Drawing upon her many years of experience working within the aerospace industry in the United States as an Agile coach, Cindy demonstrates the ease, flexibility, and power of Agile Hive for Jira Data Center using her FlyByHive Agile Release Train (ART) example. In this episode, Cindy will show you how to create and manage an ART Backlog in your Agile Hive project.
Without Further Adieu – Agile Hive for Jira Data Center
Our FlyByHive ART consists of a “team of teams”, with seven teams contributing to the development, refinement, and execution of a complex aerospace industry product solution. At a glance, we can visualize the hierarchy of our team’s organization and then by diving in, the backlog of items, features, and enablers that support the current planning interval (PI) that is about to get underway.
As Agile Hive is a fully integrated app, we still have access to all the base Jira functionality. We see for instance how easy it is to set up issue filters to see only the features and enablers for the current PI.
Clicking into any of the Jira tickets, Agile Hive Data Center extends the base functionality by giving us additional details such as which of our seven teams contribute to that feature. Also displayed are the respective children (enablers, compliance activities, or other stories) from each story and which teams are responsible.
Take It To A Higher Level
Just as easily, Agile Hive lets you navigate to and see the bigger picture with the ART PI Roadmap. At a glance, you can see the sequence and timing of the various features and enablers, as well as any other PIs that contribute to a release. Dependencies are also visible at the ART level.
In the same manner one would use to create issues of any issue type within Jira, Agile Hive Data Center once again extends the functionality by allowing you to select the issue types appropriate for the respective levels of SAFe.
Utilizing the Team Breakout Board feature of Agile Hive Data Center, one can see not only the capacity compared to the load, it’s also possible to see the story points, relationships, and cross-team dependencies. Additionally available for review are unplanned issues, and then the drag-and-drop functionality to place them into an iteration.
In the example above, we can quickly see any dependencies, in this case, highlighting some planning issues that must be addressed. With very complex systems, these views could get overwhelming, which is why Agile Hive Data Center allows you to use the Feature filter to focus on the level of detail you need at that moment.
Reporting At the Team and ART Levels
When you need that instant, all-in-one dashboard of information to understand where a team is at in their work (capacity, load, progress of work, PI objectives, risks, etc.), turn to the Team Reports. The breadth and depth of the information on this one, single screen will greatly assist the team in managing the delivery of value and improving the workflow.
Rolling all that team-level data up to the ART level, it is easy to see which teams are contributing to which issues and how that work is progressing. To this level of detail for each respective team, Agile Hive Data Center now offers Shared Services functionality. As a result, for those teams that are not fully dedicated to an ART (e.g. accounting, general compliance, etc.) we can still include their contributions.
Managing Complexity with Agile Hive
We hope you enjoyed this first blog article tied to our “Study Sessions with Cindy” YouTube series of videos. Stay tuned for more to come. If you’re ready to learn more about Agile Hive Data Center, the “SAFe® in Jira” solution, reach out to us today to schedule a demo or discussion with one of our team members.
You can also find us in the Atlassian Marketplace. To learn more about the Scaled Agile Framework (SAFe®), head over here. You can find the YouTube recording here.
We look forward to working with you!
