1. Big Data Competition Considerations

2. 2  Data important to business model in certain sectors  “Gigantic datasets … extensively analysed using computer algorithms”* What is Big Data?  Big Data growing rapidly: McAfee: companies that make the most of their data are 5% more productive and 6% more profitable than their competitors IDC: big data will generate $125 billion in 2015; will grow worldwide at CAGR of 40% (about seven times that of the ICT sector overall) but let’s be considered in assessing potential harm * Article 29 Working Party, Opinion 2013

3. 3  Transform Europe's service industries by generating a range of innovative information products and services  Increase productivity of the economy through improved business intelligence  Help to address many challenges E.g., Environmental, cybersecurity, traffic management  Improve research and speed up innovation E.g., health and epidemiological research  Reduce costs through personalized services  Increase efficiency in the public sector McKinsey: possible savings of up to €300 billion a year in the EU Advantages of Big Data

4. 4 Competition Law and Big Data  Fundamental question – what is ‘big data’? Not only personal data; also includes aggregated and anonymised data sets Used as an input in many industries (not only GAFA)  Online advertising – used to target ads  FMCG (incl loyalty schemes) – inventory management and targeting ads  Travel and local (incl user reviews and frequent flyer schemes) – load/inventory management  eMedicine and other eServices - personalisation and real-time management of patients  Search data – used to improve tail query results Nature of the data is important  Not a new phenomenon  Volume of data generated and analysed is new, as are some uses

5. 5  Data is an “input” – nothing about it warrants departure from normal application of competition rules  DG COMP has considered data as an input in a number of cases, most recently, Facebook/WhatsApp  Data increasingly used to monetise multi-sided online markets (where one side is “free” and another pays), such that the data is critical to both sides: On consumer side – improves relevance/ quality of service (attracting and retaining users) On merchant/advertiser side – delivers targeted advertising/offers (attracting advertisers)  Consumers (on “free” side) can provide data directly (e.g., in user profiles) or indirectly (e.g., what they view/listen to, where they go)  One data set can be used in multiple markets Competition Law and Big Data

6. 6  Like other inputs there are key threshold questions to imposing an obligation to share data (Bronner framework, essentially): Replicability  User multi-homing  Multiple layers in stack have/access same data Exclusivity Degree to which data is important to competing in a downstream/related market  Assess each type of data in context of market in issue – need to understand how each market functions, parameters of competition, sources of data and market positions of entity(ies) to determine potential for competitive harm Competition Law and Big Data

7. 7  Competition concerns may arise Exclusive access to data used to raise rivals’ costs or otherwise disadvantage rivals (preventing entry and expansion)  Reviewed in Google/Doubleclick, M-commerce, Publicis/Omnicom Potential network effects – where entity has market power that enables control over further data collection, incentives (and ability) to exploit existing power might increase  Reviewed in Google/Doubleclick, Facebook/WhatsApp Potential cross-platform network effects – e.g., advertising and user services  Under consideration in Google search Potential scale effects – e.g., tail search queries  Under consideration in Google search Competition Law and Big Data