This report crosses Datto RMM alert data (135,387 alerts across 264 companies) with Autotask ticket SLA metrics (67,521 tickets) to test whether companies generating high alert volumes also experience degraded SLA performance. Two data sources, one question: is your RMM alert noise drowning your service desk?
This report crosses Datto RMM alert data (135,387 alerts across 264 companies) with Autotask ticket SLA metrics (67,521 tickets) to test whether companies generating high alert volumes also experience degraded SLA performance. Two data sources, one question: is your RMM alert noise drowning your service desk?
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 delivery managers, operations leads, and MSP owners tracking service quality
How often: Weekly for operational adjustments, monthly for client reporting, quarterly for contract reviews
This report crosses Datto RMM alert data (135,387 alerts across 264 companies) with Autotask ticket SLA metrics (67,521 tickets) to test whether companies generating high alert volumes also experience degraded SLA performance. Two data sources, one question: is your RMM alert noise drowning your service desk?
87.3% of all alerts are informational. That means nearly 9 out of 10 alerts coming from Datto RMM carry no immediate action requirement. Only 3.9% of alerts fall into the Critical or High categories (5,253 total). The remaining 8.8% are Moderate or Low. This distribution suggests that the alert noise floor is very high, and the signal-to-noise ratio could be improved significantly by tuning informational monitors or suppressing known-good patterns.
EVALUATE GROUPBY('BI_Datto_Rmm_Alerts', 'BI_Datto_Rmm_Alerts'[priority], "Total", COUNTX(CURRENTGROUP(), 'BI_Datto_Rmm_Alerts'[alert_uid]), "Unresolved", SUMX(CURRENTGROUP(), IF('BI_Datto_Rmm_Alerts'[resolved] = FALSE(), 1, 0))) ORDER BY [Total] DESC| Company | Alerts | Alert Tickets | Total Tickets | First Response | Resolution |
|---|---|---|---|---|---|
| Client A | 26,873 | 1,105 | 2,775 | 73.7% | 88.3% |
| Client B | 9,307 | 160 | 5,458 | 88.2% | 91.7% |
| Client C | 7,430 | 494 | 1,803 | 75.4% | 87.1% |
| Client D | 5,032 | 95 | 2,376 | 86.0% | 92.5% |
| Client E | 4,086 | 521 | 2,180 | 84.9% | 90.9% |
| Client F | 3,838 | 494 | 5,290 | 87.5% | 93.7% |
| Client G | 3,437 | 114 | 1,758 | 68.6% | 86.0% |
| Client H | 2,920 | 132 | 682 | 78.6% | 89.3% |
| Client I | 2,646 | 798 | 1,002 | 92.3% | 97.5% |
| Client J | 2,033 | 156 | 6,381 | 43.2% | 79.3% |
Client A dominates the alert landscape with 26,873 alerts, nearly three times the next client. Their first response SLA sits at 73.7%, well below the 90% target. Client G (68.6%) and Client J (43.2%) show even worse first response rates. The pattern is not universal though: Client I generates 2,646 alerts but maintains a 92.3% first response rate, showing that alert volume alone does not determine SLA outcomes.
The alert-to-ticket ratio varies wildly. Client A converts about 4.1% of alerts to tickets, while Client I converts 30.2%. A high conversion rate with good SLA (Client I) suggests well-tuned monitors that create actionable tickets. A low conversion rate with poor SLA (Client B at 1.7%) suggests the service desk is overwhelmed by other work, not alert-generated tickets specifically.
EVALUATE TOPN(15,
SUMMARIZECOLUMNS(
BI_Autotask_Companies[company_name],
"Alerts", COUNTROWS(BI_Datto_Rmm_Alerts),
"TicketsFromAlerts", [Tickets - From Datto RMM Alerts],
"FirstResponseMet", [Tickets - First Response Met %],
"ResolutionMet", [Tickets - Resolution Met %],
"TicketsCreated", [Tickets - Count - Created]
),
COUNTROWS(BI_Datto_Rmm_Alerts), DESC
)When sorted by first response SLA, a loose pattern emerges: most high-alert clients cluster below the 90% target line. But Client J at 43.2% is the real outlier, with 6,381 total tickets and only 2,033 alerts. Their SLA problem is not alert-driven. It is a capacity or process issue. Meanwhile, Client I proves that high alert volumes (2,646) can coexist with excellent SLA performance (92.3%) when alerts are well-tuned and the service desk is properly resourced.
97.5% of RMM alerts self-resolve, leaving just 3,369 unresolved alerts. The alert pipeline itself works well. The concerning number is on the ticket side: all 844 open tickets are overdue. That is a 100% overdue rate on the current backlog, which means the service desk is not keeping up with existing work. When new alert-generated tickets land on top of an already-overdue queue, they contribute to SLA degradation not because of their volume, but because the queue has no breathing room.
