Data Collaborative Case Study by Michelle Winowatan, Andrew J. Zahuranec, Andrew Young, and Stefaan Verhulst: “Following the 2015 earthquake in Nepal, Flowminder, a data analytics nonprofit, and NCell, a mobile operator in Nepal, formed a data collaborative. Using call detail records (CDR, a type of mobile operator data) provided by NCell, Flowminder estimated the number of people displaced by the earthquake and their location. The result of the analysis was provided to various humanitarian agencies responding to the crisis in Nepal to make humanitarian aid delivery more efficient and targeted.
Data Collaboratives Model: Based on our typology of data collaborative practice areas, the initiative follows the trusted intermediary model of data collaboration, specifically a third-party analytics approach. Third-party analytics projects involve trusted intermediaries — such as Flowminder — who access private-sector data, conduct targeted analysis, and share insights with public or civil sector partners without sharing the underlying data. This approach enables public interest uses of private-sector data while retaining strict access control. It brings outside data expertise that would likely not be available otherwise using direct bilateral collaboration between data holders and users….(More)”.