Data Governance Template for Product Analytics

Yes, but scale the effort to the team. A 5-person startup does not need a quarterly audit process. But it does need three things: a naming convention (written in a shared doc), an event ownership list (who to call when something breaks), and a monitoring alert for zero-event-volume days. These take 1-2 hours to set up and prevent the most common data quality disasters.

The data or analytics team owns the governance policy, monitoring, and audits. Engineering owns implementation quality (events fire correctly, properties are populated). Product owns the "what" (which events exist, what they mean, what metrics they feed). In practice, the data lead writes and maintains the governance doc, and product and engineering are accountable for their respective domains.

Deprecate, do not rename. Create new events following the convention and update all dashboards and analyses to use the new events. Keep the old events firing for a migration window (30-90 days) so historical comparisons remain valid. After the window, remove the old tracking code. Document the migration in the data quality tracker.

If you use Segment, Protocols can enforce schemas and block non-conforming events. Amplitude Data (formerly Taxonomy) validates event properties. For warehouse-level quality, tools like Great Expectations, dbt tests, or Monte Carlo monitor freshness, volume, and schema drift. At minimum, set up a daily cron job that checks event volume against the previous 7-day average and alerts on anomalies.

Quarterly is the right cadence for most teams. Monthly audits burn too much time. Annual audits miss too many issues. The quarterly audit should take 2-4 hours and cover: event coverage, naming compliance, documentation freshness, unused events, and dashboard accuracy. Log findings in the data quality tracker and assign fixes to the next sprint. ---