Competition Law and Future Regulation of Value Analytics Markets

1. Introduction

Value Analytics Markets may be understood as markets in which firms use data, artificial intelligence, algorithms, predictive models and real-time analytics to determine, measure, allocate or optimise economic value. These markets can include:

• customer lifetime-value analytics;
• dynamic pricing and revenue-management systems;
• credit and risk analytics;
• advertising-value measurement;
• retail and e-commerce analytics;
• supply-chain and procurement analytics;
• financial and investment analytics;
• insurance analytics;
• health and pharmaceutical value analytics;
• algorithmic valuation of assets and services;
• AI-generated business intelligence;
• data-driven benchmarking and market intelligence.

The competition-law difficulty is that the analytical capability itself may become a competitive asset. A dominant undertaking with access to enormous datasets, superior computing infrastructure, behavioural information and sophisticated AI models may be able to convert those advantages into market power.

Thus, future competition regulation is likely to examine not merely price and market share, but also:

Who possesses the data, who controls the analytical infrastructure, who determines the valuation methodology, who receives the resulting intelligence, and whether rivals can obtain the inputs necessary to compete?

Recent enforcement illustrates this movement. The EU's Digital Markets Act has already required Google to facilitate access to certain search data for eligible competitors, while recent U.S. Google proceedings have produced data-sharing and interoperability remedies in digital markets.

2. Meaning of Value Analytics Markets

A Value Analytics Market can be represented as:

Raw Data → Data Processing → Analytics → Prediction/Valuation → Commercial Decision → Economic Value

For example:

Consumer data → behavioural analytics → purchasing prediction → customer-value score → targeted advertising → advertising revenue

or:

Transaction data → risk analytics → credit score → risk-based pricing → lending decision

or:

Seller data → marketplace analytics → product-value prediction → ranking/pricing → consumer allocation

The competition issue arises when one enterprise controls several stages of this chain.

3. Essential Features of Value Analytics Markets

A. Data intensity

Large datasets may improve the accuracy of analytical models.

B. Feedback loops

More users generate more data, which improves analytics, which attracts more users, generating still more data.

This can create:

Scale → Data → Better Analytics → More Users → More Data

C. Algorithmic advantages

Superior algorithms can enable firms to predict demand, consumer behaviour, price sensitivity and competitive responses.

D. High switching costs

Businesses may become dependent on a particular analytics provider because historical datasets, models, APIs and dashboards are difficult to transfer.

E. Network effects

Analytics platforms may become more valuable when more participants contribute data.

F. Information asymmetry

The platform may know considerably more about customers and suppliers than the businesses that depend upon it.

4. Competition-Law Issues

4.1 Abuse of Dominance

A dominant analytics provider may engage in:

• discriminatory access;
• refusal to supply data;
• tying;
• bundling;
• self-preferencing;
• exclusionary licensing;
• discriminatory API access;
• predatory pricing;
• excessive pricing for essential datasets;
• discriminatory ranking;
• exploitation of business-generated data.

The important question is whether the conduct protects or extends market power by excluding equally efficient competitors, rather than merely reflecting legitimate innovation.

5. Data as a Competitive Input

A central future issue will be whether particular datasets constitute competitively significant inputs.

Consider:

Platform A controls ten years of transaction data while Platform B is attempting to enter the analytics market.

If Platform A refuses reasonable access to the relevant data and simultaneously uses the information to offer competing analytics services, competition authorities may investigate:

1. whether the data is indispensable;
2. whether duplication is realistically possible;
3. whether access can be technically provided;
4. whether refusal excludes competition;
5. whether legitimate privacy or security justifications exist.

The traditional essential-facilities doctrine may therefore intersect with data-access regulation.

6. Self-Preferencing in Value Analytics

Suppose an online marketplace operates:

• an analytics service;
• a seller marketplace; and
• its own retail operation.

The platform can use seller-generated information to identify:

• profitable products;
• price elasticity;
• consumer preferences;
• seasonal demand;
• competitor weaknesses.

It could then use this information to favour its own products.

This creates a potential:

Information Advantage → Replication → Preferential Ranking → Customer Diversion → Market Expansion

The UK CMA's Amazon Marketplace investigation illustrates this type of concern. The CMA investigated Amazon's use of third-party seller data as well as Buy Box selection and delivery-rate practices, eventually accepting commitments.

7. Case Law 1 — Google Search (Shopping)

Case

European Commission v Google — Google Search (Shopping), Case AT.39740

Principle

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

The broader competition principle is that a dominant platform's control over an important information-distribution mechanism can become problematic when it uses that position to favour its own downstream service.

