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A Guide to understanding Agile Control Charts in Jira

As part of of being a product development company, switching to agile was one of the main strategic decisions that any software house building products is ought to take at some point.

However, one of our main concerns about switching to agile was the fact that agile development methodologies are by nature dependant on the trust in the team. It follows, that the factor of control – an essential element in any project management methodology – had to be affected.

After long debates between our management team, coupled with various educational conversations about the merits of Agile. We finally took the decision. In the article, we will present the Control element available in the agile methodology, using the very famous tool : Jira.

 

About the Agile Control Chart

The control chart is mainly concerned with the cycle time for your product. It essentially answers the question of : How fast does the issues you create move between statuses and team members ? This metric is very valuable as it provides the bases to understand the following :

1- It helps you measure the effects of process changes or on boarding new members to your agile team.
2- It helps to analyse the your teams past performance and predict the future performance .

3- Provides visibility to your team efforts to external stakeholders or product owners.

4- Helps you set goals for future performance specially if you are using a Kanban workflow.

Understanding the Agile Control Chart

In order to be able to use the control chart, you need to understand how it is calculated. the following questions and answers will help you get started :

 

What is the Cycle Time ?

The cycle time is the time taken from when work starts on an issue to when the work is completed. Off course, it depends on the workflow given; for example, if the issue is re-opened then the cycle time increases until it is completely resolved. Cycle time is calculated by the day usually.

 

What is the Rolling Average ?

The rolling average ( represented by the blue line on the chart ) is issue based not time based. For all issues presented on the chart, the rolling average ( at each point of time ) is calculated by taking an issue, X issues before the benchmark issue and X issues after the benchmark issue, then averaging the cycle time. Jira uses %20 of total issues displayed to calculate the rolling average. As Jira Explains it

 This method produces a steady rolling average line that shows outliers better (i.e. rolling average doesn’t deviate as sharply towards outliers). The rolling average line is also easy to understand, as the inflections are related to the positions of issues.

 

What does the Blue Shaded Area mean ?

The blue shaded area represents the the standard deviation, which is simply how much do you deviate from your rolling average. The standard deviation is very crucial in forecasting, as with a low and consistent standard deviation, you can be sure that for the following sprints ( Scrum based ) or Group of issues ( Kanban Based ) your team will not deviate a lot from their rolling average. Which gives you confidence in while talking with the product owner as your team is consistent.

 

What does a Cluster of Issues Mean ?

Worcbox is a leading software development firm in Egypt. It specialises in product development and platform building along with fleet management systems and various other software and web apps in Egypt.

Cluster of Issues As shown on Jira control Chart

A cluster of issues are issues that were created at a certain time and are either ignored, blocked ( depends on other companies, clients ….etc ), decided not to be fixed, no action is required on these issue or they require feedback and analysis. These issues are a big impediment for the team. The reasons are the following :

  1. It can significantly affect the cycle time, as it will be sitting there skewing your data and confusing your numbers.
  2. It will reduce the team velocity if it was estimated, if not, then it will damage you standard deviation.

We usually call these clusters “Outliers” , we will write another article on how to remove the noise of the outliers in the near future.

 

Usage and Indicators of the previous Agile metrics

Cycle Time : The smaller the cycle time, the more agile your team is, the more predictable your production is. Any Scrum master should remove impediments aimed as reducing cycle times.

Rolling Average : The smaller the rolling average, the more heat there is in the team. That means an issue is created, then rapidly goes to development, followed by QC and then build and deploy. Imagine if all this happened in 2 or 3 days ? that is what the rolling average is trying to quantify.

Standard Deviation : A measure of the predictability of you team. If your team is accomplishing the same cycle times with a consistent rolling average, then you are confident that by time X the product is going to be done ( in Scrum ) while it gives you confidence about your team production ( in Kanban ).[/vc_column_text][/vc_column][/vc_row]


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