Who starts a data space?
A community of practice, often a mix of public and private actors, decides to collaborate. It can be sectoral or cross-sectoral, EU-level, national, or local.
1) Define scope & mission
- Write a clear Mission Statement and success criteria.
- Frame initial use case boundaries and data types (personal & non-personal).
Output: One-page mission, early use case list.
2) Set up governance
- Constitute a Data Space Governance Authority (DSGA)—optionally/preferably backed by a legal entity (association, cooperative, etc.).
- Define decision rights, roles, and onboarding/offboarding.
- Draft the Rulebook (principles, policies, standards, agreements, enforcement, dispute resolution).
- Map applicable laws & soft law (GDPR, DGA, Data Act, sectoral laws, codes of conduct, standards, etc.).
- Specify access rights and data-management practices, including inter-data space sharing rules.
Output: Rulebook v1, participation terms.
3) Map the landscape
- Engage EU/national authorities and sector coordinators.
- Connect with peer data space initiatives (same and adjacent domains and sectors).
- Align with standards/support bodies.
Output: Stakeholder map & alignment plan.
4) Co-design use cases
- Adopt a practical co-design method for intra- and inter-data space scenarios.
- Keep the ecosystem open & inclusive (public/private orgs and end-user communities).
- Formalise agreements on scope, data, roles, and KPIs.
Output: Use case canvases with owners, milestones, and metrics.
5) Build a sustainable business model
- Define value exchange: how data/services are compensated.
- Fund infrastructure and governance operations. Infrastructure operations can require the creation of a dedicated Operating Company of the data space.
- Set incentives for broad participation (incl. SMEs).
Output: Financial model, pricing/compensation options, budget.
6) Build the technical stack
- Choose cross-sector standards (identity/trust, catalague, contracts/consent, policies/rules).
- Select sector ontologies and vocabularies and define pilot ontologies of the data space.
- Favor open standards/protocols for interoperability.
- Implement core building blocks: connectors (as-a-Service or self hosted), identity & trust framework, federated catalogue, vocabulary hub, contracting, policy engine, monitoring, billing, quality.
- Provide a collaboration UI/platform/tools (marketplace, use-case factory, contract negotiation, social network and virality), run by multiple data space participants or by a single Operating Company mandated by the DSGA.
- Add middlewares for specific needs (digital twins, agentic AI frameworks, non data sources such as Open Data and classical marketplaces).
Output: Reference architecture, MVP infrastructure, runbooks.
7) Fund the journey
- Pool resources from founders.
- Secure public/private funding for infra and use cases through EU/national/local call for tenders and proposals, or from VC/corporate funding.
Output: Funding plan & commitments.
8) Operate & grow
- Run the data-sharing infrastructure and community processes.
- Support self-hosted connectors or provide Connectors-as-a-Service.
- Maintain Rulebook, semantic hubs and catalogues.
- Recruit new participants; scale intra and inter data space use cases.
- Track SLIs/SLOs, security & compliance; report on impact and iterate.
Output: Operations dashboard, conformance reports, roadmap.