1.5 Develop data levers

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Why should I do this?

To explore various ways of addressing the data blockers that have been identified within your investment. By doing this, you can start narrowing down on solutions that require the least resources, but that are likely to have the largest impact in addressing your identified data blockers.

In this activity

In this activity you will:

• Understand that data levers are specific tools, techniques, or other solutions identified to overcome the ‘blockers’ to using data or making data FAIR. Levers are often elements within a complex system where a seemingly small shift in one area can produce a big impact on data outcomes.  
• Review examples of different data levers, such as setting up partnerships with telecom providers to establish connectivity in rural areas, or establishing data sharing agreements with local agricultural organizations to access and share data securely.
• Find solutions to help you overcome the data blockers that you have noted in Step 1.4 to help you avoid project delays caused by data-related problems.

Instructions

1) If you are a Program Officer (PO), you may want to share this page directly with your grantee, so they can act on it.

 

2) Review the examples given below across four different factors (data, capacities, ecosystem, culture) to identify your levers, referring back to the findings you would have collated for intervention types and your list of data blockers in your workbook.

 

3) Refer to the investment type examples further down the page to help you complete your workbook.

 

4) You can use the workbook (and supporting factsheet) for Step 1 here. We recommend using the same document throughout this step, so you have a single document that captures all your workings.

Data

Capacities

Ecosystem

Culture

Example data levers:

• Data sharing agreements and protocols.
• Data security and privacy protocols.
• Prior planning for data handling post-contract or after cessation of use, including retention, deletion, or other disposal methods.
• Data integration systems.
• Partnerships with networks, societies, clubs, etc.

Example data levers:

• Training programs to develop or enhance necessary skills, in particular for data literacy.
• Additional funding provisions to purchase hardware, engage in training workshops, etc.

Example data levers:

• Resource allocation strategy.
• Develop clear business models for data sharing, regulatory uncertainties, and ethical concerns.
• Consider legal aspects specific to the country of your intervention.
• Greater inclusion and participation of marginalized, vulnerable and/or minority groups in the data collected.

Example data levers:

• Local leadership engagement.
• Campaigns to address mistrust in data-driven techniques.
• Cultural sensitivity training and language acquisition.
• Building trust and transparency.

Top tip

Focus on solutions that are both feasible and impactful, maximizing the effectiveness of limited resources.

Investment types

Overview AgriConnect: a digital solutions investment AgroThrive: a policy and advocacy investment NGBT: a field research investment

Investment types

©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

AgriConnect's data levers

• Develop data sharing agreements with stakeholders.
• Revise project objectives to constrain the kinds of data used.
• Plan for, and fund purchase and installation of, data storage equipment.
• Prepare team for data analytics, including staff training.
• Focus resources on building relationships with key stakeholders.
• Include representative body for sub-populations involved in the study.
• Co-create marketing and community engagement campaigns to overcome negative perceptions.

©Gates Archive/Thomas Omondi

AgroThrive's data levers

• Develop data sharing agreements and protocols.
• Develop data security and privacy protocols.
• Partnerships with networks, societies and clubs.
• Training programs to develop or enhance necessary skills, in particular for data literacy.
• Additional funding provisions to purchase hardware, engage in training workshops, etc.
• Resource allocation strategy.
• Greater inclusion and participation of marginalized, vulnerable and/or minority groups in data collected.
• Local leadership engagement.
• Campaigns to address mistrust in data-driven techniques.
• Cultural sensitivity training and language acquisition for project staff.

©Gates Archive/Esther Mbabazi

NGBT's data levers

• Collaboration with agricultural institutions, research organizations, universities, private entities and local farmers to collect and share data on improved crop varieties.
• Conduct training programs for farmers, extension workers, and researchers on data collection methodologies.
• Capacity building at the grassroots level to enhance the availability of accurate and relevant field data.
• Establish community-based mechanisms that ensure local communities actively contribute to data collection and dissemination.
• Develop knowledge-sharing networks where experienced farmers can mentor others and share their success stories.
• Co-create community events to create awareness and build a sense of community around the adoption of modern farming techniques.

It is useful if the project teams are able to actually think about the data management lifecycle at the start, when the project is being conceptualized: Who are the stakeholders, and what kind of relationships do they have with them? Who could be the new potential stakeholders, and what kind of relationships do they need to establish with them with respect to data governance, data management and data licensing? All this actually could be documented, captured in the proposal in terms of activities, which they can then start implementing when the project goes live.

Arun Jadhav, Senior Data Architect, CABI