All these practices make defects much easier to identify and remediate. The old adage that you can’t improve what you don’t measure is just as true for DevOps as any other practice. In order to fulfill the promise of DevOps — shipping higher quality products, faster — teams need to collect, analyze, and measure numerous metrics. These DevOps metrics provide the essential data DevOps teams require to have the visibility and control over their development pipeline.

As Cycle Time is a lagging indicator, it can be hard to gain visibility into risks early when using it. It should typically be paired with strong engineering practice alongside more quantative measures of reliability. As described in Why Shipping Software Smaller Helps Deliver Better Product, it is better to optimise for flow instead of just the volume of work delivered. Improving flow allows you to deliver better business outcomes reliably whilst preventing developer burnout. The engineering team can take complete ownership of improving it without external dependencies.

Conversely, measuring this value helped us to not slow down the tempo as we also had discussions on whether the tempo was too fast. But comparing our metrics to others gives us the confidence to keep the tempo. If we experience too many errors, then we should rather work on improving the stability metrics . David’s vision is to redefine the way the world thinks about risk and to develop risk management to its rightful place as being a key driver of value creation in each of Protecht’s customers. Deployment Frequency – frequency of deployments of any kind, successful or failed.

• We recommend always to keep in mind the ultimate intention behind a measurement and use it to reflect and learn.
• The book summarises years of rigorous research from years of State of DevOps Reports, built upon 23,000 datasets from companies all around the world.
• You can compare applications from selected runtimes, entire Kubernetes clusters, and specific applications.
• Deployment frequency This is a frequency, not a count, and therefore you need the total number of successful deployments within a given time period (I’ve found that a day works well).
• Every week we collectively discussed upcoming spikes and architectural decision records 22 and looked at the four key metrics.
• Through intensive research over the past ten years, four key metrics have surfaced that highlight the progress teams are making on their DevOps journey.

As an architect, these problems and issues would previously have fallen to me alone to spot, understand, analyze, and remedy. How did we end up with these visualizations, extra details, and specific time periods? One home-baked effort resulted in the most fully featured dashboard I’ve ever worked with. After a bunch of wrestling with dates and times, we captured our raw data, made our calculations, and set about creating our graphs and other visual display elements.

Time To Restore Service

Giving the teams access to, and ensuring they understand, these metrics, as well as the model and system that underpin them, is of utmost importance if you are to reap their real benefits. That’s what allows them to discuss, understand, own, and improve the software you deliver. Both mean and median are easy to calculate from your change failure tickets’ time-to-resolution data. Take all the failure resolution times that fell within that period, calculate their mean, and report it for this metric.

Each of these “North Star” metrics is formed of multiple “Leading Indicators” which allow you to drill-down and understand how to optimise a process at a more local level. At SourceLevel, we’ve been more likely to use the 75th percentile as the default metric and keeping track of the 95th percentile as well. In contrast, the 95th percentile gives us an idea of the small portion of work left out of the count and excludes the top 5% most time-consuming changes, as we consider them outliers.

Change Failure Rate

As you have most likely guessed, the first two metrics are underpinned by what happens in your CI pipelines, and the second pair require tracking service outages and restoration. Change failure rateThe proportion of changes coming out the pipe that cause a failure in our running service. Time-on-site can be tracked for each technician, as well as averaged out across all of your technicians. You’ll want to examine this metric regularly to ensure that it aligns with your goals and expectations.

Reducing batch size is another central element of the Lean paradigm—indeed, it was one of the keys to the success of the Toyota production system. Reducing batch sizes reduces cycle times and variability in flow, accelerates feedback, reduces risk and overhead, improves efficiency, increases motivation and urgency, and reduces costs and schedule growth. Every organization aims for success – great products and services, committed and happy team members, effective processes and practices, great feedback from repeat and new customers and markets, and plenty of profit. With all the data now aggregated and processed in BigQuery, you can visualize it in the Four Keys dashboard.

Our PowerBI report also had a “Four Key Metrics” front page, which comprised the key metric numbers from each individual statistics page as well as the graphs of deployment frequency and lead time. The goal was to give people an idea of the stats in real time, rapidly and accurately. There’s one final point to make about data capture from pipelines before we move on, and that is which pipeline runs to count? Again, Accelerate isn’t explicit on this point, but you only care about the runs that succeed.

Ideally, the moment you have your MVD up, you can start work on automating collection and calculation. Perhaps you will end up using Four Keys from Google, Metrik from Thoughtworks, or various extensions for platforms, such as Azure DevOps. Pay attention to this question of access , because if your four key metrics aren’t shared with everyone, then you are missing out on their greatest strength.

