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Development of use cases in data spaces

This guide presents the key steps to design and implement use cases within an agri‑food data space. Inspired by the model from the Oficina del Dato, it provides a practical approach to developing solutions based on ethical, secure, and federated data sharing.

As a real example, we have a data space deployed for soil data analysis of agricultural fields in the Azores Islands (Portugal). This environment enables dataset publishing, trusted algorithm execution, and granular access control. Data never leaves the secure environment thanks to Compute‑to‑Data mechanisms and auditable policies. You can explore field/service cards in the Cases section, which will grow as new fields and analyses are added.

Use cases make it possible to address specific challenges in the agri‑food sector, leveraging the value that arises from sharing data among multiple actors. To do this, they rely on common technical infrastructures and collaborative governance models that ensure interoperability, trust, and data sovereignty.

Within the framework of the RegenAg‑x project, you can consult various real examples of use cases developed within the agri‑food data space in the Cases section.

Phases for developing a use case​

The development of a use case within a data space follows a structured 8‑phase model [1]. These phases ensure that the solution is viable, scalable, and sustainable, in addition to aligning with the principles of interoperability, trust, and data sovereignty.

1. Definition of the business problem​

A group of participants identifies a common opportunity to share and exploit data. This opportunity may focus on:

  • New products or services.
  • Improved operational efficiency.
  • Jointly solving sector challenges.

2. Data‑driven modeling​

Relevant information is structured to make informed decisions. This phase includes:

  • Defining a data model.
  • Incorporating tools such as artificial intelligence or advanced analytics.
  • Focusing development on data‑driven decisions.

3. Consensus and requirements​

A collaboration model is built among participants:

  • Agreement on participation rules.
  • Establishment of common policies.
  • Definition of a governance and trust model.

4. Technical design of the use case​

A technical blueprint is drawn up that captures the solutions and agreements reached. This blueprint can be based on:

  • Existing models.
  • Reusable templates or components.
  • Common technical recommendations of the data space.

5. Solution construction​

The solution is developed based on the designed blueprint. The use case may reuse or adapt existing technologies to gain efficiency.

6. Technology development​

The tools required to enable the data lifecycle are selected and integrated:

  • Platforms and infrastructures.
  • Interoperability components.
  • Access, governance, traceability tools, etc.

7. Integration and deployment​

  • The use case is integrated into the data space (if one already exists).
  • Functional and acceptance tests are carried out.
  • Compliance with agreements and requirements is ensured prior to go‑live.

8. Operation and scaling​

The use case is in operation and generates real value:

  • It can scale to other actors or similar cases.
  • The data space grows in a federated and sustainable way.
  • A continuous improvement model is activated.

Evaluation and design of use cases​

Once the key phases in the development of a use case within a data space are understood, it is essential to have methodological tools that help evaluate its feasibility and design it properly. To this end, the Oficina del Dato has published two complementary guides that facilitate this process [2]:

  • One to assess the feasibility of a use case.
  • Another to design its implementation.

These guides help transform an initial idea into a scalable, sustainable use case aligned with the principles of data spaces.

Feasibility assessment​

This guide allows you to generate, describe, and evaluate ideas for use cases that involve data sharing. It proposes a methodology in five key steps, whose ultimate goal is to make a decision on the feasibility of the proposed scenario:

  1. Use case generation: identify a concrete need that could be solved through data sharing.
  2. Scope definition: delimit the objectives, the actors involved, and the expected benefits.
  3. Potential assessment: analyze the added value that the case can bring in social, economic, or environmental terms.
  4. Study of interaction complexity: assess the level of collaboration required among the different agents.
  5. Final feasibility decision: based on the previous steps, determine whether it makes sense to move forward to its design and implementation.

The guide includes a spreadsheet template with key questions that help complete each stage in a systematic way. Download feasibility assessment template.

Use case design​

If the use case is feasible, the next stage consists of its detailed design, focusing on scalability and future reuse.

The guide addresses the design through the following actions:

  • Define the objective and scope precisely.
  • Identify the functionalities needed to share and exploit data.
  • Establish the technological, organizational, and legal enablers.

A spreadsheet with key questions is also provided to facilitate the design, as well as real examples. Download use case design template.

Conclusion​

The two guides published by the Oficina del Dato offer a clear, practical, and applicable methodological framework for developing use cases in data spaces, from the initial idea to go‑live.

Combined, they ensure that use cases are not only feasible, but also scalable, sustainable, and implementable in real contexts. This is especially relevant in the agri‑food sector, where collaboration among actors and data sharing are essential to drive a fair, efficient digital transformation aligned with the principles of sovereignty and trust.

Thus, any entity interested in participating in a data space has a structured path to evaluate, design, and deploy use cases that generate real value.

References​

[1] Use case development model for data spaces – datos.gob.es. Available at: https://datos.gob.es/es/blog/modelo-de-desarrollo-de-casos-de-uso-para-espacios-de-datos

[2] How to evaluate and design use cases – datos.gob.es. Available at: https://datos.gob.es/es/actualidad/como-evaluar-y-disenar-casos-de-uso-para-compartir-datos