When we release a new feature, it’s nice to hear from the person who built it to learn what the reasoning was behind the feature and why it was important to include in our product. With the release of our new User Metrics Report, we decided to chat with the report creator and Data Zenmaster, Andrew Fager, to see what he had to say…

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Why did you build this feature?

It has always been part of the plan to build this feature. Initially, when we were creating reports for our customers through Tableau, user metrics were one of their favorites. It is something our users really liked, so we decided to make this report available directly from the VictorOps platform.

How will this report make being on-call suck less?

This feature is really useful for showing pain spots for individual users and teams in your organization. You can see if a user is getting paged too much, and in general a high page rate tends to be associated with a loss of productivity. Maybe the high volume of pages means that those systems are too noisy and some noise reduction needs to take place, or maybe those systems are too fragile and need some bug cleanup. The less pain points in your system, the happier and more productive members of your team will be!

What is one way you think this will help teams improve?

This report is a great way to shed light on your operation. If you have to spend hours and hours on incidents that are coming in and have very high MTTR it could be a sign that you need make some changes…maybe leverage alert annotations to make a common problem take a lot less time.

How did you build this feature?

There were a fair number of iterations. Majority of the time spent building revolved around working with our customers to obtain data and turn it into usable chunks that were actionable. We did a mock up of this feature for a couple of our customers, which was a great way to obtain feedback.

What was the biggest challenge you encountered?

The assumptions associated with this report were tricky. We made a lot of assumptions that were not always correct. We would do an iteration of this report and realize that a set of data we thought would be useful actually made the report harder to understand. We needed to adjust calculations associated with the report based on our assumptions.

If this feature were a gif, what would it be?

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So, check out the new reporting feature and let us know what you think!