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Use Case Development in Agri-Food Data Spaces

This guide presents the key steps for designing and implementing use cases within an agri-food data space. Inspired by the Data Office model, it provides a practical approach to developing solutions based on ethical, secure, and federated data exchange.

Use cases provide solutions to specific challenges in the agri-food sector, leveraging the value that arises from sharing data among multiple actors. To achieve this, they rely on common technical infrastructures and collaborative governance models that ensure interoperability, trust, and data sovereignty.

What is a Use Case?​

A use case is a concrete application of data shared among different actors to solve a sectoral or social need. In the agri-food context, this could involve initiatives aimed at reducing fertilizer use, monitoring animal welfare, or generating sustainability certifications based on real data.

Within the framework of the AgrospAI project, various real examples of use cases developed within the agri-food data space can be consulted in the Cases section.

Phases for Use Case Development​

The development of a use case within a data space follows a model structured into 8 phases [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.

Fig. 1. Development of a use case within a data space. Visual representation of the process. (source: [1])

1. Business Problem Definition​

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

  • New products or services.
  • Improvement of operational efficiency.
  • Joint resolution of sector challenges.

2. Data-Based 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 developed that compiles 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 can reuse or adapt existing technologies to gain efficiency.

6. Technological Development​

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

  • Platforms and infrastructures.
  • Interoperability components.
  • Tools for access, governance, traceability, 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 before deployment.

8. Operation and Scaling​

The use case is operational and generates real value:

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

Use Case Evaluation and Design​

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

  • One for evaluating the feasibility of a use case.
  • Another for designing its implementation.

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

Feasibility Evaluation​

This guide allows for generating, describing, and evaluating use case ideas 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 specific need that can be resolved through data exchange.
  2. Scope definition: delimit the objectives, involved actors, and expected benefits.
  3. Potential evaluation: analyze the added value that the case can provide in social, economic, or environmental terms.
  4. Study of interaction complexity: evaluate the level of collaboration required among different agents.
  5. Final feasibility decision: based on the previous steps, determine if it makes sense to proceed with its design and implementation.

Fig. 2. Use case feasibility evaluation. (source: [2])

📄 The guide includes a spreadsheet template with key questions that help to complete each stage systematically.

Download the guide here.

Use Case Design​

If the use case proves viable, the next stage involves its detailed design, focusing on its scalability and future reusability.

The guide addresses design through the following actions:

  • Precisely define objective and scope.
  • Identify the necessary functionalities for sharing and exploiting data.
  • Establish the technological, organizational, and legal enablers.

Fig. 3. Use case design. (source: [2])

📄 A spreadsheet with key questions is also provided to facilitate design, along with real examples.

Download the guide here.

Conclusion​

The two guides published by the Data Office offer a clear, practical, and applicable methodological framework for developing use cases in data spaces, from the initial idea to its implementation.

Their combination ensures that use cases are not only viable but also scalable, sustainable, and implementable in real contexts. This is especially relevant in the agri-food sector, where collaboration among actors and data exchange 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 for data spaces – datos.gob.es. Available at: https://datos.gob.es/es/blog/como-evaluar-y-disenar-casos-de-uso-para-espacios-de-datos-guias-para-facilitar-el-camino