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.