This led to long-lived feature branches, and the team not getting valuable feedback on their work. Identifying this let us work with that team to determine how to slice a tricky piece of work into smaller deliverable pieces and get that value out to users sooner. Understanding how your team is performing is critical to continuous improvement. Through intensive research over the past ten years, four key metrics have surfaced that highlight the progress teams are making on their DevOps journey. High-performing teams have change failure rates in the 0-15 percent range. Mean time to recovery measures how long it takes to recover from a partial service interruption or total failure.

“if You Can’t Measure It, You Can’t Manage It”

First, encourage development teams to read up on the four key metrics. Given all these considerations, you need to treat this “highest environment” just like you would production. Treat testers and collaborating teams as your “users.” They get to define service failures. It’s not perfect to pretend this environment is production, but it’s better than nothing.

20 From experience, I’m willing to wager that this will become your focus too. 6 See the documentation for all you’d ever want to know about trunk-based development. See Extreme Programming for the original definition of pair programming. DoRa Metrics software DevOps We’re thinking about service restoration here, so something like an automatic failover is perfectly fine to stop the clock ticking. Second, make all your raw data and calculations available, as well as the calculated headline figures.

Deploy Frequency measures how many times code changes reached production in a period. It can be measure as deploy per hour, per day, per week, per month, and so on. Disciplines like Marketing and Sales have widely accepted trading languages that really help us understand how Redgate is performing. Despite being a software company, we’ve always struggled to develop this language for product delivery. Variation in delivery lead time took a bit more investigation, but highlighted teams who were struggling to deliver their work incrementally.

Our first field service metric for tracking is customer support call volume. Tracking call volume will give you a good idea of how many customers seek assistance from your team before, during, or after an appointment. If the number of calls is climbing, it may indicate your service isn’t meeting customers’ needs. An easy-to-use, cross-platform measurement tool that pulls data out of CD pipelines and analysis the four key metrics for you.

Experiences From Measuring The Devops Four Key Metrics: Identifying Areas For Improvement

While it is relatively simple to be accurate about measurements around our pipelines, the third and final source of raw information is far more open to interpretation. Having based my transformation work around many of their book’s recommendations, I certainly have no issue with any of its content. Once we’ve drilled as far as we can into our problem area, we start to review the outlier data points. For example; you may find that Pull Requests with a particularly long First Response Time are those which Haystack flags as having a Big Diff risk factor. This could tell you that your team are being deterred from reviewing particularly large Pull Requests.

Our second field service metric to track is your technician’s time on-site for appointments. This measures how long technicians are spending at customer sites and can help identify where efficiency can be improved to get more jobs done in a day – leading to additional revenue for your business. Likewise; these goals aren’t “leading indicators” or “local metrics” which tell you whether you need to increase, say, Unit Test coverage or cut build times – they measure the entire engineering health of a team. If it’s usual for your team to have one or zero deploys per day, don’t measure it in days. Once the team starts deploying more than seven times a week, you can switch for days. Switch to shorter timeframes whenever possible, but always respecting the accuracy of the information.

Featured In Development

There are other audiences for the four key metrics, but these are secondary. One secondary audience might be senior management or executive management. It is fine for them to see the metrics, but the metrics need to be rolled up and read-only. If executive management wants to know more detail, then they will come down to the teams to obtain it, and that is exactly what you want to happen. If you run a fan-in design (shown in Figure 1-5), this processing will likely be more involved.

To measure the Time to Restore Services, you need to know when the incident was created and when it was resolved. You also need to know when the incident was created and when a deployment resolved said incident. Similar to the last metric, this data could come from any incident management system. From the pure metrics perspective, I can say that the root cause of the longest system outages was the infrastructure provider. Here, we have already taken countermeasures by moving services to the cloud. It would be interesting to analyse the effect of these countermeasures on MTTR, which is part of future work.

Getting Started

This metric stresses the value of continuous development, which implies a high frequency of deployment. Teams should aim to deploy on-demand to get consistent feedback and deliver value faster to end users. The bar graph in Figure 1-8 shows our lead time for changes data, again with dates on the x-axis and now with the mean of lead time for the given day in the bars along the y-axis. However you capture your data that feeds into these calculations, make sure it all happens out in the open.

A high failure rate may indicate problems in the DevOps process and result in downtimes that cause a company a loss of revenue. Baselining your organization’s performance on these metrics is a great way to improve the efficiency and effectiveness of your own operations. https://globalcloudteam.com/ It then aggregates your data and compiles it into a dashboard with these key metrics, which you can use to track your progress over time. Measuring these metrics can help you to assess the performance of your software delivery process, Huber mentioned.