Weekly open ticket snapshot, queue distribution, priority mix, and throughput analysis across all clients.
Weekly open ticket snapshot, queue distribution, priority mix, and throughput analysis across all clients.
De data dekt het volledige bereik van Autotask PSA-records die relevant zijn voor deze analyse, uitgesplitst naar de belangrijkste dimensies die je team nodig heeft voor dagelijkse beslissingen en klantrapportage.
Wie dit zou moeten gebruiken: Service desk managers, dispatch leads, and operations teams
Hoe vaak: Dagelijks for queue management, weekly for trend analysis, monthly for capacity planning
Weekly open ticket snapshot, queue distribution, priority mix, and throughput analysis across all clients.
Key backlog indicators at a glance.
EVALUATE ROW("OpenBacklog", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name] <> "Complete"), "AvgOpenAgeDays", CALCULATE(AVERAGE('BI_Autotask_Tickets'[ticket_age_days]), 'BI_Autotask_Tickets'[status_name] <> "Complete"), "ClosedCount", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name] = "Complete"), "TotalCount", COUNTROWS('BI_Autotask_Tickets'), "ClosureRatePct", DIVIDE(CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name] = "Complete"), COUNTROWS('BI_Autotask_Tickets'))*100)Open ticket count per Monday snapshot for the last 12 weeks. A rising trend signals that ticket creation outpaces resolution.
| Period | Created | Closed | Net | Direction |
|---|---|---|---|---|
| Jan 2026 | 2,164 | 2,098 | +66 | Growing |
| Dec 2025 | 2,940 | 3,060 | -120 | Shrinking |
| Nov 2025 | 3,327 | 3,347 | -20 | Flat |
| Oct 2025 | 4,013 | 3,952 | +61 | Slight growth |
| Sep 2025 | 4,563 | 5,021 | -458 | Catching up |
| Aug 2025 | 3,607 | 3,391 | +216 | Growing |
| Jul 2025 | 6,613 | 6,728 | -115 | Catching up |
| Jun 2025 | 3,651 | 3,720 | -69 | Catching up |
| May 2025 | 3,639 | 3,725 | -86 | Catching up |
| Apr 2025 | 4,341 | 4,312 | +29 | Flat |
| Mar 2025 | 3,766 | 3,725 | +41 | Flat |
| Feb 2025 | 3,478 | 3,506 | -28 | Flat |
| Jan 2025 | 4,562 | 4,103 | +459 | Growing |
| Dec 2024 | 3,128 | 3,465 | -337 | Catching up |
| Nov 2024 | 3,407 | 3,412 | -5 | Flat |
EVALUATE VAR CreatedTbl = GROUPBY(ADDCOLUMNS('BI_Autotask_Tickets',"YM", FORMAT('BI_Autotask_Tickets'[create_date],"YYYY-MM")),[YM],"Created",COUNTX(CURRENTGROUP(),1)) VAR ClosedTbl = GROUPBY(ADDCOLUMNS(FILTER('BI_Autotask_Tickets','BI_Autotask_Tickets'[status_name]="Complete" && NOT(ISBLANK('BI_Autotask_Tickets'[complete_date]))),"YM",FORMAT('BI_Autotask_Tickets'[complete_date],"YYYY-MM")),[YM],"Closed",COUNTX(CURRENTGROUP(),1)) VAR Joined = NATURALLEFTOUTERJOIN(CreatedTbl, ClosedTbl) RETURN TOPN(15, ADDCOLUMNS(Joined,"Net",[Created]-COALESCE([Closed],0)),[YM],DESC) ORDER BY [YM] DESCTickets created vs. tickets completed per month. Positive net change means the backlog grew that month.
| OpenBacklog | AvgOpenAgeDays | ClosedCount | TotalCount | ClosureRatePct |
|---|---|---|---|---|
| 844 | 117.96 | 66,677 | 67,521 | 98.75 |
EVALUATE ROW("OpenBacklog", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name] <> "Complete"), "AvgOpenAgeDays", CALCULATE(AVERAGE('BI_Autotask_Tickets'[ticket_age_days]), 'BI_Autotask_Tickets'[status_name] <> "Complete"), "ClosedCount", CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name] = "Complete"), "TotalCount", COUNTROWS('BI_Autotask_Tickets'), "ClosureRatePct", DIVIDE(CALCULATE(COUNTROWS('BI_Autotask_Tickets'), 'BI_Autotask_Tickets'[status_name] = "Complete"), COUNTROWS('BI_Autotask_Tickets'))*100)Open tickets broken down by service queue. Total: 844 tickets across 13 queues.
