Best Practices in Coordinating Large-Scale Data Science Initiatives

• Activity Number: 66 - Meeting Client Needs: Approaches and Examples
• Type: Contributed
• Date/Time: Sunday, August 7, 2022 : 4:00 PM to 5:50 PM
• Sponsor: Section on Statistical Consulting
• Author(s): Joel Thurston* and Sallie Keller and Aaron Schroeder and Stephanie S Shipp
• Companies: UVA Biocomplexity Institute Social and Decision Analytics Division and Biocomplexity Institute, University of Virginia and Biocomplexity Institute, University of Virginia and University of Virginia
• Keywords: data commons; policy making; social justice; equity; research best practices; data science initiatives

Abstract

Utilizing data science to solve intractable societal problems has seen exponential growth over the past few years. Specialists in the statistical, computer, and social sciences, along with diverse organizations, are coming together to collaborate across academia, government, community groups, and private industry. There are clear benefits to be gained by harnessing such a breadth of expertise to address complex, systemic issues such as racial and ethnic disparities in social justice. However, as the power of solutions scale in response to size of these collaborations, so do coordination and communication challenges (e.g., problem definition, competing goals). This presentation shares best practices for successfully executing large-scale data science initiatives gleaned from projects spanning multiple states, organizations, and approaches, including: (1) building a social impact data commons to inform equity-focused policy-making in the National Capital Region; (2) building a data commons focused on rural-urban health equity in Virginia; and (3) piloting a three-state network to infuse data science into agriculture research and workforce development to improve economic mobility.