides the price of data and results in consumers receiving less than they might if prices were unbundled.
- Consumers typically lack the option to purchase goods without selling their data, keeping them in a perpetual position of information disadvantage.
- Regulatory unbundling—requiring firms to post both the price with the right to use the transaction data and the price for a private transaction—would:
- Reveal the data discount to consumers.
- Allow consumers to choose whether to sell their data based on observable tradeoffs.
- Enable consumers to transition from naive buyers to active suppliers who can demand a share of the data economy gains.
Five approaches to measuring data value
- Market prices approach
- Some data is traded in open markets (platforms such as Snowflake or Datarade).
- Market prices provide a tried-and-true signal for the subset of data represented in these marketplaces.
- Limitation: these traded data sets are not representative of economically important proprietary data that firms retain.
- Revenue approach
- Treats data as a productive asset worth the extra revenue it can generate.
- Estimates what profits would have been absent certain data (a counterfactual).
- Feasibility varies by setting; in finance it is more feasible because of observable investor behavior.
- Complementary inputs approach
- Infers the value of a firm’s data stock from resources devoted to managing and exploiting data (labor, computing power).
- Rationale: firms spend real money on people and tools only if the underlying data has implicit value.
- Correlated behavior approach
- Measures data by its behavioral footprint: alignment between actions and rewards indicates the information available to decision-makers.
- Examples: how accurately recommendations match purchases or how well firms stockpile goods that will sell.
- High covariance between actions and payoffs implies valuable data at work.
- Cost-accounting approach
- Adds up bills and treats purchased data sets as assets (as in parts of the United Nations System of National Accounts).
- Challenge: most data is bartered (consumers “pay” with information), so implicit discounts rarely appear on firm books.
- A true accounting would impute the value of the dollars or cents knocked off each purchase to encourage transactions and generate data.
- Unbundling transactions would make such cost accounting feasible.
Key observations about measurement
- Each of the five approaches captures a distinct aspect of data value: labor devoted, revenue earned, precision of actions, market price, or implicit cost.
- No single approach is infallible, universally feasible, or wholly holistic; measurement will remain imperfect.
- Moving data from intuition to quantification is essential for informed choices and sound policy; without quantification, firms can freely exploit an unpriced resource.
Policy implications and recommendations
- Require unbundling of transactions so firms must post separate prices for transactions that convey the right to use the transaction data and for private transactions without those rights.
- Promote measurement tool kits that utilize multiple approaches (market prices, revenue, complementary inputs, correlated behavior, cost accounting) depending on context.
- Use unbundling and improved measurement to empower consumers to:
- Observe the data discount.
- Opt in or out of selling data based on explicit tradeoffs.
- Evolve into active data suppliers who can demand fair value.
Conclusion
- Data is not free; consumers are paid data producers through implicit discounts embedded in bundled transactions.
- Quantifying data’s value through a combination of measurement approaches and regulatory unbundling can illuminate the hidden price of data and rebalance bargaining power between firms and consumers.
LAURA VELDKAMP, December 2025 — F&D Magazine
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- The Hidden Price of Data