Market Data Feed Monopolization Concerns

1. Introduction

Market data feed monopolization refers to the acquisition, control, restriction, or strategic exploitation of commercially important data feeds by a dominant undertaking in a manner that prevents competitors from obtaining equivalent data, raises rivals’ costs, limits innovation, or entrenches market power.

A market data feed may contain:

• real-time financial prices and quotes;
• securities-trading information;
• commodity prices;
• shipping and logistics information;
• advertising or auction data;
• search and clickstream information;
• API-generated market information;
• exchange order-book data;
• benchmark or index information;
• industrial or IoT data; and
• data generated by a digital platform's users or transactions.

Competition law does not generally prohibit a company from owning or commercializing data. The concern arises where control over data becomes a source of durable market power and the undertaking uses that control to foreclose competitors.

The principal legal theories may include:

1. abuse of dominance;
2. refusal to supply/access;
3. essential-facility theories;
4. exclusive or discriminatory data access;
5. margin squeeze;
6. tying and leveraging;
7. self-preferencing;
8. predatory or excessive pricing of data access;
9. discriminatory licensing;
10. foreclosure through technical/API restrictions; and
11. anticompetitive acquisition or consolidation of data sources.

2. Why Market Data Feeds Can Create Monopoly Power

Market data has several characteristics that can make competition particularly difficult.

A. Real-time data advantage

A feed containing prices, orders, bids, offers, transactions or user activity may lose substantial value if delayed.

A competitor cannot necessarily recreate a historical dataset and compete effectively with a dominant undertaking possessing millisecond-level or continuously updated information.

B. Network effects

More users can generate more transactions and therefore more data.

More data can improve:

• prediction;
• pricing;
• matching;
• liquidity;
• personalization; and
• algorithmic performance.

Improved performance attracts additional users, producing still more data.

This creates a feedback loop:

Users → Transactions → Data → Better service → More users → More data

C. Data aggregation

A dominant undertaking may combine several datasets:

transaction data + customer data + behavioural data + market data + proprietary analytics.

The resulting dataset may be difficult for competitors to reproduce.

D. High switching costs

Customers may depend upon a particular feed because their:

• algorithms;
• trading systems;
• risk models;
• APIs;
• databases;
• compliance systems; and
• downstream applications

have been built around that feed.

E. Technical interoperability

Even where data is theoretically available, access can be undermined through:

• restrictive APIs;
• rate limits;
• incompatible formats;
• delayed updates;
• authentication barriers;
• excessive licensing requirements;
• discriminatory technical standards; or
• withdrawal of interoperability.

Thus, formal availability does not necessarily equal effective access.

3. When Does Data Control Become an Antitrust Problem?

Ownership of data alone is normally insufficient.

Competition authorities generally need to establish some combination of:

Relevant market → Dominance → Control over strategically important data → Anticompetitive conduct → Foreclosure → Consumer/competitive harm

The crucial question is therefore:

Does control over the data feed merely reflect legitimate commercial competition, or is it being used as an instrument to exclude or disadvantage competitors?

4. Relevant Market Definition

Market definition can be unusually complicated.

A data feed may constitute:

A. A distinct product market

For example:

real-time exchange-trading data

may be distinct from:

delayed or historical market information.

B. An upstream input market

The relevant market might be:

wholesale financial market-data feeds.

The downstream market could be:

trading platforms or financial analytics.

C. A multi-sided market

A platform may simultaneously serve:

• data suppliers;
• professional users;
• retail users;
• advertisers;
• developers; and
• downstream service providers.

Competition authorities therefore have to consider interactions between multiple sides of the ecosystem.

5. Dominance

Possession of a large database does not automatically establish dominance.

Relevant factors may include:

• market share;
• uniqueness of data;
• scale and depth of the dataset;
• frequency of updating;
• historical depth;
• accuracy;
• geographic coverage;
• interoperability;
• switching costs;
• network effects;
• economies of scale;
• access to alternative datasets;
• ability of competitors to replicate the information; and
• control of an indispensable infrastructure.

