![Factors Affecting Rapid, Data-Informed Decision-Making During Crises A photo of a parent and child representing Factors Affecting Rapid, Data-Informed Decision-Making During Crises](https://edc.org/sites/default/files/styles/blog_featured/public/Data%20Driven%20Decision%20Making%2011.21.png?itok=Rww2rxI7)
Collecting and using data to inform decision-making is a well-established practice. But what does the use of data to inform action look like? And what is the feasibility of using data to adjust programming during a crisis?
To answer these questions, we explored how EDC’s international development projects practiced data-driven decision-making at the onset of the COVID-19 crisis. In March 2020, due to the lockdowns, EDC’s international projects largely shut down their in-person services and worked to meet participants’ needs remotely.
To help projects make data-driven adjustments, between May and October 2020, we used a standard survey form to collect data on the effects of COVID-19 on its participants in Honduras, Mali, the Democratic Republic of the Congo (DRC), Rwanda1, Zambia and Liberia. From that data, we discovered that projects varied in how they used data to inform their programming. By interviewing project staff, we identified two main factors that affected their use of data in decision-making: (1) existing structures to respond to shocks and stressors and (2) project scope.
Existing Structures
The first factor relates to whether a project, and/or the context in which it is situated, had existing structures in place to respond to shocks and stressors, such as a crisis. In Liberia, for example, the Accelerated Quality Education project was able to adapt to the COVID-19 pandemic lockdown due to its previous experience navigating school closures during the Ebola crisis. It quickly deployed preexisting structures, such as radio public health messaging, to meet the needs of participants remotely. The project used the data collected from EDC’s COVID-19 survey to determine which messages to broadcast.
However, in Zambia, the Let’s Read project had no recent experience with schools closing due to a crisis, so it was less prepared to quickly reach participants remotely. Project staff their used survey data to develop a strategy for shifting to remote programming.
Scope
The second factor was a project’s scope. A project can only respond to data with the time, funding, resources, and actions defined within its scope of work. In Mali, the Selective Integrated Reading Activity participated in our study to learn about student participants’ access to distance learning. A main finding of the study was that food insecurity was a major issue for students. However, this need was outside the project’s scope of work, and it could not respond programmatically.
In the DRC, the ELIKIA2 project was coming to a close, so it was not feasible for the project to adjust its programming strategy. Therefore ELIKIA applied its data to a broader learning agenda.
Conclusion
Data-driven decision-making is essential to better programming, but its use and success are affected by multiple factors. Given this complexity, we encourage monitoring and evaluation practitioners to plan both how to collect data and how to use the collected data. Such intentionality is crucial—especially when a project needs to use data in real time to respond to a crisis.
Neena Aggarwal is a monitoring, evaluation, and research assistant in EDC’s International Development Division. She managed the COVID-19 Study, which spanned six countries, and is passionate about data-driven decision-making. |
1Other projects included Honduras Reading Activity, Integrated Youth Development Activity in the DRC, and Huguka Dukore Akazi Kanoze in Rwanda.
2ELIKIA stands for Enhancing Services and Linkages for Children Affected by HIV and AIDS
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