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Videos: data from Jira and apps

The videos on this page explain Jira-specific data representation, like custom fields, issue links, and asses, as well as data from popular applications and apps.

You can always read more on our documentation: Data from JiraJira custom fields, Advanced data import options, JavaScript calculated custom fields.

Sprint reports with eazyBI

Time required: 50 minutes

Learn how to use Jira data to build insightful sprint reports with eazyBI. Understand the differences between Jira and eazyBI sprint reports, analyze multi-team and cross-project sprints, use time tracking, and more.

Slides:  eazyBI Sprint webinar 2021 sept.pdf

 

Tempo Planner reports using eazyBI

Time required: 60 minutes

Webinar recording on the integration between eazyBI and Tempo Planner as well as several useful use-cases.

Blog article: https://eazybi.com/blog/tempo-planner-reports-in-jira

 

Explore Assets data with eazyBI

Time required: 60 minutes

Webinar recording covering the integration between eazyBI and Insight and several useful use-cases.

Blog article: https://eazybi.com/blog/explore-insight-asset-management-data-with-eazybi

 

Unraveling the Chemistry Between Issues: Reporting on Linked Issues

Time required: 25 minutes

If analyzing linked issues information has ever felt a bit tricky, fear not! Follow a step-by-step guide how to import issue links using eazyBI new feature and how to build meaningful reports to find relationships between Jira issues!

I Want My Custom Fields, and I Want Them Now

Time required: 20 minutes

Creating reports based on Jira custom fields is a fundamental cornerstone of eazyBI reporting: custom fields often contain answers to your unique business questions. Getting custom fields to the final reports is not always a stroll in the park. Learn how to get your custom fields into eazyBI the easy way.

Slides: eCD2023_Custom_Fields.pdf

 

Making Sense of Unstructured Data: Using JavaScript in eazyBI

Time required: 20 minutes

You can import and analyze Jira data based on measures, dates, users, teams, and other identifiable attributes. However, how can we uncover and analyze the data that may be hidden in plain sight? Let’s explore various techniques for modifying data to make it suitable for analysis.

Slides: eCD2023_Making_Sense_of_Unstructured_Data.pdf

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