Author: Prerna Raturi & Shashank Upadhyay
The authors are students at the Symbiosis Law School, Pune.
The development of technology and internet arenas has fostered the advent of digital economy. Consumers are now well aware of various online services provided by different companies and organizations and that is why the world is moving toward online consumerism. One of the offshoots of the same is the facilitation of “big data”. This means that search engines and other online platforms are now amassing the data, pertinent to consumers’ choices and search queries, and then process the same by using various algorithms and computer software in order to provide consumer-specific services. For example, if you are a frequent flyer from Bombay to Delhi, after collecting data from your browsing history, travel search engines recommend you different options and price trends for your next journey for the same specific route. This suggests that whenever you tread online, you leave digital footprints. These footprints are being used by companies to comprehend consumer demand patterns. It helps them to organize various data-sets containing personalized information related to a specific consumer. This information is then used to determine an individual’s preferential choices and interests and to provide targeted advertisement based on the same.
Data-Related Anti- Competitive Agreements: Need for Big Data Regulation
The process of utilizing big data for the purpose of commercial exploitation seems to be of great assistance for the retailers by improving the seller-buyer relationship and efficiency of the traders. But the question that arises is why regulation of big data is a concern and more importantly, why is it even necessary to regulate big data in the first place. The biggest apprehension attached to big data is the data-related anti-competitive agreements. These agreements can of be various forms and types but more importantly, data-related anti-competitive agreements can lead to the formation of cartels, through collusion, which in turn would be employed for the purpose of price fixation. Thus, one of the major concerns of these agreements is the potential of big data to induce price cartelization. Companies which have access to data and are capable of processing such data, create a cartel to determine the price of the products and services that consumers are more inclined to. After discerning the shopping behavior of the users, companies can become conscious of the demand patterns of various products and retailers and manufacturers can regulate the price of these products accordingly.
It is natural that cartels would leverage technological innovation to facilitate their operations. Competitors can unilaterally decide to use algorithms aimed at maximising profit. This way an online distributor can always enter into an agreement with a manufacturer to sell products at a variable price depending on how much shoppers would be willing to pay for certain products, based on data obtained about their shopping habits or location based on their IP addresses. Clearly, the key to such an agreement would be the accessibility to data and ability to process it as well thus, giving rise to another type of anti-competitive agreement, exclusive agreements between data-driven firms for exclusive access to data. In 2015, for the first time, U.S. Department of Justice prosecuted an e-commerce platform, Poster Revolution, under antitrust law for being involved in algorithm-driven selling. Poster Revolution conspired with certain other sellers to fix the prices of the goods sold on Amazon by using the price algorithm.
It is pertinent to note that Section 3 of the Competition Act, 2002 prohibits anti-competitive agreements which cause or are likely to cause an appreciable adverse effect on competition (AAEC) in India. Such agreements include horizontal agreements between competitors, including cartels. Once a horizontal agreement under Section 3 of the Competition Act has been established, it is presumed that it has triggered AAEC. An unfavourable offshoot of such data-driven agreements can be an entry barrier to new entrants in the market. Through such agreements, companies can obviously concentrate the datasets, pertinent to a particular market, in their own hands restricting the entry of new competitors and eventually, eliminating the competition from the market. Further, it is not easy for new entrants to collect huge volume of datasets and afford efficient tools to analyze it.
II. Accommodating Data-Driven Agreements into Antitrust Laws
At this point, it is important to ascertain that even though data-driven agreements are catastrophic to competition in the market, it is not a general case of appreciable adverse effect on competition. This is because the key element in these agreements is the ability of a company to access volumetric data and subsequently, processing it to allure customers while hindering the competition in the market. This is suggestive of the fact that these data-driven companies are actually possessing certain facilities which are not available to their competitors, causing an unfavourable effect on the market. Therefore, in such cases of data-driven anti-competitive agreements, competition watchdogs, on the imposition of antitrust laws, can compel the data-driven companies to share their information with the other companies which are nothing but an application of “essential facilities doctrine”. This doctrine provides that if any dominant enterprise is in possession of a facility which is extremely necessary for the competitors to provide their services to the customers, such an enterprise is obligated to supply the same facility to its competitors as well.
