The Sitra rulebook was developed by a diverse group of experts from Sitra, Industry, academia, and public service to provide networks of companies and organisations with the necessary tools for sharing data and creating new services. It is designed to build trust and consider business, ethics, legal, technical, and security dimensions.
The rulebook approach, of which the Sitra rulebook model is one example, takes a holistic view to governance of data sharing initiatives (such as data spaces). It helps organisations to form new data sharing networks and implement policies and rules for them. With the aid of the rulebook approach, parties can establish a data network based on mutual trust that shares a common mission, vision, and values. The rulebook approach also helps data providers and data users to assess any requirements imposed by applicable legislation and contracts appropriately in addition to guiding them in adopting practices that promote the use of data and management of risks.
The following types of issues and problems can arise when applying the rulebook approach, generally and more specifically in the agrifood sector:
The farming sector is a very traditional sector, with farmers and other stakeholders generally hesitant to share data with third parties or adopt digital/data technologies. Some reasons for this reluctance are listed below:
To overcome the above obstacles, the benefits and risks of data sharing must be clear and explained simply, preferably within the frame of an existing, historically well-developed trustworthy environments. From the technical side, data sovereignty, transparency, and provenance trail are keys to gaining trust and engaging farmers in data sharing initiatives (DSI). Gaining the trust of farmers is a decisive and very important prerequisite, but it is not enough to actively engage farmers in the data economy. They must see clear benefits for such an endeavour, e.g. it must be clearly explained to them which new services they will get if they onboard in DSI, or which services or aspects of their life and work will be improved.
As the agri-food sector becomes increasingly data-driven, the processing and exchange of several types of data (e.g., farm data, machine data, service data, agri-supply data) intensifies. The different types of agri-food data also include personal data, which requires particular forms of protection and attention. The rules and requirements for processing personal data are proclaimed in the General Data Protection Regulation (GDPR), which defines personal data as “any information relating to an identified or identifiable natural person”; an identifiable natural person being “one who can be identified, directly or indirectly” (Article 4(1)).
In the agri-food sector, personal data thus includes farmer data (e.g. names, contact information), which relates to an identified or identifiable individual, but possibly also farm data (e.g. compliance/reporting data, livestock data), machine data (e.g. sensor data, location data), service data (e.g. contractor data, vehicle data, transport data), which can render an individual (indirectly) identifiable. Therefore, it must be noted that it is complicated to separate non-personal and personal data in the agri-food sector, and compliance with data protection laws should be prioritized. In relatively rare instances, data anonymisation may render the GDPR non-applicable, as anonymised data is no longer subject to GDPR, but in these cases anonymisation must mean processing personal data in a manner that makes it irreversibly impossible to identify individuals. In other words, the process of anonymisation requires consideration of all reasonably viable measures for converting anonymised data back to an identifiable form (e.g., combining anonymised data with other information).
Stay tuned to find out find out more about how Case Study Premium Grain Chain (Finland, 1001 Lakes) and Case Study Short Food Supply Chain (Slovenia, ITC, UM)are using the rulebook, in our next rulebook feature!
