Competition Law and Resource Analytics Market Power

Introduction

Resource analytics market power refers to a situation in which a firm gains or exercises substantial market power through the collection, control, processing, analysis, or use of data concerning valuable resources. “Resources” may include energy, minerals, agricultural inputs, water, logistics capacity, industrial assets, environmental resources, financial resources, or digital infrastructure.

Resource analytics can generate market power because the firm controlling the analytical infrastructure may possess:

• large and difficult-to-replicate datasets;
• real-time information about supply and demand;
• predictive models and algorithms;
• proprietary benchmarks;
• control over interfaces or APIs;
• information concerning competitors' customers and prices;
• network effects;
• switching-cost advantages; and
• the ability to influence resource allocation.

Competition law therefore examines not merely ownership of data, but whether control over data and analytics allows a firm to exclude rivals, exploit customers, coordinate competitors, or extend dominance into neighbouring markets.

I. Meaning of Resource Analytics

Resource analytics involves the use of data, algorithms, artificial intelligence, sensors and computational systems to analyse the availability, allocation, utilisation and pricing of resources.

Examples include:

1. Energy analytics

• electricity-demand forecasting;
• renewable-energy forecasting;
• battery optimisation;
• grid analytics;
• electricity-price prediction;
• energy-storage optimisation.

2. Mining analytics

• geological datasets;
• mineral-deposit modelling;
• predictive maintenance;
• mine-production optimisation;
• autonomous mining analytics.

3. Agricultural-resource analytics

• soil data;
• weather data;
• crop-yield predictions;
• irrigation analytics;
• fertiliser optimisation.

4. Water-resource analytics

• water-demand forecasting;
• reservoir optimisation;
• irrigation allocation;
• leakage detection;
• drought modelling.

5. Industrial-resource analytics

• equipment utilisation;
• supply-chain optimisation;
• raw-material forecasting;
• production-capacity analytics;
• predictive maintenance.

The competition concern arises when a company moves from merely providing analytics to controlling an indispensable information ecosystem.

II. Sources of Market Power

1. Data accumulation

A resource-analytics platform may continuously collect information from:

• customers;
• sensors;
• IoT devices;
• suppliers;
• competitors;
• public databases;
• transactions; and
• connected infrastructure.

The resulting dataset may become difficult for competitors to reproduce.

Competition concern

A dominant undertaking may use accumulated data to:

• improve its algorithms;
• identify profitable customers;
• predict competitors' behaviour;
• discriminate between customers;
• foreclose rival analytics providers.

III. Data Network Effects

Resource analytics frequently exhibits a data-feedback loop:

More customers → more data → better analytics → better service → more customers → still more data.

This can reinforce market power even where the underlying software is technically replicable.

The competition authority may therefore consider whether the incumbent possesses a self-reinforcing data advantage.

IV. Essential or Strategic Data

Not every commercially valuable dataset constitutes an essential facility.

The relevant questions include:

1. Is the dataset indispensable?
2. Can competitors obtain equivalent data?
3. Can the data be replicated?
4. How costly is replication?
5. Can customers switch to another analytics provider?
6. Does the incumbent control access to the underlying data?
7. Is access technically or contractually restricted?
8. Would denial of access eliminate effective competition?

This is particularly important where analytics depends upon real-time infrastructure data.

V. Refusal of Access to Resource Data

A dominant resource-analytics provider may refuse competitors access to:

• sensor data;
• API interfaces;
• historical datasets;
• industry benchmarks;
• interoperability interfaces;
• data generated by customer equipment.

Such conduct can potentially constitute abuse of dominance where the applicable legal test for refusal to deal or essential facilities is satisfied.

However, competition law generally does not impose a universal obligation on firms to share every dataset they possess.

VI. Vertical Foreclosure

Resource analytics is frequently vertically integrated.

For example:

Energy producer → grid-data platform → analytics software → energy-management service.

If the same firm operates at multiple levels, it may have an incentive to discriminate against competing analytics providers.

Possible conduct includes:

• delaying access to data;
• providing inferior-quality data;
• discriminatory API terms;
• higher access prices;
• tying analytics software to infrastructure;
• exclusive contracts;
• technical interoperability restrictions.

VII. Self-Preferencing

A platform controlling resource analytics may use its informational advantage to favour its own downstream services.

For example:

Analytics platform controls electricity-consumption data → platform also sells energy-management services → rival providers receive restricted or delayed access.

The authority may examine whether the platform uses its upstream position to disadvantage downstream competitors.

VIII. Algorithmic Discrimination

Resource analytics can facilitate highly sophisticated discriminatory practices.

A dominant firm may use analytics to classify customers according to:

• willingness to pay;
• resource dependency;
• switching probability;
• geographic characteristics;
• consumption patterns.