EVALUATE ROW(
"OpenTickets", [Open Tickets (Current)],
"Overdue", [Tickets - Overdue]
)
EVALUATE ROW(
"TotalAlerts", COUNTROWS(BI_Datto_Rmm_Alerts),
"AlertTickets", [Tickets - From Datto RMM Alerts],
"OverallFirstResponse", [Tickets - First Response Met %],
"OverallResolution", [Tickets - Resolution Met %],
"TotalTickets", [Tickets - Count - Created],
"AvgHours", [Tickets - Avg Hours Per Ticket]
)The gap between first response (80.1%) and resolution (90.2%) tells a clear story. The service desk is slow to pick up tickets, but good at closing them once work starts. This pattern is typical when alert noise or ticket volume overwhelms the triage stage. Technicians who grab a ticket tend to finish the job, but the initial acknowledgment gets delayed because the queue is too long. Reducing the inbound volume through alert tuning would directly improve the first response metric without changing how work gets done.
All 844 currently open tickets have breached their SLA. This is not an alert problem, it is a capacity problem. The service desk backlog has no buffer, so any new ticket (alert-generated or otherwise) starts behind from the moment it lands in the queue.
118,217 of 135,387 alerts carry no action requirement. Even if only a fraction create tickets, the monitoring overhead and triage burden consume attention. Suppressing or auto-resolving known-good informational patterns would reduce the noise floor and free up focus for the 5,253 Critical and High priority alerts that actually need human review.
The portfolio-wide first response rate sits 10 points below the 90% target. Resolution SLA just barely clears it at 90.2%. The bottleneck is triage speed, not resolution quality. Technicians close tickets efficiently once they start, but the initial pickup takes too long.
26,873 alerts from a single client is an anomaly. Their first response SLA of 73.7% and resolution of 88.3% both fall below target. A focused review of Client A's RMM monitoring policies could cut alert volume significantly and improve their SLA numbers in the process.
Client I generates 2,646 alerts with a 30.2% conversion rate to tickets, yet maintains a 92.3% first response SLA and 97.5% resolution rate. This shows that well-tuned monitoring policies (high conversion, low noise) can produce good outcomes even at scale. The difference is alert quality, not just alert quantity.
1. Audit Client A's RMM monitoring policies immediately. With 26,873 alerts (nearly 20% of all alerts from a single client), Client A is the highest-leverage target for noise reduction. Review their active monitors, identify informational alerts that never convert to tickets, and suppress or raise thresholds on those monitors. A 50% reduction in Client A alerts alone would cut the portfolio total by 10%.
2. Address the 844-ticket overdue backlog before tuning alerts. Alert noise contributes to SLA pressure, but the 100% overdue rate on open tickets signals a more fundamental capacity issue. No amount of alert tuning will fix SLA performance if the service desk cannot clear its existing queue. Consider temporary staffing, ticket priority triage, or closing stale tickets that no longer need resolution.
3. Suppress or auto-close informational alerts that never create tickets. 87.3% of alerts are informational. Many of these exist for audit purposes and never generate actionable work. Create a suppression policy for the top 10 informational alert patterns by volume. This reduces dashboard noise, speeds up triage for real alerts, and frees monitoring screen real estate.
4. Use Client I as the benchmark for alert policy tuning. Client I demonstrates that 2,600+ alerts can coexist with 92%+ SLA when the alert-to-ticket conversion rate is high (30%) and monitors generate actionable work. Compare Client I's monitoring policies against Client A, Client G, and Client C to identify what makes their alerts more useful and less noisy.
The [Tickets - From Datto RMM Alerts] measure counts Autotask tickets that were created directly from a Datto RMM alert through the integration. These tickets have a traceable link back to the specific alert that triggered them. Manual tickets created by a technician after reviewing an alert separately are not included in this count.
First response measures how quickly a technician acknowledges the ticket. Resolution measures when the ticket is closed. The 10-point gap (80.1% vs 90.2%) means tickets sit in the queue waiting to be picked up, but once someone starts working on them, they finish the job within SLA. This is a queue management problem, not a work quality problem.
Not necessarily. Suppression means the alert does not create a notification or ticket, but the data still gets recorded in Datto RMM. You can always review suppressed alerts in bulk through reporting. The goal is to stop informational alerts from competing for attention with Critical and High priority items in the live queue.
It is the number of alert-created tickets divided by the total number of alerts for that company. A low conversion rate (like Client A at 4.1%) means most alerts do not result in a ticket, suggesting high noise. A higher rate (like Client I at 30.2%) means more alerts produce actionable work, indicating better-tuned monitors.
There is no universal benchmark, but as a rule of thumb: if less than 10% of your alerts turn into tickets, you have a noise problem. Client I demonstrates that 30% is achievable with well-configured monitors. The goal is not 100% - some alerts serve as informational records - but every alert that creates no action and draws no review is wasted attention.
Yes. Connect Proxuma Power BI to your Datto RMM and Autotask accounts, add the AI via MCP, and ask the same question. The AI writes DAX queries, runs them against your actual data, and produces a report like this one in under fifteen minutes. All company names are automatically anonymized.
This indicates a systemic backlog rather than a few stale tickets. When every open ticket is past its SLA deadline, the queue has been running behind for a sustained period. It could stem from understaffing, ticket prioritization issues, or tickets that should have been closed but remain open. A backlog review is the first step to identify which tickets are genuinely active and which can be bulk-closed.
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