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This report presents six learnings from four pilot projects conducted by the Data Powered Positive Deviance (DPPD) initiative, a global collaboration between the GIZ Data Lab, the UNDP Accelerator Labs Network, the University of Manchester Center for Digital Development, and UN Global Pulse Lab Jakarta. The pilots seek out grassroots solutions to development challenges that range from the interaction between livestock farming and deforestation to gender-based violence and insecurity in dense urban environments in Ecuador, Mexico, Niger and Somalia. The learnings relate to the early stages of the DPPD method, originally proposed by Albanna & Heeks [1], and focus mainly on the access to, and use of digital data. They are summarized as follows: 1. Remain flexible in the face of data unavailability 2. Leverage existing partnerships for data access 3. Map and fill know-how gaps early 4. Scale with caution 5. Look at deviance over time 6. Look beyond individual or community practices and behavior 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.