Having started a non-profit working in Ethiopia, I’ve developed a keen interest toward economic and humanitarian development in developing countries. They are fascinating laboratories where many Non-governmental organizations (NGOs) and Non-profit organizations (NPOs) are experimenting with new ways to bring prosperity to impoverished communities.
What I truly find fascinating is the assumptions that many of these NGOs and NPOs have made in instituting aid programs that don’t work. Part of the issue is that they use Big Data to identify the problems (i.e., 90% of a population is suffering from malnutrition), but Big Data and quantitative studies don’t help to identify the solutions. Unfortunately, many of these organizations haven’t figured that out yet. They have made assumptions based on data as to the solutions, and most have failed miserably.
We see it every day – billions of dollars are spent on quantitative market research to increase the probability of new product launch success, yet nearly 95% of all new products fail.
Why doesn’t traditional quantitative market research adequately address these failures? Because data is great for identifying issues and problems, but not so great at deriving solutions.
So what does this have to do with Positive Deviants, market research and economic development?
Well, instead of looking at the 90% that are dying of malnutrition and diagnosing that there is a problem, a better approach to creating a solution is to look at the 10% who are not malnourished and try to find out why they differ from the norm. Unfortunately, the only way to do that is through observation and probing. Asking them individually what they do differently than everyone else that allows them to not only survive, but thrive in their environment.
It turns out that there are real life case studies that highlight the power of asking these positive deviants one-on-one what they do differently. In a new book, Aid on the Edge of Chaos, the author recounts a story of rice farmers in Vietnam. Big Data quantified that they had a problem with malnutrition in the community, with the majority of people suffering from a deficiency of protein in their diets.
Dumbfounded in trying to solve the problem, the NGO, Save the Children, decided to interview those minority families who weren’t suffering from protein deficiencies. What they found was that each family had been harvesting small shrimp, crabs and snails found in the fields and including them in their rice bowls. It turns out that the tiny shrimp, crabs and snails were providing the protein they needed in their diets, even though eating these foods were considered taboo and presumed unhealthy. This finding allowed the NGOs to help educate the communities that the shrimp, crabs and snails were indeed safe, free and beneficial to their diets. Within 6 months, there was a 67% improvement in the nutritional health of the children in these communities, leading to widespread implementation of the program.
As is often the case, Big Data helped to diagnose the problem, but could not illuminate the solution. In far too many circumstances we rely on data to do it all. However, as this example showed, we have to go deeper, roll up our sleeves and be more strategic if we to give ourselves the best shot of going down the right path. Identifying issues and solving them is more than a one-step, one methodology process. By harnessing the power of conversation with groups such as positive deviants, we can get to effective and timely solutions.