Data-powered positive deviance: Combining traditional and non-traditio nal data to identify and characterise development-related outperformer s - Albanna, Basma; Heeks, Richard; Pawelke, Andreas; Boy, Jeremy; Handl, Julia; Gluecker, Andreas (Development Engineering, 2022)
The positive deviance approach in international development scales pra ctices and strategies of positively-deviant individuals and groups: th ose who are able to achieve significantly better development outcomes than their peers despite having similar resources and challenges. This approach relies mainly on traditional data sources (e.g. surveys and interviews) for identifying those positive deviants and for discoverin g their successful solutions. The growing availability of non-traditio nal digital data (e.g. from remote sensing and mobile phones) relating to individuals, communities and spaces enables data innovation opport unities for positive deviance. Such datasets can identify deviance at geographic and temporal scales that were not possible before. But guid ance is needed on how this new data can be employed in the positive de viance approach, and how it can be combined with more traditional data to gain deeper, more meaningful, and context-aware insights. This pap er presents such guidance through a data-powered method that combines both traditional and non-traditional data to identify and understand p ositive deviance in new ways and domains. This method has been develop ed iteratively through six development projects covering five differen t domains – sustainable cattle ranching, agricultural productivity, ra ngeland management, research performance, crime control – with global and local development partners in six countries. The projects combine different types of non-traditional data with official statistics, admi nistrative data and interviews. Here, we describe a structured method for data-powered positive deviance developed from the experience of th ese projects, and we reflect on lessons learned. We hope to encourage and guide greater use of this new method; enabling development practit ioners to make more effective use of the non-traditional digital datas ets that are increasingly available.