Epic: real-time dashboard that visualizes metrics like cycle time, lead time, and deployment frequency

[Guidevit]

Task: Design the Overall Layout and Structure of the Real-Time Dashboard

Start with understanding the goals and needs of the dashboard users, in this case, the team leader. Define the key information they need to see and the most intuitive way to display this information.
Create wireframes or low-fidelity sketches of the dashboard layout and structure, keeping user experience at the forefront. Define where each key metric (cycle time, lead time, deployment frequency) will be placed and how they will be displayed.
Get feedback from users and refine the design iteratively until it meets all usability and aesthetic standards. Use design software like Figma or Sketch for creating high-fidelity mockups.
Task: Implement the Visual Components for Displaying Cycle Time, Lead Time, and Deployment Frequency Metrics

Depending on your tech stack, use suitable visualization libraries or tools (such as D3.js, Chart.js, or Plotly) to create visual components like charts or graphs for each metric.
Ensure these visualizations are intuitive, engaging, and responsive. They should also align with the overall design of the dashboard.
Integrate these visual components into the dashboard's layout, following the design approved in the previous step.
Task: Ensure Real-Time Updates of the Metrics on the Dashboard

Develop mechanisms to fetch and update the dashboard data in real-time. This might involve setting up web sockets for continuous data push or setting up periodic API calls to fetch the latest data.
Make sure the updating mechanism doesn't disrupt the user experience. For instance, if using API calls, ensure they don't slow down the dashboard or cause any jitters in the visualizations.
Implement a fail-safe mechanism or error handling to address any issues with data fetching.
Task: Add Interactive Features, Such as Filters and Drill-Down Capabilities, to Enhance the Dashboard's Usability

Add functionality for users to filter data based on different parameters like date ranges or specific metrics. This might involve creating dropdowns or sliders for users to select their criteria.
Implement drill-down capabilities so users can click on a data point in a visualization and get more detailed information. This could be displayed in a new chart or a pop-up.
Test the interactive features thoroughly for usability and performance issues. Make sure they truly enhance the user's experience and ability to make data-driven decisions.