Josh Powell at Oxfam Blog: “While development actors are now creating more data than ever, examples of impactful use are anecdotal and scant. Put bluntly, despite this supply-side push for more data, we are far from realizing an evidence-based utopia filled with data-driven decisions.
One of the key shortcomings of our work on development data has been failing to develop realistic models for how data can fit into existing institutional policy/program processes. The political economy – institutional structures, individual (dis)incentives, policy constraints – of data use in government and development agencies remains largely unknown to “data people” like me, who work on creating tools and methods for using development data.
We’ve documented several preconditions for getting data to be used, which could be thought of in a cycle:
While broadly helpful, I think we also need more specific theories of change (ToCs) to guide data initiatives in different institutional contexts. Borrowing from a host of theories on systems thinking and adaptive learning, I gave this a try with a simple 2×2 model. The x-axis can be thought of as the level of institutional buy-in, while the y-axis reflects whether available data suggest a (reasonably) “clear” policy approach. Different data strategies are likely to be effective in each of these four quadrants.
So what does this look like in the real world? Let’s tackle these with some examples we’ve come across: