Inspired and motivated by our March 2017 DataDive, the GivingTuesday team wanted to delve deeper into some of the learnings and questions that surfaced from the event, and further explore additional dynamics around philanthropic giving and GivingTuesday’s continued impact on giving globally. DataKind, the GivingTuesday team and the Bill & Melinda Gates Foundation partnered again to host a second DataDive, in August 2017, at the Bill & Melinda Gates Foundation in Seattle. For this DataDive, teams focused on further examining insights into donor behavior, continuing to improve visibility into nonprofits by building out a 990 tax form data tool, and exploring additional areas impacting philanthropic giving such as workplace giving and crowdfunding.
Over the two-day event, teams of volunteer data scientists analyzed massive new datasets and developed statistical models and tools that will not only help future initiatives around GivingTuesday, but also serve to support the social sector as a whole. The work achieved is a powerful example of what can be accomplished through collaboration between mission driven organizations, data scientists, social actors and multiple data providers. It shows how this greater sharing of knowledge, expertise and data can open up new channels of exploration and bring to light things that would not have been discovered otherwise.
Though DataDives serve as a starting point for organizations in their data journey and are meant to offer initial insights and learnings, the work from this DataDive has continued to live beyond this. In addition to follow up analysis performed by contracted data scientists building off the work of the DataDive, the prototypes and tools used to analyze 990 tax forms and the workplace giving dashboards created during the GivingTuesday DataDive can be further developed and used to help nonprofits, as well as the sector as a whole, increase philanthropic giving and fuel more social change. It is because of the initiative that GivingTuesday has shown in investing in data science techniques and in bringing together multiple data providers that this work has been possible. The work not only stands on its own, but also provides a foundation for further work to come.