Data is the fuel for the artificial intelligence algorithms that have lifted stock markets to historic highs on the promise of transforming economies. But how do we determine data’s value? Data is not mined from the ground, not forged in factories. It accumulates unseen as a by-product of modern life: Every search, click, or morning walk with a phone in your pocket leaves a residue of information that someone, somewhere, can use.
When a good has no observable price—like a government service, for example—we typically value it at cost. But data has no explicit cost. When a retailer logs sales or a map app notes your location, that is data production. Of course, firms spend lavishly to process, analyze, and transform data. They hire armies of data scientists and invest in computing infrastructure to extract patterns from the noise. But the underlying raw data is like exhaust fumes from our economic engine. How do we value something that just appears?
The truth is that data is not free. We are all paid data producers. Once we comprehend that data is produced through transactions, a deeper economic logic emerges. If a profit-maximizing firm values the data it receives from customers, it has an incentive to encourage more transactions, because more transactions mean more data. Customers buy more when they pay less. Firms that fail to offer discounts will see customers turn to competitors that do. Thus profit-maximizing firms must discount their goods and services, not out of fairness, but to generate more sales and more data.
Most of the economy today operates under this hidden bargain. Every digital purchase, every app download, every click is a dual transaction: Consumers buy a good or service, and at the same time, they sell their data. The observable price—the amount of money that changes hands—is really the net price of these two exchanges. Firms get revenue and data; consumers get products and convenience.
Price bundling
Here’s the problem: As customers, we do not know what price, what discount, we received for data. This makes it impossible to know whether we received enough. Consumers typically do not have the option to purchase a good without selling their data. Requiring two transactions at the same time—in this case, a data sale and a product purchase—is what economists call bundling. By hiding the price of data, bundling ensures that customers get less.
Imagine arriving in a foreign country with a different currency. On arrival, you pay the equivalent of $18 for a lunch that should cost $3. After a few days, you learn when to haggle, when to walk away, and what price is fair. In the digital economy, we are perpetually that first-day tourist. We sell our data every time we browse or buy. But because the transaction is bundled, we never see the price. We can’t learn from experience.
Regulations that require firms to unbundle transactions—to post both the price with the right to use the transaction data and the price for a private transaction—would throw light on the data market. Consumers could observe the data discount. Some might decide it’s worth it; others might withhold their data unless the discount is substantial. Over time, consumers would evolve from naive tourists into savvy suppliers of data, demanding their share of the data economy gains.
The challenge for economists and policymakers is to turn data—an ambient, invisible asset—into something we can count, contain, and price. Economists have begun to build a data measurement tool kit. Each approach offers a different take on “value” and will be feasible in different situations.
