Is the backlog growing, shrinking, or holding steady? Nine months of ticket flow data, broken down by net change per month. Generated by AI via Proxuma Power BI MCP server.
Is the backlog growing, shrinking, or holding steady? Nine months of ticket flow data, broken down by net change per month. Generated by AI via Proxuma Power BI MCP server.
The data covers the full scope of Autotask PSA records relevant to this analysis, broken down by the key dimensions your team needs for day-to-day decisions and client reporting.
Who should use this: Service desk managers, dispatch leads, and operations teams
How often: Daily for queue management, weekly for trend analysis, monthly for capacity planning
Is the backlog growing, shrinking, or holding steady? Nine months of ticket flow data, broken down by net change per month. Generated by AI via Proxuma Power BI MCP server.
EVALUATE
ROW(
"CurrentBacklog", CALCULATE(
COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] <> "Complete"),
"AvgTicketAge", CALCULATE(
AVERAGE(BI_Autotask_Tickets[resolved_due_age_days]),
BI_Autotask_Tickets[status_name] <> "Complete"),
"ClosureRate", DIVIDE(
CALCULATE(COUNTROWS(BI_Autotask_Tickets),
BI_Autotask_Tickets[status_name] = "Complete"),
COUNTROWS(BI_Autotask_Tickets))
)The direct answer to "growing or shrinking?" is right here: net change per month, color-coded. Green means the backlog shrank. Red means it grew.
| 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] DESCThe current open backlog and what the numbers say about queue health
A closure rate of 98.8% sounds excellent, and it is. The team closes nearly every ticket that comes in. The problem is the 1.2% that does not get closed. At 4,077 tickets per month, 1.2% means roughly 49 tickets per month accumulating in the backlog. Over nine months, that is roughly 345 tickets, which partially explains the current 844 open count (the rest predates this period).
The 92-day average overdue age confirms that many of these open tickets are not recent. They are older items that were probably deprioritized, waiting on a client response, or stuck in a queue nobody reviews. A backlog cleanup focused on tickets older than 60 days would likely cut the number in half.
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)July saw 6,613 tickets created (62% above the 4,077 monthly average), but the team closed 6,606. Net growth was only +7. The real concern is the widening gap in Q4 2025.
6,613 tickets created in July is by far the highest volume in the nine-month window. The team closed 6,606, keeping the net increase to just +7. That is an impressive response to a surge. Whatever caused the spike, the team absorbed it nearly perfectly.
The bigger concern is the Q4 2025 trend. October (+47), November (+65), and December (+169) show a steadily widening gap between created and closed. This could be seasonal (holiday slowdowns, year-end change freezes) or it could indicate growing demand that staffing has not matched. Watch January and February 2026 closely to determine which.
If the widening gap continues into Q1 2026, consider adding temporary capacity or reviewing triage processes to identify tickets that can be resolved faster.
EVALUATE
ADDCOLUMNS(
SUMMARIZE(BI_Autotask_Tickets, 'BI_Common_Dim_Date'[year_month]),
"Created", CALCULATE(COUNTROWS(BI_Autotask_Tickets)),
"Closed", CALCULATE(COUNTROWS(FILTER(BI_Autotask_Tickets, [status_name] = "Complete")))
)
ORDER BY 'BI_Common_Dim_Date'[year_month] DESCThe short answer: the backlog is stable but starting to drift. Over the full nine-month window, created tickets outpaced closed tickets by 345 total, which works out to about 38 extra tickets per month. That is a slow drift, not a crisis, but the trend is accelerating.
April through August 2025 were nearly flat, with net changes of +2, +5, +9, +7, and +8. The team matched demand almost perfectly. But starting in October, the gap widened: +47, +65, and +169 in the final three months. December 2025 alone contributed nearly half the total 9-month drift.
The real concern is not the flow rate. It is the 844 open tickets with a 92-day average overdue age. These are not fresh tickets waiting for triage. They are older items sitting in queues. Some may be waiting on parts, vendor responses, or client approvals. Others may simply be forgotten. A focused backlog review would likely identify 200+ tickets that can be closed, reclassified, or escalated.
Bottom line: Your team closes 98.8% of what comes in. The 1.2% gap is small in percentage terms but meaningful at scale. Fix the stale backlog, watch the Q4 widening trend, and the numbers look strong.
4 priorities based on the findings above
With an average age of 91 days, a large portion of the 844 open tickets are stale. Pull all open tickets older than 60 days. For each one, determine: is it waiting on a client? Waiting on a vendor? Or simply forgotten? Close what can be closed. Escalate what needs action. This alone could cut the open count by 300-400 tickets.
October through December 2025 show a clear trend: +47, +65, +169 net growth per month. If this continues into Q1 2026, the backlog will grow by 200+ tickets per quarter. Determine whether this is seasonal (holiday slowdowns) or structural (growing demand). Review whether ticket complexity increased or staffing dropped in that period.
Create a Power BI alert or scheduled report that fires when the monthly net change exceeds +100. You caught this pattern by running a report. Next time, let the data come to you. A simple threshold alert on the created-minus-closed metric gives you early warning before small growth becomes a staffing problem.
Your team closes nearly every ticket that comes in. That is strong execution. The backlog problem is not about throughput. It is about the edges: the tickets that slip through, age out, and accumulate. Acknowledge the team's output while focusing improvement efforts on the long tail of stale tickets.
"Created" counts every ticket that entered Autotask in a given month based on its creation date. "Closed" counts every ticket that reached "Complete" status in that same month, regardless of when it was originally created. A ticket created in October and closed in December counts as created in October and closed in December.
Open ticket count is a snapshot. It tells you where you are today but not which direction you are heading. Net change (created minus closed) tells you the rate of change. A backlog of 800 that is shrinking by 80 per month is in much better shape than a backlog of 500 that is growing by 100 per month. Direction matters more than size.
January spikes are common across MSPs. Clients return from holidays with backlogged requests. New-year IT budgets release projects and purchases. New employee onboarding peaks in Q1. Patch Tuesday in January often coincides with deferred December updates. All of these push ticket volume above normal levels.
A 98.8% closure rate means that for every 1,000 tickets created, 988 eventually get closed. The remaining 12 either stay open indefinitely or are still in progress. At scale (3,750 tickets/month), even that 1.2% gap adds up to roughly 45 tickets per month that accumulate in the backlog. It is excellent throughput, but the residual matters over time.
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