How to Create a Dashboard in Jira With Filters: Complete Workflow Guide
Creating effective Jira dashboards requires understanding the complete workflow from initial planning through filter creation, gadget selection, layout design, and ongoing maintenance. Teams attempting to create dashboards without a structured approach produce cluttered displays that fail to clearly communicate essential information.
Organizations mastering how to create a dashboard in Jira with filters follow systematic processes that ensure dashboard relevance. According to Tempo’s dashboard best practices, dashboards should contain no more than six gadgets per display. When teams need to show more data, they should create multiple dashboards rather than overloading a single view, as excessive gadgets canprevent audiences from understanding the intended message.
Plan Dashboard Purpose Before Creating Filters
Dashboard creation begins with defining objectives rather than selecting gadgets. Clear purpose statements guide all subsequent decisions about filters, gadgets, and layout. Without defined objectives, dashboards accumulate unnecessary information, diluting focus.
Identify the primary audience for the dashboard. Individual contributors need personal task lists. Scrum masters need sprint health indicators. Product managers need feature progress tracking. Executives need portfolio summaries.
Each audience type requires different information granularity and presentation styles. Document who will use the dashboard and what decisions they need to make with the displayed information.
Define specific questions the dashboard must answer. Sprint dashboards answer “Will we complete planned work?” Team capacity dashboards answer “Who has bandwidth for new assignments?” Release-readiness dashboards answer the question “What blocks deployment?” Bug triage dashboards answer “Which critical issues need immediate attention?” Each question implies specific metrics and corresponding filters.
According to agile dashboard guidance from Atlassian, dashboards should incite emotion or action—if the data relevance is unclear to the audience, teams should gather feedback and change the dashboard, as proper tuning typically requires one or two iterations to match team and stakeholder needs.
Determine update frequency requirements. Real-time dashboards showing current sprint progress need frequent refresh. Weekly executive summaries can be refreshed daily. Planning dashboards examining historical trends might be refreshed weekly. Refresh frequency affects gadget configuration and filter complexity. High-frequency dashboards benefit from simpler queries that execute quickly.
Create Targeted Filters for Each Information Need
After defining dashboard objectives, create filters that return precise datasets for each identified question. Filter creation is the most critical step—poorly designed filters produce irrelevant results regardless of the gadget’s quality.
Start filtering by broad queries that capture the general data domain. For sprint dashboards, begin with “project = Platform AND sprint IN openSprints()” to establish the base issue set. For bug triage, start with “project = Platform AND type = Bug AND resolution = Unresolved”. These foundation queries ensure filters examine the correct issue universe before applying refinements.
Add refinement criteria that narrow results to actionable subsets. Sprint health filters add “AND status IN (In Progress, Code Review, Testing)” to focus on active work. Bug priority filters include “AND priority IN (Critical, High)” to surface urgent items. Capacity filters incorporate “AND assignee IN membersOf(platform-team)” to scope to specific teams. Each refinement criterion should directly support one of the defined dashboard questions.
Test filters by running them in advanced search before saving. Verify returned issue counts make sense. Check that results include expected issues and exclude irrelevant ones. Review several individual issues to confirm they match filter criteria. Filter testing identifies query errors before they propagate to dashboard gadgets, where debugging becomes more difficult.
Save filters with descriptive names indicating purpose and scope. “Sprint 42 – Platform Team – Active Work” communicates more than “My Filter 5”. Include project names, team identifiers, and data types in filter names. Consistent naming conventions help users locate appropriate filters when configuring gadgets. Well-named filters reduce confusion and prevent duplicate filter creation.
Select and Configure Gadgets for Visual Clarity
Gadget selection determines how filtered data appears to dashboard viewers. Choosing appropriate gadget types for each information need ensures that data is communicated effectively. Mismatched gadgets obscure rather than illuminate insights.
Match gadget types to data characteristics and viewer needs. Use the Filter Results gadget to provide detailed issue lists when users need to see specific tickets. Use the Issue Statistics gadget for categorical breakdowns that show distribution by status, priority, or assignee. Use the Pie Chart gadget for percentage comparisons. Use the Created vs. Resolved gadget for trend analysis over time. Each gadget type serves distinct visualization purposes.
Configure gadgets immediately after adding them to dashboards. Click the wrench icon to access gadget settings. Select the saved filter providing data. Choose additional configuration options specific to each gadget type: statistic fields for Issue Statistics, column selection for Filter Results, and axis fields for Two-Dimensional Statistics. Complete the configuration before adding additional gadgets to avoid confusion about which gadget awaits setup.
Position gadgets according to visual hierarchy principles. Place the most important information in the upper left where eyes naturally focus first. Position secondary information in supporting locations.
Group related gadgets together—place sprint health near the sprint burndown to show complementary progress indicators. Arrange gadgets to guide viewers through the dashboard narrative from overview to details.
Establish Maintenance Routines to Sustain Relevance
Dashboard creation is not a one-time activity. Ongoing maintenance ensures dashboards remain relevant as projects evolve, teams reorganize, and priorities shift. Without maintenance schedules, dashboards can decay into outdated displays that show stale information.
Schedule regular dashboard reviews aligned with project cadences. Sprint-focused dashboards need review each sprint retrospective. Quarterly planning dashboards need to be reviewed before each planning cycle. Release readiness dashboards need review when the release scope changes.
During reviews, verify that each gadget still serves its intended purpose and that the displayed data remains relevant to current objectives.
Update filters when underlying data structures change. Team membership changes require updating references to “assignee IN membersOf()” to reflect the new team membership. Project reorganizations need new project lists. Workflow modifications might necessitate different status criteria. Version releases require updating the fixVersion filters. These structural changes break filters if not addressed, causing gadgets to display incorrect or empty results.
Gather feedback from dashboard users to identify opportunities for improvement. Users notice when specific information they need is missing or when irrelevant data clutters displays. Feedback mechanisms can be informal—asking during team meetings—or formal—dedicated feedback sessions. User input reveals blind spots in dashboard design that creators miss because they know the data too intimately.
Archive obsolete dashboards rather than leaving them active. Completed project dashboards no longer serve active purposes. Dashboards created for specific initiatives become irrelevant when initiatives conclude.
Archived dashboards remain accessible for historical reference but do not appear in users’ default dashboard lists. This archival practice prevents dashboard proliferation that overwhelms users with too many choices.
Creating Jira dashboards with filters requires systematic workflows starting with a clear objective definition. Planning identifies audiences, questions, and requirements before filter creation begins. Targeted filter creation provides precise datasets that address specific information needs. Gadget selection and configuration translate filtered data into visual displays suited to the viewer’s needs.
Ongoing maintenance routines sustain dashboard relevance as circumstances evolve. Organizations that follow these complete workflows produce dashboards that inform decisions rather than merely display data.
