Competition Law and Future Data Ownership and Competition Law

1. Introduction

Data has become a major input of the digital economy. Search engines, social networks, e-commerce platforms, online advertising, financial technology, cloud services, artificial intelligence and digital marketplaces increasingly compete not only through price, but through their ability to collect, aggregate, analyse, retain and exploit data.

The traditional concept of ownership is therefore difficult to apply to digital data. A single dataset may simultaneously involve:

• the individual who generated the information;
• the platform that collected it;
• an employer or business that supplied it;
• an intermediary that aggregated it;
• an analytics company that processed it; and
• an AI system that generated additional insights from it.

Competition law is consequently moving from a narrow question of “Who owns the data?” toward broader questions such as:

1. Who controls access to the data?
2. Can competitors obtain equivalent data?
3. Can a dominant undertaking use accumulated data to exclude rivals?
4. Can data be combined across services to reinforce market power?
5. Should users have portability rights?
6. Can refusing access to data constitute an abuse of dominance?
7. Can privacy restrictions themselves become a competitive parameter?
8. Should regulators treat certain datasets as essential competitive infrastructure?

Recent enforcement demonstrates this shift. The CCI's 2024 WhatsApp decision, for example, treated extensive data collection and sharing as potentially relevant to abuse of dominance, while the EU Digital Markets Act expressly provides certain data-access and portability rights against gatekeepers.

2. Meaning of Data Ownership in Competition Law

“Data ownership” is not a single legal concept.

It can encompass several different interests:

A. Ownership of the underlying information

This concerns whether the person or business from whom data originates has proprietary rights over it.

B. Control over the database

A platform may not own every individual piece of information but may control the database in which the information is organised.

C. Access rights

Competition law may focus on whether third parties can access the dataset on reasonable and non-discriminatory terms.

D. Portability

Users or businesses may have a right to transfer data from one platform to another.

E. Derived data

Algorithms can transform raw data into:

• behavioural profiles;
• rankings;
• predictions;
• risk scores;
• recommendations;
• market intelligence; and
• AI training datasets.

The resulting information can create another competitive asset.

Thus, control rather than formal ownership may become the central competition-law issue.

3. Why Data Creates Competition Concerns

3.1 Data as a competitive input

Data can be an important input for:

• search algorithms;
• targeted advertising;
• AI models;
• credit scoring;
• recommendation engines;
• fraud detection;
• autonomous systems;
• pricing algorithms; and
• customer acquisition.

A dominant undertaking controlling a uniquely valuable dataset may therefore possess a competitive advantage that rivals cannot easily reproduce.

3.2 Network effects

Digital platforms often exhibit network effects.

More users generate more data.

More data can improve the service.

A better service attracts more users.

More users generate still more data.

This produces a reinforcing cycle:

Users → Data → Better Algorithm → Better Service → More Users → More Data

Competition law must therefore examine whether control over data reinforces an existing dominant position.

4. Data Accumulation and Market Power

A large dataset does not automatically establish dominance.

Regulators must examine factors such as:

• uniqueness of the data;
• quality of the data;
• frequency of collection;
• scale;
• ability to replicate the dataset;
• availability of alternative sources;
• switching costs;
• network effects;
• data-processing capabilities;
• access to complementary datasets; and
• whether the data actually improves competitive performance.

Consequently, “big data” and “market power” are not synonymous.

The relevant question is whether control over data creates or strengthens a durable competitive advantage.

5. Data as a Non-Price Parameter of Competition

Digital services are frequently offered at zero monetary price.

Consumers instead compete or choose on parameters such as:

• privacy;
• data collection;
• service quality;
• interoperability;
• security;
• personalisation; and
• advertising intensity.

This makes privacy and data practices potentially relevant to competition law.

The European Court of Justice recognised this interaction particularly clearly in Meta Platforms v Bundeskartellamt.

6. Abuse of Dominance Through Data Control

A dominant undertaking may potentially abuse its position through:

6.1 Refusal to provide data

A dominant platform might deny access to commercially indispensable information.

6.2 Discriminatory access

The platform might provide data to affiliated businesses while denying equivalent access to competitors.

