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Resources · Understanding

What is a data space?

A trustworthy, decentralised and federated ecosystem where organisations and individuals share data and services voluntarily, while keeping control over access and reuse.

Three components.

A community of practice.

SMEs, large firms, public bodies, NGOs, research. A governance authority may represent it.

A governance framework.

A shared rulebook: principles, standards, policies, agreements, enforcement and conflict resolution, readable by people and machines.

A data-sharing infrastructure.

Modular building blocks: identity and trust, catalogues, vocabularies, contracts, connectors, policy engines, monitoring, billing.

Four principles.

  • Control and sovereignty. Data holders decide who uses what, for which purpose.
  • Privacy and security by design. Aligned with GDPR and EU law.
  • Interoperability. Common models, standards and protocols.
  • Fair value-sharing. Reuse free or compensated, on transparent terms.

What a data space is not.

  • Not open data. Access is controlled, under explicit rules, and may be paid.
  • Not a data lake. Data stays with its holder; exchange is federated.
  • Not just policy talk. It runs live services: identity, catalogues, connectors.

The benefits.

Faster collaboration.

Shared tools and rules reduce negotiation time.

Quality and compliance.

Conformance checks and audits build trust.

Scale.

Reusable blocks replace one-off integrations.

A foundation for AI.

Pooling private data for trustworthy agentic AI.

Sovereignty.

Control over access and reuse, per participant and per sector.

Network effect.

First within a domain, then across domains: a European “network of networks”.

Launching a data space in eight steps.

It all starts with a community of practice, often public and private, sectoral or cross-sectoral, European, national or local, that decides to cooperate.

  1. Define scope and mission.

    A clear mission, success criteria, first use cases and data types, personal or not.

    Output: One-page mission, early use cases

  2. Set up governance.

    Constitute a governance authority (DSGA), ideally backed by a legal entity. Define decision rights, roles, onboarding and offboarding. Draft the rulebook, map applicable law (GDPR, DGA, Data Act, sector law).

    Output: Rulebook v1, participation terms

  3. Map the landscape.

    Engage EU and national authorities, peer initiatives and standardisation bodies.

    Output: Stakeholder map

  4. Co-design use cases.

    A practical method, an open ecosystem, agreements on scope, data, roles and KPIs.

    Output: Use-case canvases

  5. Build a sustainable business model.

    Define the value exchange, fund infrastructure and governance, set incentives for broad participation, SMEs included.

    Output: Financial model, budget

  6. Build the technical stack.

    Open standards, ontologies, connectors, identity and trust, federated catalogue, contracts, policy engine, collaboration platform, and where needed digital twins or agentic AI.

    Output: Reference architecture, MVP infrastructure

  7. Fund the journey.

    Pool founders’ resources, secure public and private funding: EU, national and local calls.

    Output: Funding plan

  8. Operate and grow.

    Run the infrastructure and the community, maintain rulebook and catalogues, recruit, measure, report.

    Output: Dashboard, roadmap

At step 1, or at step 8? We can help at each of them.

Let’s talk about your project

Your next project starts with a conversation.

An idea to shape, a group to bring together, rules to evolve?

Let’s talk about your project