Metrics for advanced deployment strategies

Rollback frequency: This metric measures how often changes need to be rolled back. While a higher rollback frequency may indicate issues with the deployment process or inadequate quality assurance capabilities, it can also suggest successful usage of advanced deployment strategy capabilities with automation facilitating fast rollbacks to minimize user risk. Track this by counting the number of rollbacks and comparing it to the total number of deployments.

Deployment lead time: The average time required to successfully deploy a feature or service from the moment a deployment is triggered to when it is live in an environment. Using this metric, teams can pinpoint bottlenecks in the deployment process. Enhance this metric by optimizing deployment strategies, utilizing distributed architectures, or deploying in waves to strike a balance between speed and safety. Measure the duration from when the deployment is triggered to its completion, considering only successful deployments, and calculate the average over a specific time frame, such as weekly or monthly.

Release frequency: The frequency at which changes become accessible to end users. This metric distinguishes between deployments, which introduces new code or configurations into an environment, and releases, which make those changes accessible to end users. A high release frequency can indicate mature DevOps capabilities which enable releasing small, incremental changes that are automatically deployed and verified with confidence. Measure release frequency by counting the number of releases to production over a specified period. Compare this metric to deployment frequency to understand the correlation and derive additional insights.

Mean time to recover (MTTR): The average time taken to restore a system after a failure. This metric provides insight into the team's ability to quickly detect and address production issues. A lower MTTR indicates safer deployment practices, the use of automated rollbacks, and effective governance, quality assurance, and observability capabilities. Measure the total amount of downtime and divide it by the total number of incidents within a specific time frame.