Relevance to Value Analytics

Value analytics platforms may similarly control:

• search;
• recommendation;
• ranking;
• valuation;
• market intelligence.

If the platform systematically favours its own analytical products, competition authorities may examine whether the platform is leveraging its position from an upstream information infrastructure into an adjacent analytics market.

The issue has become even more important under the DMA, under which the European Commission has addressed self-preferencing by Google in vertical search services.

8. Case Law 2 — Google Android

Case

European Commission v Google — Android, Case AT.40099

Principle

The Android proceedings examined Google's contractual practices involving mobile-device manufacturers and application distribution.

Relevance

Value analytics frequently operates through ecosystems.

A company controlling:

Operating System → App Distribution → Data Collection → Analytics → Advertising

can potentially use one layer to strengthen another.

Future competition analysis therefore needs to examine ecosystem leverage, rather than analysing every analytical service as an isolated market.

9. Case Law 3 — Meta Platforms v Bundeskartellamt

Case

Meta Platforms Inc. and Others v Bundeskartellamt, C-252/21

Principle

The case concerned the interaction between competition law and Meta's processing and combination of personal data.

The European Court of Justice addressed the circumstances in which a competition authority may consider data-protection issues when examining potentially abusive conduct by a dominant undertaking.

Importance for Value Analytics

This is particularly important because analytics markets depend heavily upon:

• personal data;
• behavioural data;
• cross-service data;
• profiling;
• prediction;
• consumer segmentation.

The case demonstrates that competition analysis and data-governance regulation can intersect.

Consequently:

Data protection compliance can become relevant to the competitive assessment of data-driven conduct without turning competition law into general data-protection law.

10. Case Law 4 — Amazon Marketplace

Case

CMA — Investigation into Amazon's Marketplace

The CMA investigated Amazon's use of third-party seller data, Buy Box selection and other marketplace practices. The investigation was closed following commitments.

Competition significance

This is directly relevant to Value Analytics Markets because marketplace data can reveal:

• seller prices;
• sales volumes;
• product performance;
• consumer preferences;
• inventory information;
• competitive strategies.

A vertically integrated platform can therefore become both:

Data intermediary + competitor

That dual role creates a potential conflict of interest.

Future principle

Regulators may increasingly examine whether platform-generated analytics derived from business users' data can legitimately be used to compete against those same businesses.

11. Case Law 5 — Google Ad Technology

Case

United States v Google LLC — Digital Advertising Technology

The U.S. Department of Justice brought proceedings concerning Google's alleged monopolization of key digital advertising technology markets. In April 2025, the district court found Google liable for monopolization in important open-web digital advertising markets. In September 2026, the court ordered additional behavioural relief involving interoperability and data-sharing.

Relevance

Advertising is essentially a value analytics market.

The system analyses:

• users;
• audiences;
• impressions;
• advertiser demand;
• publisher inventory;
• conversion probability;
• expected advertising value.

The case therefore demonstrates how control over analytical infrastructure can affect an entire commercial ecosystem.

The 2026 remedies included requirements concerning access to and export of certain publisher data, demonstrating the increasing significance of data portability and interoperability as competition remedies.

12. Case Law 6 — Google Search — U.S. Antitrust Litigation

Case

United States et al. v Google LLC

The U.S. proceedings concerning Google's search monopoly addressed exclusionary arrangements and access to search distribution.

The subsequent remedies included requirements concerning availability of certain search-index and user-interaction data to qualifying rivals and potential rivals.

Relevance to Value Analytics

Search data has significant analytical value because it can reveal:

• consumer intent;
• product demand;
• emerging trends;
• commercial preferences;
• geographic behaviour;
• query patterns.

The competition problem therefore becomes:

Can control over historically accumulated information prevent new analytical competitors from achieving sufficient scale and accuracy to compete?

This is one of the central questions for future Value Analytics regulation.

13. Case Law 7 — Google–ITA Software

Case

United States v Google Inc. and ITA Software Inc.

The DOJ's proceedings concerning Google's acquisition of ITA Software involved a vertical merger in travel-information technology. The transaction was resolved through a final judgment containing competition-related safeguards.

Relevance

ITA's technology involved highly valuable travel-information and pricing infrastructure.

The case illustrates an important future concern:

Analytics acquisition → control over specialised information → downstream market advantage

Future merger control may therefore scrutinise acquisitions of:

• data analytics firms;
• pricing engines;
• AI valuation companies;
• risk-scoring platforms;
• market-intelligence providers.

