3.2. Approach

3.2.1. Why the data space #

Across the key strategic usage scenarios defined by DS4Skills (see “Usage Scenarios” Chapter), several ideas and functionalities are already implemented by innovative edtechs and hrtechs. One may ask, why the data space is needed?

• These functionalities need real time data about people, job and training offers. This data is today siloed and closed at each source (university, edtech, employers, institutions, etc).
• The data space will allow direct access to data at their source.
• The true innovation the data space brings is trust in a common protocol for organisations to connect their data bases so that innovative solutions can be developed on top, while they keep control of their data and can choose the solution they want.
• Current solutions offer centralised approaches asking organisations and people to provide their data to one closed silo that they don’t control and to be used only by their solution.
• The data space proposes to people and organisations to connect to one decentralised protocol to share data with any trusted solution without having to do new integrations each time.
• This allows for a next generation of digital services that can access any available dataset and be interconnected.

In the following chapters, the EU-Dune use case description serves as a concrete fictional example to illustrate a data space use case and its value.

In the “Usage Scenario” chapter, the three key strategic added-value use cases— MAP, MATCH, and FORECAST—are presented at a conceptual level. These outline potential generic scenarios that describe how a skills data space could create value through collaboration between one or more members of the data space use case. The usage scenarios presented are only generalisations not intended to be exhaustive and should not be limited to the different elements introduced in these examples. There could be a lot of other different objectives, benefits, functionalities, components, stakeholders and data interconnections in similar use cases beyond the ones listed here.

3.2.2. The different levels of the data space #

To best understand the blueprint and following sections, DS4Skills has established different structural levels that are important to describe 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 one or more 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.

3.2.3. The different roles in the data space #

Throughout the project, the DS4Skills team has worked closely with the Data Space Support Centre to align its recommendations with the DSSC’s. In this Blueprint when DSSC or DSSC Blueprint is referred, it refers to the DSSC Blueprint 0.5 public consultation version as published by the DSSC on the 10th of September 2023 [DSSC-Blueprint].

DS4Skills has also defined the set of roles that compose data spaces and data space use cases:

• Data Space Governance Authority (from DSSC): The data space participant that is accountable for creating, developing, maintaining and enforcing a governance framework for a particular data space, without replacing the role of public enforcement authorities. The DSGA can propose open-source building blocks to facilitate data transactions and the technical, business and governance aspects of those transactions.
• Service provider: A data space participant that provides a (technical or non-technical) service (data space application service), that uses data that is available within a data space, and provides results thereof to (other) data space participants. Examples include analytics services, data quality services and the like.
  • Service providers for individuals: A service provider that provides a data space application service to individuals.
  • Data space application service for organisations: A service provider that provides a data space application service to organisations.
• Data Provider: A data space participant that, in the context of a specific data transaction, technically provides data to the participants that have a right or duty to access and/or receive that data.
  • Provider of Personal Data: A data provider that provides personal data.
  • Provider of Organisational Data: A data provider that provides data about organisations.
• End users (from DSSC): Organisations that are not necessarily data space participants but that use data space services provided, for instance, in the context of a data space use case.
  • Organisational end users: end users that are legal persons.
  • Individual end users: end users that are natural persons.
• Data Intermediary (from DSSC): A data space participant that provides one or more data space enabling services (identity, catalogue, consent, contract, interoperability, etc) while not directly participating in the data transactions itself.
  • Organisational Data Intermediary: A data intermediary that provides data space enabling services to share data about organisations.
  • Personal Data Intermediary: A data space intermediary that facilitates the management of personal data in data spaces.
• Use case Orchestrator: A data space participant that is in charge of a data space use case and manages the business, organisational and governance operations of that use case.

The DS4Skills Blueprint illustrates these roles and defines their governance and business models in the context of the skills data space. These terms will be referred to throughout the document.

3.2.4. Interaction model of the roles and levels of the data space #

As is described in the previous sections, the data space model is one of complexity with different layers and roles interacting.
Here’s a high-level model of the DS4Skills Blueprint data space model:

• The Data Space Governance Authority ensures trust, interoperability and decentralisation at the data space level by:
  • providing open-source building blocks (catalogue, interoperability, consent and contract management, etc) needed by the data space participants (service providers, data providers, orchestrators) to implement their data space use cases and data transactions while maintaining control over their data.
  • certifying trusted Data Intermediaries that operate these building blocks to offer it as a service to participants, this ensures high quality infrastructure services while not relying on only one provider only.
  • Participants can also choose to operate the building blocks themselves, if so, they need to be certified as well.
• Data Space participants and data intermediaries can be part of the DSGA to decide on: which building blocks to develop, what certification criteria, etc.

These interactions are illustrated in Figure 1:

The Blueprint following chapters detail in more depth the possible use cases, the nature of the participants, the building blocks needed and overall governance and business models.

3.2.5. Methodological approach #

DS4Skills adopted an approach to identify the needs of skills & education stakeholders across Europe: what are the use cases they are working on? What are their technical, business, UX and governance needs? This happened through a set of 20 in depth interviews and the results can be found more extensively in D3.1 [Deliverable 3.1].
From these needs the project identified 5 main tracks:

• Usage scenarios: what are the main usage scenarios for the skills data space, what is the value for each stakeholder?
• Business models & Value sharing: what are the different types of business model, pricing models? What kind of value is generated and shared in the data space?
• Governance Models: what are the roles in the data space? What are their obligations? Who is in charge of what? What do we need to set up this governance?
• Technical architecture and building blocks: what are the building blocks and technical components needed to share data in a decentralised and trusted way?
• User experience: how to ensure a seamless experience for people and organisations in the data space?

Each of these tracks were set to provide precise recommendations and solutions through an iterative approach of 5 phases.

1. Scientific literature phase: Proposing recommendations extracted from scientific articles.
2. Inclusion phase: Augmenting the recommendations with elements from D3.1 and the Data Space Support Centre and with the recommendations with the expertise of DS4Skills partners.
3. Community phase: Organising a workshop organized with skills / education stakeholders to get inputs on the recommendations.
4. DSSC phase: Organising a workshop organized with the DSSC to align the blueprint with DSSC progress and recommendations.
5. Partners phase: Producing the final recommendations of each chapter through an agile and co-creation approach, based on all previous inputs and their expertise.
6. Finalization phase: Fine tuning the blueprint through a final workshop with wider audience (decision makers, other data space representatives).

The following chapters detail as such:

1. General approach: description of the overall structure, roles and different levels in the data space as well as the human-centric focus of the Skills & Education Data Space.
2. Usage scenarios: description of the main use cases DS4Skills as identified for the Skills & Education Data Space.
3. Business models: description of potential business models for the data space use cases
4. Governance models: description of the governance model and framework needed to implement the data space.
5. Technical elements: description of the technical architecture and building blocks proposed to implement the data space.
6. User Experience: user experience propositions for the data space.
7. Growth and roll out: recommendations to launch the data space and ensure wide adoption.

Across the tracks we will use the fictional use case “EU-DUNE” to concretely illustrate recommendations, described under chapter “Usage scenarios”.