DORA Metrics and Engineering Velocity: How to Measure What Actually Matters

The DORA (DevOps Research and Assessment) team at Google conducted years of research into what distinguishes high-performing software delivery organizations. Their finding: four metrics predict engineering team performance better than any other combination.

The Four Metrics

Deployment Frequency: How often does your team deploy to production? Elite performers deploy multiple times per day. Frequency is a proxy for batch size — smaller, more frequent deployments are faster and safer than large, infrequent ones.

Lead Time for Changes: The time from a code commit to that code running in production. Elite performers achieve this in less than an hour. Lead time measures the efficiency of your entire delivery pipeline.

Change Failure Rate: The percentage of deployments that cause a production incident requiring hotfix or rollback. Elite performers have rates below 5%. This measures deployment quality.

Time to Restore Service (MTTR): When a production incident occurs, how long does it take to restore service? Elite performers restore service in less than an hour. This measures incident response maturity.

Implementing Measurement

Most metrics can be derived from existing tooling: deployment frequency from your CI/CD system, lead time from git commit timestamps to deployment events, change failure rate from incident management tools correlated with deployments, MTTR from incident tracking.

Tools like LinearB, Sleuth, and Cortex automate DORA metric collection across common DevOps toolchains.

Avoiding the Goodhart’s Law Problem

“When a measure becomes a target, it ceases to be a good measure.” Treat the metrics as diagnostics, not goals. Use them to understand where constraints are, then remove the constraints.