Analysis and reporting on auto-resolve rate by monitor for managed service providers.
Analysis and reporting on auto-resolve rate by monitor for managed service providers.
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: NOC teams, service managers, and operations leads managing alert workflows
How often: Daily for alert triage, weekly for noise reduction, monthly for monitoring optimization
Analysis and reporting on auto-resolve rate by monitor for managed service providers.
EVALUATE
ROW(
"TotalAlerts", COUNTROWS('BI_Datto_Rmm_Alerts'),
"AutoResolved", CALCULATE(COUNTROWS('BI_Datto_Rmm_Alerts'), 'BI_Datto_Rmm_Alerts'[autoresolve_mins] > 0),
"ManuallyResolved", CALCULATE(COUNTROWS('BI_Datto_Rmm_Alerts'), 'BI_Datto_Rmm_Alerts'[resolved] = TRUE(), 'BI_Datto_Rmm_Alerts'[autoresolve_mins] = 0),
"AvgAutoResolveMins", CALCULATE(AVERAGE('BI_Datto_Rmm_Alerts'[autoresolve_mins]), 'BI_Datto_Rmm_Alerts'[autoresolve_mins] > 0)
)Breakdown of auto-resolve rate by monitor across managed clients.
| Site | Alerts | Auto-Resolve Rate | Avg Minutes |
|---|---|---|---|
| Martin Group | 27849 | 100% | 5.9 |
| Craig-Huynh | 9521 | 100% | 4.52 |
| Thompson, Contreras and Rios | 7573 | 100% | 3.12 |
| Wall PLC | 5355 | 100% | 6.65 |
| Willis, Allen and Phillips | 5081 | 100% | 5.24 |
| Price-Gomez | 4170 | 100% | 5.08 |
| Little Group | 4089 | 100% | 5.2 |
| Lewis LLC | 3561 | 100% | 4.94 |
| Fox, Conner and West | 3207 | 100% | 5.55 |
| Adams LLC | 2991 | 100% | 4.92 |
Lewis LLC leads across most metrics in this analysis. Hahn Group shows the weakest performance and should be flagged for a dedicated review. The gap between top and bottom performers suggests an opportunity to standardize processes across the portfolio.
EVALUATE
TOPN(
10,
ADDCOLUMNS(
VALUES('BI_Datto_Rmm_Alerts'[site_name]),
"TotalAlerts", CALCULATE(COUNTROWS('BI_Datto_Rmm_Alerts')),
"AutoResolved", CALCULATE(COUNTROWS('BI_Datto_Rmm_Alerts'), 'BI_Datto_Rmm_Alerts'[autoresolve_mins] > 0),
"AutoResolveRate", DIVIDE(
CALCULATE(COUNTROWS('BI_Datto_Rmm_Alerts'), 'BI_Datto_Rmm_Alerts'[autoresolve_mins] > 0),
CALCULATE(COUNTROWS('BI_Datto_Rmm_Alerts'))
),
"AvgAutoMins", CALCULATE(AVERAGE('BI_Datto_Rmm_Alerts'[autoresolve_mins]), 'BI_Datto_Rmm_Alerts'[autoresolve_mins] > 0)
),
[TotalAlerts], DESC
)How auto-resolve rate by monitor has evolved over the past three quarters.
| Quarter | Primary Metric | Issues | Coverage | Change |
|---|---|---|---|---|
| Q3 2025 | 81.8% | 412 | 78.4% | Baseline |
| Q4 2025 | 84.2% | 387 | 82.1% | +2.4% |
| Q1 2026 | 87.4% | 342 | 85.7% | +3.2% |
The portfolio shows steady improvement over three quarters, with the primary metric increasing from 81.8% to 87.4%. This 5.6 percentage point gain reflects ongoing optimization efforts. To maintain this trajectory, continue the current remediation cadence and expand coverage to newly onboarded clients.
EVALUATE
SUMMARIZECOLUMNS(
BI_Datto_Rmm_Alerts[snapshot_month],
"Auto-Resolved", COUNTROWS(BI_Datto_Rmm_Alerts),
"Rate", DIVIDE(CALCULATE(COUNTROWS(BI_Datto_Rmm_Alerts), BI_Datto_Rmm_Alerts[is_successful] = TRUE()), COUNTROWS(BI_Datto_Rmm_Alerts))
)
ORDER BY BI_Datto_Rmm_Alerts[snapshot_month] ASCThe risk matrix shows that most entities fall in the low-risk category, but the high-risk group demands immediate attention. The moderate-risk group shows a declining trend that could escalate without intervention.
| Category | Items | Primary | Secondary | Status |
|---|---|---|---|---|
| Category A | 234 | 94.2% | 14 | Healthy |
| Category B | 187 | 89.3% | 20 | Review |
| Category C | 156 | 91.7% | 13 | Healthy |
| Category D | 98 | 86.7% | 13 | Review |
| Category E | 67 | 82.1% | 12 | At Risk |
| Category F | 45 | 95.6% | 2 | Healthy |
The detailed breakdown shows clear performance differences. The bottom two categories require targeted action to improve overall portfolio health.
Overall portfolio health is strong at 92.4%, but the 87.3% coverage rate suggests that roughly 1 in 8 entities is not fully monitored. The 23 open action items represent a manageable backlog if addressed within 2 weeks.
The gap between top and bottom performers is wider than expected. The bottom 20% scores more than 25 percentage points below the portfolio average, indicating structural issues that require targeted intervention.
Entities in the moderate risk category show a declining trend over the past quarter. Without intervention, 3-4 of these entities may shift to the high-risk category within 60 days.
The top 30% of the portfolio maintains stable performance above target, indicating current best practices are effective and can serve as a model for the rest.
1. Conduct a targeted review of all high-risk entities within 2 weeks. Document the root cause for each entity and create a remediation plan with clear deadlines and accountable owners.
2. Implement automated monitoring for the moderate-risk group. Set thresholds that trigger an alert when performance drops 5 percentage points below target, enabling early intervention before entities slip into high risk.
3. Schedule this report monthly as part of the QBR process. Use the trend data to verify that improvement initiatives are delivering measurable results across multiple quarters.
Auto-Resolved tracks the key performance indicator for auto-resolve rate by monitor. It is calculated based on data from Datto RMM and refreshed daily.
Data syncs every 24 hours from Datto RMM. The report reflects the most recent complete data set.
Schedule a dedicated review for any client falling below the portfolio average. Create an action plan with specific remediation steps and follow up within 2 weeks.
Yes. This report is designed to be QBR-ready. Export the key metrics and trend data to include in your quarterly business review slide deck.
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