This can support:

• personalised pricing;
• discriminatory access conditions;
• targeted exclusion;
• loyalty incentives;
• discriminatory resource allocation.

The competition analysis must distinguish legitimate price differentiation from discriminatory conduct that harms competition.

IX. Algorithmic Collusion

Resource analytics can also affect horizontal competition.

Where competitors use common datasets or algorithms, algorithms may facilitate:

• rapid detection of rivals' price changes;
• automated price responses;
• market monitoring;
• coordinated capacity reductions;
• information exchange.

The central question is whether the conduct amounts to an unlawful agreement, concerted practice, or other prohibited coordination under the relevant jurisdiction.

X. Market Definition

A resource-analytics investigation may require several relevant markets.

Possible relevant markets

1. Raw resource data
2. Resource-data aggregation
3. Analytics software
4. Cloud-based analytics
5. Specialised analytics services
6. Resource-management platforms
7. Downstream resource markets

For example, an electricity analytics platform might operate simultaneously in:

electricity-data collection → energy analytics → energy-management software → electricity services.

Market definition must therefore consider substitutability, functionality, switching costs, geographic scope and customer demand.

XI. Barriers to Entry

Resource analytics can have substantial entry barriers.

Important barriers include:

• access to historical datasets;
• high computational costs;
• specialised expertise;
• proprietary algorithms;
• customer lock-in;
• interoperability limitations;
• network effects;
• intellectual property;
• cloud infrastructure;
• regulatory approvals.

A new entrant may technically be able to develop software but still be unable to reproduce the incumbent's data advantage.

XII. Competition Effects

Resource-analytics market power may produce several effects.

A. Exclusion

Rivals may be denied access to necessary data.

B. Higher prices

Customers may face excessive analytics or data-access charges.

C. Reduced innovation

Smaller analytics firms may lack sufficient datasets to compete.

D. Lower quality

A dominant platform may reduce service quality where customers have limited alternatives.

E. Reduced interoperability

Closed systems may make switching difficult.

F. Exploitation of data contributors

Customers generating the underlying data may receive inadequate access to their own information.

XIII. Relevant Case Laws

The following cases provide important principles for analysing resource-analytics market power.

1. United States v. Microsoft Corp. — U.S. Supreme Court

Principle: Market power can be reinforced through technological integration and exclusionary conduct.

Microsoft used its dominant position in PC operating systems to restrict competitive threats from alternative technologies, particularly web browsers.

Relevance to resource analytics

A resource-analytics company could similarly use control over an established technological platform to disadvantage complementary or competing analytics products.

The case demonstrates that competition law can examine technological architecture and exclusionary strategies, not merely prices.

2. Bronner v. Mediaprint — Court of Justice of the European Union

Principle: Refusal to provide access to an infrastructure does not automatically constitute an abuse of dominance.

The CJEU established a demanding framework for treating infrastructure as indispensable.

Relevance

Where a resource-analytics provider refuses access to a database, API or analytical infrastructure, the claimant generally needs to establish the relevant conditions for a refusal-to-supply/essential-facilities theory.

The mere fact that access would make competition easier is insufficient.

3. IMS Health GmbH & Co. KG v NDC Health GmbH & Co. KG — CJEU

Principle: Intellectual-property rights and market access can intersect with the essential-facilities doctrine.

The case concerned a pharmaceutical-sales information system and access to a data structure used by market participants.

Relevance

This is particularly significant for resource analytics because proprietary databases can become commercial infrastructure.

The case illustrates that competition law may intervene where control over a particular information structure prevents effective competition and the demanding conditions for compulsory access are met.

4. Google Shopping — European Commission / General Court

Principle: A dominant platform can potentially abuse its position by favouring its own specialised service in ways that disadvantage competing services.

The Google Shopping proceedings concerned the relationship between Google's general search service and its comparison-shopping service.

Relevance

The principle has wider relevance to resource-analytics platforms.

For example:

dominant resource-data platform → own analytics service → preferential treatment → competing analytics providers disadvantaged.

The important analytical issue is whether the conduct represents legitimate product improvement or exclusionary self-preferencing.

5. Slovak Telekom v European Commission — CJEU

Principle: A vertically integrated dominant undertaking can face competition-law liability where access restrictions concerning infrastructure have exclusionary effects and the applicable legal conditions are satisfied.

The case involved access to telecommunications infrastructure and downstream competition.

Relevance

The same analytical framework can be relevant where a resource-analytics company controls:

• energy infrastructure data;
• industrial-data interfaces;
• network information;
• resource-management infrastructure.

The distinction between legitimate infrastructure management and anticompetitive foreclosure remains crucial.

6. Facebook/Meta — Bundeskartellamt

Principle: Data collection and the combination of information from different services can form part of a competition-law analysis concerning a dominant digital platform.

The German competition authority examined Facebook's combination of user data obtained from Facebook and other sources.

