BIG DATA HAS LED to big predictions in the area of competition law. Coupled with algorithms used in big data analytics, big data is predicted to fundamentally change the competitive landscape within which firms price and customize products and services, and predict market trends. This has led to varying predictions for competition law and enforcement in Canada and around the world.
Competition law and competition enforcers focus on market forces rather than market outcomes. Acting as a referee of sorts, competition enforcers, such as Canada’s Competition Bureau, seek to ensure that firms compete on the merits and that consumers are well-informed through minimal intrusion.
As noted in the Competition Bureau’s recent white paper, Big Data and Innovation: Implications for Competition Policy in Canada, some believe big data will be key for competition, underpinning new waves of productivity and innovation. Others fear that big data will undermine the competitive process, including the ability of competition enforcers to effectively enforce laws. Regardless, competition laws and their enforcers have unique challenges to confront as big data analytics grow and develop.
Take cartels. As horizontal agreements between competitors to fix prices, allocate markets or limit output, cartels have been characterized as an assault on Canada’s open market. A cartel is per se illegal, requiring neither proof of market power nor anticompetitive effects; competition enforcers need only prove an offence by establishing an agreement between competitors to collude.
Data and algorithms may afford competitors the ability to collude without an agreement. The OECD paper Algorithms and Collusion: Competition Policy in the Digital Age refers to this as algorithmic collusion. For example, self-learning algorithms, which learn and readapt to the actions of other market participants, may enable collusion without human intervention, let alone an agreement.
Mergers and monopolist practices are being tested. Laws prohibit certain means by which a firm may create, enhance or maintain market power. Increasingly, data is a competitive asset, the essential input in a firm’s product or service; access to and control over data can thus confer market power.
On the one hand, data can create economic benefits such as efficiencies and innovative products and services at minimal or no consumer cost. On the other hand, firms that control data can erect entry barriers and foreclose competition, resulting in higher prices or diminished consumer choices. Being big in big data is not necessarily bad, but a case-by-case competitive analysis is required.
Deceptive marketing laws are also being challenged. Materially false or misleading representations to the public that promote a business interest are prohibited. In principle, these laws would capture a firm’s false or misleading representations regarding its collection and use of consumer data. But how will these provisions be applied to big data analytics? Data is no longer incidental to advertising. It is a game-changer, and arguably the principal method to assess buying behaviour, since consumers leave their digital footprints every time they use the internet and social media. In these circumstances, what, if any, level of disclosure could and should be provided to consumers regarding the use of a consumer’s data? Again, deceptive marketing laws were not drafted with these circumstances in mind.
Competition laws in Canada date back to 1889, and the legislative drafters could not have anticipated the emergence of big data and the algorithms used in big data analytics. Accordingly, is Canada in need of amended competition laws and enforcement tools, or will the status quo suffice? The competition community is debating that fundamental question; stay tuned.
Antonio Di Domenico is a partner and regional leader of the Antitrust/Competition & Marketing Group at Fasken Martineau DuMoulin LLP in Toronto. He is also a former counsel to Canada’s Commissioner of Competition, and a Lexpert Rising Star in 2017.