Governance Design Layers
Before defining the already mentioned governance building blocks, it is recommended to first establish a clear understanding of how governance needs to be structured and what it should enable. The suggested layered approach below supports the design of an evolving governance structure, capable of adapting as the data space matures while maintaining clear responsibilities for strategic direction, operational execution, stakeholder participation, and independent oversight. Governance frameworks are typically developed iteratively, based on existing best practices, example governance models and continuous input from stakeholders. For this reason, in this iSHARE Data Space Template, there is not only a suggested co-creation methodology, including some guiding questions that stakeholders can utilise, but also the below suggested design layers that you can follow.
Layer One draws insights from governance models from other European data spaces, like GDDS, EMDS, CEEDS, ETDS, Catena-X, EBSI, ToIP/DC4EU, AgriDataSpace, DS4SSCC, Health-X, and EMREX. It concludes 9 main Design principles that Data Space Governance should follow when created. The second layer is derived from stakeholder input and from the requirements directly defined in the data space itself. This can differ per data space.
Applying a layered governance approach helps to:
ensure alignment between stakeholders,
support a successful governance design,
reduce complexity,
support a structured path towards implementation and scaling,
and enable transparency and accountability, meanwhile.
Within iSHARE, this approach is supported as a practical way to design and implement governance. It provides a solid foundation that can be reused and adapted across different data spaces, while ensuring consistency with common principles and frameworks.
Layer 1 – Governance Design Principles
This layer outlines the core principles that underpin any robust data space governance model.
01. Governance as the foundation
The "soft infrastructure" that enables trust and balances interests.
Governance acts as the defining element of a data space, enabling trust between participants, balancing public and private interests, and aligning legal, technical, business, and ethical dimensions.
Enables trust between participants
Balances public and private interests
Supports scalability and sustainability
Enables secure, sovereign data exchange
02. Multi-layer governance structure
Four nested layers from ecosystem to participant level.
Effective data spaces consistently apply a layered approach spanning from broad ecosystem alignment down to individual participant obligations.
Ecosystem: cross-space alignment
Data space governance: core rules
Use case/domain: context-specific
Participant: access, rights, obligations
03. Role of governance authorities
A facilitator and steward, not an overly centralised controller.
A governance authority is essential but should act as a facilitator or steward, often complemented by delegated or federated governance structures.
Maintains and enforces the rule framework
Defines participation conditions
Ensures regulatory compliance
Oversees trust and interoperability
It does not replace public enforcement authorities.
04. Subsidiarity and federation
Decisions taken at the lowest appropriate level.
Governance should follow the principle of subsidiarity, escalation occurs only when broader coordination is required, enabling flexibility and bottom-up feedback.
Flexibility across domains
Bottom-up feedback loops
Coexistence of multiple models
05. Trust as a core element
Addressed both technically and organisationally.
Trust frameworks should combine technology (credentials, policies) with governance processes such as audits and accountability mechanisms.
Identity and credential management
Verification mechanisms
Access control policies
Trust registries and oversight
06. Data Space Rulebooks as Key Instruments
The primary governance artefact is modular and versioned.
Best practices include modular design and clear separation between baseline and domain-specific rules.
Defines roles, rights, and obligations
Mandatory vs optional rules
Integrates legal requirements
Evolves through structured versioning
07. Separation of governance and operations
Governing entity and operating entity are kept distinct.
A clear distinction between the governing entity (rules, oversight) and the operating entity (execution, daily operations) avoids conflicts of interest and enables scalability.
Avoids conflicts of interest
Enables scalability
Supports outsourced operations
08. Inclusivity and balance
Fair representation, transparency, and accountability.
Voting structures and board composition should prevent dominance by single actors and ensure fair representation of all stakeholders, including smaller participants.
Fair stakeholder representation
Inclusion of smaller participants
Balance of public and private interests
Transparency and accountability
09. Evolution over time
Designed as an evolving system, not a rigid structure.
Governance typically progresses through formation, operation, improvement, and long-term sustainability. Overly rigid or premature structures should be avoided.
Initial formation phase
Operational phase
Continuous improvement
Long-term sustainability
Key implications and requirements
A governance framework should satisfy all of the following design requirements.
Be multi-layered and federated
Clearly define Governance Authorities and delegated scopes
Embed a Trust Framework (technical + organisational)
Separate governance from operations
Enable subsidiarity and bottom-up feedback
Use a modular, evolving Rulebook
Ensure inclusive, balanced representation
Editor’s note: This section synthesises governance principles derived from analysis of existing European data space initiatives and reference frameworks. It informs the design of the Data Space Governance Framework but does not prescribe a fixed organisational structure.
Layer 2 – Governance Capabilities (Operational Perspective)
This layer defines what governance should enable within a data space from an operational perspective.
Governance capabilities should be derived from:
the specific use cases,
the needs of participating organisations,
and the operational context of the data space.
These capabilities may include, for example:
participant onboarding and offboarding,
access and usage control,
compliance monitoring,
dispute resolution,
lifecycle management of rules and policies,
coordination across domains and stakeholders.
Editor's Note: This section should be informed by the Data Space itself, including the use cases and the participants who are designing the data spaces. They can provide/ should provide their specific, governance related requiremnts here.
Examples:
Clear Participation and Onboarding Paths
The Governance Framework shall define clear participation categories and onboarding paths, including Anonymous Users, pre-registered users, Participants, and cross-data-space users, enabling all users to understand what is required to participate (trust, governance and onboarding requiremnets) and what capabilities are available at each stage. This will also allow Cross- Data spcae accessibility, and Graduated Access for Non-Onboarded Users.
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