Abstract: When we’re talking about social impact, we’re not simply measuring and improving a single bottom line number. Improving lives can be measured in many different ways, and (as any data scientist knows) you can only optimize what you measure. How do we think about the work of a data scientist when our objective is less clear? Our new approach joins the quantitative insights from data science with qualitative insights of human-centered design. The interplay between data and design is a powerful new way of thinking about how data can improve products, outcomes, and ultimately lives. In addition to the concrete ways in which data scientists and can use design thinking in their work, we present a case study of increasing usage of mobile money among poor and rural farmers in Tanzania. Through both qualitative and quantitative insights, we designed and tested new products to provide financial services to people who have never had access to a bank account before. Like the Wonder Twins, data science and human centered design are better together.
Bio: Peter is a co-founder at DrivenData, whose mission is to bring the power of data science to the social sector. DrivenData builds algorithms and software for non-profits, NGOs, and governments, and also engages a global community of data scientists is online competitions that leverage data for the greater good. Recently he has worked on projects in smart school budgeting, anti-human trafficking, crop yield modeling, and digital financial services for rural populations. He earned his master's in Computational Science and Engineering from Harvard. Previously he worked as a software engineer at Microsoft and earned a BA in philosophy from Yale University.