A particularly important distinction is:

Large dataset ≠ automatically dominant position.

The competition concern becomes stronger where competitors cannot realistically reproduce the relevant dataset within a commercially viable period.

6. Refusal to Provide Market Data

One of the most important theories is refusal to supply.

A dominant undertaking may:

• completely refuse access;
• terminate an existing feed;
• refuse API access;
• restrict redistribution;
• provide inferior data;
• impose discriminatory access conditions; or
• make access commercially impracticable.

The legal analysis must distinguish legitimate protection of:

• intellectual property;
• cybersecurity;
• confidentiality;
• privacy;
• investment incentives

from exclusionary conduct.

7. Essential-Facility Considerations

The essential-facility doctrine can become relevant where the feed is effectively indispensable for competing downstream services.

Typical considerations include:

1. Is the data facility controlled by a dominant undertaking?
2. Is access indispensable?
3. Can competitors realistically reproduce the information?
4. Is duplication technically or economically feasible?
5. Does refusal eliminate effective competition?
6. Is there an objective justification?
7. Can access be provided without destroying legitimate incentives to invest?

The threshold is generally high.

Courts are cautious about turning competition law into a general obligation to share commercially valuable assets.

8. Data Feed Discrimination

A particularly serious problem arises when a dominant undertaking supplies data to competitors but provides better data to itself.

For example:

Such conduct may support theories involving:

• discrimination;
• self-preferencing;
• leveraging;
• refusal to deal;
• margin squeeze; or
• abuse of dominance.

9. Margin Squeeze Through Data Feeds

A dominant undertaking may operate at two levels:

Upstream: supplies market data.

Downstream: competes using that data.

It may charge downstream rivals an excessively high price for the feed while using the same data internally at a much lower effective cost.

The conceptual structure is:

High wholesale data price + aggressive downstream pricing = potential margin squeeze

The relevant question is whether an equally efficient downstream competitor could profitably compete after purchasing the dominant firm's data input.

10. Self-Preferencing

A platform may provide market data to independent users while simultaneously giving its own downstream services:

• earlier access;
• richer data;
• greater API capacity;
• superior analytics;
• privileged information;
• preferential ranking; or
• exclusive technical functionality.

This can transform control over the feed into a mechanism of vertical foreclosure.

Self-preferencing is particularly important where the dominant undertaking simultaneously controls:

the infrastructure + the data + the downstream application.

11. Excessive Pricing of Market Data

Another possible theory is excessive pricing.

A dominant undertaking might charge unusually high prices for access to a data feed.

However, excessive pricing claims are difficult because:

• data creation may involve substantial investment;
• the value of information is difficult to quantify;
• pricing can legitimately reflect innovation;
• high prices may attract alternative data suppliers; and
• competition law generally does not regulate ordinary commercial prices.

The strongest case arises where there is evidence of very high prices combined with exclusionary restrictions and lack of realistic alternatives.

12. Data Quality Discrimination

Competition harm need not involve outright denial.

A dominant undertaking could supply:

• delayed information;
• incomplete information;
• lower-resolution data;
• fewer data fields;
• unreliable API access; or
• inferior technical documentation.

If rivals receive materially inferior inputs while the dominant undertaking retains superior internal access, the conduct can have exclusionary effects.

13. Licensing Restrictions

Licensing arrangements can also produce foreclosure.

Potentially problematic conditions include:

• prohibiting redistribution;
• prohibiting combination with other data;
• restricting algorithmic analysis;
• limiting the number of users;
• prohibiting downstream resale;
• imposing discriminatory geographic restrictions;
• restricting cloud processing; and
• requiring customers to purchase unrelated services.

Such restrictions must be assessed according to their competitive effects and legitimate commercial justification.

14. Important Case Laws

1. IMS Health GmbH & Co. OHG v NDC Health GmbH & Co KG

Court: Court of Justice of the European Union
Year: 2004

This is one of the most important cases concerning access to commercially valuable information infrastructure.

