Applying the Data Powered Positive Deviance Method to Identify Grassroots Solutions Using Digital Data

This report presents six learnings on how to use non-traditional data sources to support a Positive Deviance approach to development practice. They are derived from four pilot projects, jointly conducted by the UNDP Accelerator Labs Network, the GIZ Data Lab, and the University of Manchester, that have tested the Data Powered Positive Deviance (DPPD) method in Ecuador, Mexico, Niger, and Somalia. The report is written for development practitioners, data analysts, domain experts, and more generally anyone interested in using new data sources and technologies to uncover successful local solutions to development challenges.