49 - Story Telling with ASPIRE

Abstract Information: Dashboards have become a powerful and widespread tool to visualize and tell stories through data. At the Substance Abuse and Mental Health Services Administration (SAMHSA), we developed an internal performance dashboard, Advancing SAMHSA Programs in Reaching Equity (ASPIRE), with the goal of utilizing stories in the dashboard data to support learning and promote data driven decision making. Dashboards have been effective tools to support performance monitoring, dissemination of data, funding decisions, meetings, presentations, and more. When data are presented through dashboards, opportunities for storytelling become more accessible and comprehensive. During ASPIRE’s development, SAMHSA learned lessons on dashboard designs that best engage target audiences and relay stories of clients through visualizations. Lessons learned include, clarifying your primary audience, engaging target audience early, using various charts and maps, having a color scheme, and including extensive filtering options. ASPIRE is a new SAMHSA venture but has already begun telling stories of the geographic and demographic reach of SAMHSA’s discretionary programs clients. These observations will serve as primary support for future data driven decisions impacting clients because of SAMHSA’s gains in cultural understandings and demographic data. The ASPIRE dashboard is helping make data matter by bringing the stories of the people to life.

Relevance Statement: This project is relevant and important to the practice of evaluation because it demonstrates ways to tell stories through the visualization of dashboards and lessons on how to include key stakeholders in the process of dashboard development. This project is an example of how we can use data visualization to expand the reach of data accessibility for decision making within the government and other organizations.

References: Kruglov, A., Strugar, D., & Succi, G. (2021). Tailored performance dashboards-an evaluation of the state of the art. Peer J Comput. Sci, 7(625). http://doi.org/10.7717/peerj-cs.625