A science of delivery underscores the importance of a data-driven and rigorous process to understand what works, under what conditions, why, and how. Too often in international development, conclusions are made without understanding counterfactuals and the assumption is that we can replicate success without understanding its constituent elements.
Traditional development models capture knowledge when it is too late to apply lessons to live projects. We need ways to develop and challenge our hypotheses while we execute and be less concerned about the accuracy of our original hypotheses. We need to try many things, look for positive deviants where they exist, and better understand why some results are better than others despite similar circumstances. What are we not seeing and what could we learn if only we admitted we might be working with misinformed assumptions? Can we be rigorous without being rigid and open without being undisciplined?
In essence, how can we design and implement in a way that allows change to be incorporated into what we are doing as we are doing it, within the framework of our overall impact and outcome goals within agreed timeframes.
Disclaimer – the views expressed in this document do not reflect the official positions of DFID or Cambridge Education. This document was posted with permission from Ian Attfield, Senior Education Adviser, DFID Tanzania