6.3 Self-preferencing

A platform could use proprietary data to favour its own downstream products.

6.4 Data tying

Access to one service could be conditioned upon permission to collect or combine additional data.

6.5 Excessive data collection

A dominant undertaking could impose extensive data-collection conditions that competitors could not impose.

6.6 Data leveraging

Data accumulated in one market may be used to strengthen a position in another market.

6.7 Data portability restrictions

Restricting users or businesses from transferring valuable information can increase switching costs and reinforce market power.

7. Six Major Case Laws

Case 1: Meta Platforms v Bundeskartellamt — Germany/EU

Court: Court of Justice of the European Union
Case: C-252/21
Judgment: 4 July 2023

This is one of the most important authorities concerning data and competition law.

The German competition authority objected to Facebook making use of its social-network service conditional upon extensive combination of Facebook data with “off-Facebook” data obtained from other websites, apps and Meta services.

The CJEU held that a competition authority examining abuse of dominance under Article 102 TFEU may, where necessary, consider whether the undertaking's processing of personal data complies with the GDPR, subject to the required cooperation with data-protection authorities.

Competition-law significance

The case demonstrates that:

• privacy can constitute a competitive parameter;
• data collection can be relevant to abuse of dominance;
• competition authorities may need to consider data-protection rules;
• dominant platforms cannot necessarily separate privacy practices from competition analysis.

Future significance

This case supports a more integrated approach to competition + privacy + data governance.

Case 2: In Re Updated Terms of Service and Privacy Policy for WhatsApp Users — India

Authority: Competition Commission of India
Case: Suo Motu Case No. 01/2021 and related proceedings
Important order: 18 November 2024

The CCI examined WhatsApp's 2021 privacy-policy update.

The policy required users to accept expanded data-sharing arrangements to continue using the service. The CCI found abuse of dominance involving unfair conditions and the use of WhatsApp user data in ways that affected competition in online advertising. It imposed a monetary penalty of ₹213.14 crore and behavioural directions.

The subsequent NCLAT proceedings in 2025 substantially upheld the CCI's findings concerning the relevant abusive conduct.

Competition-law significance

The case is important because it treats:

Data collection → data sharing → cross-platform data advantage → competitive foreclosure

as a possible chain of anticompetitive effects.

Principle

Data-related terms imposed by a dominant digital platform can potentially constitute an abuse where they:

• exploit users;
• reduce meaningful choice;
• create data advantages;
• disadvantage competitors; or
• reinforce dominance in related markets.

Case 3: Matrimony.com v Google — India

Authority: Competition Commission of India
Cases: 07/2012 and 30/2012
Decision: 31 January 2018

The CCI examined Google's conduct in online search and search advertising.

Among the allegations was preferential treatment of Google's own vertical services in search results. The CCI delineated a relevant market for online search advertising and found Google dominant in that market.

Data relevance

Although this was not principally a “data ownership” case, it is important for understanding the future relationship between data control, search dominance and vertical integration.

A search platform possesses extensive information concerning:

• user queries;
• preferences;
• behaviour;
• advertisers;
• clicks;
• commercial intent.

Such information can potentially provide an advantage to the platform's own downstream businesses.

Future principle

Competition authorities may therefore need to examine whether:

control over user-generated information combined with control over a gateway market enables preferential treatment of affiliated services.

Case 4: Google Android — CCI

Authority: Competition Commission of India
Case: 39/2018
Decision: 20 October 2022

The CCI found Google dominant in relevant markets involving licensable mobile operating systems and Android app stores and identified several practices involving pre-installation, restrictions and leveraging.

The CCI concluded, among other things, that Google's practices could protect its position in search and other related markets.

Data significance

Android provides a particularly important ecosystem because operating-system control can generate access to:

• search behaviour;
• application usage;
• location-related information;
• device information;
• user preferences;
• advertising information.

Therefore, ecosystem control + data accumulation + default placement can reinforce competitive advantages.

Future principle

Data competition cannot always be analysed independently from:

• operating systems;
• app stores;
• default settings;
• advertising;
• search;
• interoperability.

