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Why a metric looks off

Diagnostic checklist for unexpected metric values.

Updated today

A metric looks wrong. Before assuming a bug, walk through this checklist — most "wrong" metrics turn out to be configuration issues.

Is it actually wrong, or just unexpected?

The most common case: the metric is computed correctly but reflects something you didn't realize was happening. Click Show details on the metric card to see the underlying items. Often the data tells a true story you weren't prepared for.

Step 1 — Check the time range and filters

  • Make sure the date range is what you expect.

  • Clear all filters and re-run. If the number changes, a filter was hiding part of the population.

Step 2 — Check status mappings

Most flow metric errors trace back to status mappings:

  • If a state is misclassified as "active" when it should be "waiting", cycle time will look too long.

  • If "done" isn't the right terminal status, throughput will be wrong.

  • Open the integration's status mapping under Administration → Productivity Tools and verify each status.

Step 3 — Check user and team mappings

  • Unmapped users contribute zero to per-team metrics.

  • Bots or system accounts mapped as humans inflate activity numbers.

  • Verify mappings in Administration → Users.

Step 4 — Check the integration's data quality

  • For DORA: are deployment and incident events actually flowing? If not, MTTR and Change Failure Rate will be empty or misleading.

  • For flow: do Jira issues have proper status-change history? Bulk-edited issues sometimes lack history rows.

Step 5 — Compare to a known-good period

Switch the date range to a period where you know what the metric should be. If that range looks correct, the issue is data-source-specific to the new range. If both look wrong, the issue is configuration.

When to contact support

If you've worked through the steps above and the metric still doesn't reflect reality, reach our team via the Intercom Messenger. Include:

  • The exact metric and dashboard.

  • The time range and filters applied.

  • The expected vs. observed value with a sample of items if possible.

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