For years, the prevailing wisdom in digital regulation has been straightforward: more data equals more power. In this view, the sheer volume of information held by a tech giant creates an insurmountable moat, naturally leading to market dominance and the stifling of competition. However, a new economic perspective is challenging the ‘data equals power’ narrative, suggesting that the accumulation of data is not a guaranteed ticket to monopoly power.
Recent research conducted by 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, argues that the relationship between data and market power is far more nuanced than previously assumed. Their work, detailed in “Data-Driven Competition: Implications for Enforcement and Merger Control,” posits that the outcome for consumers depends not on how much data a firm possesses, but on how that data is actually deployed.
This shift in thinking has significant implications for how antitrust regulators evaluate “theories of harm” in the digital age. If data accumulation does not automatically lead to exclusionary power, the legal frameworks used to block mergers or penalize dominant firms may need to be recalibrated to distinguish between data used for innovation and data used for extraction.
The Tension Between Innovation and Extraction
At the heart of the economists’ argument is a critical distinction between two different ways firms utilize information. According to Taylor, data is not a monolithic asset; its effect on a market depends entirely on the business model it supports.
On one hand, data can be used to enhance the quality of a product. When a company uses data to refine a search algorithm, improve logistics, or personalize a recommendation, it creates a “positive feedback loop.” In this scenario, better data leads to a better product, which attracts more users, who in turn generate more data. This cycle can intensify rivalry as firms compete aggressively to offer the best possible user experience.
data can be used for “value extraction.” This occurs when a firm uses its information advantage not to improve the service, but to maximize revenue from existing users—for instance, through sophisticated price discrimination or by increasing the intensity of targeted advertising. In these cases, the user experience may stagnate or decline even as the firm’s profits grow.
The researchers suggest that the “data equals power” narrative fails because it ignores this tension. If a firm focuses solely on extracting value, it may actually break the feedback loop that sustains its growth, as users eventually migrate toward competitors who use data to provide a superior deal.
Recalibrating Regulatory ‘Theories of Harm’
This distinction creates a complex challenge for enforcement agencies. De Cornière warns that regulators often conflate different types of competitive harm, which can lead to inconsistent legal arguments. Specifically, he suggests that agencies should avoid pursuing theories that are simultaneously “exclusionary” and “exploitative.”
The logic is structural: a firm that is successfully exclusionary—meaning it uses data to build a product so superior that no one else can compete—is fundamentally different from a firm that is exploitative, using its dominance to squeeze more money out of a captive audience. By attempting to prove both at once, regulators may undermine the consistency of their own cases.
Measuring the Impact of Data Strategy
To move beyond theoretical assumptions, Taylor suggests that regulators glance toward observable engagement metrics. The difference between innovation and extraction is often visible in the data itself:
- Innovation indicators: Higher user engagement, improved search accuracy, and increased retention rates.
- Extraction indicators: Increased ad frequency, volatile pricing shifts for individual users, and stagnant product quality despite data growth.
The Role of Data Trade in Merger Analysis
The research also introduces a counterintuitive finding regarding corporate mergers. Traditional antitrust logic suggests that when two data-rich companies merge, the resulting concentration of data is almost always harmful to competition. However, de Cornière argues that the impact depends heavily on whether “data trade”—the ability to buy or sell data between firms—was possible before the merger.
If data trade is hampered or impossible (perhaps due to strict privacy regulations or technical silos), a merger might actually benefit the consumer. In such cases, the merger allows the combined entity to internalize the value of data across different markets for the first time, potentially creating incentives to improve products to attract a broader user base.
Conversely, if data can be easily traded on the open market, a merger is more likely to be harmful. In this environment, firms may use a merger specifically to limit others’ access to critical data, thereby creating a bottleneck that prevents competitors from entering adjacent markets.
| Usage Strategy | Primary Goal | Effect on Competition | Consumer Outcome |
|---|---|---|---|
| Product Improvement | Higher Quality | Intensifies Rivalry | Better Services |
| Value Extraction | Revenue Max | Increases Dominance | Price Discrimination |
| Data Hoarding | Market Entry Barrier | Exclusionary | Reduced Choice |
What This Means for the Future of Big Tech
For industry leaders, this framework suggests that data strategy is no longer just a technical concern, but a regulatory one. Companies that can demonstrate that their data collection leads to measurable improvements in consumer surplus are more likely to withstand antitrust scrutiny than those whose data is used primarily for monetization.
For regulators, the task is to shift from a quantitative assessment (how much data does this firm have?) to a qualitative one (what is the firm doing with that data?). The burden of proof is shifting toward identifying the specific mechanism of harm—whether it is the exclusion of rivals or the exploitation of users.
Disclaimer: This article is provided for informational purposes and does not constitute legal or financial advice regarding antitrust compliance or merger strategy.
As digital markets continue to evolve, the next critical checkpoint for these theories will be the upcoming series of court rulings and regulatory updates regarding the Digital Markets Act (DMA) in the European Union and ongoing antitrust litigation in the United States, which will test whether courts accept a more nuanced view of data-driven power.
We invite readers to share their perspectives on the balance between data privacy and market competition in the comments below.
