DORA metrics are a set of four key performance indicators—deployment frequency, lead time for changes, mean time to recovery (MTTR), and change failure rate (CFR)—used to measure software delivery and operational performance. They help engineering teams benchmark their DevOps maturity, identify bottlenecks, and drive continuous improvement. For more, see our DORA metrics guide.
Common pitfalls include using incorrect definitions (e.g., misclassifying change failure rate or lead time), focusing on individual metrics in isolation, and treating DORA metrics as leading indicators for engineering excellence rather than lagging indicators. It's crucial to understand the intent behind each metric and use them as part of a broader improvement strategy. See the original blog post for detailed examples.
Organizations should use DORA metrics as signals for areas needing improvement, not as the sole measure of high performance. They should measure the entire development flow, use all four metrics together, and focus on continuous delivery discipline. DORA metrics should be contextualized with organizational architecture, mentoring, and product management practices. Read the full blog post for more guidance.
Focusing on a single DORA metric (e.g., delivery frequency) can be dangerous if not balanced with quality and batch size signals. Using all four metrics together provides a holistic view of software delivery performance and helps avoid unintended consequences. This approach ensures that improvements in one area do not negatively impact others.
Faros AI provides scientifically accurate, end-to-end measurement of DORA metrics, ensuring correct definitions and attribution. The platform integrates data from the entire SDLC, not just proxies from Jira or GitHub, and offers actionable insights, benchmarks, and team-specific recommendations to drive real improvement. Learn more at our DORA metrics page.
Faros AI offers a detailed guide on DORA metrics, including definitions, measurement best practices, and common pitfalls. Access the guide at our blog post about DORA metrics and how to measure them.
Autodesk adopted DORA metrics as the foundation for its productivity framework, enabling all engineering teams to baseline their current state and drive improvements aligned with business outcomes. For more, see the Autodesk case study.
DORA metrics are closely tied to continuous delivery practices. High-performing organizations use DORA metrics to identify obstacles to daily software delivery and focus on removing those barriers, as recommended in the original blog post and industry research.
DORA metrics are lagging indicators for engineering excellence and product management, but can be leading indicators for IT performance. They should be used alongside other signals to drive improvement.
To avoid misuse, ensure you understand the correct definitions, measure the full development flow, use all four metrics together, and contextualize them within your organization's broader improvement strategy. Faros AI provides resources and platform support to help you implement DORA metrics correctly.
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Faros AI provides tools to measure, track, and analyze all four DORA metrics with scientific accuracy. It integrates data from the entire SDLC, supports custom workflows, and offers actionable recommendations to drive engineering excellence. For more, visit our DORA metrics resource page.
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Faros AI integrates with the entire SDLC, supports custom workflows, and provides accurate metrics from the full lifecycle of code changes. It offers out-of-the-box dashboards, deep customization, and actionable team-specific insights. Competitors are limited to Jira/GitHub data, require specific workflows, and lack customization. Faros AI also provides proactive intelligence and enterprise-grade compliance. See details.
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Since Accelerate was published in 2018, “DORA metrics” have become increasingly popular for measuring IT performance. More vendors are creating dashboards or integrating these metrics into their existing pipeline tooling. However, the context is getting lost in the race to deliver these features to customers.
Since Accelerate was published in 2018, “DORA metrics” have become increasingly popular for measuring IT performance. More vendors are creating dashboards or integrating these metrics into their existing pipeline tooling. However, the context is getting lost in the race to deliver these features to customers.
Since Accelerate was published in 2018, “DORA metrics” have become increasingly popular for measuring IT performance. More vendors are creating dashboards or integrating these metrics into their existing pipeline tooling. However, the context is getting lost in the race to deliver these features to customers.
First, what are the DORA metrics?
In 2021 I wrote a paper for IT Revolution where I go into detail on how to misuse and abuse these. Today, let’s cover some high-level tips to consider before attempting to use these.
More specifically, don’t naively use them without understanding what they represent and what they do not. Having good DORA metrics does not mean you are a high-performing organization. Delivering very stable, very small batches of useless crap doesn’t make you high performing. However, delivering large batches and/or having an unstable system will definitely cause a negative impact on your business performance. Do not use them to track positive performance. The correct way to use them is as an indicator for things that could be improved so you can investigate “what’ and “how”.
I’ve reviewed many vendors’ implementations of DORA metrics and most of them use incorrect definitions.
Most tools define “Change Fail %” as the percentage of changes that cause an outage or otherwise need to be backed out. Nope. Read “Accelerate”.
“…result in degraded service or subsequently require remediation (e.g., lead to service impairment or outage, require a hotfix, a rollback, a fix-forward, or a patch).”
So, a change that results in a defect. Any defect.
Another that is almost always measured incorrectly is “lead time”. This is almost always measured from when the code is checked in until it is delivered, but that’s only the automated portion. In a follow-up response to a critical book review, Jez and Nicole state,
“But again, going back to first principles, going from starting to write code to checking in, and from releasing to getting feedback from production, should be fast and low variability processes and therefore belong in the delivery domain.”
Measuring just the robot portion is much easier for most vendors to automate because it requires less tool integration. However, it tells you almost nothing about where improvement opportunities exist. Most of the issues are upstream of there. Measure the entire development flow.
There are more incorrect definitions that tools use. Read “Accelerate”, understand the intent, and don’t blindly trust the implementation of a tool.
“This quarter we’ll focus on improving delivery frequency. next quarter we’ll focus on the next metric.”
Rapid delivery without a disciplined quality process is just dangerous. Speed isn’t the goal. Increased quality feedback is the goal. We need signals for quality and batch size.
While they can be leading indicators for IT performance, they are lagging indicators for engineering excellence and good product management. Measuring how frequently working code is integrated into the trunk and the wait times for handing off work will help identify things that will improve the DORA outcomes.
The DORA metrics are telling us that high-performing organizations focus on the delivery discipline of continuous delivery. Focus on “why can’t we deliver working software daily?” and fix those things.
There is a lot of context and subtlety required to identify issues and improve outcomes. Simply publishing DORA metrics is usually a destructive act. Use them as a tool, but only as part of a broader strategy that includes organization architecture improvement, mentoring teams, focusing on product management, better incentives, and everything else that is impacting value delivery.
(This post was originally published on August 13 by Bryan Finster on his blog post titled: 5-Minute DevOps: DORA Metrics Tips)
Three challenges keep surfacing in conversations with engineering leaders: productivity measurement, actions to take, and what real transformation actually looks like.
