How JLINC Enables Governed Data Spaces

In looking forward to the MyData Global 2023 conference this week, I thought it might be a good idea to post some thoughts on how the JLINC protocol enables and underpins governed data ecosystems, also known as Data Spaces.

Before doing so, it might be a good idea to set out what we think it takes to run an effective governed data ecosystem.

Firstly, it should be safe to assume that any Data Space will have articulated boundaries, i.e. a scope, and some form of authority/ decision-making body.

That scope requires a governance model which will define the rules of the space, and how to join. The scope will also determine whether the dataspace is closed or open in its architecture. A closed data space is one in which membership is limited to specific, invited entities. An open data space is one in which any appropriate entity can join, provided they meet the requirements of the governance model.

A governance model requires an information sharing agreement through which space members confirm that they will adhere to the rules of the space. All members of the data space will sign the space agreement. The agreement is what enables all parties to understand the provenance of the data shared within the space, and thus can reliably build this data into their processes and data pipelines.

The agreement will point to various roles that might exist in the space, the registration methods, identity management, data definitions, schema, the API’s or other connection methods, and to its business model; i.e.what payments are made by whom, to whom for which data transaction types.

The diagram below sets out a typical data space; it shows multiple parties connecting into a governed data space in a many to many data sharing scenario.


The JLINC protocol is a simple but modern and robust means to underpin a governed data space. In the JLINC model the agreement, management of (decentralised) identifiers, anagent representing each data sharing/ data using entity, the schema, master data management, data portability, interoperability, and an audit log of everydata transaction are all tightly integrated. The transaction log enables the space business model making it easy to see and account for who has contributed and consumed what data. That simple, integrated model means the designers and users of the space can focus more on value maximisation for the various stakeholders, and less on the technical intricacies.

That model should apply in any governed data space, including the major projects in and around the various EU Data Spaces. It should also apply in the interesting model proposed by Vivvi Lahtenooja around ‘What are personal data spaces?’ in which she sees these as a space for each individual, rather than as a collective space for personal data. This new framing would take a bit of getting used to and working, but it could help move beyond the data storage focus. As with all data spaces that will really boil down to the scope of the personal data space, the schema that underpins it, and the agreement that all parties accessing such a space sign up to. All are on the agena for the MyData conference this week; so hopefully that dial can be moved in the right direction.