Is data really that important to a business?
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Antitrust / competition law data issues
Global | Publication | February 2021
Access to data
Manufacturers of connected devices or information service providers (such as operators of social media platforms) control access to data which they collect through the provision of their goods and services, by way of contract and / or technical measures. The exclusive possession or control of such data can have antitrust / competition law considerations, giving rise to access disputes.
Sector-specific data access rules are dealt with elsewhere (see Availability of Sector-specific Data as a Result of Market Failure). Here we deal with non-sector-specific data access antitrust / competition law issues.
EU
In the EU the legal threshold required to force access to data held by a single corporate group on antitrust / competition law grounds is very high.
The Court of Justice of the EU has developed four conditions that need to be fulfilled for an action based on antitrust / competition law principles to lead to an obligation to license the use of commercially-held information:
- That the data is indispensable for the downstream product.
- That there would not be any effective competition between the upstream and downstream product.
- That refusal prevents the emergence of the second product.
- There is no objective reason for the refusal.
These high thresholds may be relaxed in the near future. The EU Commission has observed that, where the negotiation power of the different market participants is unequal, market-based solutions alone might not be sufficient to: (1) ensure fair and innovation-friendly results; (2) facilitate easy access for new market entrants; and (3) avoid lock-in situations.
A number of measures are already in place in the EU to provide data access for users to address any imbalance:
- The Regulation of Free-Flow of Non-Personal Data encourages porting of data in a “structured, commonly used and machine-readable format including open standard formats” in order to minimize risk of vendor lock-in (see Prohibition on Localization of Data).
- The EU Commission has identified areas of market failures that antitrust / competition law cannot solve, and has legislated for data access in these identified areas (see Availability of Sector-specific Data as a Result of Market Failure).
- Some member states have applied principles from consumer protection laws (like the Directive on Unfair Contract Terms) to B2B transactions.
Faced with the reality that the majority of data is in the hands of just a few, the EU Commission is considering implementing rules (envisaged to appear in an EU Digital Services Act) banning dominant platforms from hoarding data, and to compel them to open up access to their datasets, boosting availability of data and competition.
Other areas of reform under consideration include:
- Implementation of new ex ante competition “tool” enabling the EU Commission to intervene in markets before they “tip” into being dominated by one or two providers (after which entry by new providers is more difficult).
- Additional scrutiny of possible effects on competition of large-scale data accumulation through acquisitions.
For more information on the EU proposals, see EU Commission Launches Consultations on Ex Ante Antitrust Tool and Platform Regulation.
Canada
Canadian jurisprudence has confirmed that controlling significant amounts of data is a source of market power, and a dominant market participant restricting the access and use of data may raise antitrust / competition law concerns.
For example, a 2017 court decision held that a real estate trade association abused its dominant market position by restricting access, use and dissemination of data from a real estate listing service it controls.1 This decision may open the door for organizations to become subject to Competition Bureau investigations, when it is believed that control of data is being misused to punish or exclude competitors.
China
Under Article 13 of the PRC Anti-Monopoly Law (AML), monopoly agreements (i.e. agreements, decisions or other concerted practices which eliminate or restrict competition), are prohibited.
Information exchange does not constitute a cartel or a monopoly agreement in and of itself under the AML. However, if businesses act in a concerted way because of the information exchange, the existence of the concerted action together with the information exchange may be construed as constituting a cartel or of reaching a monopoly agreement.
As such, information exchange may violate the AML in two ways – either it is part of a wider cartel agreement, or it leads to a concerted practice. The main difference between the two is that the latter does not contain any written or oral, express or implied agreement between the competitors.
In the case of a wider cartel agreement, information exchange is a form of reaching and implementing the agreement, while concerted practice may be established by the finding of an exchange of information (or even the opportunity for such an exchange) and subsequent parallel competitive behaviors.
All cases involving information exchange that have been sanctioned in China so far are cases containing a wider cartel agreement involving price fixing and in some instances market allocation between competitors.
However, while the law is less settled in China that information exchanges are in and of themselves an issue, most multinational businesses operating in China adopt a careful approach as exchange of commercially sensitive information between competitors can easily be considered to lead to concerted practices, if not part of a wider cartel agreement.
Singapore
Under Singapore’s competition law, a refusal by a dominant firm to supply data to other players in the market may theoretically be anti-competitive if there is evidence of substantial harm to competition and if the behavior cannot be objectively justified.
There has not been any such case in practice and the competition authority would have to meet a high threshold in order to prove its case, as a business generally has the freedom to decide with whom it will deal. However, if the competition authority is able to establish infringement, it is empowered to direct the dominant firm to provide access to its data.
Data sharing
To what extent can data be shared between competitors?
EU
In the EU sharing of competitively sensitive data between competitors is likely to be regarded as a restriction of competition.
Whether information is competitively sensitive, and the likely effects of an information exchange on competition, must both be analyzed on a case-by-case basis, and so require specialist advice. However, as a general guide, disclosure to a competitor of particularly sensitive information, such as relating to future pricing intentions, is likely to be regarded as anti-competitive by object and thus a serious infringement of competition law, regardless of the market share of the participants. In such cases there is no need for the competition authority to prove any actual effect on the market arising from the information exchange.
Where potentially sensitive information needs to be shared (for example, in order to achieve a pro-competitive benefit), safeguards should be put in place to reduce the competition law risk, such as effectively aggregating and anonymizing the information to reduce its competitive sensitivity.
