Jira Service Management Time-in-Status: 5 Common Mistakes and How to Fix Them

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A quick intro: you’re probably measuring the wrong clock

Most “why is MTTR up?” puzzles aren’t talent problems; they’re measurement problems. If you time tickets across weekends, mix “Doing” with “Waiting,” and ignore rework loops, your dashboards will confidently guide you to the wrong fix. Then morale dips, SLAs wobble, and Monday’s stand-up becomes a weather report instead of a plan.

Goal: make time-in-status fair, explainable, and useful—especially for distributed teams. Measure work, not people; find friction, not culprits.

The silent killers hiding in your metrics

🚩 Wall-clock bias. Weekend and holiday hours inflate “delay” for teams who aren’t working then. Your best agents look slow for the most human reasons.

🚩 Doing ≠ Waiting. Blending real work with approval queues or “Waiting for Customer” hides where to improve and turns every discussion into vibes.

🚩 Rework loops. Ping-pong between Support ↔ Customer or Dev ↔ QA spikes cycle time without adding value, and it’s invisible if you only watch “Done.”

🚩 Blended averages. One “average cycle time” across all priorities and channels tells a comforting, misleading story; the outliers you need to fix get sanded off.

🚩 Dashboard drift. Definitions vary by team. If “Working” means different things in different queues, trends stop being comparable, and decisions get political.

Mistake #1: measuring 24/7 when your team works business hours

If you count every minute on the wall clock, your charts critique childcare, sleep, and Saturdays more than process. That’s how you get an “urgent” midnight spike that is really just a weekend.

Fix: define a work calendar per queue/region (hours, time zone, holidays). Track both Business Hours (fairness & SLAs) and Wall-Clock (customer experience). When these disagree wildly, your handoffs—not your people—are the issue.

Time in Status tip: apply calendars once; re-use across Time in Status and Average Time views so every chart speaks the same time language.

Mistake #2: treating every status as “work”

If “Waiting for Customer,” “Waiting for Vendor,” and “Pending Approval” get lumped into “work,” your throughput looks healthy while your customers are stuck in limbo.

Fix: group statuses into Working, Waiting (external), and Internal Review. Make it a coaching metric: reduce the share of Waiting month over month. If “Waiting” dominates, improve request templates, approvals, or customer nudges before adding headcount.

Time in Status tip: use Status Groups and read Average Time by group; that single chart changes the conversation from “work harder” to “remove this wait.”

Mistake #3: celebrating volume while flow degrades

Teams learn to close the easy stuff first; velocity looks great while age quietly climbs. If “tickets closed” is up but Work Item Age and Cycle Time trend up too, you’re starving the complex work.

Fix: add Cycle Time, Work Item Age, and WIP to your regular review. Watch 4-week moving averages. If age rises while volume rises, rebalance intake policies, SLAs by priority, and swarm rules for aging tickets.

Time in Status tip: Time in Status per Date trends age and cycle time day by day; the built-in charts make “flow health” visible without a data export.

Mistake #4: ignoring reopens and ping-pong (your MTTR tax)

Every reopen is an interest payment on unclear acceptance criteria. Every Support ↔ Customer volley is a form not asking the right question. Loops are where time goes to die.

Fix: measure re-entries to key statuses and back-and-forth transitions (Support ↔ Customer, Dev ↔ QA). Run one small experiment—better intake form, tighter “Definition of Ready,” clearer acceptance criteria—and re-measure next sprint.

Time in Status tip: Status Count surfaces re-entries; Transition Count reveals ping-pong chains so you can target the exact loop that’s taxing MTTR.

Mistake #5: averaging everything together

“Average cycle time: 18h” is trivia if Priority 1 chat tickets are 3h and email P3s are 40h; one number hides two realities.

Fix: segment by Priority, Channel (email/chat/portal), Customer tier, Product area, or Region/shift. Review Average Time and share of Waiting for each slice. Pick one “slice of the month” to improve—momentum beats perfection.

Time in Status tip: build a report based on a specific criterion—a specific label, priority, etc. The biggest wins usually live in one or two slices, not everywhere.

 

A 30-minute self-audit

• Pick last month and your top 2 queues.
• Turn on work calendars; rerun.
• Group statuses into Working / Waiting / Review.
• Trend Average Cycle Time and Issue Age.
• Check Status Count (re-entries) and Transition Count (ping-pong).
• Build one Pivot by Priority & Channel.
• Choose one change to reduce Waiting next month. Review results.

Small, boring wins compound. Fix one wait state, one handoff, one loop—repeat.

Guardrails so metrics help people (not punish them)

1. Publish definitions once. What counts as “Working”? Which hours count? Consistency beats cleverness and prevents dashboard arguments.
2. Coach privately, trend publicly. Use individual views for support and growth; share team trends for alignment and safety.
3. Prefer moving averages over spikes. A bad week ≠ , a broken system; look for slope, not drama.
4. Instrument the process you can improve. If a metric can’t lead to a process change, retire it.

Where Time in Status fits

You can do everything above without manual timers or exports. Time in Status app reads Jira history, respects work calendars, lets you group statuses, and gives you Average Time, Time in Status, Status/Transition Count, Per-Date trends, Pivots, and dashboard gadgets you can drop into Jira or Confluence. It’s process telemetry, not surveillance.

If you try only three things

• Turn on Business-hours reporting per queue/time zone.
• Create Working vs Waiting groups and review monthly.
• Add a Transition Count widget to hunt loops.

Then ask, “What single change would shave the most Waiting next month?”

If you want numbers that are fair and actionable, not just “big,” try Time in Status for Jira on a single queue for a month. Map calendars, group statuses, and add a loop chart. Decide with your team if the insights beat your current rituals. If you’d like a second pair of eyes, our team would be happy to meet with you at a demo call.