🎯 Scatter Plot Gadget | Detecting Outliers in Jira

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If you're managing agile teams or tracking performance in Jira, you’ve probably dealt with outliers — those tasks that take much longer (or shorter) than the rest. They can distort your average metrics, hide bottlenecks, or signal process inefficiencies.

In this article, we will explore what a Scatter Plot is, examine and analyze examples of Scatter Plot charts by Time Metrics Tracker, and provide insights on how you can leverage it to improve your team's performance in Jira.                  

🔵 What is Scatterplot?

A Scatterplot is a type of chart that displays individual data points (dots) to show the relationship between two variables.
In our case:

• X-axis = Time (e.g., date of issue transition)
  
  
• Y-axis = Time metric (e.g., Cycle Time, Lead Time)

Each dot represents a Jira issue — and its position tells a story.

⚠️ What is Outlier?

An outlier is a data point that differs significantly from other values in a dataset. In Jira time tracking, this means a task that took much longer or shorter than most others.

                   

Here’s a simple visual explanation:

Task	Cycle Time (Days)	Outlier?
Issue A	4	❌ No
Issue B	5	❌ No
Issue C	3	❌ No
Issue D	18	✅ Yes – unusually long
Issue E	0.5	✅ Yes – unusually short

Even though the average might look “normal,” these extremes are hiding a bigger story.

📊 How the Scatter Plot Gadget Helps 

The Scatter Plot Gadget maps each Jira issue as a single dot, based on:

• 📆 X-axis: Transition date (when it moved through statuses)
  
  
• ⏱ Y-axis: Time metric (Cycle Time, Lead Time, etc.)

That means:

• Clusters of dots = Normal behavior
  
  
• Dots that fly high or drop low = Outliers you need to investigate
  
  

🛠 Use Case: Identifying and Analyzing Outliers Using the Agile Scatter Plot

An Agile Project Manager is using Jira and Time Metrics Tracker to monitor and optimize their team's workflow. They specifically track Cycle Time—how long tasks take to move through their workflow stages.

Problem

Upon reviewing their Agile Scatter Plot, the Project Manager immediately notices several significant outliers—dots that sit far above the typical task duration. These unusually high values indicate tasks with Cycle Times significantly longer than the team's average.

Identification of Outliers

The Scatter Plot visually highlights:

• A major outlier marked with Cycle Time of approximately 1047 days (Issue QAT-107).

• Another notable group of outliers clustered around the 250-300 days mark, significantly longer than the typical range, which is under 100 days.

Investigation Steps

The Agile Project Manager clicks on issue QAT-107, observing detailed insights:

• Issue Type: Task

• Cycle Time: 1046.95 days

• Transition Date: Sep 12, 2024

Upon investigation, potential issues could include:

• Extended periods of task blockage or waiting

• Repeated status transitions or reassignments

• Incorrect initial estimation or misclassification of task complexity

Actions and Improvements

Armed with these insights, the Project Manager takes corrective actions:

• Performs a root-cause analysis to understand why specific tasks took excessively long

• Adjusts planning and estimation practices to better reflect real complexities

• Implements periodic status reviews to prevent tasks from becoming severely delayed in the future

Results

By regularly using the Agile Scatter Plot Gadget to identify and address outliers proactively, the Agile Project Manager achieves:

• Improved accuracy in task estimations

• Reduced overall Cycle Time variability

• Enhanced team efficiency and predictability

🔮 How to Predict Future Performance Using Scatter Plots

By analyzing the historical data shown on the Agile Scatter Plot, the Project Manager can predict future performance through these steps:

1. Trend Analysis: Notice if there's a consistent increase or decrease in Cycle Time over months or quarters. For instance, seeing recurring clusters of high-duration tasks can suggest that future tasks may face similar delays.

2. Identify Seasonality: If certain periods, such as specific months or project milestones, consistently result in higher Cycle Times, the Project Manager can anticipate these busy periods and plan accordingly.

3. Forecasting: Use historical issue performance data, especially outliers, to better estimate future tasks. For example, tasks similar to QAT-107 could be flagged early for additional monitoring and resources.

4. Proactive Planning: Adjust sprint planning and resource allocation based on identified trends and historical outliers to prevent future delays.

🤔 Questions & Answers

Q: What should I do when I find an outlier?

A: First, open the issue and review its history. Look for comments, reassignments, status loops, or blockers. You may also want to review its scope — was it overestimated?

Q: Are short-time outliers also important?

A: Absolutely! They might indicate:

• Automation success
  
  
• Process optimization
  
  
• Underreported work (watch out!)
  
  

Short durations could be examples of best practices you can replicate.

Q: How can I highlight only the outliers in the gadget?

A: While the gadget doesn’t have a native “highlight” function, you can:

• Sort by time values
  
  
• Use conditional formatting to flag high/low performers

🚀 Wrapping Up

Agile Scatter Plot Gadget helps you quickly spot unusual tasks in your workflow. Regularly using this visual tool helps you plan better, avoid delays, and makes project management simpler and more effective.

Have you tried using Scatter Plot to detect outliers in your Jira projects?
👇 Share your experience in the comments below!