Case 5: hiQ Labs v LinkedIn — United States

Court: U.S. Court of Appeals for the Ninth Circuit
Decision: 2022

This case involved access to publicly available LinkedIn profile information.

hiQ used automated scraping to collect public professional information from LinkedIn and develop analytics products. LinkedIn attempted to block the access.

The Ninth Circuit affirmed preliminary relief preventing LinkedIn from denying hiQ access to publicly available member profiles. The court also noted that LinkedIn did not possess an ownership interest in the user-contributed data equivalent to exclusive ownership; LinkedIn had a non-exclusive licence to the data.

Competition significance

The case illustrates a crucial distinction:

Platform control ≠ necessarily exclusive ownership of all user-generated data.

This is particularly important for future data markets.

If a platform can selectively prevent competitors from accessing publicly available information, questions can arise concerning:

• exclusion;
• unfair competition;
• data access;
• innovation;
• competitive foreclosure.

Future principle

Data-access regulation may increasingly distinguish between:

1. genuinely proprietary confidential data;
2. user-generated data;
3. publicly available information;
4. aggregated data; and
5. derived analytical information.

Case 6: FTC v Facebook / Meta — United States

Authority: U.S. Federal Trade Commission
Proceeding: FTC v Facebook, Inc.

The FTC alleged that Facebook unlawfully maintained monopoly power in personal social networking through a course of conduct including acquisitions of Instagram and WhatsApp and restrictions concerning API access. The case remains an important U.S. competition-law authority concerning platform control and access.

Data significance

The case illustrates how data can become strategically important when a platform:

• controls a large user network;
• accumulates user information;
• controls developer access;
• determines interoperability conditions; and
• acquires potential competitors.

Competition principle

Data may contribute to competitive entrenchment even where the conduct being challenged is not formally described as “data ownership.”

The broader question is whether control over data and ecosystem access contributes to maintaining monopoly power.

8. Additional Important Authority: Cambridge Analytica

In re Cambridge Analytica LLC — FTC

The FTC brought proceedings concerning deceptive collection and use of Facebook information by Cambridge Analytica.

The FTC alleged that information relating to tens of millions of Facebook users was obtained through an application and used for profiling and targeting.

This was principally a consumer-protection/data-governance proceeding rather than a conventional dominance case.

Nevertheless, it demonstrates why competition authorities increasingly have to consider:

• data provenance;
• consent;
• data sharing;
• third-party access;
• data aggregation; and
• downstream exploitation.

9. Data Portability as a Competition Remedy

Data portability can reduce switching costs.

For example:

Platform A → User data → Platform B

If users cannot transfer their information, moving to a competing platform becomes more difficult.

Portability can therefore facilitate:

• multi-homing;
• entry;
• innovation;
• switching;
• interoperability; and
• contestability.

The EU Digital Markets Act expressly provides data-access and portability mechanisms for certain gatekeeper services. The European Commission describes these rights as intended to provide businesses and users with access to valuable data and promote competition and innovation.

10. Data Access and Essential Facilities

One of the most difficult future questions is whether certain datasets should be treated as an essential facility.

A dataset could become particularly significant where:

1. it is uniquely valuable;
2. competitors cannot reasonably reproduce it;
3. access is necessary to compete;
4. the data controller is dominant; and
5. refusal substantially impairs competition.

However, mandatory access should not automatically follow merely because a dataset is commercially useful.

Regulators must balance:

• incentives to collect data;
• investment incentives;
• privacy;
• cybersecurity;
• confidentiality;
• intellectual property;
• commercial autonomy; and
• competitive access.

11. Data Tying

A particularly important future concern is data tying.

Suppose a dominant platform provides Service A but requires the customer to permit collection and combination of information relating to Service B.

The competitive concern could be:

Dominant Service A → Mandatory Data Collection → Data Advantage → Service B → Entrenchment

The WhatsApp case demonstrates the increasing relevance of this theory in digital markets.

12. Data-Based Self-Preferencing

A platform may possess information about third-party businesses operating on its ecosystem.