This doctrine was examined by the CCI in the case of Arshiya Rail Infrastructure Limited (ARIL). The CCI held that Container Corporation of India (CONCOR) was not dominant in the relevant market but as an obiter on the issue of access of terminals of CONCOR held that essential facilities doctrine can be only be invoked in certain circumstances (a) technical feasibility to provide access; (b) possibility of replicating the facility in a reasonable period of time; (c) distinct possibility of lack of effective competition if such access is denied and (d) possibility of providing access on reasonable terms. Further, in the landmark judgement of Shamsher Kataria v. Honda Siel Cars India Ltd., the DG concluded that spare parts, diagnostic tools, manuals, etc., of each OEM, would constitute essential facilities for the independent repairers to be able to provide consumers with effective after-sale repair and maintenance work. This would be essential for independent repairers to be able to effectively compete with the authorized dealers of the OEMs. The Commission pointed out that the essential factors to be taken into account in determining whether spare parts of each OEM would constitute essential facilities for independent repairers are: (a) control of the essential facility by the monopolist; (b) the inability to duplicate the facility; (c) the denial of the use of the facility; and (d) the feasibility of providing the facility. Therefore, access to such technology was critical for any entity undertaking after-sales services to be able to compete effectively on the market.
Essential facilities doctrine is not an unknown or unexplored arena for CCI. It was been examined and invoked by CCI time and again, which speaks of the viability of the doctrine in competition law. Therefore, through the application of essential facilities doctrine under Indian competition law regime, it can be determined that this doctrine can be efficiently used to crack down on the challenges imposed by big data. In cases where data-driven companies enter into agreements to restrict the possession of their data in order to impede the competition in the market, by virtue of essential facilities doctrine CCI can necessitate the access of the data sets to other competitors. This is an appropriate and adequate remedy especially when such data sets are voluminous and unique, difficult to be replicated by the new entrants thus, affecting the competition in the downstream market.
As every coin has two different sides, the utilization of big data by various multinational companies is beneficial to the customers in some ways but at the same time is detrimental to their own interests and competition in the market. Thus, the enforcement of competition law in data-related markets is now needed to be quickening. CCI is focusing on the big data sector to prevent anti-competitive activity. The CCI, in its Google decision, has stated that the commission would be acknowledging the benefits of data-driven innovation but this would not prevent big data from the scrutiny for possible anti-competitive conduct. Consequently, the Commission also imposed a fine of Rs 136 crore on Google for unfair business practices in the Indian market and abusing its dominance to create search bias and manipulation. Therefore, the best outcomes can be secured by deterring firms from forming cartels in the first place. Strong sanctions are a fundamental component of an effective antitrust enforcement policy against hardcore cartels. Imposing sanctions, which would compel the wrongdoers to share peculiar and beneficial data sets with other competitors, could be a better option to confront the present issues of data-driven anti-competitive agreements.
 US Department of Justice, Former E‑Commerce Executive Charged with Price Fixing in the Antitrust Division’s First Online Marketplace Prosecution, Press release April 6, 2016, available at, https://www.justice.gov/opa/pr/former-e-commerce-executive-charged-price-fixing-antitrust-divisions-first-online-marketplace.
 The Competition Act, 2002, § 3(3), No. 12, Acts of Parliament, 2003 (India).
 Autorité de la concurrence and Bunderkartellamt, Competition Law and Data, May 2016, Pages 12-13, available at http://www.autoritedelaconcurrence.fr/doc/reportcompetitionlawanddatafinal.pdf.
 Arshiya Rail Infrastructure Limited (ARIL) v. Ministry of Railways and Ors., (2013) 112 CLA 297.
 Shamsher Kataria v. Honda Siel Cars India Ltd., (2014) CCI 26.