Case Study: Steve Kenei, Technical Analyst at Development Initiatives in Nairobi, Kenya

April 10, 2014 in Uncategorized

As part of the Open Development Toolkit, we’ll be talking to people based in aid-recipient countries who work with international aid data to try and work out what the needs of data users are, what else might be useful, and what people need to use data more effectively in their work, as the ‘research’ stage in the project. Here’s the first in the series of case studies, with Steve Kenei, who works with Development Initiatives in Nairobi, Kenya. Steve is a Technical Analyst, working to support a team of data analysts and researchers, and we spoke via Skype on April 10th, 2014.

Colleagues at Development Initiatives come to me to find out how/where to get the data they need, to convert it into a format they can work with, and to manipulate the data.

A common issue that I come across is people coming to me with PDFs, and needing the data in a reusable format; it’s an old problem, and it’s getting boring!

I normally work directly with the IATI Datastore, but for people with less technical knowledge, there’s a number of problems with it. Even before you get to the technical skills needed to manipulate the data, even the language used isn’t useful for the average layperson: the words and terms used assume that people will understand and know what an ‘activity’, or a ‘transaction’, or a ‘budget’ is, as the very first step, for example. Then it’s difficult to know what you need in order to answer your question or even if it’s possible to answer what you’re looking for with data from the IATI datastore.

I usually point people towards donor portals, if they know who they want the data from—DFID’s DevTracker for example, or the UNDP’s open data portal. My colleagues also had a two-day training on using the OECD data, so now they can access this data themselves without any problems; this was really useful for them.

From my experience, people don’t often need disaggregated figures or even detailed geocoded data. They’re more interested in big aggregated figures that they can use in a ‘headline’ style, rather than the small, detailed data. For example, the level of knowing how much is being spent in a certain country within a certain sector is adequate, or how much a certain donor is spending in a certain country.

There was one time that a colleague of mine wanted to know how much money was being spent on HIV/AIDS prevention + support in Western Kenya, but we couldn’t find anything. We looked at the IATI datastore, directly on the Global Fund site, but we didn’t get anything.

If you are working with aid data in your work, we’d love to hear from you! Get in touch with zara.rahman[at], or drop @OpenDevToolkit a line on Twitter; your input would be so valuable to help us understand what we can best do to support you in your work!

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