Let us mitigate the adverse impact of data monopolies

In a data economy, companies with the largest data pools have outsized, unbeatable techno-economic advantages.
In a data economy, companies with the largest data pools have outsized, unbeatable techno-economic advantages.


  • Mandatory data sharing would give new digital players better odds of success and thus foster innovation

According to Forbes, the top 10 global companies by market capitalization in 2023 included four technology companies, one social media platform, and one online marketplace. In 2009, their collective number was one. In 14 years, digital companies, led by data usage, have challenged the neoclassical approach to business. Data is the new oil, analytics the refinery, and artificial intelligence the gasoline powering the digital economy. Interestingly, the marginal utility of data never diminishes. Also, even though data is non-rivalrous, its access and use can be denied.

This has created new challenges, especially for competition policy. Its existing framework all over the world, based as it is on a neoclassical economic model, never anticipated zero-pricing business models by digital services. However, it is pertinent to note that zero pricing does not imply zero cost. When users avail of digital services, they inevitably reveal rich information about themselves and others, which constitutes information cost. Their exposure to advertisements while using zero-priced platforms forms attention cost.

Data as a barrier to entry: Digital markets are characterized by network effects, driven by feedback loops. As the Gopalakrishnan Committee noted, a company with a large user base is able to collect more data to improve the quality of its service and thereby acquire new users—known as the ‘user feedback loop.’ Additionally, companies are able to explore user data to improve targeted advertisements and monetize their services, resulting in additional funds from investors to improve service quality and attract more users—known as the ‘monetization feedback loop’. Hence, a data-rich incumbent is able to strengthen its position in the market, thus creating a formidable entry barrier for new entrants. This position gets further entrenched over time due to additional network effects. New customers join not because of the quality of the product but the size of the network.

Hence, digital businesses are governed by competition driven by a concentration of data combined with analytics, feedback loops and network effects. While the latter confers enormous power on them to innovate, it also creates entry barriers that foreclose markets, thus forming a vicious cycle.

In a data economy, companies with the largest data pools have outsized, unbeatable techno-economic advantages. For example, studies have shown that increasing a speech corpus size by 5 times reduces word-error-rate (i.e. errors in speech-to-text translation) by 10% or more. The old adage “we are what we eat" has relevance, for the end product/service is dependent on the data it is fed. Smaller firms, even if they are equipped with a superior idea/production technology, face higher marginal costs of innovation as they lack access to the large pile of user information that the field’s dominant firm has.

Further, the technologies required to store and process data can be costly. The cost structure is characterized by high economies of scale and scope and can therefore facilitate the market concentration of big data in the hands of a few players. For instance, according to estimates from Synergy Research Group, Amazon Web Services, Azure and Google Cloud accounted for a 65% share in the market for cloud infrastructure.

The way forward: Against this backdrop, the notion of “beneficial ownership of non-personal data," as proposed by the Gopalakrishnan Committee, assumes significance. The committee lays down that startups/businesses should have access to anonymized metadata generated out of data collected by different data businesses and governments. This will enable potential users to identify opportunities to combine data from multiple data businesses or governments to develop innovative products and services. For this, tech platforms must grant access to the corpus of data in their possession. In the absence of enabling regulation, the world’s big data-powered behemoths will never share this data.

An argument is often made that nothing prevents a competitor from collecting similar data-sets. This is akin to saying that one could have taken on Standard Oil by digging an oil well using a shovel. It is impossible for any small entity to replicate the data storage and processing powers of big players. In merger cases, what qualifies for antitrust review—beyond thresholds of turnover, assets or deal value—must take into account the value of data and its control by merging parties. Also, an argument can be made for the application of ‘The Essential Services Doctrine,’ which grants competitors the right to access essential facilities of monopolists to the extent that facility duplication would be economically infeasible and denial of its use would inflict a severe handicap on market entrants. Most importantly, regulators should ensure that antitrust remedies do not harm consumer privacy. Data minimization, purpose limitation, collection limitation and storage limitation should be the principles governing our antitrust remedies.

Conclusion: Tech companies are the growth engines leading us into the future. However, an engine needs railroads and fuel to function. Railroads were provided by the government by way of infrastructural support and the fuel by us in the form of data. The aim is not to stop the engine, but to ensure that the free and fair market conditions that allowed these companies to flourish exist for all small and new players too.

These are the author’s personal views.

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