As Product Design Lead for Data Cloud, I led the end-to-end design of native Dashboards from early strategy through launch. My scope included the canvas, tables, text tiles, filters, parameters, saved views, drilldowns, embedding, sharing, and permissions, as well as the migration from the incumbent dashboard product.
Dashboards as a decision surface
Dashboards are most valuable when they do more than summarize data. I designed Rippling Dashboards to help teams move from a high-level signal to the governed reports and source records behind it.
Global filters, parameters, and saved views let people shape the analysis, while drilldowns preserved the context behind each result. The same dashboard could be embedded on the homepage or in an app such as Payroll and shared across roles, with row-level security keeping each viewer in the right scope.
From context to decision
- Orient. Start from a prebuilt dashboard shaped by role, organization, and permissions. Or create a custom dashboard.
- Explore. Use global filters, parameters, and saved views, then drill into the underlying report or records.
- Share. Share with people, teams, or groups using viewer, editor, and owner roles while row-level security stays intact.
Admins could preserve canonical templates while users reordered, hid, or added approved tiles, balancing consistent definitions with personal workflows.
A system designed to scale
Dashboards required more than isolated features. I created shared patterns for tables, text tiles, filters, canvas interactions, sharing, and embedding. The system supported denser layouts without sacrificing readability and became a reusable analytics surface across Rippling, including dashboards created with Rippling AI.