5.0 Developing a FAIR data strategy

To develop a considered strategy that will increase the likelihood of successfully implementing FAIR principles

Why should I do this?

To define the processes, technologies and governance measures needed to help you ensure data quality, availability, accessibility and security within your investment and to improve the chances of successful FAIR implementation.

 

Articulating this strategy will also help you outline how your investment will collect, manage, analyze and use data to achieve its objectives.

 

A well-implemented FAIR data strategy can help your projects maximize the value of their data while minimizing risks associated with data misuse or mismanagement.

 

Download this factsheet for more insights.

Deciding when to use a DMAP or a data governance policy?

Think of a data governance policy as your high-level strategy. It sets the principles and guidelines for how data should be handled within your organization or project. It is like the blueprint—outlining what is important, defining roles, and stating the commitment to data quality, privacy, and security.

 

In contrast, a DMAP is the detailed implementation plan. It outlines the day-to-day specifics of how data will be collected, stored, shared (made FAIR), and preserved throughout the project’s life cycle. A DMAP puts the data governance principles into practical steps and tasks, ensuring alignment with the strategy while focusing on the management of specific datasets.

 

In short, use a data governance policy to guide your overall approach to data, and a DMAP when you need to get specific about managing and accessing project-level data.

 

Consider the size and complexity of the project

For larger, more complex projects, having both is ideal. The data governance policy provides strategic direction, while the DMAP ensures operational consistency. However, for smaller projects, a DMAP may include essential governance/strategic elements, especially if no organizational-level policy exists.

 

Working with existing organizational policies

If an existing organizational data governance policy is in place, align your DMAP with it, referencing the policy to ensure consistency. This helps avoid redundancy and maintains coherence in how data is managed.

 

Collaborating partners with different policies

When multiple partners with different organizational data governance policies collaborate, use the DMAP to harmonize approaches. Firstly, ensure all stakeholders agree on a shared vision and goal for FAIR data in the project. Then establish clear guidelines within the DMAP for addressing differences or conflicts between the policies, creating a cohesive plan for the project’s specific needs.

Below is an introduction to the key concepts you will come across in this step. As you complete an activity, you may need to refer back to some of these key concepts.

©Gates Archive/Mansi Midha ©Gates Archive/Mansi Midha

Every investment project is unique

The application of the six steps will vary accordingly. To provide examples that align with your project, common characteristics of AgDev investments were researched and three ‘investment types’ were developed.

©Gates Archive/Alissa Everett

Recently, a large African-led organization, AgriConnect, has decided to make its data processes FAIR. Its work focuses on scaling agricultural innovations to improve smallholder livelihoods, and ultimately increase food security across the continent.

©Gates Archive/Thomas Omondi

The well-established policy and strategy organization AgroThrive works to improve enabling conditions for people across the AgDev ecosystem (including smallholder farmers), with the goal of improving smallholder livelihoods, and ultimately increasing food security.

©Gates Archive/Esther Mbabazi

NourishGen BioTech (NGBT) is a multinational research organization committed to combating global hunger, addressing gender disparities, and mitigating the impact of climate shocks on vulnerable populations. Its lab-to-field approach has already improved nutritional outcomes for millions of people by optimizing crops for widespread planting.

A key outcome of the data ecosystem mapping exercise in Step 2 was a widespread acknowledgement of the importance of data sharing agreements and a data sharing policy. This meant that we were also able to put Step 5 into action.

Melissa Allan, Project Coordinator, CABI

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