To best understand the blueprint and following sections, DS4Skills has established different levels that are important to understand the structure of the data space.
Three different levels are considered and mentioned across the document:
- Data space level: It is the data sharing infrastructure level. A data space is a distributed structure defined by a governance framework, that enables trustworthy data transactions between participants while supporting trust and data sovereignty. Data space is implemented by one or more infrastructures and supports one or more use cases.
- Data space use case level: A specific setting in which two or more use case participants rely on a data space to create value and implement a particular usage scenario. Value can be interpreted as business, societal or environmental value.
- Data space use case participant level: A data space participant that is engaged with a specific data space use case and may have one or more roles in it.
These distinctions are needed as for instance the business model of the data space level is not the same as the business model of the use case level. Moreover, governance issues are different according to each level: a data space needs to set some generic onboarding criteria to join, and a data space use case can add on to them.
These distinctions are also fundamental from a trust point of view: the main innovation the data space brings is trust in a decentralised and common protocol to share data. Distinguishing between the data space level and the data space use case level allows to separate the data from its use, organisations and people are no longer enclosed in the solutions that use their data: they have tools to control their data and share it with any solution and use case. Similarly, solutions and use cases are not dependent on only the data they have: they can access the data present in any other solution or use case, provided they have the authorisation to do so.
All this will be more thoroughly defined and illustrated in the next chapters.
