DevOps Metrics and KPIs
DevOps seems promising, and lots of organizations have already integrated it or are
planning to commit to it. But Gartner has predicted that through 2022, 75% of DevOps
initiatives will fail to meet the expectations.
A bold prediction. Isn’t it?
Well, it is, as it puts a huge investment on the stake ( almost 2.9 billion
USD).
So, should you use it or not?
Definitely. You should use DevOps because it has a lot more to offer than one might
think, but instead of just focusing on the benefits alone, your focus should be on
making it workable for your organization.
And one of the best ways to do it is by measuring DevOps performance through KPIs and
metrics.
If you don’t know about DevOps KPIs and metrics, then keep reading this
post.
What Are DevOps KPIs and Metrics?
The DevOps methodology involves lots of phases that are interdependent. If one
collapses other follows. So it is better to track the performance of each stage through
pre-defined KPIs and metrics.
KPIs and metrics are nothing but parameters that monitor the progress of the DevOps. If
something is happening too often, and a particular metric suggests it is a bad sign for
proceeding further, you should introspect and move ahead. If you overlook it, chances
are you’re heading for a partial or complete failure.
Another significant thing is that there are lots of DevOps KPIs and metrics that you
should consider for a comprehensive DevOps strategy.
Essential DevOps KPIs and Metrics:
- Deployment Frequency: Deployment frequency indicates how often
new features are launched. This frequency can be measured on a daily or weekly
basis. Many organizations prefer to track deployments daily to improve efficiency.
In an ideal situation, the frequency of deployment should be stable or it can
increase gradually. A sudden decrease in the deployment frequency indicates faults
within the existing workflows. More deployments are considered better but to some
extent. Higher frequency results in increased deployment time or higher failure
rate. In that case, it is good to restrict deployment increases until the existing
issues are resolved.
- Change Volume: DevOps is known for making changes often, but
these changes should be impactful, not incremental. In simple words, it does not
matter whether you change or alter the functionality of a feature in a week or
month, instead, it should create an impact, not a disturbance. Sometimes making
changes too often points out the inefficiency of the development process that can be
tracked by the change volume metric.
- Deployment Time: It measures the application’s deployment
time after it gets approval. If the deployment time increases, there should be a
further investigation to check whether deployment volume is reduced or not. A
shorter deployment is always preferred but it should not come at the cost of
accuracy. If the number of errors is increased, it means that deployments occur too
promptly.
- Failed Deployment Rate: It monitors how many times the deployment
led to outages or other issues. The failed deployment rate should be as low as
possible as increasing failed deployment rate suggests the dysfunction in the
workflow.
- Change Failure Rate: The change failure rate refers to a release
that leads to unexpected outages or other unplanned failures. A low change failure
rate indicates that deployments are occurring regularly and quickly. Contrary, a
high change failure rate suggests application instability that leads to a bad user
experience.
- Time to Detection: It’s important to know that a low change
failure rate is not the indicator of an ideal application. The primary focus of the
developer should be to search out for solutions to minimize or even eradicate
failures. To do so they need to catch issues quickly as they arise. That’s why
the time to detection KPI comes in handy as it determines whether current response
efforts are adequate or not. If there is more involvement in time for detection
it’s a clear sign that there are possible bottlenecks that could ruin the
entire workflow.
- Mean Time to Recovery: Once you detect the failed deployments or
changes, you should also track the time taken to address the problems so that the
entire application can get back on track. The mean time to recovery is an important
metric that monitors your ability to respond appropriately to identified issues.
Prompt detection means nothing if you are not able to correct the issue on time.
That’s why MTTR is given preference in the DevOps community because it’s
a key performance indicator metric.
- Lead Time: Lead time is used to measure how long it takes for a
change to occur. This metric can be used at various phases, from the beginning,
which is the idea initiation, to the deployment, and production phase. Lead time
offers significant insight into the efficiency of the entire development process. It
measures the current ability to meet the customer demand. If there is a long lead
time it indicates that there are some serious bottlenecks while a shorter time
indicates that feedback is addressed quickly.
- Defect Escape Rate: Each software deployment includes the risk of
sparking new defects. These issues can be discovered with user acceptance testing
but sometimes these errors are found by the end-user. Since errors are the natural
part of the development process, the development team should plan to deal with these
issues before the development process. Defect escape rate comes into play to deal
with such scenarios. It monitors the reality by accepting that issues will arise and
they should be discovered as early as possible. The defect escape rate checks how
often defects are discovered in the preproduction phase versus during the production
phase. This metric alone provides essential insights into the quality of software
releases.
- Defect Volume: This matrix is related to the previous metric but
to some extent. Instead of focusing on the defects, it monitors the volume of
defects. Some defects are expected but a sudden increase in the defects is not a
good sign. If there is a sudden increase or a high volume of defects is monitored,
it is the indicator that the development process or test data management may have
some crucial issues to fix.
- Availability: This metric measures the amount of downtime for a
particular application. It can monitor the availability of a particular application
as complete or partial. Less time downtime is always better. But some instances,
planned or unplanned, may require you to spend time to correct the issues so the
application can be available to the users once again. The availability metric tracks
the downtime for both scenarios as the hundred percent availability for a particular
application is not realistic.
- Service Level Agreement Compliance: Most companies prefer to
operate according to service level agreements. These agreements are held between the
client and the service providers to increase transparency. The service level
compliance KPIs provide necessary accountability so that it can ensure that the
client’s expectations are met.
- Unplanned Work: Due to some issues or problems you can encounter
unplanned work. The unplanned work rate metric (UWR) measures how much time you
dedicate to unplanned work. Generally, UWR will not exceed 25%. In case if there is
a UWR it reveals that there has been time wasted on unexpected errors that were not
detected in the workflow. Sometimes UWR and the rework rate RWR, both metrics are
done together to know that how much time it took to address new tickets.
- Customer Ticket Volume: Like the defect escape rate, API suggests
that not all the defects are bad but they should be caught early. However, if the
end-user is reporting any error and the customer ticket volume of such errors is
high, it indicates that there are issues in production or testing.
- Cycle Time: This metric tracks the functionality of an
application on a border level. From the early stages to the user feedback, it tracks
all the processes. Generally, a shorter cycle time is preferred but defects should
also be discovered once they arise.
Conclusion
DevOps is creating a buzz but switching over technology is never easy. No doubt that it
is helpful and better for scaling. However, it can also collapse like the other
methodologies, still, you need not panic.
If you incorporate these DevOps KPIs and metrics into the practice, you avert the risk
of failing.
