Definition and some examples

Unlike the term ‘personal data’, Swiss legislation does not provide a definition of the term ‘non-personal data’. In the Federal Act on Data Protection (FADP, SR 235.1), it is only defined in contrast to the definition of personal data as “all data that is not personal data”.


However, this definition does not provide a basis for determining unequivocally whether data should be considered ‘personal’ or ‘non-personal'. Yet it is a distinction that is central because the strict provisions of the FADP apply to the processing of personal data, but not to the processing of non-personal data.


Meteorological data, topographical data, cadastral and machine-generated data (e.g. from machine tools on industrial production lines) unambiguously constitute non-personal data.


Information that is mandatory for the labelling of foodstuffs or an inventory of tourist attractions in a town or region is also considered non-personal data. In fact, this type of data is already being pooled (as one of many ‘access’ solutions) and exploited commercially by different stakeholders jointly within the same market in Switzerland.

  
  

‘Smart farming’

Access to non-personal data is of considerable economic importance. The simplest way to illustrate this is to use the example of agriculture. If a tractor, tank, silo, milking machine, barn, etc. is equipped with appropriate sensors, huge amounts of non-personal data can be accumulated on a farm. This data can significantly increase efficiency not only for farmers, but also in various other sectors. For example, non-personal data produced by the farm is of interest to producers of agricultural machinery, feed and seed. This non-personal data is also of interest to globally operating government agencies and agricultural traders in order to adapt logistics at an early stage. In the finance and insurance sector, on the other hand, this information is of great value for the appropriate use of capital.

  

  

‘Multimodal Mobility’

In the future, it will be possible to put together and buy tailor-made offers, such as several means of transport, with a single click online or via a mobile application. This will make it easy, for example, to combine public transport, shared vehicles such as cars, bicycles and scooters, as well as taxis and other mobility options. This will enable the available transport offers to be used and exploited in a more targeted manner and the overall transport system to become more efficient and sustainable. The most important prerequisite for this is to have secure and the simplest possible access to the non-personal data of the various stakeholders. This is why the Federal Council has instructed the Federal Department of the Environment, Transport, Energy and Communications to set up the National Data Infrastructure on Mobility (NaDIM) as a public service, with a view to exchanging non-personal mobility data (timetables, availability, location, fares, etc.).

  

  

Tourism

The local and regional websites of tourism service providers (i.e. hotels, restaurants, tourist attractions, key players in mobility, cultural and entertainment venues, etc.) can only compete to a very limited extent with the global platforms active in tourism (e.g. Google, Booking.com, Airbnb, etc.). This makes it even more important to make their own non-personal data (schedules, prices, services) and content openly accessible on global platforms and thus visible to a wide audience. In addition, a customer does not usually use a sole tourist service in one location, but visits the area and wishes to use a range of tourist offers in that region. Renowned tourism organisations, for example, have started to cooperate to make their data available on various larger platforms instead of only on their own websites and applications.

  

  

Train delays

The European Commission estimates that real-time notification of train delays in the EU can save 27 million working hours. It estimates that this is the equivalent of €740 million in labour costs.

  
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