Relevance

Resource analytics can similarly create market power through data aggregation across multiple sources.

The competition analysis may therefore consider whether:

multiple datasets + dominant platform + restricted alternatives

create a competitive advantage that rivals cannot realistically reproduce.

XIV. Additional Relevant Case: Google Android

Google Android — European Commission

The Android proceedings involved several practices concerning Google's position in mobile operating systems, including tying and restrictions affecting competing services.

Relevance to resource analytics

The case demonstrates how dominance in one technological layer can be leveraged into adjacent markets.

A resource-data company could potentially use:

dominant data infrastructure → analytics software → downstream resource-management services

to extend market power into neighbouring markets.

XV. Competition-Law Theories Applicable to Resource Analytics

XVI. Resource Analytics and Merger Control

Resource analytics is particularly significant in digital and data-driven mergers.

A large resource company may acquire:

• an energy-data startup;
• an AI analytics provider;
• a satellite-data company;
• a mining-data platform;
• an agricultural analytics firm;
• a predictive-maintenance company.

Even where the target has relatively low current revenue, the transaction may raise concerns because of its data assets and future competitive significance.

Authorities may examine:

1. elimination of a potential competitor;
2. accumulation of unique datasets;
3. foreclosure of competing analytics providers;
4. interoperability restrictions;
5. vertical integration;
6. increased barriers to entry;
7. innovation competition.

XVII. Dynamic Competition

Resource analytics requires particular attention to innovation competition.

Traditional market-share analysis may underestimate market power because a company with modest present sales could possess:

• unique datasets;
• superior predictive technology;
• important patents;
• a rapidly growing user network;
• strategically valuable infrastructure connections.

Consequently, competition authorities may examine future competitive constraints as well as existing market shares.

XVIII. Remedies

Potential remedies depend upon the competitive harm established.

1. Data-access remedies

A dominant company may be required, where legally justified, to provide access to specified datasets.

2. API interoperability

Authorities may require technically reasonable interoperability.

3. Non-discrimination

The platform may be prohibited from giving its own downstream service preferential access.

4. Data portability

Customers may be allowed to transfer relevant data to alternative providers.

5. Separation remedies

In particularly serious circumstances, structural or functional separation may be considered.

6. Behavioural commitments

These can include:

• transparent access conditions;
• non-exclusive licensing;
• equal API access;
• prohibition of discriminatory terms.

XIX. Challenges for Competition Authorities

Resource analytics creates several enforcement difficulties.

1. Data quality

Two datasets may appear similar but differ substantially in accuracy and timeliness.

2. Algorithmic opacity

Authorities may have difficulty determining how an algorithm produces competitive outcomes.

3. Rapid technological change

Market power can develop faster than conventional market-definition exercises can capture.

4. Multi-sided platforms

The same analytics platform may serve:

• resource producers;
• consumers;
• governments;
• infrastructure operators;
• advertisers;
• competing service providers.

5. Privacy and competition overlap

Data-access remedies must coexist with privacy, cybersecurity and sector-specific regulation.

XX. Analytical Framework

A competition authority assessing resource-analytics market power can proceed through the following framework:

Identify the resource

↓

Identify the data generated by the resource

↓

Identify who controls the data

↓

Define the relevant data/analytics markets

↓

Assess market power

↓

Examine barriers to replication

↓

Examine network effects and switching costs

↓

Identify exclusionary or exploitative conduct

↓

Assess actual or potential competitive harm

↓

Consider efficiencies and legitimate business justifications

↓

Design proportionate remedies

XXI. Distinguishing Data Ownership from Market Power

An important principle is:

Control of valuable data ≠ automatic dominance.

A dataset is more likely to contribute significantly to market power where it is:

• unique;
• difficult to replicate;
• continuously updated;
• commercially indispensable;
• protected by strong network effects;
• connected to infrastructure;
• combined with superior analytical capabilities.

Conversely, market power is less likely where equivalent information is:

• publicly available;
• commercially obtainable;
• easily replicated;
• available from multiple providers.

XXII. Conclusion

Resource analytics can become a source of market power when control over data, infrastructure, algorithms and analytical capabilities creates an advantage that competitors cannot effectively reproduce.

Competition law therefore needs to examine the entire chain:

Resource → Data → Analytics → Platform → Allocation → Downstream Market

The most significant competition concerns include data foreclosure, refusal of access, interoperability restrictions, self-preferencing, tying, exclusive data arrangements, algorithmic coordination and data-driven mergers.

The principles emerging from Bronner, IMS Health, Microsoft, Google Shopping, Slovak Telekom, Facebook/Meta and Google Android demonstrate that competition law can address technological and data-based sources of market power, while maintaining important distinctions between legitimate innovation and exclusionary conduct.

The central legal question is ultimately not simply “Who owns the data?”, but:

“Does control over resource data and analytics enable the undertaking to