📊How to Turn Jira Work Item History Exports into Actionable Analytics

[#1]

Jira captures all the changes that your team makes - each status change, reassignments, deadline update, and priority increase. However, most teams never analyze history.

Exporting Jira work item history to Excel doesn’t bring just raw data; it provides a clear picture of process stability, workload balance, and delivery discipline. And that is where true management insight starts.

What Is the Best Way to Export Jira Work Item History

To get useful analytics, you must have structured, bulk history data, not per-work-item logs.

Using the Issue History for Jira (Work Item History) app (by SaaSJet), you can export the complete project work item history in a clean, structured CSV/Excel format for analysis.

What you can export:

• Status transitions (from → to)
• Assignee changes
• Priority updates
• Due date shifts
• Time spent changes
• Change timestamp and user
• Metadata of work items (created date, project, etc.)
• Custom fields changes

Every change is saved as a single row, and the file can be used in Pivot Tables and charts at once.

Also, you can use filters and specify:

• Project(s)
• Date range
• Specific updaters
• Selected work items

This makes sure that your dataset meets your reporting requirements.

To summarize, Issue History for Jira converts Jira change logs into analytics-ready data, designed for reporting and management.

How to Generate and Export a Work Item History Report

To build analytical reports, you first need to generate and export history data from Issue History for Jira (Work Item History) app. Let’s explore how to do it.

1️. Select scope. Choose the project (or space) and optionally filter by updater/updaters.

2️. Define the date range. For example, it can be the current month, the previous month, or the previous year, etc. 

3️. Adjust columns (optional). Customize which fields are visible, such as Status, Assignee, Priority, Due Date, or Time Spent, using Columns menu.

4️. Export the report. Click Export in the top-right corner. 

The app generates a structured CSV/Excel file where each row represents a single change, with the old and new values separated. 

The file is ready for analysis in Excel, Google Sheets, or BI tools immediately.

📊How to Build a Reopen & Rework Trend Chart from Exported Data

After exporting structured history data using Issue History for Jira (Work Item History) app, you can then analyze workflow stability by creating a Reopen & Rework Trend Chart. This report demonstrates the frequency of back-moving work in the process, which is a major indicator of the quality and clarity of requirements.

✅ STEP 1: Create a Month Column

Add a new column to the report called Month. In the first row of the new column, enter:

=TEXT(A2,"yyyy-mm")

A2 = Date of change. Copy down. Now each event is assigned to a month.

✅ STEP 2: Create a Reopen Flag Column

Use this formula:

=IF(AND(I2="DONE",J2="REOPENED"),1,

IF(AND(I2="DONE",J2="IN PROGRESS"),1,0))

✅ STEP 3: Build the Reopen Trend Pivot

• Select your full dataset (including Month and Reopen Flag columns).
• Go to Insert → Pivot Table.
• Click Create.

Then, you can configure the Pivot Table. For example:

• Rows → Month
• Values → Reopen Flag

As in our report, we have only data by February, so we received such a report: 

Or you can analyze reopened tasks by Assinee, choosing the option - Sum of Reopen Flag for each Assignee:

✅ STEP 4: Create the Chart

1. Click inside the Pivot.
2. Insert → Line Chart (recommended).
3. Rename it: Reopen & Rework Trend

If you only have one month, it will show one point. When you expand the date range, the trend line becomes meaningful.

✅ STEP 5: Calculate Reopen Rate % (Optional)

In your Pivot Table editor, choose Values → Key → COUNTUNIQUE. That’s your Total Work Items Per Month. Add to the table Reopen Rate column (=SUM of Reopen Flag/COUNTUNIQUE of Key). You will get such a result:

Now you’re not showing activity. You’re showing process health. If next month it drops, for example, to 12%, that’s a measurable improvement.

📊How to Build Due Date Shift Report

Now we go to planning reliability. Due Date Shift Report is an indicator of one important thing: how often do deadlines move? Not how many work items are done. Not how fast.

✅ STEP 1: Create Due Date Shift Flag

Add a new column: Due Date Shift Flag. If column K = Due date (old) and column L = Due date (Changed), then:

=IF(AND(L2<>"", K2<>L2),1,0)

This checks:

• New date exists
• Old date ≠ New date

Rows where the date moved should show 1.

✅ STEP 2: Count Shifts Per Month and Assignee

Create Pivot:

• Rows → Month
• Values → Sum of Due Date Shift Flag
• Columns → Assignee 

Now you see:

This shows how often planning changed and who is responsible for the task.

✅ STEP 3: Measure Planning Stability (%)

Create another Pivot:

• Rows → Month
• Values → COUNTUNIQUE of Key

Then calculate: Deadline Shift Rate % = SUM of Due Date Shift Flag / COUNTUNIQUE of Key

✅ STEP 4: Measure Delay Size (Optional but Powerful)

If you want deeper insight, you need to create a new column called Delay Days.

Formula:

=IF(L2<>"", L2 - K2, 0)

Where column K = Old Due Date and column L = New Due Date.

The column Delay Days shows the number of days added.

Then, create another Pivot:

• Rows → Issue Type, Key, Month
• Values → Delay Days

So, this table shows how many days were added to each work item’s deadline. For example, the task with a key SUP-28 had its deadline extended by 28 days in total.

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In the next articles, I will present additional analytical reports to help analyze exported work item history from Jira.

[s_gridnevskii]

I guess it can be done by rest api

<server>/rest/api/latest/issue/<issuekey>?expand=changelog

for datacenter and for cloud

<server>/rest/api/latest/issue/<issuekey>/changelog

Then we can ask gemini or any other neural network to get info from json and display in appropriate format.

[Natalia_Kovalchuk_SaaSJet_]

Hi s_gridnevskii!

Yes, it is possible to get Jira work item history using REST API; however, it will be much more complicated than, for example, using Issue History for Jira (Work Item History) app.

Using the Jira API means writing scripts, managing authentication, handling pagination, and building logic to extract changelogs. That's developer work.

When using the Issue History for Jira, you don't need coding, no API limits, no headaches. You receive a ready-to-export Excel or CSV file, a structured, readable history. It is perfect for admins, auditors, PMs, and compliance teams.

Moreover, the API provides raw data, while Issue History for Jira app gives structured, audit-ready reports.

Also, API is not a very good solution if we are talking about audit use cases related to enterprise teams, as they usually need bulk history export across multiple projects, comment tracking, field-level change history, date-range filtering, easy sharing with auditors, no dependency on developers. 

And if we talk about using Gemini or another AI tool to process exported Jira data, I think such an approach can raise serious security and compliance concerns.

[Elena_Communardo Products]

Hi @Natalia_Kovalchuk_SaaSJet_ Great breakdown! This goes beyond “exporting data” and actually shows how to measure process health. The reopen rate and due date shift metrics are especially practical for spotting workflow instability and planning discipline issues. Looking forward to the next analytics examples 👏

[Natalia_Kovalchuk_SaaSJet_]

Hi Elena_Communardo Products! Thank you for your feedback!