Community Veterans Engagement Board Project
Visualizing the Future Through Data
Project Duration: 6 months
Project Type: Federal
Agency Partner: Department of Veterans Affairs (VA)
Stakeholders: Veterans Experience Office, Veterans Health Administration, Veterans Benefits Administration, National Cemetery Administration
Design Phases: Research
Skills: Design Strategy, Research, Writing, Data Visualization
Product Design Output: Next Generation Community Veterans Engagement Boards Report
The Community Veterans Engagement Boards (CVEBs)1 were started by the Veterans Experience Office as a way to knit together the frequently fragmented community and VA service offerings available to veterans. The original CVEB developed organically in San Diego, where the leaders of the three VA Administrations and veteran service organizations sought to limit turf wars and undesirable service competition through actively collaborating with each other to support veterans. The network of useful redundancies2 they created have resulted in one of the healthiest and most support veteran communities in the country. Referencing the San Diego model, VEO brought CVEBs to communities across the country in just two years. In 2017, VEO asked the design team to study the now-extant CVEBs to find out what might be their desired futures, how to support them, and how to continue to grow robust communities.
We conducted a series of workshops in five communities across the country, using a variety of methods to parse how they perceived themselves, surface their service gaps, and support them in envisioning their next steps. The resulting report contained a profile of diverse communities, common points, and direction on how VA might use these burgeoning communities to pilot new collaborative programs and spread best practices.
Below, find some data visualizations from the report. This project challenged me to codify how designers might visualize qualitative data into charts, what it means to make visual as well as text-based logical arguments, and how to both accurately and acutely show a qualitative process in the language of quantitative data, without misrepresenting that data.