“M365 Lighthouse Workload Health Monitor”
Autotask PSA Datto RMM Datto Backup Microsoft 365 SmileBack HubSpot IT Glue Lighthouse All reports
AI-GENERATED REPORT
You searched for:

M365 Lighthouse Workload Health Monitor

Analysis and reporting on workload health monitor - unhealthy status detection for managed service providers.

Built from: M365 Lighthouse
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

M365 Lighthouse Workload Health Monitor

Analysis and reporting on workload health monitor - unhealthy status detection 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 · Lighthouse
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 › M365 Lighthouse Workload Health Monitor
What you can measure in this report
Summary Metrics
Healthy Workloads by Client
Workload Health Monitor Trend (3 Quarters)
Alert Risk Matrix
Alert Detail by Category
Device Fleet Health Overview
Key Findings
Strategic Recommendations
Frequently Asked Questions
Healthy Workloads
Degraded
Unhealthy
AI-Generated Power BI Report
M365 Lighthouse Workload Health Monitor

Analysis and reporting on workload health monitor - unhealthy status detection 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
Healthy Workloads
202 (68.9%)
Onboarded tenants
Degraded
269 (91.8%)
Compliant
Unhealthy
91
69 ineligible + 22 disabled
Tenants Affected
14
With issues
View DAX Query - Summary Metrics
EVALUATE ROW("TotalTenants", COUNTROWS('BI_Lighthouse_Tenant'), "Active", CALCULATE(COUNTROWS('BI_Lighthouse_Tenant_Status_Information'), 'BI_Lighthouse_Tenant_Status_Information'[onboarding_status] = "active"), "GDAP", CALCULATE(COUNTROWS('BI_Lighthouse_Tenant_Status_Information'), 'BI_Lighthouse_Tenant_Status_Information'[delegated_privilege_status] = "granularDelegatedAdminPrivileges"), "Ineligible", CALCULATE(COUNTROWS('BI_Lighthouse_Tenant_Status_Information'), 'BI_Lighthouse_Tenant_Status_Information'[onboarding_status] = "ineligible"), "Disabled", CALCULATE(COUNTROWS('BI_Lighthouse_Tenant_Status_Information'), 'BI_Lighthouse_Tenant_Status_Information'[onboarding_status] = "disabled"))
2.0 Healthy Workloads by Client

Breakdown of unhealthy status detection across managed clients.

Contoso Ltd
94.2
Fabrikam Inc
83
Woodgrove Bank
71
Tailspin Toys
59
Adventure Works
47
Litware Inc
35
ClientHealthy WorkloadsDegradedUnhealthyTenants AffectedStatus
Contoso Ltd94.2%481814Good
Fabrikam Inc86.7%441713Good
Woodgrove Bank79.1%401512Warning
Tailspin Toys71.6%361411Warning
Adventure Works64.1%331210Critical
Litware Inc56.5%29118Good

Contoso Ltd leads across most metrics in this analysis. Adventure Works 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 - Healthy Workloads by Client
EVALUATE
SUMMARIZECOLUMNS(
    BI_Lighthouse_WorkloadHealth[company_name],
    "Healthy Workloads", COUNTROWS(BI_Lighthouse_WorkloadHealth),
    "Degraded", CALCULATE(COUNTROWS(BI_Lighthouse_WorkloadHealth), BI_Lighthouse_WorkloadHealth[status] = "Active")
)
ORDER BY [Healthy Workloads] DESC
3.0 Workload Health Monitor Trend (3 Quarters)

How unhealthy status detection 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 - Workload Health Monitor Trend (3 Quarters)
EVALUATE
SUMMARIZECOLUMNS(
    BI_Lighthouse_WorkloadHealth[snapshot_month],
    "Healthy Workloads", COUNTROWS(BI_Lighthouse_WorkloadHealth),
    "Rate", DIVIDE(CALCULATE(COUNTROWS(BI_Lighthouse_WorkloadHealth), BI_Lighthouse_WorkloadHealth[is_successful] = TRUE()), COUNTROWS(BI_Lighthouse_WorkloadHealth))
)
ORDER BY BI_Lighthouse_WorkloadHealth[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 Healthy Workloads measure?

Healthy Workloads tracks the key performance indicator for unhealthy status detection. It is calculated based on data from Microsoft 365, M365 Lighthouse and refreshed daily.

How often is this report updated?

Data syncs every 24 hours from Microsoft 365, M365 Lighthouse. 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.

Generate this report from your own data

Connect Proxuma Power BI to your PSA, RMM, and M365 environment, use an MCP-compatible AI to ask questions, and generate custom reports - in minutes, not days.

See more reports Get started