Six Steps To Implementing Fair And Responsible Data Practices
21 January 2025, Africa: The FAIR Process Framework is a structured six-step approach designed to promote the adoption of FAIR (Findable, Accessible, Interoperable, Reusable) principles and responsible data practices.
Its goal is to equip grant makers (project officers), researchers, or stakeholders in the agricultural development field with the tools to harness the full potential of data. By simplifying the implementation of FAIR principles, the framework ensures that data-driven projects maximize impact and sustainability.
The six-steps of the framework offer manageable and practical activities that support teams implementing FAIR data strategies from concept through to completion. Here is an overview of each step, with practical examples involving CABI-linked projects where applicable.

Step 1: Discovery
The first step focuses on defining data intervention types to identify and categorize data-related activities required by an investment or project. This helps teams understand the scope and nature of the data and ensures they consider all aspects of data management.
It involves defining and documenting problems, goals, activities, challenges, and solutions to support FAIR data practices. Further, it clarifies technical jargon related to data-rich projects and uses a system-focused framework to understand how data interacts with capacities, ecosystems, and culture.
It lays the groundwork for sustainable data-driven projects.
Step 2: Understanding
In this step, teams assess the enabling environment – the external factors that influence data investments. By mapping the data ecosystem, identifying personas, and analyzing enablers and barriers, strategies can be tailored to specific contexts.
Engaging stakeholders provides critical insights, addresses challenges such as regulatory constraints and infrastructure gaps, and leverages existing resources and opportunities. Thus, this ensures strategies are both context-sensitive and locally relevant.
Step 2 was implemented by CABI and the Open Data Institute (ODI) as part of the ‘FAIR and open data in agronomy programmes’ project funded by the Gates Foundation. This involved helping agronomy projects in Ethiopia and India to understand how data and value is created and shared amongst different actors.
Step 3: Planning
Planning involves cataloguing and identifying data assets used or generated by a project. A comprehensive inventory provides clarity on input and output data, metadata attributes, and potential for reuse or integration.
Creating a detailed data inventory uncovers opportunities for analysis and collaboration. As a result, this step also ensures compliance with data policies and prioritizes data assets for FAIR alignment. It establishes a foundation for implementing FAIR principles across a data portfolio.
For example, CABI led an intensive data inventorization process following the guidance in Step 3 as part of the ‘Guiding Acid Soil Management Investments in Africa’ (GAIA) project. This involved the CGIAR and national partners from Tanzania, Rwanda and Ethiopia.
Key project assets and insights on acid soils (soil properties, lime requirements, crop yields etc) are now available through a dashboard published in 2024.
Step 4: Co-developing
The purpose of this step is to develop a shared FAIR goal that builds awareness, trust, and alignment among stakeholders. Consequently, this promotes commitment to FAIR principles and enable sustainable, informed resourcing decisions for achieving FAIR compliance across an investment.
Teams use workshops and discussions to establish actionable FAIR principles and create a roadmap for data stewardship and FAIR implementation.
This step ensures all stakeholders have a shared understanding of FAIR practices, which encourages collaboration and clarity within projects. This step has been applied across several CABI projects including GAIA and Rwanda Soil Information Services (RwaSIS).
Step 5: Strategy
This step creates the rulebook for data management – a FAIR data strategy. This will increase the likelihood of successful FAIR implementation. Teams develop policies, management plans, and agreements that guide how they will collect, share, and use data responsibly.
This strategy outlines how a data investment will collect, manage, analyze and use data to achieve its objectives while minimizing risks associated with data misuse or mismanagement. Thus, a well-articulated strategy transforms FAIR principles into actionable policies and procedures and builds resilience into data practices.
Step 6: Implementing
The final step focuses on developing technological foundations to support implementing FAIR in a data investment. This bridges theory and practice by translating a strategy into actionable technical solutions.
A robust FAIR implementation plan ensures that a project achieves scalable, futureproof data management, enhanced discoverability and interoperability of data and secure and ethical data handling. They also prioritize secure and ethical data handling. This step turns a FAIR vision into reality by creating robust, sustainable data management practices.
Conclusion
The FAIR Process Framework provides a roadmap for organizations to harness the power of data responsibly and effectively. Although the framework has only recently launched, there are already practical examples such as those mentioned above where we’ve shown how specific steps of the framework have added value in some of CABI’s projects.
Following these six steps, agricultural development projects maximize impact, ensure sustainability, and contribute to a future where data drives meaningful change. You can explore the framework further at www.fairprocessframework.org.
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