In recent years, organizations across the globe have embarked on the DevOps journey to reimagine their business and stay ahead. And DevOps has indeed empowered businesses to develop and deliver software quickly and reliably and drive organizational performance. However, only a few organizations are successful at realizing the full potential of DevOps implementation. The reason is that they are able to measure DevOps. Let's delve deep into why businesses need to measure DevOps:
“You can’t improve what you don’t measure”. This is the mantra that businesses must live by in the DevOps ecosystem.
A DevOps transformation often requires significant investment in terms of time, money, and resources. Moreover, it requires modernizing everything from communication practices to training methodologies to software tools. So, in order to ensure that the DevOps journey doesn't go astray, businesses need the ability to assess DevOps performance benchmarks, clearly and accurately. Making DevOps measurable is imperative to define and invest in processes that work, continuously improve the practices according to the market change, track performance, and ensure peak productivity. (Looking to learn more about DevOps observability? Read our blog here: DevOps observability: What is it and how to implement it?)
Once you have implemented DevOps, it’s time to know whether it helped you gain value. It’s imperative to measure the value delivered by the new collaboration and culture. Wondering how? DevOps metrics or Key Performance Indicators (KPIs) are the answer here.
DevOps metrics and KPIs are the quantifiable measures that directly reveal the performance of the DevOps initiatives. They help you gain visibility into the software development processes and accordingly identify areas of improvement. More specifically, DevOps metrics and KPIs enable DevOps teams to measure the performance of collaborative workflows and track the progress of achieving high-level goals including increased quality, faster release cycles, and improved application performance.
Read: 13 DevOps KPIs every leader should track
Over time, innumerable metrics and KPIs came into the limelight, pushing businesses into a corner on which metrics to track. Taking due heed of this challenge, Google Cloud’s DevOps Research and Assessment (DORA) team has extended its support.
The DORA team has conducted research for seven years to identify the key metrics that precisely indicate the performance of the DevOps initiative. During the research, the team collected data from over 32,000 professionals worldwide and analyzed it to gain an in-depth understanding of DevOps practices and capabilities that drive performance. The team identified four key metrics, namely Deployment Frequency (DF), Lead Time for Changes (LT), Mean Time to Restore (MTTR), and Change Failure Rate (CFR), that serve as a guide to measure the performance of the software development team.
Every business, irrespective of its DevOps maturity, needs the DORA metrics as they are a great way to enhance the efficiency and effectiveness of their DevOps processes. While deployment frequency and lead time for changes help teams to measure velocity (software delivery throughput) and agility, the change failure rate and time to restore service help measure stability (quality). These metrics enable teams to find how successful they are at DevOps and identify themselves as elite, high, medium, and low performing teams.
Let’s delve into the details on how to measure the four key software delivery performance metrics:
Deployment frequency measures how often an organization releases software to the production. In other terms, it refers to the frequency of successful code deployments for an application. Among all the four metrics, deployment frequency needs only one table, making it the easiest metric to measure. However, calculating the frequency is somewhat tricky. It is very easy to calculate the daily deployment volume, but the metric is in terms of frequency, not volume.
In general, one deployment for a week is the standard. But, high-performing companies make up to seven deployments a week. If you deploy on most working days, then you fall under the "deploy daily” category. Likewise, if you deploy most weeks, you will fall under “deploy weekly”, and then “monthly” and so forth. Deployment Frequency metric can also be used to measure how often new features or enhancements are being delivered.
According to the State of DevOps 2021 report by Google Cloud, the deployment frequency for various performers is as follows:
Elite performer: "On-demand (multiple deploys per day)"
High performer: More than 1 deployment/week and less than one/month
Medium performer: More than 1 deployment/month and less the 1/6-months
Low performer: Less than 1 deployment/6-months
If your organization is falling under the low-performer category, you must leverage an automated deployment pipeline that automates new code testing and feedback mechanism. This reduces the time to recovery and time to delivery. When the deployments are automated, it allows the organization to shift-left in terms of development, security and quality, and resolve issues within a quick span of time. Automating also reduces soak time, approval time significantly, and increases speed to market.
Lead time for changes metric refers to the total time it takes a code commit to reach the production environment. Simply put, this metric measures the velocity of software delivery. The lower the lead time for changes, the more efficient the team is in deploying code.
To measure the LT metric, you need two timestamps– when the code commit occurred and when the deployment occurred. So, for every deployment, a list of all the changes included in the deployment must be prepared. This can be done easily using triggers with an SHA mapping to the commits. With the list at your disposal, you can glean the timestamps and then calculate the median lead time for changes.
Elite performer: Less than an hour
High performer: 1 day to 1 week
Medium performer: 1 to 6 months
Low performer: More than 6 months
To improve lead time to changes, DevOps teams must include automated testing in the development process. Your testing team can educate your Dev teams to write and automate tests. The change lead time can also be reduced by introducing more regression unit tests, so any regressions introduced by code changes can be identified as early as possible.
The mean time to restore metric refers to the time taken by the business to recover from a failure in production. In other words, it is the time required to recover and address all the issues introduced by a release.
In order to calculate the mean time to restore, you need to know the time when the incident occurred and when it was addressed. You also need the time when the incident occurred and when a deployment addressed the issue.
Elite performer: Less than an hour
High performer: Less than 1 day
Medium performer: From 1 day to 1 week
Low performer: More than 6 months
To improve the time to restore, businesses have to implement robust monitoring processes and swift recovery practices. This enables teams to deploy a go-to action plan for an immediate response to a failure. Businesses can also start investing in auto-healing mechanisms and prediction techniques to identify failures that may happen, where they can proactively identify or anticipate issues and resolve them well before they occur.
The change failure rate metric is the percentage of deployments causing a failure in production. Simply put, it is the percentage of changes made to a code that resulted in incidents, rollbacks, or any other production failure. Thus, this metric is considered the true measure of quality and stability.
The change failure rate is calculated from two things – the number of attempted deployments and the number of failed deployments. When tracked over time, this metric provides the details on the amount of time the team is spending on resolving issues and on delivering new code.
DevOps teams must focus on change failure rate instead of the number of failures. This axes the false convention that the failures reduce with the number of releases. So, teams must push releases more often, and in small batches, to fix the defects easily and quickly. It is also ideal to ensure that all CI/CD processes have been followed religiously, which includes resolving critical vulnerabilities and bugs in code, proper regression techniques in place, and automated performance testing as well.
Elite performer: 0-15%
High performer: 16-30%
Medium performer: 16-30%
Low performer: 16-30%
Any organization, regardless of its business size and industry, trying to drive its DevOps journey in the right direction, must start focusing on the above precise set of DORA metrics. However, it is not all about collecting all the DORA metrics across the CI/CD ecosystem. In order to improve DevOps productivity, businesses must be able to aggregate the metrics into a meaningful dashboard. This is where Opsera’s Unified Insights tool comes in.
Our Unified Insights tool is a powerful DORA metrics dashboard that enables businesses to aggregate DORA metrics into a single and unified view. It helps gain end-to-end visibility into the metrics in true CI/CD categories, including Pipelines, Planning, SecOps, Quality, and Operations. Moreover, this persona-based dashboard provides DevOps analytics targeting vertical roles, including developers, managers, and executives, to empower you to understand your DevOps processes from both practitioner and managerial perspectives and take better technical and business decisions.
Opsera’s Unified Insights tool provides contextualized logs and reports for faster resolution and improved auditing and compliance. You can swiftly diagnose the failures and view the RCA for faster troubleshooting. This ability helps you improve the time to recovery and time to deploy.
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