As an engineering VP or manager, you don’t have consistent metrics to show the business what your teams are working on. Plus, it’s hard to prove that what they’re working on aligns with business needs. What you need is a way to quantify your engineering org’s efficiency so you can justify scaling your team up, and that means using DevOps metrics.
By using DevOps metrics and key performance indicators (KPIs), you can effectively gauge your DevOps effectiveness and success—trust us, this is tried and true.
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What Are DevOps Metrics?
DevOps metrics are specific data points that show you how well a DevOps software development pipeline is operating. These stats are quantitative assessment tools that help you evaluate your DevOps’ effectiveness.
You can use these metrics to monitor team operations and technical skills. And they can enable your DevOps to immediately locate and eliminate any process bottlenecks.
If you want to discover bottlenecks to continuously improve teams’ performance, you can always rely on DevOps Research and Assessments (DORA) metrics—deployment frequency, cycle time, change failure rate, and mean time to restore. They have become the standard way for CTOs and VPs of Engineering to get a high-level overview of how their organizations are performing.
But DORA metrics aren’t a perfect solution because they’re lagging indicators and they don’t tie directly to business outcomes. Too much focus on DORA metrics ignores the bigger picture. So let’s look at a few key metrics you should include with the 4 DORA metrics.
Top 7 DevOps KPIs
DevOps metrics are plentiful, so you need to narrow in on the key performance indicators relevant to your business needs. We’ve compiled this list of 7 DevOps KPIs to help you get started.
1. Cycle Time
Cycle time measures the amount of time from work started to work delivered, typically from first commit to release to production. It’s also helpful to measure each of the 4 phases of cycle time separately—coding time, pickup time, review time, and deployment time.
Teams with shorter cycle times mean they’re delivering quality code more often. And this means they’re able to deliver more features quickly and reliably.
2. Deployment Frequency
Deployment frequency measures how often an organization successfully releases to production. If your teams are deploying often, that signals a healthy and stable CI/CD pipeline. In turn, this improves your user experience and keeps delays low.
3. Change Failure Rate
Change failure rate measures the percentage of failed deployments to production. In a perfect world, you’d have a failure rate of zero. Unfortunately, in the real world, we tend to find the best teams under 15%.
4. Mean Time To Recovery
Mean Time to Recovery (MTTR) measures how long it takes an organization to recover from a failure in production. If your team is quickly able to resolve production bugs or outages, you’ll have a better user experience when things hit the fan.
5. PR Size
Pull request (PR) size is a metric calculating the average code changes (in lines of code) of a pull request. Large PRs can be a huge bottleneck and impact your team’s efficiency.
Watch this presentation from Interact to understand just how badly PRs can affect your productivity. We even give you some tips to solve these issues!
6. Rework Rate
Rework refers to code that is rewritten or deleted shortly after being added to your code base. LinearB considers changes to code that have been in your code-base for less than 21 days as reworked code.
Your rework rate is the percentage of changes that are considered rework for a given timeframe. Ideally, you’d keep this rate as low as possible, as low rework rates mean your teams are writing clean, efficient code and have more time to focus on delivering new features.
7. Planning Accuracy
Planning accuracy is the ratio of the planned and completed issues or story points out of the total issues or story points planned for the iteration. Nobody loves telling the execs, sales, customer success, and marketing that all their plans have to change because engineering missed a deadline.
It’s inevitable that you’re going to have scope creep and unplanned work each iteration, but keeping a high planning accuracy means you’re delivering on your promises to the rest of the business most of the time.
In addition to measuring these 7 DevOps KPIs, knowing how your teams compare against industry standards helps you justify where you need to focus improvement efforts. We studied nearly 2,000 teams in our Engineering Metrics Benchmarks study to determine what makes engineering teams elite. Then, we incorporated these benchmarks into our analytics dashboards so you can see at a glance how your team is performing and contrast that against the industry average.
Improving Your DevOps KPIs
Once you’ve identified the key metrics to measure your software development process efficiency and benchmarked where your teams stand, you can create an action plan and strategies to improve your numbers.
As we’ve learned with CI/CD, automation is key to dramatic improvement in engineering productivity and team health. So rather than try to micromanage your teams to improve DevOps metrics, provide them with the automation tools that help them self-improve.
LinearB can help you measure all the DevOps KPIs listed above, but we’ve also developed engineering improvement recipes for efficiency, quality, and planning accuracy. They all follow these basic steps:
- Benchmark your metrics
- Set your goals
- Automate improvement
Our developer bot, WorkerB, notifies your teams when PRs aren’t aligned with established team goals. It also automates low-value tasks like creating Jira tickets and reviewing small PRs directly from Slack.