IMS Health controlled a particular pharmaceutical data structure used by pharmaceutical companies.

The CJEU considered the circumstances in which refusal to license an intellectual-property-protected resource could constitute an abuse of dominance.

The Court identified demanding conditions associated with compulsory access, including circumstances where access was indispensable, refusal was capable of eliminating competition in a downstream market, and access was unjustified.

Relevance to market data feeds

The case demonstrates that:

Control over an information structure does not automatically create an obligation to license it.

However, where the information resource becomes indispensable for downstream competition, refusal can potentially engage Article 102 TFEU.

15. Magill

Radio Telefis Éireann (RTÉ) and Independent Television Publications Ltd v Commission

Court: CJEU
Year: 1991

The case concerned television programme information controlled by broadcasting organizations.

The broadcasters refused to provide comprehensive programme information that was necessary for a new weekly television guide.

The CJEU developed the exceptional circumstances under which refusal to license protected information could constitute abuse.

Importance

Magill is highly relevant to market data because it establishes the conceptual foundation for analysing:

exclusive control over commercially indispensable information.

The case demonstrates that intellectual-property protection cannot necessarily be used as an absolute shield against competition law.

16. Bronner

Oscar Bronner GmbH & Co. KG v Mediaprint

Court: CJEU
Year: 1998

Bronner concerned access to a newspaper distribution system.

The Court adopted a strict approach to compulsory access.

A facility is not automatically essential merely because obtaining an alternative would be:

• expensive;
• inconvenient; or
• commercially difficult.

Relevance

This principle is extremely important for market data feeds.

A competitor cannot simply argue:

“The dominant firm's data would make competition easier.”

The stronger question is:

Can the competitor realistically obtain or generate an alternative data source?

If alternatives exist, compulsory access becomes much harder to justify.

17. Slovak Telekom

Slovak Telekom a.s. v European Commission

Court: CJEU
Year: 2021

This case concerned access to telecommunications infrastructure and exclusionary conduct involving a dominant operator.

The Court examined refusal/access conditions and margin-squeeze-related issues.

Relevance to data feeds

The case illustrates how a dominant upstream infrastructure provider can potentially harm downstream competition by controlling the conditions under which rivals obtain an essential input.

The principles can be adapted to:

data infrastructure → data feed → downstream digital service.

18. Deutsche Telekom

Deutsche Telekom AG v Commission

Court: CJEU
Year: 2010

The case is a major authority concerning margin squeeze.

A vertically integrated dominant undertaking could breach competition law where the relationship between its upstream and downstream prices made effective downstream competition impossible.

Relevance

The same logic can arise where:

Dominant data provider → sells feed to rivals → competes downstream using the feed itself.

If the wholesale price is sufficiently high relative to downstream prices, rivals may be unable to compete effectively.

19. Google Shopping

Google Search (Shopping) v European Commission

Court: General Court of the European Union
Year: 2021

The case concerned Google's treatment of its comparison-shopping service within general search results.

The Court upheld the central finding that Google's conduct could constitute abusive leveraging where the dominant search infrastructure systematically favoured its own downstream service and disadvantaged competitors.

Relevance to market data

Google Shopping is highly relevant to data-feed self-preferencing.

A comparable situation could arise where a dominant data platform:

1. controls a critical data feed;
2. provides access to competitors;
3. gives its own downstream service preferential access or functionality; and
4. thereby diverts competitive opportunities toward its own service.

20. Microsoft

Microsoft Corp. v Commission

Court: General Court
Year: 2007

Microsoft involved interoperability information and the ability of competitors to develop interoperable products.

The case demonstrated that withholding strategically important technical information can, in exceptional circumstances, produce exclusionary effects.

Relevance to market data

Market data is increasingly delivered through:

• APIs;
• software development kits;
• protocols;
• machine-readable formats.

Consequently, control over technical access to data can be as important as ownership of the underlying information.

21. United States v. Terminal Railroad Association

Court: U.S. Supreme Court
Year: 1912

This is a foundational U.S. antitrust case concerning control over essential transportation infrastructure.

