The Do’s and Don’ts of using DORA Metrics

by Tola Ore-Aruwaji

If you're a part of the software development world, you are likely familiar with DORA (DevOps Research and Assessment) metrics, a standard for assessing the effectiveness of software delivery and operational performance. DORA defines four key metrics that drive performance:

  • Lead Time for Changes
  • Deployment Frequency
  • Mean Time to Restore (MTTR)
  • Change Failure Rate

These metrics provide critical insights into your software delivery performance and can illuminate areas for improvement. However, like any measurement tools, DORA metrics can be both effectively and ineffectively used. Misinterpretations or misuse of these metrics can lead to a false sense of progress or counterproductive practices.

In this blog, we'll dive into the do's and don'ts of using DORA metrics, with an emphasis on how to effectively map deployment time with deployment failure rate. We will also discuss why it's essential to measure performance at a team level rather than focusing on individual contributions.

Using DORA Metrics - The Right Way

Lead Time for Changes

Lead Time for Changes measures the amount of time it takes for a code commit to go into production. It is a good indicator of how quickly your team can deliver features, bug fixes, and updates. However, it is crucial to remember that faster isn't always better. Rapid code changes can lead to more errors if not paired with robust testing and quality assurance processes.

Deployment Frequency

Deployment Frequency measures how often deployments occur. While a higher frequency generally indicates a high-performing team, it's important not to sacrifice stability and quality for speed. High deployment frequency should be accompanied by low failure rates and quick recovery times to ensure balance.

Mean Time to Restore (MTTR)

MTTR measures the average time it takes to restore a service after a failure. Lower MTTR indicates that your team can quickly recover from failures. However, it is not a license to ignore the failure rate. A low MTTR should not excuse a high failure rate.

Change Failure Rate


Change Failure Rate measures the percentage of changes that result in a failure. The goal should be to have as low a failure rate as possible. However, don't let this metric push your team into excessive caution, slowing down the rate of change and innovation.

Deployment Time and Deployment Failure Rate

One common misconception is that faster deployment automatically equates to a better-performing team. This isn't always the case. Deployment time should be mapped with the deployment failure rate to get a comprehensive view of performance.

For example, if a team consistently deploys quickly but experiences high failure rates, it may be a sign that quality assurance steps are being skipped or not effectively catching issues. Speed without quality can lead to technical debt, rework, and service interruptions, which are detrimental to long-term performance.

Conversely, a slower but more cautious team that ensures high-quality deployments with fewer failures could be seen as more effective. Therefore, while the speed of deployment is crucial, it's equally important to consider the quality and reliability of those deployments.

Fast deployment can become problematic when the emphasis on speed leads to a neglect of other critical elements of software delivery, such as thorough testing, code review, and quality assurance. If the priority becomes deploy as fast as possible, the risk of error can increase, which may lead to a higher deployment failure rate.

Deployment failure rate refers to the percentage of deployments that result in a failure. This could be a minor issue requiring a quick fix or a major problem that takes down your production environment. An increase in the deployment failure rate may be a symptom of rushed or improperly tested deployments, which is where the connection with deployment time comes in.

To effectively map deployment time with deployment failure rate, you should look at these two metrics in relation to each other. Here's how:

  • Speed versus Stability: If you have fast deployments but a high failure rate, this indicates that the pace of deployments may be compromising their stability. The focus should then be on improving testing and QA processes to ensure that code changes are stable before they're deployed.
  • Slow Speed, Low Failures: On the other hand, if your deployments are slow but the failure rate is low, this suggests that your attention to quality and stability might be slowing down the deployment process. The challenge here is to find ways to maintain the quality while accelerating the deployment pace.
  • Fast and Stable: The optimal situation is to have a fast deployment time with a low failure rate. This indicates that your team has found a balance between speed and stability, delivering quality code changes to production rapidly.

Team-Level Performance Measurement

A common pitfall in performance measurement is the overemphasis on individual contributions. While individual performance is important, the focus should be on team-level measurement.

Software development is fundamentally a collaborative process that involves cross-functional teams working together. Individual metrics can often overlook the broader context of a person's work and could potentially incentivize behaviors that aren't beneficial for the team as a whole.

In contrast, team-level metrics promote a more holistic view of performance. They encourage collaboration, shared accountability, and collective improvement. They also align better with DevOps principles that value the overall flow of value through the system, rather than the efficiency of individual components.

The Don'ts of Using DORA Metrics

While DORA metrics can provide valuable insights, there are some common pitfalls to avoid:

  • Don't use DORA metrics in isolation: DORA metrics are most effective when used together to provide a comprehensive view of your software delivery performance. Using a single metric in isolation could lead to an incomplete or skewed understanding of your team's performance.
  • Don't use DORA metrics to blame or punish: These metrics should be used as learning and improvement tools, not as a way to punish or blame. Using them for punitive measures can create a culture of fear, discourage innovation, and damage team morale.
  • Don't use DORA metrics as the only performance indicators: While DORA metrics are valuable, they aren't the only measures of performance. Other factors, such as customer satisfaction, code quality, and product functionality, are also essential.

Mastering DevOps: A Comprehensive Guide to Using (and Avoiding Misuse of) DORA Metrics

In conclusion, while deployment speed is crucial in today's fast-paced software development landscape, it's important to maintain a holistic view. Quick deployments that frequently fail can be more detrimental than slower, more stable releases. By viewing deployment time and deployment failure rate together, you can ensure a balance between the speed of delivery and the quality of the software you produce.

We hope this guide has shed light on the effective use of DORA metrics in your DevOps journey. As you move forward, remember to balance speed and quality, and always consider your team's performance holistically.