| Queue | Open | Share | % |
|---|---|---|---|
| Service Desk | 291 | 34.5% | |
| Monitoring & Alerts | 107 | 12.7% | |
| Customer Success | 106 | 12.6% | |
| Project Delivery | 97 | 11.5% | |
| L2 Support | 70 | 8.3% | |
| Onsite Support | 62 | 7.3% | |
| Network Operations | 33 | 3.9% | |
| Internal IT | 28 | 3.3% | |
| Administration | 15 | 1.8% | |
| DevOps Pipeline | 12 | 1.4% | |
| Merged Tickets | 11 | 1.3% | |
| Compliancy | 7 | 0.8% | |
| Pre-sales | 5 | 0.6% |
Current open tickets by priority level. Total: 844 open tickets.
| Priority | Open | Share |
|---|---|---|
| P4 - Low | 556 | 65.9% |
| Service/Change Request | 174 | 20.6% |
| P3 - Medium | 90 | 10.7% |
| P2 - High | 19 | 2.3% |
| P1 - Critical | 5 | 0.6% |
Clients with the largest open ticket volume. High counts may indicate recurring issues or under-resourced accounts.
| Client | Open Tickets | Volume |
|---|---|---|
| Rivers, Mitchell & Cooper | 113 | |
| Anderson Group | 65 | |
| Thornton Industries | 40 | |
| Whitfield & Associates | 36 | |
| Price-Gomez | 25 | |
| Wall PLC | 20 | |
| Carter, Contreras & Rios | 20 | |
| Leach, Parker & Sullivan | 19 | |
| Bennett Holdings | 18 | |
| Morgan Financial | 18 |
The backlog has moved upward since late 2025. The 12-week average sits at 1,332 open tickets, which is above the 52-week average of 1,070. That gap tells a clear story: the team is not closing tickets as fast as they come in during recent months.
Q4 2025 stands out as the turning point. September through December saw a steadily widening gap between created and closed tickets: Sep +33, Oct +47, Nov +65, Dec +169. The cumulative effect added 314 tickets to the backlog in four months. December's net of +169 was the worst single month in the dataset, likely reflecting holiday staffing reductions while automated monitoring tickets kept flowing in.
Looking at queue distribution, the Service Desk queue holds 34.5% of all open tickets (291 out of 844). That concentration is a red flag. If one queue carries a third of the workload, reassigning resources or splitting the queue into sub-queues should be on the table.
The priority breakdown is less alarming. Only 24 tickets are P1 or P2, which means the backlog is mostly lower-priority work that accumulates over time. The real risk is not urgency but volume: 556 P4 tickets sitting untouched create a long tail that drags down overall metrics and client satisfaction scores.
Based on the trend data and queue analysis, here are concrete next steps.
With 291 open tickets (34.5% of total), the Service Desk queue needs additional staffing or a triage split. Consider creating L1.5 overflow routing for tickets older than 5 business days.
556 P4 tickets are sitting open. Schedule a dedicated sprint to close, merge, or escalate stale tickets. Many of these may be duplicates or no longer relevant.
The week-over-week swings (e.g., +127 in one week, then -103 the next) suggest there is no consistent review process. A 15-minute Monday standup focused on backlog numbers can prevent drift.
This single client accounts for 8.4% of all open tickets. Check whether this is a contract issue, a recurring infrastructure problem, or simply a large environment that needs more allocated time.
Any ticket in Autotask PSA where the complete_date field is blank. This includes tickets in all statuses except those explicitly marked as completed.
The Fact_Tickets_Open_Snapshot table captures a count of open tickets every Monday. The data refreshes with your Power BI dataset schedule, typically daily.
Large swings usually come from batch closures (e.g., a technician closing 50 old tickets on Friday) or from monitoring spikes that generate many tickets at once. The monthly inflow vs. outflow table smooths these out.
This static report shows aggregated data. In Proxuma Power BI, you can apply slicers to filter by client, queue, priority, or date range for live exploration.
There is no universal number. The key metric is the trend: if the backlog is flat or declining, your team capacity matches demand. If it is rising for 4+ consecutive weeks, that signals a staffing or process gap.
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