A group of railroads controlled critical terminal facilities and effectively prevented competing railroads from obtaining equivalent access.

Relevance

Although the case predates modern digital markets, its fundamental principle is highly relevant:

Collective or dominant control over an indispensable bottleneck can create exclusionary power.

Modern data exchanges and centralized feeds can sometimes operate as information bottlenecks.

22. Aspen Skiing

Aspen Skiing Co. v. Aspen Highlands Skiing Corp.

Court: U.S. Supreme Court
Year: 1985

The Supreme Court found liability where a dominant undertaking terminated a profitable cooperative arrangement with a rival despite the prior relationship and lack of apparent legitimate business justification.

Relevance to data feeds

Aspen Skiing is relevant where a dominant data provider:

• historically supplied data;
• cooperated with a downstream competitor;
• abruptly terminates access; and
• has no convincing legitimate business justification.

It is particularly relevant to termination of an existing data-sharing relationship.

23. Trinko

Verizon Communications Inc. v. Law Offices of Curtis V. Trinko, LLP

Court: U.S. Supreme Court
Year: 2004

Trinko substantially limited the circumstances in which U.S. antitrust law requires a dominant firm to assist competitors.

The Court emphasized that forced sharing can weaken incentives for firms to invest in infrastructure.

Relevance

For market data:

A company does not automatically violate antitrust law merely because it refuses to share its proprietary data.

This is an important counterweight to essential-facility arguments.

24. The Core Legal Tension

The case law reveals two competing principles.

Principle 1 — Competition protection

A dominant undertaking should not be permitted to use control over an indispensable information resource to eliminate effective competition.

Magill, IMS Health, Microsoft, Google Shopping

Principle 2 — Protection of investment incentives

Competition law should not automatically require companies to share valuable infrastructure or intellectual property with competitors.

Bronner, Trinko

The central legal challenge is therefore to determine whether the data feed is:

merely valuable
or
effectively indispensable for competitive participation.

25. Market Data Feed Monopolization Through Different Conduct

26. Data Feed Monopolization and Network Effects

The concern becomes stronger where data creates a self-reinforcing competitive advantage.

For example:

Dominant platform

↓

Large user base

↓

More transactions

↓

More real-time data

↓

Better algorithms

↓

Better service

↓

More users

↓

Still more data

This creates a data-network-effect loop.

Eventually, even a technically capable competitor may be unable to reproduce the incumbent's information advantage.

27. The "Data Cannot Be Replicated" Problem

A particularly important question is whether competitors can generate substitute data.

Consider:

Easily replicable data

A competitor can collect the same publicly observable information.

Low competition concern.

Moderately replicable data

A competitor can reproduce the dataset but needs substantial investment.

Intermediate concern.

Non-replicable data

The data results from:

• proprietary transactions;
• historical accumulation;
• network participation;
• exclusive contracts;
• unique infrastructure; or
• legally protected relationships.

Much stronger foreclosure concern.

28. Market Data as a Bottleneck Input

A data feed may become a bottleneck where:

No meaningful downstream competitor can operate without it.

Examples might include:

• exchange trading data;
• dominant payment-network transaction information;
• critical shipping-rate feeds;
• benchmark information;
• dominant advertising-auction data;
• cloud telemetry;
• proprietary mobility data.

But indispensability must be demonstrated rather than assumed.

29. Objective Justifications

A dominant undertaking may have legitimate reasons for restricting access.

These can include:

• cybersecurity;
• privacy;
• intellectual-property protection;
• data accuracy;
• regulatory compliance;
• capacity constraints;
• fraud prevention;
• system stability;
• confidentiality;
• protection against data scraping.

Therefore, competition authorities should consider whether a less restrictive alternative could address the legitimate concern.

For example:

Instead of completely denying API access, could the provider impose reasonable authentication, rate limits and security requirements?

If yes, complete exclusion may be harder to justify.

30. Remedies

Where monopolization is established, possible remedies include:

A. Access remedies

Require non-discriminatory access to the feed.

