Service Desk Dashboard

Metrics

Last Updated

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Central time

All-time tickets

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Full Jira ticket count

All-time resolved

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Currently Done/resolved

Last 30 days created

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Current rolling activity

Last 30 days resolved

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Current rolling activity

Highest-volume window

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830 tickets created

Reporting windows

A concise leadership view of three rolling timeframes plus all-time performance.

Window	Created	Resolved	Open	Median resolution	Resolution rate
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Recurring and critical issues

Recurring themes are approximate summary-keyword buckets from the past 30 days. Critical queue is aggregate only.

Recurring themes

#	Theme	Tickets
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Critical queue snapshot

Metric	Value
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Open priority	Tickets
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High / Highest tickets

Current open High and Highest priority tickets from Jira.

Issue Key	Summary	Current status	Priority
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Backlog age expectations

Tickets	Ticket age	Classification	Expected action
—	3–5 business days	Aging	Technician reviews status and provides an update
—	6–10 business days	Backlog	Review ownership, blocker and next action
—	11–30 business days	Aged backlog	Team lead review; escalation/closure plan required
—	30+ days	Critical/stale backlog	Management review; resolve, close, escalate, or formally document why it remains open

Monthly volume trend

Created tickets by calendar month across the full dashboard dataset.

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Last 30 days ticket volume by location

Created tickets by Jira Location field for the current rolling 30-day window.

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Top request types

Highest-volume service categories across the rolling dataset.

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Top issue types

Work item classification by volume.

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Priority mix

Executive snapshot of demand severity across the rolling dataset.

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Executive takeaways

The last 30-day window reflects active throughput, while the prior rolling windows provide trend direction. Backlog is shown separately from window-open counts because created-minus-resolved within a reporting window is not the same as current operational backlog.