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Developing Use Cases in Agrifood Data Spaces

Data spaces make it possible to build collaborative solutions where different actors in the agrifood sector can combine datasets, algorithms and services under a shared framework of trust. This section explains how use cases are designed and deployed within the RegenAg-X ecosystem, following a practical, outcome-oriented methodology aligned with European principles of data sovereignty and interoperable federated architectures.

As a real working reference, RegenAg-X operates a production dataspace in the Azores Islands (Portugal), where local farmers publish soil datasets and allow trusted algorithms to run inside secure Data Rooms through Compute-to-Data.
Data never leaves the protected environment, and access is controlled through verifiable and auditable policy mechanisms. The Use Cases section includes real examples that will grow as more parcels, datasets and services are incorporated.

Use cases are the primary vehicle for addressing concrete challenges in the agrifood sector, unlocking value that emerges when data is shared across multiple stakeholders. They rely on:

  • a common technical infrastructure (Web3 identities, catalogs, connectors, Data Rooms, marketplace),
  • shared governance rules,
  • and transparent policies that guarantee data sovereignty and interoperability.

Methodology for Developing a Use Case​

Developing a use case inside a data space follows a structured, phased process. This ensures that the final solution is feasible, scalable and aligned with the technical and organisational principles of European data spaces.

Below are the eight phases used in RegenAg-X, expressed in an original structure but conceptually compatible with best practices from the Spanish Data Office and the EU dataspace community.

1. Identifying the challenge and opportunity​

Participants identify a shared need or improvement area that can be solved by combining or sharing data.
Typical motivations include:

  • creating a new product or service,
  • improving operational efficiency or automation,
  • addressing sector-wide challenges that require collaboration.

2. Understanding and structuring the available data​

This phase examines which datasets exist, who owns them, their quality and how they must be organised to support the use case.
Key tasks include:

  • defining the data model,
  • identifying relevant sources,
  • deciding whether to apply AI, predictive analytics or simulation models.

3. Alignment among participants​

Stakeholders agree on the collaboration framework:

  • participation conditions,
  • access and usage policies,
  • expected outcomes and value,
  • governance and trust arrangements.

The aim is to create a clear, shared foundation before any technical work begins.

4. Functional and technical design​

A design document is created describing:

  • the purpose and workflow of the use case,
  • which components of the data space are required (Compute-to-Data, Data Rooms, catalogs, marketplace…),
  • integration needs and interoperability requirements.

The design may reuse existing patterns or modular components from the Gaia-X and Ocean Enterprise ecosystems.

5. Solution development​

Based on the approved design, the solution is built:

  • development or adaptation of algorithms,
  • creation of data pipelines,
  • implementation of automated processing logic,
  • definition of metrics, dashboards or reporting mechanisms.

Where possible, existing components are reused to accelerate the process.

6. Integration of technologies and services​

All required elements are integrated to enable the full data lifecycle:

  • interoperability connectors,
  • verifiable identities,
  • semantic catalogs and metadata,
  • governance and auditing services,
  • secure compute infrastructure (Compute-to-Data, Data Rooms).

The goal is to ensure end-to-end functionality inside the data space.

7. Deployment and validation​

The use case is embedded in the RegenAg-X dataspace, where the following tests are performed:

  • functional and integration tests,
  • access tests with real policies,
  • Compute-to-Data validation,
  • conformance and traceability checks,
  • final acceptance by the participants.

Only after these validations is the use case promoted to operational status.

8. Operation, scaling and continuous improvement​

Once operational:

  • the value generated is measured,
  • enhancements are implemented,
  • the use case is extended to new datasets or stakeholders,
  • new services or algorithms are incorporated.

This cycle ensures the dataspace grows federatively, preserving sovereignty and trust.

Tools for Evaluating and Designing Use Cases​

To support this process, two types of methodological tools are typically used:

1. Viability assessment​

This helps determine whether a use case is worth pursuing. It includes:

  • identifying the problem or need,
  • analysing the expected value (economic, environmental, social),
  • understanding collaboration requirements,
  • assessing risks and complexity,
  • making a go/no-go decision.

2. Detailed design​

If viable, the next step is designing the use case with focus on scalability and reuse:

  • precise definition of scope and objectives,
  • identification of required functionalities,
  • technical, organisational and legal enablers,
  • integration architecture,
  • access and usage policy models.

Together, these tools help transform an initial idea into a scalable and implementable use case inside the dataspace.