Hello! We are Alex Tran, Piva Bell, David Okutu, Hanna Camp, and Amy Joce from Mercy Corps! We are a team of MEL professionals working on enhancing data-driven decision making in humanitarian response programs.
Lessons Learned
We’ve seen that comprehensive and timely data can improve a humanitarian response team’s ability to adaptively manage programs. But this is easier said than done! Often, we see a trade-off in analysis – more comprehensive analysis often means less timeliness as more time is needed to conduct deeper analysis. On the flip-side, quicker results often mean less comprehensiveness as teams don’t have time to conduct deeper analysis often beneficial for enhanced decision making. However, by following the steps in this rad resource, it’s possible to achieve both! Overall, we reduced the average time of availability for comprehensive results (inclusive of data cleaning, analysis, reporting) from 3-5 weeks to as little as one day!
Timeliness of data availability PRIOR to aligning data automation with adaptive management frameworks – 3-5 weeks.
Timeliness of data availability AFTER to aligning data automation with adaptive management frameworks – 1-2 days.
Rad Resource
Step 1. Pre-Identify key data that influence decision making and measure performance
Set up adaptive management frameworks prior to the start of program activities. In this case we adapted the 3 R’s framework to help identify key response questions, the data that answer them, when that data is needed, and how that data can be interpreted to enhance decision making.
- Right Questions – Identify overarching questions that the team need to answer to determine if they are performing well, and help inform decisions
- Right Data – Identify data, variables, and results that are needed to to answer those overarching questions
- Right Timing – Identify when/how often teams need that data
- Right Decisions – Identify how the data be interpreted to help humanitarian response team members answer the key questions
In the above example, we have the following overarching question – “Is the emergency response program effectively communicating with program participants in order to ensure fair and equitable access to services?” We have then identified data that we believe answer this question, when we need that data, and how we can interpret that data.
Step 2. Pre-code data cleaning and analysis into your mobile data collection and analysis tools
Next step – pre-code data collection and analysis tools to automate data cleaning, analysis, visualization, and reporting!
We created standard mobile data collection tools using XLS forms that have key data/variables that helped us answer if emergency response team members were communicating with program participants effectively. We then connected the tools to pre-coded data visualization dashboards.
Step 3. Collect data
Collect data, when returning to an area with internet sync your data, and then on your analysis tools click run/refresh!
Step 4. Work with program team members to interpret results and take action!
Comprehensive analysis (all results are disaggregated by location, gender, and services received) are then immediately ready! For example, the Power BI dashboard below was used by the Mercy Corps Indonesia emergency response team within 1 day after data collection was complete and they were able to adjust their approach to participant engagement/communication in a flood response that was set to begin just days after the earthquake response. The team credited the very timely and comprehensive analysis with ensuring they were able to adapt their approach in time for the next response.
We hope this process can be as helpful to you as it was to us! If you come up with any adaptations, we would love to hear about it. Until then, happy automating!
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