What I (Kelly Gannon) have Learned this Month about Data Collection
(This post is written by our M&E Fellow, Kelly Gannon, and posted by me, Gabrielle Blocher)
I have spent much of this first month absorbing and observing as much as I can during our transition from FT7 to FT8. It’s been great to connect the work I was doing remotely from the US since February to the project in Kenya. David has been incredible at bringing me up to speed on the operations and strategies of the M&E team on the ground here. While I have learned a lot, one aspect of our work as M&E really stuck out in my first couple weeks.
For me, the questions as to the what, why and how of data have driven (and complicated) a lot of what I do personally, academically, and professionally. In graduate school I struggled with the idea of linking data to human rights in my research projects. Numbers are an extremely powerful tool wielded by donors, governments, diplomats, activists, and development workers to plan policy, set benchmarks and be motivated to act. But what can be observed and quantified never tells the entire story and numbers can easily be manipulated.
As a Monitoring and Evaluation Program Manager, these questions as to the what, why and how need to always be asked at each and every step of program and operational measurements. Its something I need to constantly be reminding myself to do as I am in the transition process here in Kenya. The why and the what are previously what I concentrated my focus on when analyzing and thinking through ideas. Very quickly after I arrived though, I was reminded of the importance of the how question.
I went with my team to Ngochoni where we are doing data collection on last year’s harvest yield of new farmers who joined Nuru this year. The amount of effort that goes into three data points (GPS coordinates, number of acres farmed in 2011 long rains, and total number of bags of maize harvested) is much more complex then I would have thought from my desk in NYC. Most shambas (farms) are not neat, one acre plots. Some are surrounded by bushes, others have odd shapes with a banana grove in the middle, rows are not straight and evenly spaced, and many plots are far from the home. Furthermore, asking the farmer for the number of bags collected is not a simple answer either. The farmer might not have bagged all the maize in the standard 90kg bag to sell, it might be stored in a homemade granary or collected in large tins.
Despite these challenges and others, I was also extremely inspired and excited about the work I’m a part of. The local staff here is competent and understands the shortfalls of the information collected as well as the strengths. Data is a messy, difficult and challenging tool—but that is part of the reason I like it. If you only spend time looking at neat numbers as they are presented in a report, it is easy to forget how each and every number is so complex. As we finish collecting harvest yield and start to shift to data entry and then data analysis and reporting, we have to remember where the information originated in order to accurately tell the story.