Autonomy
Every organisation retains its own responsibilities, systems and decisions.
Sharing data across organisations
Collaborate with data without giving up control.
Data sharing becomes complex when autonomous organisations have different interests, responsibilities and systems. A dataspace provides the governance context to make it manageable.
Sovereign participant ecosystem
Participant AOwn data & policy
Participant BOwn data & policy
Participant COwn data & policy
Participant DOwn data & policy
Identity
Agreements
Evidence
Federated exchange No central data store
Why this matters now
Europe needs data to flow—without losing control.
Data is essential for AI, public services, innovation and competitiveness. At the same time, geopolitical tension and technology dependencies make it critical for organisations to preserve control, continuity and freedom of choice.
Europe therefore combines two ambitions: making more high-quality data available and strengthening European data sovereignty. Common European data spaces are a practical way to achieve that balance.
2020
European Data Strategy
A single market for data and common data spaces in strategic domains.
2025Data Union Strategy
Unlock data for AI, simplify rules and strengthen European data sovereignty.
NowFrom policy to practice
Organise data sharing through shared governance, interoperability and verifiable conditions.
Data sovereignty
Sovereignty is not isolation.
It is the ability to collaborate deliberately while participants retain control over data, conditions and dependencies.
ControlDetermine who may use data, for what purpose and under which conditions.
Freedom of choiceLimit lock-in through open standards, portability and replaceable services.
ResilienceMaintain continuity when providers, jurisdictions or circumstances change.
Trusted opennessCollaborate internationally on fair, secure terms consistent with European values.
The role of a dataspace
A dataspace makes this ambition operational: autonomous participants work with shared governance and standards while access and usage conditions remain explicit and verifiable.
The architectural distinction
Shared value does not require central ownership.
Central stores, data lakes and analytical environments can remain valuable within an organisation. The problem arises when central aggregation becomes the required operating and governance model for collaboration between autonomous organisations.
| Dimension | Central aggregation | Federated dataspace |
|---|---|---|
| Data location | Copies are collected in a shared central environment. | Data remains with, or close to, the participant responsible for it. |
| Control | The central operator becomes the operational control point. | Participants retain operational control and responsibility. |
| Access | Access depends primarily on permissions managed centrally. | Availability and use follow agreed, machine-actionable conditions. |
| Governance | Technology, rules and enforcement are concentrated. | Rules are shared while responsibilities remain distributed. |
| Dependency | Collaboration depends on one shared solution and operator. | Participants connect through interoperable services and standards. |
Where InformationGrid fits
InformationGrid supports the participant side of this federated model: it turns domain knowledge and events into governed data products that can be used locally or made available through a dataspace under agreed conditions.
Governed exchange
Every provision of data passes explicit checks.
Data providerOwn infrastructure
TrustWho is participating?
Usage policyWhat use is permitted?
GovernanceDo shared rules allow it?
Data consumerApproved purpose
Data is made available between participants according to agreed conditions. The exchange creates tamper-evident evidence of what was agreed and observed.
Composable ecosystem
Different roles without mandatory central infrastructure.
01Data Rights Holder
02Data provider
03Data consumer
04Dataspace authority
05Onboarding provider
06Infrastructure provider
When does a dataspace fit?
Not every integration needs a dataspace.
A dataspace helps when…
- multiple autonomous parties participate
- shared governance is required
- access and use depend on context
- new participants must join at scale
A direct integration may be enough when…
- only two fixed parties are involved
- purpose and conditions are stable
- no ecosystem-wide governance is needed
- scalable onboarding is not required
From understanding to delivery