A snapshot of 844 overdue tickets across 14+ clients from Autotask PSA. This report breaks down where overdue volume concentrates, how long tickets have been sitting past their due date, and which accounts need immediate attention. PSA
A snapshot of 844 overdue tickets across 14+ clients from Autotask PSA. This report breaks down where overdue volume concentrates, how long tickets have been sitting past their due date, and which accounts need immediate attention. PSA
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
A snapshot of 844 overdue tickets across 14+ clients from Autotask PSA. This report breaks down where overdue volume concentrates, how long tickets have been sitting past their due date, and which accounts need immediate attention. PSA
Current overdue ticket status pulled from the live Autotask PSA dataset. Overdue = due_datetime < NOW() with no complete_datetime.
EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Tickets'[status_name], "TicketCount", COUNTROWS('BI_Autotask_Tickets'))Top 14 clients ranked by overdue ticket count. Severity badge: red = 30+ overdue, amber = 15-29. Avg Days Overdue shows how long tickets have been sitting past their due date on average.
| Client | Overdue | Severity | Avg Days Overdue |
|---|---|---|---|
| Client A | 113 | Critical | 110 |
| Client B | 65 | Critical | 90 |
| Client C | 40 | Critical | 80 |
| Client D | 36 | Critical | 110 |
| Client E | 33 | Critical | 96 |
| Client F | 25 | Warning | 93 |
| Client G | 20 | Warning | 97 |
| Client H | 20 | Warning | 78 |
| Client I | 19 | Warning | 94 |
| Client J | 18 | Warning | 71 |
| Client K | 18 | Warning | 146 |
| Client L | 18 | Warning | 103 |
| Client M | 18 | Warning | 73 |
| Client N | 18 | Warning | 137 |
EVALUATE
TOPN(12,
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[company_name],
FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[due_datetime] < NOW() &&
NOT(ISBLANK('BI_Autotask_Tickets'[due_datetime])) &&
ISBLANK('BI_Autotask_Tickets'[complete_datetime])
),
"OverdueCount", DISTINCTCOUNT('BI_Autotask_Tickets'[ticket_id]),
"AvgDaysOverdue", AVERAGE('BI_Autotask_Tickets'[due_date_age_days])
),
[OverdueCount], DESC
)How long overdue tickets have been sitting past their due date. Grouped by age bucket from the due_date_age_category field. Longer bars and redder colors indicate deeper aging problems.
EVALUATE
SUMMARIZECOLUMNS(
'BI_Autotask_Tickets'[due_date_age_category],
FILTER('BI_Autotask_Tickets',
'BI_Autotask_Tickets'[due_datetime] < NOW() &&
ISBLANK('BI_Autotask_Tickets'[complete_datetime])
),
"Count", DISTINCTCOUNT('BI_Autotask_Tickets'[ticket_id])
)With 113 overdue tickets and an average of 110 days past due, Client A is the single biggest concentration of risk in the portfolio. This is not a handful of recently missed deadlines. These tickets have been sitting for nearly four months on average. Any SLA review or client-facing report will surface this immediately. Addressing Client A alone would cut the total overdue count by more than a tenth.
The age distribution shows that roughly 75% of overdue tickets have been past due for more than 60 days. This is a structural issue, not a temporary spike. New tickets are being handled on time (zero due today, zero due this week), but the old backlog keeps growing because nothing forces these tickets back into active workflows. Without a dedicated cleanup effort, the 90+ day bucket will only get larger.
The fact that no tickets are currently due today or this week means the team is not falling behind on new work. Current-period SLA adherence appears healthy. The overdue problem is entirely historical. This is good news because it means a focused cleanup sprint can reduce the backlog without competing with incoming ticket flow.
The 844 overdue tickets represent a backlog that has been accumulating for months. The data shows this is concentrated in a small number of clients (the top 5 alone account for 287 tickets, or 34% of the total) and is heavily skewed toward older aging buckets.
Step 1: Triage the top 5 clients. Start with Client A (113 tickets, 110 days avg). Pull those tickets into a dedicated board or queue. Classify each one as "still actionable," "waiting on client," or "should be closed." Many tickets aging 90+ days may already be resolved informally but never closed in Autotask. A single pass could eliminate 20-30% of the backlog.
Step 2: Set a weekly overdue review cadence. Add a 15-minute weekly checkpoint where the service manager reviews the [Tickets - Overdue] measure filtered by due_date_age_category. Focus on anything that crossed into 30+ days that week. The goal is to stop tickets from aging into the 90+ day bucket.
Step 3: Build an auto-escalation rule. Any ticket where due_date_age_days exceeds 14 should trigger an automatic notification to the assigned resource and their team lead. Autotask workflow rules can handle this natively. The current data shows that once a ticket passes 14 days overdue, it tends to stay overdue indefinitely.
Step 4: Track progress monthly. Use this same report structure as a monthly checkpoint. The target: reduce the 90+ day bucket by 50% within 60 days, and bring total overdue below 400 within 90 days.
A ticket is overdue when its due_datetime is in the past and it has no complete_datetime. The field due_date_age_days tracks how many days have passed since the due date. This report does not use is_sla_overdue (which does not exist in the data model). Instead it filters directly on due_datetime < NOW() combined with ISBLANK(complete_datetime).
The [Tickets - Due Today] measure counts tickets whose due_datetime falls on the current calendar date and are still open. Zero means no tickets are reaching their deadline today. The 844 overdue tickets all have due dates that have already passed, some by more than 100 days. This is a backlog problem, not a current-day pressure problem.
Yes. Copy any query from the toggles above and paste it into DAX Studio or the Power BI Desktop performance analyzer. The queries reference standard Proxuma data model tables and measures that exist in every Proxuma Power BI deployment. Replace company_name values with your own client names to filter for specific accounts.
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