P02 / Project dossier
GHCP Adoption Telemetry
Designed skill-level GitHub Copilot telemetry for internal teams, creating a low-code path to measure adoption, usage, and indicators of return on investment in Dataverse.
Challenge
Internal teams needed a clearer view of GitHub Copilot adoption and the value created by individual skills. Aggregate usage alone could not show which capabilities were being adopted or where investment was producing meaningful engagement.
The measurement model needed to be lightweight enough to adopt without introducing another complex telemetry platform.
Approach
Designed a skill-level telemetry model that connected usage signals to adoption and ROI indicators. Introduced a novel low-code pattern that wrote telemetry directly to a Dataverse table, reducing custom infrastructure while keeping the data accessible to internal teams.
What changed
Created a practical telemetry foundation for understanding which GitHub Copilot skills were being used and how adoption changed over time.
The direct-to-Dataverse pattern gave internal teams a simpler route to analyze usage and develop evidence-based views of skill-level return on investment.
Why it matters