Jira Histogram Chart: Visualizing Time Distribution based on Time Metric

This guide will help you navigate the Histogram Chart, understand its components, customize intervals, and interpret the data effectively. Use these insights to make informed decisions and improve project outcomes.

1. Accessing the Histogram Chart 

1.1 Locate the Chart Icon:

• Find and click on the chart icon in the switcher panel to open the Histogram Chart.

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2. Understanding the Histogram Chart 

2.1 2.1 Automatic Histogram Formation (Single Time Metric)

• If your project has configured only one time metric, the histogram will automatically display issue distribution over time.

2.2 Selecting a Time Metric (Multiple Time Metrics)

• For projects with multiple time metrics:

  1. Open the dropdown menu.

  2. Select the desired time metric.

  3. The histogram will refresh to show data for the selected metric.

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2.3 Key Components of the Histogram Chart

• Issue Distribution: Shows the number of issues grouped by time intervals.

• Average Line: Indicates the average time for issues based on the selected metric.

• Default Bins: The histogram defaults to 7 bins, each representing a distinct time interval.

3. Customizing the Histogram Chart 

3.1 Adjusting Intervals 

You can modify intervals to better suit your analysis needs:

• Bin Width: Define the number of bins (up to 50).

This determines the number of bins displayed in the histogram. A "bin" is essentially a grouping range for your data. You can set up to 50 bins, each representing a specific time interval. For example, if your data spans 50 days and you set 10 bins, each bin will represent 5 days.

• Underflow Value: Sets the endpoint of the leftmost bin.

This is the smallest value included in the leftmost bin. It defines where the data grouping starts. Example: If your underflow value is set to 0, the first bin will group all issues with a time metric of 0 or slightly above.

• Overflow Value: Sets the starting point of the rightmost bin.

This is the largest value included in the rightmost bin. It defines where the data grouping ends. Example: If the overflow value is set to 50, the last bin will include all issues with a time metric of 50 or less.

How to Adjust These Settings:

• Use the dropdown menu to input your desired values for Bin Width, Underflow Value, and Overflow Value.

• After making changes, press Enter or click outside the dropdown to apply them.

3.2 Default Interval Calculation

By default, intervals are calculated as:

• (Overflow Value − Underflow Value) ÷ 5 This ensures a balanced representation of your data.

4. Interpreting the Histogram Chart

To make sense of the histogram:

• Bins: Each bin shows the number of issues within a specific time interval. A higher count in a bin indicates that many issues fall within that time range, which might signify a bottleneck or a common trend in issue resolution.

• X-Axis: Displays time intervals. Check these intervals to understand how long issues remain unresolved or in progress. For example, if most issues are clustered in shorter intervals, it may indicate efficient processes.

• Y-Axis: Represents the number of issues at each interval. Look for peaks or valleys:

  • Peaks might highlight frequently occurring time frames, potentially signaling consistent processing times or recurring challenges.

  • Valleys might indicate exceptions, where issues take significantly longer or shorter periods to resolve.

Tips for Analysis:

1. Identify Trends:

   • Examine where most issues fall. This could reveal common processing times or areas needing improvement.

2. Spot Outliers:

   • Look for bins with unusually high or low counts. These could represent anomalies or areas requiring further investigation.

3. Compare Metrics:

   • If using multiple time metrics, compare histograms to identify differences in processing times across various workflows or teams.

4. Review the Average Line:

   • Use the average line as a benchmark to determine whether most issues align with typical processing times or deviate significantly.