My ART has successfully used Jira Align for one remote PI Planning so far. As the RTE, I am continually looking for ways to improve PI Planning, and I have four questions about Jira Align related to this:
1. Is there a way to measure the % of points spent on Business Features, Enablers, and Tech Debt/Maintenance at the ART level and the team level? We want to start using a capacity allocation policy to guide how we allocate our effort across the ART and need a way to measure it.
2. Is there a way for teams to present their capacity and load for each iteration during PI Planning?
3. For a new team that doesn't have a velocity yet, or a team whose velocity is expected to change due to a change in team members, is there a way to enter a team level velocity override to use in PI Planning?
4. Does the Innovation and Planning Iteration contribute to a team's average velocity? Is there a way to exclude it?
Hi Carolyn!
That's awesome to hear you were able to use Jira Align for your PI Planning! Glad to hear it was successful!
For question #1, there are a couple reports I'd recommend you take a look at to get an idea of where you're investing your time. If you set the Feature Category you can leverage the Investment Trend Report (great to see how many stories are being invested into each category). If you use the Strategic Driver field on Epics you can also get a similar view on the Strategic Driver Allocation Report (you can set allocations at the PI level too in order to see how you compare to planned investment). At a points level you can use the Points Delivered by Type report to see where points are being spent each sprint.
For questions #2 and #3, I'd recommend checking out the following article:
When viewing the story backlog you can visualize it using "Sprint View" to show load vs velocity, as well as override velocity for a given sprint within a PI.
For question #4, the IP sprint does contribute to the teams average velocity (think of the IP type as meta data that lets the team know they shouldn't plan much for the sprint).
I hope this help!
Mark
Hi Mark,
This is helpful!
That story backlog Kanban view looks like exactly what we need.
Is it possible to configure the drop-down options in either the Feature Category or Strategic Driver fields? Or, is there any way to do reporting on a custom field?
Carolyn
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You betcha! I actually prefer those fields over the Type field because you can configure them. To configure you'll go to...
Administration > Settings > Platform > Dropdowns Tab
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Hi @Carolyn Suorsa! I wanted to correct something in my original response to you. If you are using the sprint type of IP, it actually is not included in velocity calculations (we'll skip over it in our calculations). This was an oversight on my part (*face palm*). If you simply rename a sprint to IP sprint (not changing the type) then it will be included, so make sure to update the type field if you want to exclude it. Sorry about that.
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Carolyn:
1) Let me research this one a bit more. There are some views and filters you can set in some different areas that should get you what you are looking for.
2) One of the Jira Align Solutions Architects Rob Phillips wrote a great article about this here: https://community.atlassian.com/t5/Jira-Align-articles/Using-Jira-Align-s-Backlog-Kanban-Sprint-View-to-Visualize-SAFe/ba-p/1294415
3) In the above article, it mentions also how you can override sprint velocity in the backlog page. You can also override velocity
4) I don't believe the IP sprint counts in the average velocity allocation. However, from my experience there are so many variables with planned velocity for iteration, I've seen teams have to override most, if not all iteration planned velocity. I'm looking for definitive info on if it counts or nots in the metrics. Either way, my recommendation is for the team to confirm if the average velocity set by the system is correct or not. In most cases it's not and you override it.
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Ha - looks like Mark and I answered at the same time. I think between the two of us you are all set!
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Very useful tips from Mark and Tom. Thank you for sharing.
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