“Datto RMM Stale Alerts”
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Datto RMM Stale Alerts

Analysis and reporting on stale alerts - unresolved over 7 days for managed service providers.

Built from: Datto RMM
How this report was made
1
Autotask PSA
Multiple data sources combined
2
Proxuma Power BI
Pre-built MSP semantic model, 50+ measures
3
AI via MCP
Claude or ChatGPT writes DAX queries, executes them, formats output
4
This Report
KPIs, breakdowns, trends, recommendations
Ready in < 15 min

Datto RMM Stale Alerts

Analysis and reporting on stale alerts - unresolved over 7 days 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

Time saved
Alert noise drowns out real issues. This report separates signal from noise so your team focuses on what matters.
Alert hygiene
Stale monitors, noisy devices, and misconfigured thresholds waste technician time. This report finds them.
Operations data
Evidence for tuning alert policies, adjusting thresholds, and improving response workflows.
Report categoryRMM & Alert Management
Data sourceAutotask PSA · Datto RMM · Datto Backup · Microsoft 365 · SmileBack · HubSpot · IT Glue
RefreshReal-time via Power BI
Generation timeUnder 15 minutes
AI requiredClaude, ChatGPT or Copilot
AudienceNOC teams, service managers
Where to find this in Proxuma
Power BI › RMM & Alerts › Datto RMM Stale Alerts
What you can measure in this report
Summary Metrics
Stale Alerts by Client
Stale Alerts Trend (3 Quarters)
Alert Risk Matrix
Alert Detail by Category
Device Fleet Health Overview
Key Findings
Strategic Recommendations
Frequently Asked Questions
Stale Alerts
Oldest Alert
Avg Age
AI-Generated Power BI Report
Datto RMM Stale Alerts

Analysis and reporting on stale alerts - unresolved over 7 days for managed service providers.

Demo Report: This report uses synthetic data to demonstrate AI-generated insights from Proxuma Power BI. The structure, DAX queries, and analysis reflect real MSP data patterns.
1.0 Summary Metrics
Stale Alerts
6,953
310 sites
Oldest Alert
96,319 (all resolved)
100% auto-resolve
Avg Age
14.3 days
Stale alert average
Sites Affected
28
Of 142 sites
View DAX Query - Summary Metrics
EVALUATE ROW("Devices", COUNTROWS('BI_Datto_Rmm_Devices'), "Sites", DISTINCTCOUNT('BI_Datto_Rmm_Sites'[Name]), "Alerts", COUNTROWS('BI_Datto_Rmm_Alerts'), "RebootPending", COUNTAX(FILTER('BI_Datto_Rmm_Devices', 'BI_Datto_Rmm_Devices'[Reboot_Required] = TRUE()), 1))
2.0 Stale Alerts by Client

Breakdown of unresolved over 7 days across managed clients.

Lewis LLC
342
Martin Group
83
Wall PLC
71
Ramos Group
59
Hahn Group
47
Anderson Group
35
ClientStale AlertsOldest AlertAvg AgeSites AffectedStatus
Lewis LLC342471428Good
Martin Group315431326Good
Wall PLC287391224Warning
Ramos Group260361121Warning
Hahn Group233321019Critical
Anderson Group20528917Good

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.

View DAX Query - Stale Alerts by Client
EVALUATE
SUMMARIZECOLUMNS(
    BI_Datto_Rmm_Alerts[company_name],
    "Stale Alerts", COUNTROWS(BI_Datto_Rmm_Alerts),
    "Oldest Alert", CALCULATE(COUNTROWS(BI_Datto_Rmm_Alerts), BI_Datto_Rmm_Alerts[status] = "Active")
)
ORDER BY [Stale Alerts] DESC
3.0 Stale Alerts Trend (3 Quarters)

How unresolved over 7 days has evolved over the past three quarters.

Q1 2026
87.4%
Q4 2025
84.2%
Q3 2025
81.8%
QuarterPrimary MetricIssuesCoverageChange
Q3 202581.8%41278.4%Baseline
Q4 202584.2%38782.1%+2.4%
Q1 202687.4%34285.7%+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.

View DAX Query - Stale Alerts Trend (3 Quarters)
EVALUATE
SUMMARIZECOLUMNS(
    BI_Datto_Rmm_Alerts[snapshot_month],
    "Stale Alerts", 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] ASC
4.0
Alert Risk Matrix
Categorizing clients by alert severity and device health.
HIGH RISK
4 entities
Performance significantly below portfolio average. Immediate action required.
MODERATE RISK
7 entities
Performance below target but stable. Review within 2 weeks.
LOW RISK
12 entities
Performance above target level. Standard monitoring sufficient.
NOT ASSESSED
3 entities
Insufficient data available for risk assessment.

The 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.

5.0
Alert Detail by Category
Granular breakdown of alert types and resolution status.
CategoryItemsPrimarySecondaryStatus
Category A23494.2%14Healthy
Category B18789.3%20Review
Category C15691.7%13Healthy
Category D9886.7%13Review
Category E6782.1%12At Risk
Category F4595.6%2Healthy

The detailed breakdown shows clear performance differences. The bottom two categories require targeted action to improve overall portfolio health.

6.0
Device Fleet Health Overview
Overall health indicators across the managed fleet.
92.4% health score
Portfolio Health
87.3% of 100%
Coverage
23 action items
Open Items

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.

7.0
Key Findings
!

Performance Gap Requires Attention

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.

!

Declining Trend in Moderate Risk Group

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.

Top Performers Remain Consistent

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.

8.0
Strategic Recommendations

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.

9.0
Frequently Asked Questions
What does Stale Alerts measure?

Stale Alerts tracks the key performance indicator for unresolved over 7 days. It is calculated based on data from Datto RMM and refreshed daily.

How often is this report updated?

Data syncs every 24 hours from Datto RMM. The report reflects the most recent complete data set.

What should we do about poor performers?

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.

Can we use this in QBR presentations?

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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