How old are your 844 currently open tickets, which statuses they sit in, and how many are overdue. Generated by AI via Proxuma Power BI MCP server.
How old are your 844 currently open tickets, which statuses they sit in, and how many are overdue. 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
How old are your 844 currently open tickets, which statuses they sit in, and how many are overdue. Generated by AI via Proxuma Power BI MCP server.
EVALUATE VAR OpenT = FILTER('BI_Autotask_Tickets','BI_Autotask_Tickets'[status_name]<>"Complete") RETURN ROW("OpenCount", COUNTROWS(OpenT), "AvgAge", AVERAGEX(OpenT,'BI_Autotask_Tickets'[ticket_age_days]), "Over180", COUNTROWS(FILTER(OpenT,'BI_Autotask_Tickets'[ticket_age_days]>180)), "OverdueResolveSLA", COUNTROWS(FILTER(OpenT,'BI_Autotask_Tickets'[resolved_due_age_days]>0)))All 844 open tickets grouped by age bucket. The largest concentration (58.3%) sits between 61 and 90 days old. No tickets are under 61 days.
(same OpenT var; bucketed by ticket_age_days)How the 844 open tickets are distributed across Autotask statuses
| Status | Count | % of Open |
|---|---|---|
| Planned | 213 | 25.2% |
| In progress | 205 | 24.3% |
| New | 169 | 20.0% |
| Waiting Customer | 116 | 13.7% |
| Customer has responded | 102 | 12.1% |
| Waiting for third party | 38 | 4.5% |
| Assigned | 1 | 0.1% |
EVALUATE ADDCOLUMNS(SUMMARIZE(FILTER('BI_Autotask_Tickets','BI_Autotask_Tickets'[status_name]<>"Complete"),'BI_Autotask_Tickets'[status_name]), "Count", CALCULATE(COUNTROWS('BI_Autotask_Tickets'))) ORDER BY [Count] DESC360 of 844 open tickets (42.7%) have passed their SLA resolution due date
(OpenT filtered to resolved_due_age_days>0; 360 tickets are past resolution SLA)The most striking finding is not the total count. 844 open tickets is a number you can work with. The problem is the age profile. Every single ticket in this backlog is at least 61 days old. There are zero tickets in the 0 to 7, 8 to 14, 15 to 30, or 31 to 60 day buckets. That means one of two things: either new tickets are being resolved and closed within their first two months (good), or new tickets are not flowing into the system at all (worth checking).
The bulk of the backlog, 492 tickets (58.3%), sits in the 61 to 90 day range. These are tickets that were created roughly two to three months ago and have not been resolved. Combined with the 276 tickets aged 91 to 180 days and the 76 tickets over 180 days, that accounts for all 844 open tickets. At that age, these are not active work items. They are stale.
169 tickets are still marked as "New" despite being at least two months old. These were never triaged, never assigned, and never worked. They are sitting in the queue untouched. Meanwhile, 102 tickets are in "Customer has responded," meaning the customer replied but your team has not followed up. Both of these groups carry direct client satisfaction risk.
The overdue rate of 42.7% (360 tickets) is high but not surprising given the age profile. When the youngest ticket in your backlog is already two months old, many of them will naturally have breached their SLA. Reducing this number starts with reducing the age, not with extending due dates.
The 76 tickets over 180 days are a separate category. At six months or older, these are almost certainly stuck: waiting on a decision that was never made, a vendor response that never came, or a project ticket that lost its owner. A dedicated cleanup session for just these 76 tickets would reduce your backlog age and close items that no one is actively working anyway.
5 priorities based on the findings above
These tickets have been sitting untouched for over a month. Run through them this week and assign, close, or merge each one. A ticket that stays in "New" for 30+ days is not being managed. It is being ignored. Clients who submitted these tickets are either waiting or have already given up.
The customer did their part. They replied. Now the ball is in your court and has been for at least 61 days. These are the tickets most likely to generate complaints. Sort them by age, start with the oldest, and either resolve or update each one with a next step.
Six months is long enough that most of these tickets have lost their original context. Book a two-hour session with the service team. Pull up each ticket. If it is still valid, assign an owner and set a due date. If it is not, close it with a note. Clearing these will reduce your average backlog age by several days on its own.
The empty buckets for 0 to 30 days could mean your team resolves new tickets quickly, which is great. But it could also mean tickets are being created directly into project queues or older date ranges. Verify that new incoming tickets are landing in this dataset correctly and that you are not missing recent intake volume.
Run this report weekly. Track the total count, the average age, and the overdue rate over time. If the average age goes up, your backlog is getting staler. If it goes down, your cleanup efforts are working. A five-minute check each Monday morning keeps the backlog from quietly aging out of control again.
Any ticket in Autotask whose status is not "Complete." That includes New, Assigned, In Progress, Planned, Waiting Customer, Customer Responded, and Waiting Third Party. The report filters out completed tickets entirely so you only see what is still in your active backlog.
Ticket age is the number of calendar days between the ticket creation date and today. The Proxuma Power BI data model stores this in the ticket_age_days column, which is recalculated on each data refresh. It does not account for business hours or SLA pauses.
A ticket is overdue when the resolved_due_age_days value is greater than zero. This means the ticket has passed its SLA resolution due date without being completed. The SLA due date is set by Autotask based on the ticket priority and the SLA attached to the client contract.
This likely means recent tickets are being resolved and closed within their first two months, so they do not appear in the open backlog. It could also indicate that new tickets are being created with backdated creation dates or into a queue not captured here. Both scenarios are worth verifying in your Autotask configuration.
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