EU Horizontal Co-operation Guidelines
The sharing of the following kinds of data could, depending on circumstances, be held to be anti-competitive, according to the EU’s Horizontal Co-operation Guidelines, and so require careful consideration:
- Prices (for example, actual prices, discounts, increases, reductions or rebates), customer lists, production costs, quantities, turnovers, sales, capacities, qualities, marketing plans, risks, investments, technologies and R&D programs and their results.
- If companies compete with regard to R&D, it is the technology data that may be the most strategically valuable.
- The strategic usefulness of data will depend on its aggregation and age, as well as the market context and frequency of the exchange.
Owing to the increasing importance and market power that can be conferred by exchange of data, as at the date of publication the EU Commission plans to revise its guidance on data sharing and pooling arrangements by means of an update of the Horizontal Co-operation Guidelines.
United States
With respect to non-personal data, US antitrust enforcers have taken action in the merger context with respect to data. In Bazaarvoice (2014), the Justice Department (DOJ) successfully challenged a 2012 consummated merger involving companies that provide software platforms for online ratings and reviews (R&R) of products created by consumers that manufacturers and retailers host, share, distribute and display. The court found:
- A relevant market for R&R platforms, and noted that the merging parties had called themselves duopolists in this market.
- That the merged firm likely would be able to charge monopolistic prices.
To settle the case, DOJ required Bazaarvoice to divest all of the assets it had acquired in 2012.2
Federal Trade Commission cases
The other antitrust enforcer, the Federal Trade Commission (FTC), in 2014 charged that CoreLogic Inc’s proposed US$661 million acquisition of DataQuick Information Systems, Inc. from TPG VI Ontario 1 AIV L.P. would likely substantially lessen competition in the market for national assessor and recorder bulk data.
The FTC’s settlement order3 required CoreLogic to license to Renwood RealtyTrac national assessor and recorder bulk data as well as several ancillary datasets that DataQuick provided to its customers. The order allowed RealtyTrac to offer customers the data and services that DataQuick offered and to become an effective competitor in the market.
In CDK/Auto-Mate, the FTC sued to block a merger of two digital technology platforms where firms were current competitors, but one was a market giant – close to a duopolist – while the other was far smaller. The complaint4 alleged harm to current competition, but focused even more sharply on harm to future, or nascent competition. That harm arose from the smaller competitor’s substantial efforts to remake itself into a greater competitive threat going forward. The parties abandoned the transaction after the FTC filed a lawsuit.
Singapore
The Competition and Consumer Commission of Singapore (CCCS) recognizes that businesses regularly exchange information on a variety of matters legitimately, with no harm to competition. In general, competition is unlikely to be harmed when the data that is shared is:
- Historical.
- Sufficiently aggregated and cannot be attributed to a particular business.
- Not sensitive, strategic or confidential; and
- Shared with consumers of government agencies.
However, data sharing among competitors may be anti-competitive where it allows a business to have access to or draw inferences from a competitor’s commercially sensitive information, such as pricing or production figures.
The potential anti-competitive effects of data sharing are greater:
- In markets which only consist of a few companies.
- Where data sharing is frequent.
- Where the data shared is commercially sensitive, strategic and confidential; and
- Where the data shared is limited to a select group of businesses in the market, to the exclusion of their competitors and buyers.
Footnotes
1
Toronto Real Estate Board v. Commissioner of Competition (2017), 2017 FCA 236.
2
https://www.justice.gov/atr/case-document/file/488816/download
3
https://www.ftc.gov/news-events/press-releases/2018/06/ftc-approves-final-order-adding-requirements-2014-order-remedy
4
https://www.ftc.gov/enforcement/cases-proceedings/171-0156/cdk-global-automate-matter
The myriad ways in which data may be used
The value that can be gained from data by businesses will inevitably lead to an increase in the use of data to improve daily operations and to develop new products, services and processes.
Nature of data, ownership, and control
In many jurisdictions pure information, or data, is not considered to be property. This is because a claim to property in intangible information presents obvious definitional difficulties.
Ways of protecting data
There is a patchwork of different rights, intellectual property rights and contract rights that may apply to data. Understanding the way in which these rights come into play enables a business to understand how its data assets can be protected.
Disruptive technology data: case studies
Disruptive technologies, such as AI, IoT, AVs, distributed ledger technology (DLT), cryptocurrencies and smart contracts, generate many different forms of data. What are the particular characteristics of such data, and to what extent can intellectual property rights or other rights protect them?
The regulatory environment for data
In this section, we review the EU’s position with regards to industrial and non-personal data and look at whether other jurisdictions have similar initiatives.
Free flow of data
Data location laws (in relation to industrial and non-personal data) can be restrictive (as in banking secrecy laws, which may require some types of data to remain onshore or to be “localised”) or liberalising (as in laws that ban the prohibition of export of data from a locality).
Data for re-use
In furtherance of the objective of leveraging existing datasets paid for by public funds, a number of jurisdictions have sought to make public sector information (PSI) available to industry.
Antitrust / competition law data issues
The exclusive possession or control of data can have antitrust / competition law considerations, giving rise to access disputes.
Contractual considerations in sharing data
The uncertain nature of intellectual property rights in data means that “contract is king” in data transactions.
Managing Data
Data is an incredibly valuable resource for businesses, enabling organisations to effectively operate and to make business improvements. In order to exploit this value most effectively, businesses must invest in good data management.
Liability
Errors, incompleteness or biases within data may flow through, and be amplified by, data analytics process outputs upon which a business's strategic and investment decisions may depend, potentially causing business losses. In this section we deal with liability arising out of use of data / datasets that are in some respect sub-optimal.