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How to Export Jira to Excel – All Methods Explained

How to Export Jira to Excel All Methods Explained

Jira is a leading project management tool for software development teams. It helps project managers easily keep track of the project’s progress through issue tracking.

The native dashboard and reporting features of Jira offer a complete overview of the project’s progress. But there are limitations when it comes to customizing the report’s functionality. Therefore, project managers often require exporting Jira to Excel to create custom reports.

You can export Jira to Excel in multiple ways. This tutorial covers all the different methods you can use to export from Jira to Excel explained with screenshots. You’ll also learn an efficient approach to automate the Jira to Excel export on a schedule. If you wish, you can check it right away in our video tutorial.

Why do you need to export Jira to Excel?

Exporting Jira to Excel can make reporting easier, as it is a familiar spreadsheet tool. Besides this, creating Jira reports in Excel serves many benefits, such as:

In short, Jira export to Excel makes reporting clear even for those who are not used to the Jira dashboards.

How to export from Jira to Excel? – 4 methods

You can export Jira to Excel in four ways:

Let’s explore each of the methods to export Jira to Excel.

Jira export to Excel automatically using Coupler.io app

Coupler.io is a data analytics and automation platform that enables data integration with 60+ data sources, including LinkedIn Ads, Asana, and many more. Its Jira to Excel connector lets you auto-import data into Excel at regular intervals to build self-updating data workflows. It offers data transformation features, which you can use to transform the data before importing.

In addition to this, you can connect to multiple data sources at once and blend the data before importing.

Connect Jira to Excel & export data on a schedule

To connect Jira to Excel, sign up for a free Coupler.io account. In your account, create a new importer with Jira as the source & Microsoft Excel as the destination. Click the Proceed button and configure the following settings in the importer:

Source Configuration:

Click Continue. The following source list screen will appear.

You can add any additional data source from here if you want. Click Transform Data to preview and transform the data from Jira before importing it to Excel.

Transform Data:

Now, the data extracted from the source will appear on the screen. Here, you can filter, sort, hide, or add columns per your requirements. In this example, I only wanted six columns from Jira.

Once you’re done with the data transformation, click the Proceed button at the top-right corner.

Destination Configuration:

Once you’re done configuring the destination settings, click Finish and Proceed to complete the importer setup.

Now, click Save and Run to save the Jira to Excel importer and run it.

On a successful run, the importer will get the required data from Jira to Excel.

You can see the data imported in Excel below.

Now, you can configure the above importer to auto-export Jira to Excel at regular intervals.

Open the importer, and switch on the Automatic data refresh option as shown below. Set a preferred interval, days of the week, and time. Click Update Schedule to save changes.

Now, Coupler.io will run the Jira to Excel importer as per the schedule and keep the Excel sheet updated with the latest data. Now, that’s a lot of time saved!

Export Jira issues to Excel using the Jira Cloud add-in

Add-ins in Excel are tiny software that adds new features to the spreadsheet tool. Jira offers an official Excel add-in, which you can use to import the required data.

BTW, Coupler.io is available as an Excel add-in as well. 

You can follow the steps below to export Jira data to Excel using the Jira Cloud Add-in.

Install Jira Cloud for Excel:

Once the Jira Cloud for Excel add-in has been successfully installed, you need to connect your Jira account to it.

Connect Jira Account:

Once you’ve connected your Jira account, you can use the Jira Cloud plugin to import the required data into Excel.

Import Data from Jira:

Caution: Importing data through the Jira Cloud add-in will replace all the existing data in the sheet.

You can also configure the Jira Cloud add-in to import only specific columns; follow these steps to do that:

Now, on the next refresh, the add-in will only import the selected columns from Jira. You can manually refresh the data by clicking the Refresh button. You can also set up automatic data refresh by using these steps:

Now, the data will be refreshed every time you open the Excel spreadsheet. Thus, you’ll always have the latest data to work on!

Manually export from Jira to Excel

The most basic way to export from Jira to Excel is by using the native export option in Jira. It offers you to export users, groups, and issues to .csv files, which you can open easily in Excel. It is the most straightforward method when you want to just click on an export button to get your Jira data downloaded as an Excel file.

Let’s see how to export issues from Jira to Excel.

In the Jira dashboard, go to Issues and expand the Export Issues button on the top-right of the screen. Click Export Excel CSC (all fields) to download all issues.

Now, open the downloaded .csv file in Excel.

Ta-daa! You’ve completed a manual Jira to Excel export.

Import Jira API results to Excel Power Query

Application Programming Interface (API) is a language of information exchange between software applications. You can use the Jira API to get the required data into Excel using Power Query.

Power Query is a tool that lets you connect to multiple types of data sources, including APIs. Since the 2016 version, it has been included in Excel as Get & Transform Data.

How to generate a Jira API key?

To export Jira to Excel via API, you’ll first need to generate an API Key for your Atlassian account. Like your password, an API key is used to authenticate any request.

To generate the Jira API key, go to Atlassian API Tokens and click Create API Token.

Now, enter a name for the API token and click Create. You can name it anything; it is just for your reference.

Now, the generated API token will appear in the popup. Copy and save it safely.

Once you have the API Key or Token ready, you can use it in Excel to make API requests and import the desired data from your Jira account.

How to use Power Query to export Jira to Excel?

Open an Excel Worksheet where you want to import data from Jira. Navigate to Data > Get Data > From Other Sources > From Web.

Now, a pop-up will appear on the screen. Select the Basic option and enter the Jira API URL you want to call. For example:

https://yoursite.atlassian.net/rest/api/2/issue/{issue_id}

Read Jira REST API documentation to know more about the data you can pull.

Click OK to proceed.

Now, an authentication window will appear to access web content. Choose the Basic option.

You’ll be asked to provide the following details:

Click Save.

Now, on successful authentication, the response will be displayed in the Power Query Editor.

Navigate to the data you want to import and click Into Table in the top-left corner.

Now, the selected data will be converted to a table. Click the Close & Load button to load the data into the Excel sheet.

You can see the requested data has been successfully imported to Excel from Jira via API.

You can use various REST API endpoints to get different metrics from Jira into Excel via API. This is tedious and requires solid technical knowledge. It is not a recommended method to export Jira data to Excel unless you’re technically proficient.

What is the best method to connect Jira to Excel?

Exporting Jira to Excel unlocks new opportunities for project reporting and analytics. In this post, we discussed four different ways to export Jira to Excel. Each of these methods has its pros and cons.

On balance, Coupler.io is the best method to connect Jira to Excel. It requires no technical skills and can be set up using a few easy steps. It also supports automatic data sync at regular intervals, data transformation, and other powerful data analytics features.

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