Big data e concorrência: uma avaliação dos impactos da exploração de big data para o método antitruste tradicional de análise de concentrações econômicas

Ragazzo, Carlos Emmanuel Joppert
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A feature of digital markets is the generation and analysis of a “torrent” of data which is being considered as a key aspect of many business emerging in the context of the “Internet of Things”. The word big data reflects this trend towards collecting, acquiring, storing and processing great volumes of digital data to create economic value. Online platforms’ business models are frequently based on exploiting data, in particular personal data, which are used as an input to improve and personalize services and product that they offer. Until recently, antitrust authorities had not carefully analyzed the impacts of the use of big to a competition policy, but this situation has been changing with the emergence of discussions about anticompetitive concerns raised by the exploitation of this capacity. In light of this, this work aimed at investigating if and to which extent the exploitation of big data in digital markets may be considered a comparative advantage that raises antitrust risks and, in this case, how an analysis of this competitive variable should be incorporated in the traditional antitrust approach to mergers and acquisitions. This investigation identified that, under certain conditions, big data capacity may result in a relevant competitive advantage, giving raise to anticompetitive concerns in the context of mergers and acquisitions. In a general manner, these concerns may be analyzed within the scope of the phases of the classic antitrust method, and there is no need, at this moment, for a new methodologic framework specifically applicable to the analysis of transactions involving firms which business models are preponderantly based on the use of data. Notwithstanding, certain tools might need to be adapted or enlarged by the Brazilian antitrust authority, mainly to take into account non-price competition dimensions, such as quality, innovation and privacy, as well as particular features of big data and its ecosystem in the assessment of the transaction’s effects and efficiencies, as well as potential remedies.

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