In this post, I’ll clearly define DORA Metrics, explain their importance, and provide guidance on effectively tracking them to enable better data-driven decision-making for your teams.

What Are DORA Metrics?

DORA stands for DevOps Research and Assessment. It’s a research group acquired by Google that studies the factors behind efficient and effective software delivery. Based on extensive research, they’ve identified four critical metrics indicative of the overall health and performance of software engineering teams.

The four DORA Metrics are:

1. Cycle Time (Lead Time): How long does it take from committing code to running it in production?
2. Deployment Frequency: How often do your teams release to production?
3. Change Failure Rate: What percentage of deployments result in failures?
4. Mean Time to Recover (MTTR): How quickly can your team recover from production issues?

Let’s delve deeper into each metric and understand why each matters for your team’s health.

Why DORA Metrics Matter

Tracking DORA Metrics allows your engineering teams to achieve an optimal balance between rapid deployment velocity and consistently high-quality software. These metrics highlight bottlenecks, pinpoint process inefficiencies, and indicate areas needing improvement.

However, simply tracking metrics is not enough. As leaders, we must interpret these insights and act strategically, initiating improvements that enhance both productivity and quality.

Here’s a breakdown of each DORA Metric, why it matters, and how you can leverage it strategically.

The Four DORA Metrics and How to Track Them

Cycle Time (Lead Time)

Cycle Time measures the duration from code being written to its deployment to production.

Cycle time typically includes four sub-metrics:

• Work In Progress (WIP): Time from first to last commit on a branch.
• Review: Average time spent in code review.
• Merge: Time from review completion to code merge.
• Release: Time from merge to release.

Think of cycle time as your overarching “health metric”. A shorter cycle time indicates a highly efficient pipeline. Longer cycle times reveal bottlenecks that require attention and improvement.

Deployment Frequency

Deployment Frequency represents how often your team pushes code to production, directly measuring your team’s agility and delivery velocity.

A high deployment frequency suggests your team is delivering value rapidly, responding quickly to user needs. Ideally, teams should aim to deliver smaller, incremental updates frequently. Small changes are easier to troubleshoot, test, and roll back if necessary.

It’s important to remember that deployment frequency alone doesn’t speak directly to quality. For a complete picture, this metric must be analyzed alongside others, particularly Change Failure Rate and MTTR.

Change Failure Rate

Change Failure Rate measures the percentage of deployments that fail. Definitions of “failure” vary, but common examples include bugs, incidents, rollbacks, or customer-impacting issues.

A lower change failure rate indicates your engineering practices produce stable, high-quality code. If your failure rate remains high despite focused improvements (e.g., enhanced review processes, automated testing), it signals the need to reassess and iterate on your strategies.

Mean Time to Recover (MTTR)

Mean Time to Recover (MTTR) measures your team’s resilience—the average duration it takes to restore services after a production incident occurs.

A low MTTR indicates that your team is effective at quickly identifying, diagnosing, and resolving production issues, minimizing disruption and downtime.

To effectively improve MTTR, monitor related metrics like:

• MTTA (Mean Time to Acknowledge): Time it takes teams to acknowledge incidents.
• MTTRepair (Mean Time to Repair): Actual time spent resolving the issues.

MTTR reveals how responsive and prepared your teams are in crisis scenarios.

Leveraging DORA Metrics for Strategic Improvement

The goal of tracking DORA Metrics is not merely to accumulate data, but to use these insights strategically to drive continuous improvement.

Identify where bottlenecks occur. Investigate why certain metrics aren’t meeting your expectations. Implement targeted changes, monitor results, and continuously iterate on these processes.

Summary

As a seasoned engineering leader, I’ve seen firsthand how DORA Metrics empower data-driven decision-making, enabling our teams to deliver faster and more reliably while continuously improving quality.

By systematically tracking and responding to these four critical metrics—Cycle Time, Deployment Frequency, Change Failure Rate, and MTTR—you can build a high-performance software delivery organization.

Consistently leverage these insights to optimize your team’s efficiency, resilience, and quality.