DevOps

DevOps Metrics: Measuring DevOps Success

Written byJohn Terra
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Updated onFebruary 27, 2024

So, your organization has adopted the DevOps methodology for your software development and delivery needs. Great! DevOps, a portmanteau of “software development” and “IT operations,” stresses communication, collaboration, and integration and delivers a quality product.

But how do you know if your DevOps methods are working? You need reliable benchmarks that show if you and your development team are on track. You need metrics.

This article explores the concept of DevOps metrics, including a definition, the different types, their importance, how to measure success, and more, including how online DevOps training can help you practically master them.

By now, we already know what DevOps is, so we’ll forgo a detailed definition. But let’s see what we mean by DevOps metrics.

What are DevOps Metrics?

DevOps metrics and DevOps Key Performance Indicators (or DevOps KPIs for short) are data points and statistics that correlate to the team’s DevOps model performance and measure the process’s efficiency, as well as reveal any points of friction between the phases of the continuous delivery pipeline. Metrics track the team’s progress toward achieving their decided overarching goals.

They are considered the industry standard for evaluating the quality and reliability of software delivery within an organization. In tracking these metrics, the DevOps team can identify bottlenecks slowing delivery and causing deployment code failures.

In summary, DevOps metrics offer data-driven insights that teams continuously improve and deliver better quality software and more value to customers.

Also Read: A Comprehensive List of Top DevOps Tools for 2024

What’s the Difference Between DevOps KPIs and DevOps Metrics?

You often see the terms DevOps metrics and KPIs for DevOps used together, often interchangeably or with little differentiation. But are they the same thing?

Short answer: no. Long answer: KPIs are directly tied to specific strategic goals, but metrics are data points. KPIs for DevOps are directly tied to goals and used for measuring performance against set objectives. Metrics, however, are much broader and contain various data points used for analysis, yet they may not directly align with key objectives.

For example, an organization sets a key performance indicator to increase customer satisfaction. The organization would track metrics such as customer churn rate, page views, bounce rate, customer satisfaction rate, Net Promoter Score, average resolution time, etc. So, KPIs can have multiple metrics connected to them.

The Primary DevOps Goals

The main goals of DevOps are:

Velocity. You want to ship your code as fast and frequently as possible. Your results will vary dramatically based on the product type, team, and risk tolerance.
Quality. Quality entails how good the product is, with as few errors and bugs as possible. For some companies, the goal of high product quality supersedes production speed.
Application performance. Performance is related to quality, but there are subtle distinctions. Performance could cover application speed and portability, ease of use, and the overall user experience.

The priority of the above goals depends on the company and the product or service they offer.

Now that we know the purpose of DevOps, what kinds of DevOps metrics exist?

Types of DevOps Metrics

Many different metrics are used for DevOps; depending on who you ask, you will get varying numbers of how many metrics exist. But here’s our take, starting with four DORA metrics.

DORA metrics, also referred to as the four key metrics, were developed by Google’s DevOps Research and Assessment team (hence, DORA) to improve DevOps efficiency and communicate the performance to the organization’s stakeholders, thereby improving business results.

The four key metrics are:

Deployment frequency. “How often does the team successfully release into production?” Deployment frequency measures how often code changes get successfully released to production. As a rule, smaller or more frequent deployments present less risk and place the team in continuous delivery. Elite teams can perform on-demand deployments; low-performing teams typically produce large deployments over a series of months, often impacting velocity and increasing the risk and impact of deployment failure.
Change failure rate. “How many deployments failed?” Change failure rate measures the percentage of attempted deployments that result in a production failure requiring a bug fix or a roll-back. This metric measures the stability and efficiency of the team’s DevOps processes. Change failure rates above 40 percent can mean poor testing procedures, usually requiring the teams to make more changes than necessary, eroding efficiency. Elite teams hover within a 0-15 percent change failure rate, and low-performing teams range between 16-30 percent.
Mean lead time to changes. “How long does it take for committed code to be released into production?” Lead time for changes is the time a developer’s committed code takes to enter production. This metric is an early indicator of process issues and helps development teams identify bottlenecks slowing down software delivery. Elite teams take less than an hour from when the code is checked to when it’s deployed, while a low-performing team can spend more than six months making and deploying changes.
Mean time to recovery. “How quickly can the DevOps team resolve problems and restore service? The time to restore service is called the mean time to recovery (MTTR), which measures how long the team can recover from a production failure. This metric is especially significant from an operational perspective since the quicker the team responds, the better the customer’s experience. To measure recovery time, the team needs to know the incident’s timestamp, when it was resolved, and what deployment resolved it. Elite teams typically take less than an hour to get things running again, but a low-performing team could take more than six months.

