"Team DevOps effectiveness scorecard" dashboard template
This dashboard template is only available for Atlassian Data Lake connections that include Jira data and have scope of data set to “All”. Read more about how to connect to the Atlassian Data Lake.
This dashboard displays a set of key scorecards and metrics to measure and improve the overall effectiveness of your engineering team.
Controls on this dashboard
Use the following controls to configure the dashboard:
Current date interval | “Calendar” control to filter charts to display data relating to issues completed during the selected date range. |
|---|---|
Date bucket | “Dropdown” control to group data by week, month, quarter, or year. |
Project name | “Dropdown” controls to specify the teams that this dashboard should focus on. If you reset these controls, the charts will look at issues across all teams. |
Issue types to configure as bugs | “Dropdown” control to select specific issue types that you want this dashboard to consider as bugs. If you reset this control, the charts will look at issues of all types. |
You can filter all charts on the dashboard by date, project, and issue types to configure as bugs.
Charts on the dashboard
Quick overview
The quick overview gives a holistic view of your engineering health over the last four weeks, helping you see where your strengths and weaknesses are. The trends besides the number indicate the movement of the metrics displayed compared to the previous 4 weeks.
Each chart has additional charts on this dashboard that provide more details related to the metric.
Development cycle time
The average time it takes for your code to be available in production.
Keeping the cycle time to a lower value ensures smooth delivery of work in a timely manner. Breaking work down into smaller chunks and frequently pushing code are some of the effective practices to bring down cycle time.
Observe the development cycle time, the overall issue cycle time, and the pull request cycle time to spot any anomalies in the delivery chain. For instance, if there's a significant difference between the issue cycle time and development cycle time, it's important to review processes outside of core development to understand the gap.
Deployment frequency
How often a change is deployed to a production environment.
Deploying frequently and in smaller chunks ensures quick recovery from any incidents in production by isolating each change. Use continuous integration and continuous deployment practices to maintain a healthy pipeline for changes and reduce overhead linked to manual deployments.
Look at the following complimentary stats related to deployment to understand the pain points and areas of improvement:
Average deployment frequency
Median commits per deployed issue
Successful production deployment rate
Successful production deployments
Median deployment time
Maximum deployment time
Issues opened vs. bugs raised
This chart displays the number of issues and bugs (bugs, defects, and more as mapped in the “Issue types to consider as bugs” control) created over the specified date interval.
Practices like test automation and integrated testing step in the pipeline help reduce the overall number of bugs discovered after deployment.
Understand the chart better by looking at the additional stats about the failures:
Number of bugs raised
Number of critical bugs raised
% of issues raised as bugs
Mean time to resolution
The time it takes to recover from failures in production.
Mean time to resolution (MTTR) helps you understand how ready your team is to recover from any mishaps in production. Use practices like frequent code deployments and continuous delivery to quickly isolate the problem area and revert back to the previous healthy state with minimal disruptions.
You can also compare the MTTRs for each severity level (critical, high, medium, and low) to get deeper insights.
Activity breakdown
Breakdown of the activities that make up most of the development time.
Use the chart to understand the percentage breakdown of the activities as well as the median time taken for each activity to identify bottlenecks in the overall delivery lifecycle.
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