This Week: Diagnosing Data

As more and more data becomes available, it’s a natural next step to begin diagnostics of what is out there. Expectations are rising as we learn what information is most useful, what could be available and the format it’s most useful in. 
CGD recently published The Financial Flows of PEPFAR: A Profile to take a look at where funding was flowing-to which countries and which prime partners. The results were interesting and prompt further questions about the selection process for who receives funding. 
The report showed that whether a country was designated a “focus country” was the number two predictor that a country received PEPFAR funding. In addition, all but 3 of the top 25 prime partners, which together received 58% of PEPFAR funding, were based in the US. Check out the report for more analysis and reasoning behind these trends. 
What I found disappointing was that much of the analysis was based off 2008 figures since more recent data wasn’t published in a usable format. Considering the changes that have taken place in development in recent years, it would be interesting to see changes in these trends throughout the years. You can access the data set here.
Top 10 busiest airports via
Ushahidi gives an interesting example of how easy it is to make quick assumptions about data, and
further consideration shows a different story. It’s a good reminder that behind the charts, graphs, and figures are anecdotes, politics, and often messy realities or even just simple reasoning.
One author on Aid Leap questioned the accuracy of popular financial estimates for development needs. Will £2 a month really save a child when the poverty line is $1.25 a day? The author ends with suggesting that adapting the current development system would cost less and benefit more, but gives no suggestion for what adaptions might make such a difference, making it a hard point to argue.
My geocodingsenses were tingling as I read the reaction to the portal by Degan Ali, the Executive Director of Adeso, an NGO based in Kenya who remarked that, “From this I cannot tell which parts of the country are served, or even the objectives of the projects or the local organizations that are doing the real work on the ground.” 
These are concerns that can be answered. You can see examples of project level information available mapped in Nepal:, World Bank projects worldwide:, Malawi:, initial work in Haiti: you can look forward to future geocoded datasets in Senegal, Uganda, and Timor-Leste. 
The diagnosis? It’s improving.