Now, let’s look at nine other standard DevOps metrics.

Mean time to detection. Also called MTTD, this DevOps metric measures the average time between when an issue arises and how long it takes to discover it. This metric directly relates to application performance monitoring as the primary way to spot problems, though customer tickets can also help.
Defect escape rate. Quality assurance (QA) isn’t perfect, and there will inevitably be some errors that slip through the net and enter production. The defect escape rate measures the number of bugs that “escape” testing and is subsequently released into production. This metric helps teams determine the effectiveness of their testing procedures and the overall quality of the software release.
Customer ticket volume. Customer feedback and support tickets are a critical part of highlighting application problems. The more bugs and issues customers encounter, the more they’ll complain to your company.
Application usage and traffic. The application usage and traffic metrics monitor the number of users accessing your system and report many other metrics, such as system uptime. After a company deploys its new software, it wants to ensure everything usually operates by knowing how many users are accessing the system and the number of transactions occurring.
Cycle time. Cycle time measures how long the DevOps team takes to deliver once they begin working on a task.
Deployment time. Not to be confused with deployment frequency, deployment time measures how long it takes to deploy a release into testing, development, or production environments. This metric lets teams see where they can improve deployment and delivery methods.
Automated testing. This metric measures the percentage of the code subjected to automated testing. Automated testing typically means greater code stability, although there’s still room for manual testing to play a role in software development. The DevOps team’s goal is a higher percentage of code covered by automated testing.
Mean time between failures. The mean time between failures (MTBF) measures the average time between repairable shortcomings in a product or system. This reliability and availability metric highlights how well the DevOps team can prevent and reduce potential issues and failures. The higher the MTBF rate, the more reliable the system.
App availability. This vital DevOps metric measures the proportion of time an application is fully operational and accessible to meet end-user needs. The goal is to meet the KPI gold standard of the Five 9s or 99.999 percent.

Also Read: How to Enable Virtualization: A Guide for Aspiring DevOps Professionals

Why are DevOps DORA Metrics Important?

DORA metrics give the project manager insight into the two critical predictors of the engineering team’s success: throughput and stability. Throughput is the speed at which the application is delivered to the end user. Stability is the application’s reliability in performing as designed without failure.

Throughput is measured by deployment failure and the changes’ lead time. Stability is a DevOPs metric calculated by the time spent restoring services and the change failure rate. These metrics work together in tandem and emphasize that one can potentially impact the performance of the other. They clearly show overall team health and how well the internal DevOps processes work. Consequently, leaders can use the metrics to speak up for their team and improve the customer’s experience.

How to Prioritize the Right DevOps Metrics for Your Team

DevOps teams track the metrics listed to maximize efficiency and improve product delivery. However, there is yet to be an accepted universal standard for which guidelines and their hierarchal importance should be tracked.

However, the four DORA metrics are an excellent way to kick things off as you begin scaling DevOps. These four metrics offer insight into your DevOps practices’ stability and throughput and can act as a compass, pointing your team toward what can be improved.

You should also define a solid reason and a clear goal for why you’re tracking specific metrics. Instead of tracking every possible metric, consider sticking with the ones most likely to boost your business value. There’s a fine line between tracking metrics for making actionable improvements and monitoring metrics for ego’s sake.

Do You Want to Learn More About DevOps?

If you want to change careers and enter the field of DevOps, or you are currently a DevOps professional who wants to upskill, consider this 36-week online DevOps bootcamp. You will learn skills that will help bridge the gap between development and operations, including continuous deployment and DevOps tools such as Ansible, Docker, Terraform, and Kubernetes. In addition, the course also provides masterclasses on artificial intelligence topics such as prompt engineering and generative AI.

According to Glassdoor.com, DevOps engineers earn an annual average of $108,951. So, sign up and learn the necessary skills for a rewarding, in-demand position in DevOps.