14. Algorithmic Collusion

Value Analytics Markets create a new form of competition concern.

Suppose competing firms use similar pricing analytics.

Each algorithm observes:

• competitors' prices;
• demand;
• inventory;
• consumer response.

Algorithms may then adjust prices rapidly.

Potential outcomes include:

Competitor data → algorithmic observation → automated response → parallel pricing

Competition authorities will need to distinguish between:

Legitimate parallel conduct

Independent optimisation based upon market conditions.

and

Anti-competitive coordination

Actual communication, agreement or concerted conduct facilitated by algorithms.

The future challenge is particularly significant because algorithms can potentially coordinate faster than traditional human decision-making.

15. Algorithmic Information Exchange

Competitors may use a common analytics provider.

For example:

Manufacturer A → analytics platform ← Manufacturer B

If the analytics platform receives competitively sensitive information from both manufacturers and distributes sufficiently detailed information back to them, the arrangement could raise concerns involving:

• information exchange;
• coordination;
• market transparency;
• price alignment;
• output coordination.

The regulatory focus will increasingly shift from who communicates with whom to what information the analytical infrastructure enables competitors to observe.

16. Value Analytics and Merger Control

Traditional merger analysis often considers:

• market share;
• concentration;
• entry barriers;
• efficiencies;
• unilateral effects;
• coordinated effects.

Future analytics acquisitions may require additional questions:

Data concentration

Will the transaction combine uniquely valuable datasets?

Model concentration

Will competitors lose access to an important analytical model?

Feedback-loop effects

Will the merger allow the combined entity to obtain more data and thereby improve its analytics?

Vertical foreclosure

Could the merged firm deny analytics to downstream rivals?

Killer acquisitions

Could an incumbent acquire a promising analytical start-up before it becomes a meaningful competitor?

17. Data Portability as a Competition Remedy

Future regulators may increasingly require:

• machine-readable data portability;
• API access;
• interoperability;
• export of historical analytics;
• model portability;
• interoperability standards.

The recent Google search-data measures under the DMA provide a concrete example of regulatory movement toward mandated data access where market power makes such access competitively significant.

18. Interoperability

Interoperability can prevent analytics providers from creating closed ecosystems.

For example:

CRM → Analytics Provider A → Pricing Engine

should potentially be capable of becoming:

CRM → Analytics Provider B → Pricing Engine

without requiring the customer to reconstruct years of analytical history.

Thus, interoperability can reduce:

• switching costs;
• lock-in;
• entry barriers;
• dependency on dominant providers.

19. The Problem of Analytical Opacity

A further future issue is algorithmic opacity.

Businesses may not know:

• why their products receive a particular valuation;
• why their advertisements receive particular prices;
• why their sellers receive particular rankings;
• why their credit or risk score changes;
• why their products receive lower visibility.

Competition law may therefore increasingly examine:

Whether opaque analytical systems can be used to discriminate against competitors while making the discriminatory mechanism difficult to detect.

20. Predatory Analytics

Traditional predatory pricing involves selling below cost to eliminate competitors.

In analytics markets, the equivalent could be:

Free or below-cost analytics → rapid customer acquisition → competitor exit → dependency → subsequent monetisation

For example, a dominant platform could offer advanced business analytics for free while imposing substantial costs on independent competitors providing comparable services.

Authorities would need to distinguish:

• legitimate innovation;
• introductory pricing;
• cross-subsidisation;
• genuine efficiency;
• exclusionary below-cost strategies.

21. Tying and Bundling

A dominant firm could bundle:

Cloud Computing + Analytics + AI Model + Data Storage + Business Intelligence

A customer may technically have a choice, but switching one component may make the others less useful.

Competition analysis could therefore examine:

1. dominance in the tying product;
2. separate demand for the tied product;
3. coercion or economic pressure;
4. foreclosure;
5. legitimate efficiencies.

22. Refusal to Provide Analytical Inputs

A dominant analytics platform might control a critical:

• dataset;
• API;
• benchmark;
• industry index;
• valuation database;
• risk model;
• transaction database.

A refusal to provide access could become particularly significant where:

• the input cannot realistically be replicated;
• competitors depend upon it;
• access can technically be supplied;
• denial excludes competition;
• no adequate legitimate justification exists.

This represents a possible evolution of the essential-facilities doctrine.

23. Discriminatory Analytics

A platform might provide:

High-quality analytics → own business

but:

Lower-quality analytics → independent businesses

Potential discrimination could involve:

• ranking;
• access speed;
• data granularity;
• API functionality;
• model accuracy;
• latency;
• pricing;
• forecasting capability.