B. API access

Mandate technically effective API interoperability.

C. Data portability

Permit users to transfer relevant data to competing providers.

D. Non-discrimination

Require equivalent access conditions for competitors and the dominant undertaking.

E. Firewalls

Prevent commercially sensitive upstream data from being improperly transferred to downstream competitive units.

F. Licensing remedies

Require reasonable and transparent licensing terms.

G. Structural remedies

In exceptional circumstances, separation of:

data infrastructure + downstream competitive service

may be considered.

31. Key Evidentiary Questions

A competition authority investigating market data monopolization should examine:

1. Who owns the data?
2. How is the data generated?
3. Can competitors reproduce it?
4. How quickly does the data become stale?
5. Are there alternative feeds?
6. What are the switching costs?
7. Is the feed indispensable?
8. Does the dominant firm compete downstream?
9. Does it provide itself superior access?
10. Are competitors subjected to discriminatory conditions?
11. Are access prices economically sustainable?
12. Has access previously been supplied?
13. Why was access restricted?
14. Is there an objective justification?
15. What would happen to competition without access?
16. Can a less restrictive remedy preserve competition?

32. Emerging Digital-Market Dimension

Modern competition law increasingly encounters a more complicated phenomenon:

The dominant undertaking may not simply own data; it may control the entire data-generation ecosystem.

For example:

Device → App → Platform → Transaction → Data → Analytics → AI model → Recommendation → New transaction

The competitive advantage may therefore come from controlling the entire data pipeline, rather than merely possessing a database.

This makes conventional market-definition and dominance analysis more difficult.

33. Relationship With AI

AI significantly increases the importance of market data feeds.

AI systems can use real-time feeds for:

• prediction;
• algorithmic trading;
• demand forecasting;
• autonomous pricing;
• fraud detection;
• logistics optimization;
• recommendation systems; and
• automated investment decisions.

A dominant data provider that restricts high-quality real-time data can therefore indirectly restrict competition in AI-enabled downstream markets.

The problem may become:

Data monopoly → AI performance advantage → downstream market dominance → additional data acquisition.

This can create another feedback loop.

34. Overall Legal Test

A useful analytical framework is:

Step 1 — Identify the data

What exactly does the feed contain?

Step 2 — Define the relevant market

Is the feed itself a market, or is it an input into another market?

Step 3 — Establish dominance

Does the undertaking possess durable market power?

Step 4 — Determine indispensability

Can competitors reasonably obtain or reproduce equivalent information?

Step 5 — Identify conduct

Was access:

• refused;
• restricted;
• delayed;
• degraded;
• overpriced;
• tied;
• discriminated against; or
• technically obstructed?

Step 6 — Establish foreclosure

Did the conduct materially impair competitors' ability to compete?

Step 7 — Examine effects

Consider:

• prices;
• quality;
• innovation;
• entry;
• consumer choice;
• data diversity; and
• downstream competition.

Step 8 — Assess justification

Does the undertaking have a legitimate, proportionate reason?

Step 9 — Design remedy

Choose the least intrusive remedy capable of restoring effective competition.

35. Conclusion

Market data feed monopolization is not simply a question of who owns the most data. The central competition-law question is whether control over a strategically important information resource is being converted into durable exclusionary power.

The strongest cases generally arise where:

a dominant undertaking controls a difficult-to-replicate data feed + competes downstream + restricts or discriminates against access + causes substantial foreclosure + lacks adequate objective justification.

The major cases—Magill, IMS Health, Bronner, Microsoft, Deutsche Telekom, Slovak Telekom, Google Shopping, Terminal Railroad, Aspen Skiing and Trinko—collectively establish an important balance:

• competition law can intervene against strategic exploitation of indispensable information and infrastructure;
• but courts remain reluctant to impose generalized data-sharing obligations;
• indispensability, foreclosure, proportionality and objective justification remain central;
• and the analysis becomes particularly important when data control, network effects, APIs, AI and vertical integration combine to create a self-reinforcing digital bottleneck.