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Policy Brief: Towards a Data Space for Skills Blueprint

This brief presents an overview of key findings related to stakeholder needs and preferred solutions in the field of skills data. It offers valuable insights to guide the development of the DS4Skills Blueprint and serves as the first feedback for the Data Spaces Support Centre (DSSC).  

It is based on the previous findings in “The interim report on requirements and design approaches”, which conducted extensive research on existing platforms, stakeholder interviews, and co-creation sessions involving 20 skills data initiatives, to understand their design approaches, needs, and envisioned use cases for future applications of skills data. 

The service design approach used in the research process allows for a comprehensive understanding of the solutions, motivations, and methods employed by these initiatives in building skills data spaces. It covers the development of solutions, the drivers behind skills data space creation, and the critical areas and challenges involved. 



…or read a summary here ⤵️

1. Research results 📝

The analysis of our research reveals several challenges and opportunities to strengthen the development of a data space for skills, and highlights important learnings across seven key themes 

  • Theme 1: A human-centred approach is crucial for sharing skills data and creating value in skills data spaces.  
  • Theme 2: Semantic interoperability, data quality, and AI capabilities are essential for effective skills data sharing.  
  • Theme 3: Building blocks for skills data spaces include standard elements, human centricity, AI, and Personal Data Intermediaries.  
  • Theme 4: Business models in skills data initiatives vary, with some emphasizing profit and others focusing on the greater common good.  
  • Theme 5: Data ecosystems in skills data spaces are complex and lack standardized governance models, posing challenges for management.  
  • Theme 6: Challenges in readiness and growth include financing, governance, legal frameworks, engagement, political support, and agreements.  
  • Theme 7: Services and applications in skills data spaces can be categorized into four distinct categories, offering value to individuals. 

2. Introduction of the essential technical elements 🛠️

 The following are key concepts that were identified for the different building blocks in the Skills Data Space: 

Building block 

Key concepts identified 

Interoperability building blocks 
  • Standardised vocabularies (semantics, data models, and APIs) recommended for improved interoperability. 
  • Examples of standards in use: JSON-LD, Open Badges v2 (OBv2), and 
  • Ideal future building blocks: universal plugin for integrating solutions, W3C Verifiable Credentials standard, digital wallet interoperability, JSON-LD for skills data models, semantic translators, GraphQL API, and Open Badges v3 (OBv3). 
Trust building blocks 
  • Key factors: security, anonymity, pseudonymity, explainability, consent, and contracts. 
  • Recommendations: secure data architectures, consent and contract management based on Kantara Consent Receipt and ODRL standards. 
  • Current standards: IDP, SSO, and Verifiable Credentials. 
  • Ideal future building blocks: decentralised protocols, self-sovereign identity (SSI) management, smart contracts, and decentralised AI training. 
Data value building blocks 
  • Key elements: data accessibility, comparable data, transparency, and interoperability of metadata and data. 
  • Recommendations: collaboration on a common vocabulary, Gaia-X catalogues, JSON dashboards. 
  • Ideal future building blocks: non-fungible tokens (NFTs) for gamification, FAIR principles, and data vault modelling. 
AI building blocks 
  • Need for building blocks to evaluate algorithms against ethical criteria. 
  • Recommendations: follow internationally recognised AI ethical guidelines (OECD, UNESCO). 
  • Ideal future building blocks: AI modelling platforms, ethical AI, edge AI translators, and image recognition support. 
Governance building blocks 
  • Agreement on multi-level governance system and data sharing contracts. 
  • Recommended model: Sitra Rulebook. 
  • Suggestions: public/private governance, cooperation, reciprocity, data intermediaries, personal data intermediaries (PDI), mutualised efforts, common building blocks. 
  • Business building block: tracking value contribution and redistributing generated value. 


3. Nine conditions for success 🚀

The following list groups the 9 key conditions to create the Blueprint for the Skills Data Space and for other DSSC activities, as well as perspectives about what actions and changes would be necessary to enable the development of a data space for skills in line with the principles described above and in the EU data strategy. 



Actions and Changes Needed 

1️⃣ Permission to experiment 
  • Data spaces require experimentation to move from ideas to operational solutions. 
  • Encourage a culture of iteration and experimentation.  
  • Accept and learn from failures. 
2️⃣ Cultivate champions 
  • Commitment and confidence among individuals working on data spaces are crucial. 
  • Find and encourage individuals passionate about building data spaces.  
  • Support them in spreading awareness, securing funding, and developing solutions. 
3️⃣ Unity of the small 
  • Data spaces empower medium and smaller players by increasing their access to data and improving services. 
  • Promote collaboration and data sharing among smaller actors in the field. 
4️⃣ Broad awareness and recognition 
  • Proper communication of the value proposition is essential for uptake of data spaces. 
  • Clearly communicate the idea of data spaces to stakeholders, decision-makers, and policymakers.  
  • Invest in raising awareness and understanding of data spaces. 
5️⃣ Renewal 
  • Moving to decentralised and open data ecosystems requires significant transformation. 
  • Renew legislative frameworks to accommodate data spaces.  
  • Transform legacy systems that inhibit the implementation of data spaces.  
  • Foster public-private collaborations for effective development. 
6️⃣ Public-private partnerships 
  • Public funding has been essential, but there is a desire for self-sustainability in data space initiatives. 
  • Seek partnerships between public and private sectors for future developments.  
  • Establish agreements regarding funding and profit sharing. 
7️⃣ Collaboration on infrastructures and building blocks 
  • Collaboration is needed to create functional mechanisms for data transmission and sharing. 
  • Facilitate collaboration across countries and companies through a public-private EU data infrastructure consortium.  
  • Share knowledge, tools, and processes to optimize resources and reduce costs. 
8️⃣ Interoperability 
  • Technical interoperability has been emphasized, but other forms of interoperability are needed. 
  • Build foundations of legal, semantic, functional, and operational interoperability.  
  • Consider ecosystem-level interoperability. 
9️⃣ Trustworthy system 
  • Reliability, safety, and good governance are critical for data spaces. 
  • Establish trustworthy governance mechanisms for data spaces.  
  • Admit validated and reliable actors into the data space 


4. Recommendations for the DSSC 💡

The Data Spaces Support Centre (DSSC) has an important role for the development of European common data spaces, capacity to coordinate and mobilise activities and knowledge sharing between engaged stakeholders, and strong connections with European regulatory bodies.  

Therefore, DS4Skills recommends that the DSSC take the following actions:     

  • Clearly articulate and promote a concrete value proposition of data spaces, especially for skills data spaces, to engage key stakeholders and assess resonance across different domains. 

  • Emphasize the importance of a human-centric approach in data spaces in its communications and resources, integrating it in the early design phases and prioritizing tools for Personal Data Intermediaries to ensure data control and compliance with individuals’ requests. 
  • Collect and share success stories and learnings, including challenges and failures, to foster a culture of experimentation and co-learning in data spaces. 
  • Coordinate small-and-medium commercial actors to strengthen their market position and efficiency, promoting unity among smaller players. 
  • Facilitate public-private budgeting and strategizing exercises, supporting structured dialogue between public and private actors to assess long-term costs and develop sustainability and cost-mitigation strategies. 
  • Encourage the European Data Innovation Board (EDIB) to address legal interoperability and consistent application across EU Member States. 
  • Encourage the European Commission to support for experimentation in developing data spaces and recommend mechanisms like regulatory sandboxes or synthetic data to foster innovation without non-compliance concerns. 
  • Propose how certain technical building blocks can fit into the overall data spaces blueprint, such as User Experience, data analytics, and artificial intelligence.