In today's digital business world, data is among the most valuable resources and often the key to success. By continuously collecting and analyzing data, companies can not only respond quickly and agilely to changes but also make informed decisions, optimize processes, and, above all, increase their productivity and efficiency.
The growing importance of data becomes increasingly evident amid rapid progress. With the increasing complexity of teamwork and projects, it becomes essential to harness the data generated within workflows. Where do teams invest a lot of time? Where are bottlenecks, and what impact does their work have on the company's performance? Data helps gain a comprehensive overview and define data-driven actions that guide companies toward business success.
Atlassian has also recognized the power of data and has introduced a platform that enables intuitive data analysiseffectively putting an end to the problem of teams spending more time analyzing data than gaining insights. With Atlassian Analytics, Enterprise Cloud customers of Jira Software, Jira Service Management, and Confluence are provided with a powerful tool to collect, visually analyze, and transform data from all team activities and projects into insights — improving collaboration and achieving goals more quickly.
What is Atlassian Analytics?
Atlassian Analytics is a cloud-based visualization platform that processes data in various formats and consolidates it into dashboards. The necessary information is extracted from the Atlassian Data Lake—a queryable database containing pre-modeled, enriched data from Atlassian products used within the organization. Once the Datenstream is set up, teams can create interactive and easily customizable charts and gain a comprehensive overview of their projects and workflow through a user-friendly low-code/no-code interface.
Key features of Atlassian Analytics
- Real-time data visualization: With Atlassian Analytics, projects can be monitored in real-time, and data can be presented in meaningful graphics. This allows for early trend detection, timely responses to unfavorable developments, and course corrections. The visualization options are highly versatile, with data being represented in tables, pie charts, bar charts, and numerous other formats, making it easily understandable and suitable for the intended use and audience.
- Pre-built templates for various use cases: Atlassian Analytics offers ready-to-use templates for service management, asset management, content management, and DevOps use cases. These templates simplify the setup and initiation of data analysis projects, saving valuable time.
- Customized data analysis: Atlassian Analytics allows data analysis according to individual requirements. The platform provides a powerful visual SQL interface for conducting custom analyses, providing teams with insights tailored to the specific needs of the organization.
- Integration of database connectors: Atlassian Analytics seamlessly integrates with Atlassian products like Jira and Confluence, but it also allows data retrieval from other sources, offering database connections that enable teams to access non-Atlassian data sources like Snowflake, Amazon Redshift, Google BigQuery, Microsoft SQL Server, PostgreSQL, and others. This allows for consolidating data from various sources and gaining a holistic view of projects.
- Historical data analysis: Atlassian Analytics stores data over an extended period, enabling teams to track historical trends and developments. This is crucial for developing long-term strategies and monitoring improvements over time.
- Efficient collaboration: Atlassian Analytics supports team collaboration. Team members can embed content, add comments, and manage permissions, facilitating the sharing of insights and promoting collaboration throughout the organization.
How does Atlassian Analytics work?
Atlassian Analytics speeds up decision-making at all levels: businesses gain a complete insight into their teams' workflow, can create interactive dashboards to reduce misinterpretations, and break down silos across teams and products.
Use Case: Development leaders and product owners have the ability to access dashboards that provide information on the number of issues created and closed, average processing time, the current status of open issues, and associated risks. These dashboards offer a comprehensive overview of all projects and allow for a detailed examination of specific project information—especially important when there is an unusual or unexpected data drop. In such cases, analysts or product managers can be tagged in a comment and are immediately involved to solve the problem
In this way, Atlassian Analytics can help identify bottlenecks or recognize teams that may be overloaded.
And this is how teams gain a complete insight into their projects and workflow with Atlassian Analytics:
1. Select data sources: Atlassian Analytics seamlessly accesses the Atlassian Data Lake, where teams can choose their desired data sources. This flexibility allows them to specify precisely from which products and instances they want to retrieve data. They can include or exclude projects (such as Jira Software, Jira Service Management, and Opsgenie), assets, or areas (like Confluence) according to their needs. Administrators also have the ability to set up multiple connections to the Data Lake to meet the requirements of different teams.
2. Get started with pre-built templates: Once a connection to a product instance is established, a world of pre-built dashboards and charts becomes available. These pre-built templates are specifically designed to meet the needs of both business and IT teams.
Use Case: Service managers can access pre-built dashboards to assess created and resolved requests, the average CSAT score, the percentage of SLA violations, and much more. They can investigate service quality by identifying all incidents and the average time to resolution. They can even delve into the quality of specific services.
No template fits? Pre-built dashboards can be customized to meet the individual needs of teams. Users have the option to create personalized dashboards according to their preferences, whether it's by renaming fields, removing charts, or rearranging elements.
3. Run SQL based on custom preferences: By running SQL queries directly in Atlassian Data Lake, it's possible to create completely custom dashboards and charts across various products or instances. Not familiar with SQL? No problem! Visual SQL allows you to create custom charts and dashboards without code and programming skills by quickly and easily adding columns, filters, and intuitive connections between records. If additional precision is required, the option to switch to written SQL queries is available to fine-tune queries and dive deeper into the data.
Use Case: Technical teams can use visual query to pull in stories and bugs assigned to them from Jira Software alongside any change requests or incidents from Jira Service Management to assess where they should prioritize their time or understand correlation between features delivered and incident requests generated.
4. Explore various visualization options: With Atlassian Analytics, teams have the choice of a wide range of chart types and graphics to best meet their visualization needs. The best part: Atlassian Analytics recommends a chart type based on the queried and transformed data that most effectively represents the data.
5. Comment, download, and embed: Atlassian Analytics dashboards and charts are inherently collaborative and promote teamwork. Users can add comments to charts and mention colleagues with questions or remarks. Additionally, dashboards and charts can be easily shared by embedding them in Confluence pages using Smart Links. This enables even more comprehensive collaborative analysis with different teams within the organization.
6. Integrate your own data and dive even deeper: Data from Atlassian products becomes even more powerful when combined with other critical business data sources. Together, they offer deeper insights into projects and the ability to align performance with business outcomes. With these combined analyses, leaders can be confident in making informed decisions based on comprehensive insights.
Use Case: Teams can retrieve customer NPS ratings from the CRM and visualize this metric alongside data on incident resolution from Jira Service Management to assess how improvements in service quality impact customer satisfaction. Similarly, growth data from a Snowflake database or Google Sheets can be incorporated alongside Jira Software data on engineering performance to evaluate how innovations affect revenue goals.
Summary
Atlassian Analytics is more than just a data analysis tool; it's a crucial step toward data-driven efficiency. All relevant sources and data, both cross-team and cross-project, converge in one place and can be simultaneously exported to other preferred Business Intelligence tools through various database connections, where they can be visualized. The platform provides insights that can be of great value to every department, from business to service to IT. By using Atlassian Analytics, companies can better understand how their teams operate, paving the way for even more successful collaboration.
Want to learn more about Atlassian Analytics? In our datasheet you will find additional information about the platform as well as some more use cases and benefits. Or contact us — we're happy to help you.
