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Economic and Economic and Economic and Economic and Regulatory Analysis of Data Platforms and Data Platforms and Value Creation

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Economic and Economic and Economic and Economic and Regulatory Analysis of Data Platforms and Data Platforms and Value Creation Economic and regulatory analysis of data platforms and value creation models of the on demand economy Project no. 732546 DECODE DEcentralised Citizens Owned Data Ecosystem D2.2 Economic and regulatory analysis of data platforms and value creation models of the on demand economy Version Number: V1.0 Lead beneficiary: CNRS Due Date: December 2017 Author(s): Stefano Lucarelli (CNRS), Elena Musolino (CNRS), Giulia Rocchi (CNRS), Sophie Ciacciarelli (CNRS), David Laniado (Eurecat), Ricard Espelt (UOC), Enric Senabre (UOC), Mayo Fuster Morell (UOC), Francesca Bria (IMI) Editors and reviewers: Marco Ciurcina (Polito), Eleonora Bassi (Polito), Tom Symons (NESTA) Dissemination level: PU Public PU PP Restricted to other programme participants (including the Commission Services) RE Restricted to a group specified by the consortium (including the Commission Services) CO Confidential, only for members of the consortium (including the Commission Services) Approved by: Francesca Bria, Chief Technology and Digital Innovation Officer, Barcelona City Council (IMI) Date: 30/12/2017 This report is currently awaiting approval from the EC and cannot be not considered to be a final version. H2020–ICT-2016-1 DECODE D.2.2 Economic and regulatory analysis 2 of data platforms Contents EXECUTIVE SUMMARY 5 1. BIG DATA IN THE DIGITAL ECONOMY 9 1.1 Defining Big Data: an open issue 9 1.2 Current and expected global industry trends 17 2. BIG DATA AND PRIVACY 19 2.1 The main differences between the EU and the US with regard to privacy protection 19 2.2 The key innovations introduced by the General Data Protection Regulation 20 2.3 The uncertain impact of the GDPR: optimistic, sceptical and dramatic previsions 23 2.4 The major obstacles to the expansion of European data-driven businesses. Some mainstream views 25 2.5 Towards an alternative framework: stressing the role of the little-known data broker industry 28 3. DATA-DRIVEN PLATFORMS AND COMPETITION 32 3.1 The peculiarity of data as a good 32 3.2 The structure of the big data market: challenges in defining potential competition issues 33 3.3 Is there a link between privacy and competition? 38 3.4 Why Big Data is susceptible to raise competitive concerns 39 3.5 Two/multi-sided markets/platforms: a literature review 41 4. DATA-DRIVEN VALUE CREATION MODELS: SELECTED INCUMBENT PLATFORMS’ CASE STUDIES 47 4.1 Incumbent platform companies: a global economic and geographical overview 47 4.2 Internet search services: the case of Google Search 53 4.2.1 General description 54 4.2.2 Google Search’s business model 57 4.2.3 Value creation model: a simplified framework 60 4.2.4 Risks connected to the current value creation model 61 4.2.5 Alternative approaches 73 4.3 Social media: the case of Facebook 75 H2020–ICT-2016-1 DECODE D.2.2 Economic and regulatory analysis 3 of data platforms 4.3.1 General description 75 4.3.2 Facebook’s business model 80 4.3.3 Value creation model: a simplified framework 84 4.3.4 Risks connected to the current value creation model 85 4.3.5 Alternative approaches 95 4.4 On-demand platforms: the case of Uber 98 4.4.1 General description 100 4.4.2 Uber’s business model 103 4.4.3 Value creation model: a simplified framework 104 4.4.4 Risks connected to the current value creation model 105 4.4.5 Alternative approaches 119 4.5 On-demand platforms (bis): the case of Airbnb 120 4.5.1 General Description 120 4.5.2 Airbnb’s business model 122 4.5.3 Value creation model: a simplified framework 125 4.5.4 Risks connected to the current value creation model 129 4.5.5 Alternative approaches 134 CONCLUSIONS 135 REFERENCES 137 H2020–ICT-2016-1 DECODE D.2.2 Economic and regulatory analysis 4 of data platforms Executive Summary Author: Stefano Lucarelli and Francesca Bria DECODE – DEcentralised Citizens Owned Data Ecosystem – aims to develop practical tools to protect people’s data and digital sovereignty. The project is building towards a data-centric digital economy where citizen data, generated by the Internet of Things (IoT) and sensor networks, is available for broader communal use, with appropriate privacy protections. As a result, companies, cooperatives, local communities and citizens will be able to use that data to build data- driven services that better respond to individual and community needs. This means rethinking the thorny questions around the ownership, control and management of personal data from an economic, legal, regulatory and technical dimension 1. The DECODE deliverable “ Economic and regulatory analysis of data platforms and value creation models of the on demand economy ” presents two main aims: (i) Proposing an analysis of regulatory models for data platforms; (ii) Clarifying the logic of value creation that characterises the so-called on-demand economy, by analysing four case studies of dominant platforms. Both the objectives are necessary to develop T2.2 (“ Data-driven disruptive and commons based economic models ”) within the WP2 (“ Decentralised Governance and Economic framework: Commons data platforms for digital sovereignty ”). This deliverable analyses the dominant data-driven economic models, and clarifies the boundaries between the major data driven platforms and the so called on-demand platform economy, then it defines the different areas affected by currently existing regulatory models. This analytical work is essential to be able to by propose in our future work economic, regulatory, and technical alternatives to the dominant, centralised and monopolistic economic models of data-driven platforms, which exploit network effects and capture rent from network externalities , thus hindering innovation and eroding citizens’ digital sovereignty (D 2.4). This will contribute to the overall objective of DECODE to identify and experiment new political, economic, and legal regimes that recognize social and communal rights to data. A conceptual map of the different models of the data-driven economy can be represented as follows: 1 Reclaiming Europe’s digital sovereignty: https://www.ft.com/video/da525a75-0276-4261-a6fe-d5a4a950f157 H2020–ICT-2016-1 DECODE D.2.2 Economic and regulatory analysis 5 of data platforms D2.2 “ Economic and regulatory analysis of data platforms and value creation models of the on demand economy ” What kind of regulation? What kind of new collective production models & alternative ownership regime for data? Can we regulate data driven platforms to promote a transition towards alternative approaches of collective production models for a wealth that is equally distributed? Can Europe develop alternatives? D. 2.4 “ Data driven disruptive commons-based models ” H2020–ICT-2016-1 DECODE D.2.2 Economic and regulatory analysis 6 of data platforms Both data-driven platforms and the on-demand economy are areas characterized by incisively innovative dynamics. Regulatory interventions in these fields, characterised by continuous evolution, are constantly confronted with the difficulty of defining the actual extent of regulation itself. In fact, what can happen is that innovative dynamics create business models that fall outside of existing applicable norms. Consequently, the first section of this deliverable tries to contribute to a definition of ‘Big Data’, in the conviction that data extraction and management represents the driving force of value creation models within platform capitalism. Understanding the value of data and the emergence of big data management is key to defining the regulatory models necessary to favour the development of alternative approaches of data-driven collective production where wealth is equally distributed, data is treated as a common good and not as a commodity, and citizens can regain data sovereignty. In the first part we provide a pragmatic definition of Big Data, looking at the evolution of its frequency in Web search queries, shows the main groups of concepts and actors around it, and highlighting the relevance of key players and powers like Google and Facebook. The second section focuses on one of the most debated topics in data regulation: privacy and data protection. In particular, the main differences between EU and USA about privacy protection are presented and discussed, emphasising the opportunities for Europe with the new General Data Protection Regulation to propose new data- driven models that are privacy enhancing and rights respecting. Attention is then paid to data marketplaces and the role played by data brokers, which remains still underinvestigated and opaque (which will then be recalled in the discussion related to Google’s and Facebook’s data extractivist value creation models). Finally, we maintain that the optimal trade-off between the need for innovation and the need for personal data protection and data sovereignty has not yet been achieved. To address this gap, and develop privacy-enhancing tools that allow people to regain control over their data is one of the main objectives of the DECODE project. The third section is focused on another basic aspect for regulatory models: platform competition in data-driven markets, we question, among other things, the relationship between citizens’ privacy and fair competition in data marketplaces. The economic literature has shown that the two-sided nature of platforms dealing with Big Data makes price a secondary factor among the determinants of the dynamics between the different sides of the platform. It is extremely significant that research tends to signal the relevance of a concept known as Small but Significant Non-transitory Decline in Quality. This aspect deserves attention in future DECODE research, especially within the upcoming phases related to project’s pilots. It is also significant that the reviewed literature tends not to consider the specificities of the logics of value creation that characterize the concrete economic realities dominating platform capitalism. H2020–ICT-2016-1 DECODE D.2.2 Economic and regulatory analysis 7 of data platforms Following this line of thought, in the fourth and last section we will present four case studies, which may be considered as benchmarks respectively in the context of integrated platforms (Google search and Facebook) and transaction platforms (Uber and AirBnB).
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