Being a data-driven organization is advantageous for your business at all levels, according to Pluralsight. The productivity and efficiency of workflows and processes will be improved by using data to inform decision-making. But more importantly, it enhances the entire developer experience and helps team leads advocate for their staff.

Our engineering insights platform, Pluralsight Flow, allows our own engineers to significantly cut down on wait times while simultaneously enhancing collaboration. Our staff are aware of the integral relationship between each indicator in Flow, yet using every feature of an insights platform right away might be overwhelming. In light of this, we questioned our technical leadership about the KPIs that they found most valuable prior to the word “Go.” By concentrating on these data points, your software development team may become a data-driven model of efficiency. They are also simple to comprehend and use as you get used to some of the finer details of Flow and the general health of the team.

Coding statistics

Days of Coding

Coding Days, according to Flow, are any days where an engineer adds code to the project. Engineering work can take many different forms, including developing code, assessing other people’s work, and debating specifications and architecture.

Coding Days provides a broad overview of “do employees have time to develop solutions in the code base, and are they adhering to the rules?”What are the best practices for modest, frequent commits? This is related to the effectiveness of approaches like continuous integration and delivery, where technologists prioritize short, frequent commits.

It’s crucial to remember that there are intricacies to what influences coding days, so if a team has a low week of coding days, it’s not necessarily a bad thing. The idea is to use Flow to notify you of that condition and then have a discussion with the team to work out the specifics.

Measures of collaboration

Unapproved PRs

In a perfect world, PRs would never be merged without first being reviewed. Unreviewed PRs can be a major source of issues, even when they are modest or created by senior developers. Because of this, a manager needs to be informed each time a PR is merged without review.

Simply expressed, this data point serves as a danger indicator. Large quantities of unreviewed PRs shouldn’t be merged because they’re more likely to result in a greater change failure rate or subpar customer-facing code.

Response Time

This data point examines how quickly a reviewer responds to a comment that is specifically directed to them. Are team members clearing each other’s paths promptly so that code can be put into use?

Cycle time for tickets

Cycle time is the amount of time it takes for a ticket to move from its initial Active position to its ultimate Done status. Cycle times will not be calculated for any tickets that are categorized as Active or Not begun at the moment. When tickets are redirected from a done status, the cycle time is updated.

The length of time it took to complete a ticket should be calculated using this statistic. Only the time engineers spent on the ticket is included in the cycle time. Lead time before the ticket entered an Active status when product managers, designers, or stakeholders gave requirements is not included.

Waiting time

The amount of time a ticket spends in the queue is called the queue time. The engineer constructing the ticket might need to wait for team members’ comments, quality assurance, or reviews. These phases of the ticket life cycle are captured by queue time, which helps engineers work more effectively and without obstacles.

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