For example:

Marketplace → seller data → platform obtains sales information → platform launches competing product

Potential concerns include:

• copying successful products;
• preferential ranking;
• discriminatory access;
• preferential advertising;
• use of competitor data; and
• leveraging marketplace data into downstream markets.

This creates a future competition-law question:

Should a platform be permitted to use competitively sensitive information generated by its business users to compete against those same businesses?

13. Data Portability and Interoperability

Data portability alone may not be sufficient.

A user may technically be able to export data but still face difficulties if the competing platform cannot use it.

Therefore, future regulation may increasingly focus on:

Data portability

Ability to move data.

Interoperability

Ability of different systems to work together.

Real-time access

Ability to access continuously updated information.

API access

Machine-readable access to information.

Standardisation

Common formats for transferring data.

The EU's current DMA framework illustrates this movement toward legally structured data access and interoperability obligations.

14. Data Ownership and Artificial Intelligence

AI makes data ownership substantially more complicated.

AI developers may require:

• training datasets;
• user interaction data;
• behavioural data;
• copyrighted materials;
• synthetic data;
• proprietary databases;
• domain-specific datasets.

A company possessing a uniquely valuable dataset may therefore obtain a substantial AI advantage.

Future competition disputes may concern:

A. Training-data foreclosure

A dominant platform prevents AI rivals from accessing valuable information.

B. Exclusive data agreements

A platform enters exclusive arrangements preventing competitors from obtaining comparable datasets.

C. Data feedback loops

AI service → users → interactions → data → better AI → more users.

D. AI ecosystem leveraging

A company uses data obtained from one dominant service to strengthen an AI service in another market.

E. Data scraping restrictions

Platforms may restrict competitors from collecting information that is publicly accessible.

15. Synthetic Data and Competition

Synthetic data creates another issue.

If a dominant undertaking generates synthetic datasets using its uniquely valuable proprietary data, the resulting dataset may become a competitive asset even though the synthetic dataset does not contain identical copies of the original information.

Competition authorities may therefore need to distinguish:

Raw Data → Processed Data → Derived Data → Synthetic Data → AI Model

The competitive value can exist at every stage.

16. Data Sharing Agreements

Data-sharing agreements can produce both pro-competitive and anticompetitive effects.

Potential benefits

• improved innovation;
• reduced duplication;
• better fraud detection;
• improved safety;
• increased interoperability;
• new products;
• lower costs.

Potential risks

• exchange of competitively sensitive information;
• coordinated pricing;
• exclusion of competitors;
• discriminatory access;
• creation of data monopolies;
• collective dominance;
• increased entry barriers.

Therefore, competition law must distinguish legitimate data collaboration from information exchange that facilitates coordination.

17. Data Pools and Data Cartels

Competitors may jointly create a data pool.

For example:

Competitor A + Competitor B + Competitor C → Common Data Pool

This can be legitimate where it improves innovation.

But the arrangement may become problematic if competitors use the pool to exchange:

• prices;
• output;
• customers;
• strategic plans;
• costs;
• future business decisions.

Thus, data pooling can create a new form of algorithmic information exchange.

18. Data as a Barrier to Entry

Data can create entry barriers through several mechanisms.

First-mover advantage

The incumbent possesses years of historical information.

Learning effects

More data improves algorithms.

Network effects

More users create more data.

Switching costs

Users cannot easily transfer accumulated information.

Economies of scope

A company can combine information from multiple services.

Feedback effects

Better service generates more users, which produces more data.

The resulting structure can become:

Data Advantage → Quality Advantage → User Advantage → Further Data Advantage

19. Future Regulatory Models

Future competition regulation is likely to move toward several complementary models.

Model 1: Data portability

Users can transfer their data.

Model 2: Business-user access

Businesses receive access to data generated through their activities.

Model 3: Interoperability

Competing services can interact.

Model 4: Non-discriminatory access

Dominant platforms cannot selectively provide data to affiliated businesses.

Model 5: Data-use restrictions

Data collected for one purpose cannot automatically be exploited for unrelated competitive purposes.

Model 6: Data silos

Certain sensitive datasets may need organisational separation.

