In this blog post, you will learn how you can connect JIRA to Tableau to extract data from JIRA and create appealing visualization to uncover insights.

Data Visualization helps businesses make data-driven decisions in the following key areas

  • Prediction of customer trends and behavior.
  • Forecasting of sales volume and factors that influence sales.
  • Increase in business productivity.
  • Analyze, interpret, and deliver data in more meaningful ways.

Pre-requisites

  1. Working knowledge and access to JIRA 6.4 +.
  2. Working knowledge and installation of Tableau desktop 9.4+.
  3. Basic knowledge of JQL(JIRA Query Language) and JIRA terminology.

What is JIRA

JIRA is a proprietary tool provided by Atlassian and is the most powerful work management tool used for bug tracking, issue tracking, and project management.

Nowadays, JIRA has evolved and is used for all kinds of use cases, i.e., from requirements and test case management to agile software development.

What is Tableau

Tableau is a robust and broadly used Business Intelligence (BI) and Data Visualization tool.

It is the fastest-growing tool in the BigData industry, and it is easy to deploy, learn, and use.

Tableau creates exceptional masterpieces with visual elements like colors, tables, and charts that help businesses execute market-driven decisions. 

How to Load Data from JIRA to Tableau

  • Step 1 – Log into your JIRA account and make sure you have the admin access.
  • Step 2 – Click the admin dropdown and choose Add-ons.
  • Step 3 – Search for AIO Tableau Connector for JIRA and click on Free trial to download and install your app.
  • Step 4 -AIO icon will appear on the JIRA Web UI upon the successful addition of Add-on (marked in red in the below image). 
  • Step 5 – Click on the icon and select My Connector URL to generate the custom URL to connect to Tableau.
  • Step 6– Copy the above-generated link and go to Tableau Desktop.
  • Step 7 – On the Tableau home screen, click on the Web Connector and paste the link copied above.
  • Step 8 – It will take some time to load the extracted from JIRA. Upon a successful load, the below screen will appear. 
  • Step 9 – Add the JQL (JIRA Query Language) to select the project or add any filers. Provide the access code and click on Continue.
  • Step 10 – Select all the required fields to import on Tableau.
  • Step 11 – It will take some time to fetch all the data from JIRA, and once successfully done, data will look as it does below.
Jira to Tableau: Add JQL Query
Jira to Tableau: Jira Data
  • Step 12 – Go to the Worksheet and add the Rows and Columns to prepare visualizations.
    1. The left panel will show the available metrics and dimensions.
    2. The middle one will show the data selected for visualization.
    3. The right group provides the option to choose different types of charts and graphs.

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Why Integrate Jira and Tableau?

  • Open issues, project pace, hours worked on a project, and other Key Performance Indicators (KPIs) may all be tracked with Jira.
  • These indicators are beneficial because they assist organizations in making informed resource planning decisions.
  • Executives can look back at previous data to see where improvements might be made.
  • Jira provides a wealth of information on the technologies that enable your operations.

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Common Use Cases of Jira Tableau Integration

For both large and small organizations, data analysis has become a significant approach. Jira is a valuable data source for businesses and IT professionals.

  1. Data Visualization
  2. Bringing Multiple Data Sources Together
  3. Identifying Performance Patterns and Increasing Productivity

Conclusion

Data analysis and visualization are essential in business to understand problems faced by an organization, and to explore data in meaningful ways.

In this blog post, you got introduced to the Connector, which can help you load data from JIRA into Tableau to create appealing visualizations.

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Vishal Agrawal
Technical Content Writer, Hevo Data

Vishal Agarwal is a Data Engineer with 10+ years of experience in the data field. He has designed scalable and efficient data solutions, and his expertise lies in AWS, Azure, Spark, GCP, SQL, Python, and other related technologies. By combining his passion for writing and the knowledge he has acquired over the years, he wishes to help data practitioners solve the day-to-day challenges they face in data engineering. In his article, Vishal applies his analytical thinking and problem-solving approaches to untangle the intricacies of data integration and analysis.

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