Whether you’re creating Jira dashboards or Confluence pages to report on your Jira data, it’s important that those views tell a story. You want your audience to come away from your meetings with a solid idea of what’s going on and what needs to happen next. However, sometimes the charts and reports you’re sticking on your dashboards aren’t telling quite the right story.
For example, most of the reports you see in Jira count issues: how many issues are in a specific status, how many are assigned to a specific user, etc. And almost all of the native Jira dashboard gadgets use issue counts.
But does issue count tell you everything you need to know to make decisions with your Jira data? In a lot of cases, no. There are several other very valuable metrics you should be reporting on if you want to tell your teams a good, solid story about what’s going on.
This is where our Atlassian reporting add-ons, Custom Charts for Jira and Custom Jira Charts for Confluence, come in mighty handy. With the calculation feature you can display the sum or average of any number field in your instance, and even compare date fields.
Let’s walk through some of the ways this feature can help an agile team create relevant dashboards that tell actionable data stories.
Using the Calculate dropdowns
Configuring your charts to calculate things other than issues is as simple as selecting from the Calculate dropdown. Just open your Custom Charts gadget or macro, select your source and chart format, and then open up the Calculate dropdown. Use the second dropdown to sort through available fields and date comparison options, and then choose whether you want the sum or average of that data.
If you select the date comparisons like Time Since or Time Until, more dropdowns will appear to allow you to pick which date fields you’d like to look at.
For agile teams, measuring by issues is not half as valuable as measuring by whichever estimation statistic your team uses. For many teams, this is story points, but any numeric value can be reported on using Custom Charts. Look at the example chart above to see how we can get a useful overview of how many story points each team member completed during each sprint. We can see the total story points per sprint along the bottom.
This is going to be more valuable than simply counting issues per sprint because, as we know, not all issues are sized exactly the same. Each person completing three stories in a single sprint tells much less of a story than the number of points they represented. It’s important to take this into consideration when you’re building your sprint reporting – staying away from simply counting issues allows teams to adjust how they think about their work.
For teams using Jira’s native time tracking functionality, being able to report on time spent on issues is going to be very important. Custom Charts is not meant to replace apps like Tempo Timesheets, which are complete time-tracking solutions, but if you’re looking to generate reports on time spent at the issue level, charts like the funnel above can be very useful.
This chart shows the average time logged against Jira issues based on issue type. From this we can see that stories, on average, take a lot more time to complete than bugs or tasks do. This information can be particularly helpful when planning future work.
If you are a Kanban team working on sizing your stories consistently, this can be a great tool to see if your team is on track or if there needs to be an improvement in how work is broken down. If there is a large gap in the time it takes to complete your stories, then your team will likely struggle with projecting completion dates and keeping the work flowing through your board. The chart above could be because you had a few massive stories that are skewing the data, or maybe your bugs were all tiny! Regardless, this chart is a great point to start that conversation.
There are four date comparison options available in Custom Charts. Any date or date-time field in your Jira instance can be used to calculate the following:
- Time until a specific date
- Time since a specific date
- Time between two dates
- Time to resolution
In the example above, we calculated the average time since the issues were created, broken up by issue type. As an agile team, this can provide some information on the average age of tickets, and how long they are staying in your backlog. We’ve filtered down to only open issues, so we can see that generally bugs are kept open for less time than stories. This is useful information to see how the team is doing with bug fixes or more urgent tickets. Additionally, we can see whether our backlog is getting stale. It is important that teams are working through their backlog, otherwise you’ll likely see tickets that are no longer relevant or not important enough to be worked on.
All of the above calculations can also be shown cumulatively in Custom Charts if needed. And although our users most commonly use issue count to generate cumulative totals in charts like Created versus Resolved Issues, you can use other number fields as well. For example, one of our clients uses cumulative totals to track how much budget they are spending over a period of time.
Cumulative totals can also be used to measure progress, like in the chart above, which shows story points as they’re being completed per epic. This view allows you to compare burnups of completed issues in each epic, which can tell you how they are progressing. Just like the first chart in this article, this one is looking at story points. The need to understand how larger bodies of work are progressing in Jira is extremely common, and using your estimation statistic at this level encourages folks at the product level to think about work the same way that the developers are.
Combine all of these gadgets in Custom Charts for Jira and Custom Jira Charts for Confluence and you’ve got a compelling and highly visual dashboard. One that’s very useful for product teams or Scrum Masters because it helps tell a better, clearer story about how work is being broken down and completed. Throw in some of the standard board reports as well and you’ll have all the ingredients for a great data story. A strong narrative, powerful visuals, and most importantly, the right data.
Christopher is a self-confessed nerd who’d probably take the cake on Mastermind if Star Trek: Voyager was his specialist subject. He writes fiction about time travel, conspiracies and aliens; loves roller coasters, hiking and Christmas; and hates carpet, rom-coms and anything with chilli in it. He’s written extensively for technology companies and Atlassian partners and specializes in translating complicated technical concepts, specs and jargon into readable, benefits-driven copy that casual readers will understand.