2017-2019 will be landmark years for Data Economy[1] and particularly with regards to cooperative, connected and automated mobility - also known as connected cars or self-driving cars.

Recently in Rome, March 23, 29 European countries, Members of the European Union and of the European Economic Area, have signed a Letter of Intent (LoI) to intensify cooperation on testing of automated road transport in cross border test sites.

This LoI emphasises the need to properly adapt international road traffic legal environment and vehicle legislation to technological advancements and the need to address the data ecosystem, taking into consideration data ownership, safety matters and the opportunities of the Data Economy and the free movement of data.

The signature of this LoI was preceded by the signature of an MoU in March 2, between the European Automotive – Telecom Alliance (EATA), an alliance between telecom and automobile industries to promote automated mobility EATA and the 5G Automotive Alliance (5GAA). a cross-industry global association formed in September 2016 by players in the telecommunications and automotive sectors.

Connected and Automated Driving (CAD) will undoubtedly have a significant impact within the data economy legal environment, particularly with regards to IP rights over machine-generated data, liability and safety in the context of emerging technologies, free flow of data and portability of non-personal data.

Raw non-personal machine-generated data already raises sensitive ownership issues since it is generally not protected by IP rights – such data is not the result of an intellectual effort and/or have any degree of originality – nor by the sui generis right of the Database Directive (96/9/EC) – except if the creation of such a database involves substantial investment in the obtaining, verification or presentation of its contents. However, the impact of CAD in the data economy justifies proper legal protection with regards to data ownership which may pass through legislative initiatives and policy frameworks.

Liability principles and rules will have also to be revisited with CAD, since current contractual and non-contractual liability rules – and particularly the Directive on liability for defective products (85/374/CEE) ("Products Liability Directive") – may not give satisfactory answers to this new challenges[2]. Insurance industry will have also a primary role in this respect.

Finally, it’s worth to mention that machine-generated data generally may involve personal data process – in which case the data protection framework and, in particular, the GDPR, will apply – together with non-personal data – which are not covered by data protection rules and might not be protected by IP or sui generis rights or even by the trade secrets framework that will emerge from the Trade Secrets Directive.

According to the EU Commission[3], the increasing importance of raw non-personal machine-generated data in the data economy will justify a proper legal framework in order to:

  • Improve access to anonymous machine-generated data: Through sharing, reuse and aggregation, machine-generated data becomes a source of value-creation, innovation and diversity of business models.
  • Facilitate and incentivise the sharing of such data: Any future solution should foster effective access to data, taking into account, for example, possible differences in bargaining power between market players.
  • Protect investments and assets: Any future solution should also take into account the legitimate interests of market players that invest in product development, ensure a fair return on their investments and thereby contribute to innovation. At the same time, any future solution should ensure a fair sharing of benefits between data holders26, processors and application providers within value chains.
  • Avoid disclosure of confidential data: Any future solution should mitigate the risks of disclosing confidential data, in particular to existing or potential competitors. In this regard it should also allow for proper data classification to be performed, prior to the assessment of whether or not a certain piece of data can be shared.