Community Data: What to Measure and Why?
summary
Explains how community metrics move from raw counts to actionable insight by codifying questions into metrics and visualizations, using Augur OSS feeding a Postgres datastore and visualized with Dash and Plotly. Shows concrete tactics: split 120 total contributors into active vs drifting, repeat vs flyby (repeat threshold ~3–4 contributions), measure commit depth and repo bus factor, track conversion rates for Fedora’s “double contributors by 2025” goal, and notes a Red Hat pipeline that can produce a polished chart in a couple of hours.
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chapters (18)
- 0:00Start
- 0:15What Is the Value of Community Metrics
- 2:54Expose an Area of Improvement or Highlight Your Strengths
- 3:38Community Impact versus Business Impact
- 4:09The Community Impact
- 5:16Codifying Problems and Metrics
- 5:40Analysis Angles
- 6:21Number of Contributors over Time
- 7:17Repeat versus Flyby Contributors over Time
- 7:58Depth of Commits
- 11:06Living Cycle
- 11:35Breaking Down Your Focus Area and the Perspectives
- 13:15Select the Specific Data Points Needed
- 13:44Visualization
- 14:51Step Three Which Is Analysis in Action
- 15:40How Community and Community Initiatives Can Be Impacted or Implemented
- 21:49Analysis Lessons Learned
- 25:51Closing Thoughts
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