Why do a few individuals, communities or organisations achieve significantly better results than their peers? The positive deviance approach tries to answer this question.
The story began in 1990, the Vietnamese government invited Save the Children (SCF) to help overcome the problem of child malnutrition. Jerry Sternin, the SCF Programme Director, was asked to demonstrate impact within six months and decided to try the idea of positive deviance. Building on past workhe undertook a village survey of child height and weight, looking for positive deviants: children from poor families, living among high malnutrition rates, who were nonetheless well-nourished.
In the pilot survey, he found six such families and began to study them intensively (see Figure 1). By observing the food preparation, cooking and serving behaviours of these families, he found three consistent yet rare behaviours. Mothers of positive deviants:
- washed their children’s hands every time they came in contact with anything unclean;
- added to their children’s diet tiny shrimps from the rice paddies, and the greens from sweet potato tops; and
- fed their children less per meal but more often: four to five times per day compared to two times in non-positive deviant families.
Sternin and his team then scaled out those simple, affordable, community-inspired practices and, within two years, this had reduced malnutrition by 80% in 250 communities, rehabilitating an estimated 50,000 malnourished children.
Figure 1: Jerry Sternin speaking to mothers in a village in Vietnam
The simple power of the positive deviance (PD) approach has led to its successful application in more than 60 countries across the globe. Yet PD still faces a number of challenges to its diffusion and implementation. As a result, we decided to investigate whether big data might help address those challenges, via a systematic review, published in the Electronic Journal of Information Systems in Developing Countries.
A priori, big data provides opportunities in relation to two main PD challenges.
1. Time, Cost and Sample Size. Relying on in-depth primary data collection, the PD approach is time- and labour-intensive with costs proportional to sample size. As a result, PD sample sizes are traditionally small. Statistically and practically, this can make it hard to identify positive deviants, given their relative rarity (see Figure 2). By contrast, cost of gathering big data tends to be very low since it often makes use of already existing “data exhaust” from digital processes. With big data thus covering large – often very large – sample sizes, greater numbers of PDs can be identified, and generalisation to even-larger populations is easier.
Figure 2: Positive deviants in a normal distribution
2. Domain and Geographic Scope. To date, most applications of PD have been highly concentrated. In a recent systematic literature review, 89% of applications in developing countries were in public health, 83% were in rural communities, and just four countries had hosted roughly half of all PD implementations. A simultaneous review of big data in developing countries, on the other hand, showed datasets and demonstrated value across a much wider set of domains and locations. As a result, big data could help positive deviance to break from its current path dependency.
To assess these and other benefits that big data may bring to the PD approach – relating to behaviour identification, methodological risk, and scalability – a “big data-based positive deviance” research project has been designed and is underway. The project is currently identifying positive deviants from large-scale datasets in the education and agriculture domains, with results planned to emerge in 2019.
For further details on the challenges of positive deviance and the opportunities offered by big data, please refer to the review article.