Economists Challenge the ‘Data Equals Power’ Narrative

Data has become the decisive input in digital markets, but its effects are neither uniform nor predictable. That’s forcing regulators and firms to confront a more complex set of trade-offs than traditional competition frameworks anticipated.

That was the throughline of a Competition Policy International interview, conducted by the PYMNTS-owned publication, with Alexandre de Cornière, professor of economics and director of the Competition Policy and Regulation Center at the Toulouse School of Economics, and Greg Taylor, professor of digital markets and competition at the Oxford Internet Institute, University of Oxford.

Their research, detailed in “Data-Driven Competition: Implications for Enforcement and Merger Control,” rejects the premise that data accumulation alone determines market power.

How Data Is Used Determines Competitive Outcomes

Taylor drew a clear line between two distinct economic effects. “Data is not this monolithic thing, but it can be used in many different ways, in different kinds of business models,” he said.

He continued: “That data is going to make firms better able to monetize their customers, and that makes each customer more valuable. That is going to spur firms to compete more intensely to attract those valuable customers.”

However, he paired that with a second observation: that data can also be used to make firms more efficient at “extracting value from those customers, for example by enabling price discrimination.”

This tension sits at the center of data-driven competition. When data is used to improve services, it can raise quality and intensify rivalry. When it is used to refine targeting or pricing, it can increase revenue without improving outcomes for users.

The Dynamic Nature of Data

De Cornière emphasized that data must be evaluated over time. “Data has a dynamic dimension,” he said, explaining that more users today generate more data tomorrow, which then shapes future competition.

The dynamic quality of that information flow underpins the idea of feedback loops. Firms that attract users collect more data, which can be used to improve products and draw in additional users.

Yet that loop is not guaranteed to reinforce dominance. Taylor explained the condition that determines whether it holds. “We need to believe that having better data allows firms to produce better products, and that brings more users, and with them, more data.”

If that condition is met, the cycle strengthens a firm’s position. If it is not, the loop can stall. Taylor noted that when data is used primarily to extract value from users rather than improve the product, “that is not going to be something that attracts a lot of people to your products, and that will then break the cycle.”

Positive Feedback Versus Consumer Harm

The distinction between improving products and extracting value explains why data can produce sharply different outcomes across markets.

When firms use data to refine recommendations, search results or logistics, the result is often higher engagement and stronger competition for users. When firms use data to increase advertising intensity or tailor pricing, the outcome may be higher revenue with little improvement in user experience.

Taylor pointed to measurable indicators as a way to distinguish between these outcomes. “If we think the data is being used mostly to train better search algorithms, then we should be able to use engagement metrics to see if that is reflected in the way consumers interact with those algorithms,” he told CPI. “If data is being used to target ads, that is something that we could measure as well.”

For industry leaders, that framework ties data strategy directly to observable outcomes. It also signals where regulatory scrutiny is likely to focus.

Separating Theories of Harm

The interplay between these effects complicates enforcement. De Cornière cautioned against combining incompatible theories when assessing competitive harm.

“For consistency of the argument, agencies should probably avoid pursuing theories of harm that are both exclusionary and exploitative at the same time,” he said.

The reason is structural. A firm that uses data to improve products and attract users may build a durable competitive position. A firm that uses data to extract value risks undermining its own growth by reducing the attractiveness of its offering.

Regulators therefore face a choice. They must determine which mechanism is operating in a given market and build their case accordingly.

Data Trade and the Structure of Mergers

Those distinctions become more pronounced in merger analysis. The research highlights the role of data trade, particularly whether firms can share or sell data before combining.

De Cornière described a key result: “The merger is more likely to benefit consumers if data trade is hampered or impossible.”

In such cases, firms may not be able to monetize data across markets before a merger. Combining those firms can create incentives to improve products in order to collect more data, which can increase consumer surplus.

When data can be traded, the risks shift. Firms may use mergers to limit access to data, reducing competition in adjacent markets.

That places data trade at the center of merger review. Regulators must examine whether firms exchange data prior to a deal, whether they plan to do so and why such exchanges may be constrained.

Evaluating When a Deal Should Proceed

The framework also requires regulators to examine the source of those constraints. If data cannot be traded because of privacy rules, allowing firms to combine data through a merger may conflict with those protections. If constraints arise from coordination problems or concerns about misuse, mergers may enable more efficient use of data.

De Cornière framed the issue as a question of incentives. When firms cannot trade data, they may not fully internalize its value across markets. A merger can change that calculation by aligning incentives and encouraging firms to improve their offering to attract users.

For regulators, the task is to determine which effect dominates in each case. For firms, the task is to understand how data strategy affects both growth and scrutiny.

As de Cornière said, the logic ultimately depends on whether data leads firms to offer better outcomes: “More data needs to allow the firm to offer a better deal to consumers.”