Big data e concorrência: uma avaliação dos impactos da exploração de big data para o método antitruste tradicional de análise de concentrações econômicas
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Dissertação |
Idioma: | por |
Título da fonte: | Repositório Institucional do FGV (FGV Repositório Digital) |
Texto Completo: | https://hdl.handle.net/10438/20312 |
Resumo: | 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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Monteiro, Gabriela Reis PaivaEscolas::DIREITO RIORivera, Amanda Athayde Linhares MartinsSampaio, Patrícia Regina PinheiroRibas, Guilherme Favaro CorvoRagazzo, Carlos Emmanuel Joppert2018-03-02T13:37:48Z2018-03-02T13:37:48Z2018-02-02https://hdl.handle.net/10438/20312A 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.Uma característica de mercados digitais é a geração e análise de uma “enxurrada” de dados, o que tem sido considerado um elemento chave de muitos negócios que emergem no cenário da “Internet das Coisas”. O termo big data reflete essa tendência de coletar, adquirir, armazenar e processar grandes volumes de dados digitais para criar valor econômico. Os modelos de negócio das plataformas online frequentemente se baseiam na exploração de dados, em particular os de natureza pessoal, que são usados como insumo para melhorar e personalizar os serviços ou produtos que oferecem. Até recentemente, as autoridades antitrustes ainda não haviam se debruçado completamente sobre as implicações do uso de big data para uma política de defesa da concorrência, mas essa situação tem se modificado com o surgimento de discussões sobre as preocupações anticompetitivas suscitadas pela exploração dessa capacidade. Dessa forma, esta dissertação buscou investigar se e em que medida a exploração de big data em mercados digitais pode ser considerada uma vantagem comparativa que suscita riscos anticompetitivos e, nesse caso, como a análise dessa variável competitiva pode ser incorporada ao método antitruste tradicional para o controle de estruturas. Esta investigação identificou que, em determinadas situações, a capacidade de big data pode representar relevante vantagem competitiva, gerando diversas preocupações concorrenciais no contexto de concentrações econômicas. De forma geral, essas preocupações anticompetitivas podem ser analisadas dentro do escopo das etapas do método antitruste clássico, não se verificando, neste momento, a necessidade de um novo arcabouço metodológico que seja especificamente aplicável ao exame de operações envolvendo agentes econômicos cujos modelos de negócio se baseiem preponderantemente em dados. Não obstante, determinadas ferramentas desse método precisarão ser adaptadas ou alargadas pela autoridade concorrencial brasileira, principalmente para que sejam levadas em consideração outras dimensões competitivas não relacionadas a preço, como qualidade, inovação e privacidade, bem como particularidades do big data e do ecossistema de sua exploração na avaliação dos efeitos e das eficiências da operação, assim como de eventuais remédios.porBig dataData analyticsDadosPrivacidadeMétodoMetodologiaAntitrusteConcorrênciaCADEDireitoBig dataDireito antitrusteVantagem competitivaConcorrênciaBig data e concorrência: uma avaliação dos impactos da exploração de big data para o método antitruste tradicional de análise de concentrações econômicasinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/openAccessreponame:Repositório Institucional do FGV (FGV Repositório Digital)instname:Fundação Getulio Vargas 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dc.title.por.fl_str_mv |
Big data e concorrência: uma avaliação dos impactos da exploração de big data para o método antitruste tradicional de análise de concentrações econômicas |
title |
Big data e concorrência: uma avaliação dos impactos da exploração de big data para o método antitruste tradicional de análise de concentrações econômicas |
spellingShingle |
Big data e concorrência: uma avaliação dos impactos da exploração de big data para o método antitruste tradicional de análise de concentrações econômicas Monteiro, Gabriela Reis Paiva Big data Data analytics Dados Privacidade Método Metodologia Antitruste Concorrência CADE Direito Big data Direito antitruste Vantagem competitiva Concorrência |
title_short |
Big data e concorrência: uma avaliação dos impactos da exploração de big data para o método antitruste tradicional de análise de concentrações econômicas |
title_full |
Big data e concorrência: uma avaliação dos impactos da exploração de big data para o método antitruste tradicional de análise de concentrações econômicas |
title_fullStr |
Big data e concorrência: uma avaliação dos impactos da exploração de big data para o método antitruste tradicional de análise de concentrações econômicas |
title_full_unstemmed |
Big data e concorrência: uma avaliação dos impactos da exploração de big data para o método antitruste tradicional de análise de concentrações econômicas |
title_sort |
Big data e concorrência: uma avaliação dos impactos da exploração de big data para o método antitruste tradicional de análise de concentrações econômicas |
author |
Monteiro, Gabriela Reis Paiva |
author_facet |
Monteiro, Gabriela Reis Paiva |
author_role |
author |
dc.contributor.unidadefgv.por.fl_str_mv |
Escolas::DIREITO RIO |
dc.contributor.member.none.fl_str_mv |
Rivera, Amanda Athayde Linhares Martins Sampaio, Patrícia Regina Pinheiro Ribas, Guilherme Favaro Corvo |
dc.contributor.author.fl_str_mv |
Monteiro, Gabriela Reis Paiva |
dc.contributor.advisor1.fl_str_mv |
Ragazzo, Carlos Emmanuel Joppert |
contributor_str_mv |
Ragazzo, Carlos Emmanuel Joppert |
dc.subject.eng.fl_str_mv |
Big data Data analytics |
topic |
Big data Data analytics Dados Privacidade Método Metodologia Antitruste Concorrência CADE Direito Big data Direito antitruste Vantagem competitiva Concorrência |
dc.subject.por.fl_str_mv |
Dados Privacidade Método Metodologia Antitruste Concorrência CADE |
dc.subject.area.por.fl_str_mv |
Direito |
dc.subject.bibliodata.por.fl_str_mv |
Big data Direito antitruste Vantagem competitiva Concorrência |
description |
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. |
publishDate |
2018 |
dc.date.accessioned.fl_str_mv |
2018-03-02T13:37:48Z |
dc.date.available.fl_str_mv |
2018-03-02T13:37:48Z |
dc.date.issued.fl_str_mv |
2018-02-02 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/masterThesis |
format |
masterThesis |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
https://hdl.handle.net/10438/20312 |
url |
https://hdl.handle.net/10438/20312 |
dc.language.iso.fl_str_mv |
por |
language |
por |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.source.none.fl_str_mv |
reponame:Repositório Institucional do FGV (FGV Repositório Digital) instname:Fundação Getulio Vargas (FGV) instacron:FGV |
instname_str |
Fundação Getulio Vargas (FGV) |
instacron_str |
FGV |
institution |
FGV |
reponame_str |
Repositório Institucional do FGV (FGV Repositório Digital) |
collection |
Repositório Institucional do FGV (FGV Repositório Digital) |
bitstream.url.fl_str_mv |
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Repositório Institucional do FGV (FGV Repositório Digital) - Fundação Getulio Vargas (FGV) |
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|
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1819892965595676672 |