Model 7: Data trustees/intermediaries

Independent institutions may manage access to strategically important datasets.

Model 8: Sector-specific data access

Healthcare, finance, energy, transport and telecommunications may receive specialised access regimes.

20. Competition Law and Data Protection Law

The future legal framework will increasingly require coordination between:

Competition authorities

Focus on:

• market power;
• exclusion;
• exploitation;
• entry barriers;
• consumer welfare.

Data-protection authorities

Focus on:

• lawful processing;
• consent;
• purpose limitation;
• data minimisation;
• individual rights.

Digital regulators

Focus on:

• interoperability;
• portability;
• platform obligations;
• gatekeeper conduct.

Intellectual-property authorities/courts

Focus on:

• database rights;
• copyright;
• trade secrets;
• proprietary algorithms.

The Meta v Bundeskartellamt judgment illustrates this institutional overlap and expressly requires cooperation between competition and data-protection authorities in appropriate circumstances.

21. Indian Legal Framework

In India, future data-competition disputes are likely to involve interaction between:

Competition Act, 2002

Especially:

• Section 3 — anti-competitive agreements;
• Section 4 — abuse of dominant position;
• Section 5 — combinations;
• Section 19 — information and investigation;
• Section 26 — investigation;
• Section 27 — orders against abuse.

Digital Personal Data Protection Act, 2023

Relevant to personal-data processing and individual data rights.

Sectoral regulation

Depending on the market, regulators such as RBI, TRAI, IRDAI and other sectoral authorities may also become relevant.

The CCI's WhatsApp proceedings demonstrate that Indian competition law is increasingly addressing the competitive consequences of data collection and cross-platform data sharing.

22. Future Competition Problems

The following issues are likely to become increasingly important:

23. Six Core Case-Law Principles

24. Emerging Legal Test for Data-Related Competition Cases

A useful analytical framework is:

Step 1 — Identify the data

What type of information is involved?

Step 2 — Identify the controller

Who technically controls collection, storage and access?

Step 3 — Determine the relevant market

Where does the data provide a competitive advantage?

Step 4 — Establish market power

Does the undertaking possess substantial market power?

Step 5 — Examine replicability

Can competitors reasonably obtain equivalent data?

Step 6 — Examine conduct

Is there:

• refusal;
• discrimination;
• tying;
• self-preferencing;
• exclusive dealing;
• excessive collection;
• interoperability restriction; or
• data leveraging?

Step 7 — Establish competitive effects

Does the conduct:

• foreclose competitors;
• raise entry barriers;
• reduce innovation;
• increase switching costs;
• reduce consumer choice; or
• reinforce dominance?

Step 8 — Consider efficiencies

Could the practice produce:

• innovation;
• security;
• privacy;
• quality improvements;
• lower costs?

Step 9 — Select remedy

Possible remedies include:

• access;
• portability;
• interoperability;
• non-discrimination;
• data separation;
• restrictions on cross-use;
• behavioural commitments; or
• structural remedies in exceptional cases.

25. Future Direction

The central transformation is from “data ownership” to “data governance and competitive access.”

Competition law is unlikely to treat every dataset as an essential facility or every large database as a monopoly asset. Instead, future enforcement will probably focus on circumstances in which control over data produces durable market power or enables exclusionary or exploitative conduct.

The most important emerging concepts are therefore:

Data control → Data access → Data portability → Interoperability → Data leverage → Algorithmic advantage → Market power

The European DMA's current data-access measures, including measures concerning access to Google Search data, show that regulation is moving beyond traditional ex-post antitrust enforcement toward ex-ante obligations for powerful digital gatekeepers.

26. Conclusion

Future competition law will increasingly regard data as a strategic competitive resource rather than merely an informational asset.

The principal legal question will not simply be “Who owns the data?” but:

Who controls access to the data, how that control affects competition, and whether the resulting advantage can be legitimately maintained?

The major cases involving Meta, WhatsApp, Google, LinkedIn and Facebook demonstrate different dimensions of this transformation.

The future regulatory model is therefore likely to combine co