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In my work with Atlassian's largest customers, I see how Jira is a business-critical application, and not just for managing software development, but to gain insight into how the business is performing. Enterprise customers use Jira reports and project data to calculate SLAs, defect rates, project progress, as well as analyze trends over time. Project data analysis can tell a company what's happening now and enable them to plan for what's next.
In order to get these insights, our Jira Server customers have come to depend on the ability to extract data from the UI, REST API, and Marketplace apps. (Some even pull data directly from the database—though we generally discourage that, since our schema updates can cause breaking changes). One customer told me how they wrote a custom Java plugin to emit events into their data lake. In the self-managed world of Jira Server and Data Center, administrators have complete control over the infrastructure so they can effectively move data around however they like.
BI reporting in cloud vs. server: Different...but shockingly similar
When customers, especially those in enterprises, are considering a cloud migration, the lack of infrastructure control can feel a bit disorienting at first. While every business has unique challenges moving from self-managed to our cloud products, I have seen common misunderstandings about cloud reporting options that may be holding some companies back. Specifically, the belief that moving to cloud will result in the loss of access to their project data and they won’t be able to analyze it with their BI tools.
Fortunately, that’s not always the case. I’ve put together a few scenarios we see at our enterprise Jira Server and Data Center customers involving types of Jira data and popular BI tools, and their equivalents in Jira Cloud.
The tool configurations below are only a few examples, but there are many BI tools in the Atlassian Marketplace (for both Jira and Confluence) that can help unlock advanced business intelligence needs.
Scenario 1: Using an on-premise data warehouse with Tableau
The AIO app can sync your Jira cloud data to an on-prem Tableau data file, which can then be consumed by your on-prem Tableau instance. Upshot is, you don’t have to relicense for a Tableau cloud, and don’t need to reconfigure your infrastructure for reporting.
The data is secured with OAuth 2.0 and encryption-at-rest, and allows you to use JQL, Jira Cloud’s REST APIs, and webhooks (for automatic synchronization).
This solution has worked well for one customer who is pulling daily reports on ~250,000 issues. For them, it takes ~2mins to synchronize nearly 1 million records across 15,000 issues per day in cloud.
The AIO Tableau Connector can synchronize issues, projects, comments, worklogs, change history, users, and customer fields.
Scenario 2: Multiple data sources feed into a data warehouse
If you need to perform in-depth analysis of your Jira Server data, along with other data types, Stitch with Alteryx is a great way to go.
Use Stitch to sync your Jira Cloud data down to your local data warehouse. From there, you can use Alteryx to perform the in-depth analysis you need, including the visual workflow tool in Alteryx Designer to prepare data for analysis, and the Alteryx Server platform to run and share analytic models and reports.
Our customer who is using this solution is running on a Jira Cloud instance with 100,000 issues and daily and weekly reporting.
Alteryx uses JQL and Jira Cloud’s REST APIs. The data is secured with API tokens and encryption-at-rest.
Alteryx is a great option when you’ve got many data sources to integrate for analysis and Stitch can keep that setup intact. Alteryx can synchronize Jira data including issues, projects, comments, worklogs, change history, users, and custom fields.
Scenario 3: Rapid configuration of ETL
With Jira Server: Local or cloud data warehouse, various BI tools
With Jira Cloud: Stitch Jira Cloud ETL connector
If you’ve been using a data warehouse (or are considering using one) you likely know that setting up and maintaining one is not an insignificant effort. Again, using Stitch you can sync your Jira Cloud instance to your local on-prem or cloud data warehouse, and from there perform your analysis using your preferred BI tools.
Stitch uses Jira Query Language (JQL) and Jira Cloud’s REST APIs. The data is secured with API tokens and encryption-at-rest.
Using the Stitch ETL you can synchronize issues, projects, comments, worklogs, change history, users, custom fields, and more.
Scenario 4: Advanced reporting and business intelligence needs
This solution works for both Jira Server and Jira Cloud.
Whether you’re using Jira Server or Jira Cloud, eazyBI is a great solution for accessing and storing your Jira data for analysis. In addition to accessing Jira Cloud data via REST API, eazyBI can map data from many disparate sources of data such as CSV, MS Excel, SQL databases, and Google Sheets to an eazyBI data cube. You can maintain all your data sources and wire them together with powerful, customizable reporting functionality.
eazyBI utilizes JQL, Jira Cloud’s REST APIs, and webhooks for automatic synchronization. The data is secured with JSON Web Tokens (JWT) and encryption-at-rest.
This solution our customer implemented is running on a Jira Cloud instance with 250,000 issues and daily/weekly reporting.
The eazyBI can synchronize data from your Jira instance including issues, projects, comments, worklogs, custom fields, and more.
We know access to your Jira data is important to gain insights and support good decision-making. As we’ve expanded our cloud ecosystem we’ve made sure that customers can access their data to use in BI tools much like they have with our on-prem server products, in part, through strong partnerships with the vendors in our Marketplace. These four solutions are just a few approaches to integrating Jira Cloud with BI tools.
If you’re considering moving to cloud, make sure to visit the Cloud Migration Center for more best practices, guides, and resources. Also check out our other article as part of our cloud app series on "migrating to cloud with apps" and "building cloud apps - a vendor perspective. "