Future regulation may therefore require non-discriminatory analytical access in particularly concentrated markets.

24. Digital Markets Act and Value Analytics

The EU's DMA provides an important regulatory model because it supplements traditional competition enforcement with ex-ante obligations for designated gatekeepers.

For example, the Commission's 2026 specification proceedings concerning Google's search data required measures to facilitate sharing of anonymised search data with eligible search competitors.

This indicates a shift from:

Ex-post prohibition

towards:

Ex-ante access + interoperability + non-discrimination + transparency

That model could become influential for future value analytics markets.

25. Role of AI

AI will substantially transform Value Analytics Markets.

AI systems can generate:

• predictive pricing;
• automated valuations;
• customer scores;
• investment predictions;
• demand forecasts;
• competitor intelligence;
• fraud scores;
• risk classifications.

The competitive advantage may therefore shift from data ownership alone to:

Data + compute + model + feedback + distribution

A company possessing all five can potentially create a powerful self-reinforcing competitive ecosystem.

26. Future Regulatory Framework

A comprehensive regulatory framework could contain the following components:

27. Competition Authorities and Evidence

Future investigations will require increasingly sophisticated evidence, including:

• algorithmic audit trails;
• API logs;
• data-access records;
• model-training records;
• version histories;
• pricing outputs;
• ranking changes;
• A/B testing;
• internal strategy documents;
• data-flow maps;
• switching-cost evidence;
• model-performance comparisons.

Traditional market-share evidence alone may become insufficient.

28. Remedies

Possible remedies include:

Structural remedies

• divestiture;
• separation of business units;
• prohibition of acquisitions.

Behavioural remedies

• non-discrimination;
• access obligations;
• fair licensing;
• data portability;
• interoperability.

Technical remedies

• APIs;
• data-export tools;
• standardised formats;
• interoperability protocols.

Governance remedies

• independent audits;
• algorithmic monitoring;
• compliance officers;
• technical trustees.

Merger remedies

• data firewalls;
• licensing obligations;
• restrictions on data combination;
• interoperability commitments.

The recent U.S. Google ad-tech remedies demonstrate the increasing importance of technical interoperability and data-sharing obligations in digital competition cases.

29. Indian Competition-Law Perspective

In India, Value Analytics Markets would principally engage the Competition Act, 2002, particularly:

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

The Competition Commission of India can potentially examine digital analytics conduct through concepts such as:

• relevant market;
• dominance;
• entry barriers;
• consumer dependence;
• network effects;
• data advantages;
• leveraging;
• denial of market access;
• discriminatory conditions.

India's digital competition framework is also increasingly relevant because analytics businesses frequently operate across several interconnected digital markets.

30. Key Doctrinal Development

The traditional competition model can be represented as:

Price → Market Share → Market Power → Consumer Harm

The Value Analytics model increasingly becomes:

Data → Analytics → Prediction → Control → Market Power → Competitive Effects

This represents a significant conceptual transformation.

31. Six Major Future Competition Questions

Future authorities are likely to ask:

1. Who owns the data?

Ownership alone may not determine competitive significance, but control over unique data can create substantial advantages.

2. Who controls the analytical infrastructure?

The owner of the analytical layer may influence downstream competition.

3. Can rivals reproduce the analytics?

If replication is difficult, entry barriers may be substantial.

4. Can customers switch?

Data portability and interoperability determine the practical cost of switching.

5. Does the platform compete with its own users?

This is particularly important for marketplaces and vertically integrated ecosystems.

6. Does the analytical system facilitate coordination?

Competitively sensitive information and algorithmic interaction can potentially facilitate coordinated conduct.

32. Important Case-Law Principles — Consolidated

33. Conclusion

Value Analytics Markets represent an emerging competition-law frontier in which economic power may derive less from ownership of physical assets and more from control over information, prediction and analytical infrastructure.

The principal future competition concerns will involve:

1. data concentration;
2. analytical economies of scale;
3. AI-driven feedback loops;
4. self-preferencing;
5. data-based exclusion;
6. algorithmic coordination;
7. analytics-as-a-service lock-in;
8. interoperability;
9. data portability;
10. AI and analytics mergers;
11. discriminatory access to analytical infrastructure; and
12. control over essential datasets or models.

The evolution visible in Google Shopping, Meta v Bundeskartellamt, the Amazon Marketplace investigation, and the more recent Google search and ad-tech proceedings suggests that competition law is increasingly concerned with control over the informational infrastructure through which markets operate, not merely with conventional price competition.