This report crosses Datto RMM alert data (135,387 alerts across 90+ companies) with N-able backup telemetry (169 active devices, 92.9% success rate) to find which companies generate the most device alerts while running failing or absent backups. Two data sources, one question: are your noisiest endpoints also your least protected?
This report crosses Datto RMM alert data (135,387 alerts across 90+ companies) with N-able backup telemetry (169 active devices, 92.9% success rate) to find which companies generate the most device alerts while running failing or absent backups. Two data sources, one question: are your noisiest endpoints also your least protected?
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 MSP owners monitoring backup compliance
How often: Daily for operations, weekly for management review, monthly for client reporting
This report crosses Datto RMM alert data (135,387 alerts across 90+ companies) with N-able backup telemetry (169 active devices, 92.9% success rate) to find which companies generate the most device alerts while running failing or absent backups. Two data sources, one question: are your noisiest endpoints also your least protected?
| Company | Alerts |
|---|---|
| Martin Group | 27,849 |
| Craig-Huynh | 9,521 |
| Thompson, Contreras and Rios | 7,573 |
| Wall PLC | 5,355 |
| Willis, Allen and Phillips | 5,081 |
| Price-Gomez | 4,170 |
| Little Group | 4,089 |
| Lewis LLC | 3,561 |
| Fox, Conner and West | 3,207 |
| Adams LLC | 2,991 |
The pattern is stark. 7 of the top 10 alert-generating companies have zero backup data. These are not small accounts: collectively they generate 59,294 RMM alerts. The three companies that do have backup data (Client F, G, J) show 100% success rates, but Client G only covers 2 devices despite generating 3,437 alerts.
EVALUATE TOPN(10, GROUPBY('BI_Datto_Rmm_Alerts', 'BI_Datto_Rmm_Alerts'[site_name], "Alert_Count", COUNTX(CURRENTGROUP(), 'BI_Datto_Rmm_Alerts'[alert_uid])), [Alert_Count], DESC) ORDER BY [Alert_Count] DESCThe quadrant breakdown is concerning. The top-left cell is the danger zone, and it holds the majority of your alert volume. These 7 companies account for 43.8% of all RMM alerts in the dataset, and none of them have backup protection visible in the N-able data.
This could mean two things. Either these companies genuinely have no backup solution deployed, or the backup data has not been linked to the correct company record in the data model. Both scenarios need investigation.
| Client | Alerts | Backup Rate | Devices Backed Up | Devices w/ Issues | Risk Level |
|---|---|---|---|---|---|
| Client F | 3,838 | 100% | 26 | 2 | Medium |
| Client G | 3,437 | 100% | 2 | 0 | Medium |
| Client J | 2,033 | 100% | 6 | 1 | Low |
| Client K | 1,792 | 100% | 11 | 4 | Medium |
| Client L | 1,486 | 100% | 4 | 0 | Low |
| Client M | 1,198 | 100% | 8 | 1 | Low |
| Client N | 949 | 94.4% | 17 | 3 | Medium |
| Client O | 914 | 100% | 13 | 2 | Low |
| Client P | 897 | 81.8% | 9 | 0 | Medium |
| Client Q | 828 | 80.0% | 8 | 0 | High |
| Client R | 776 | 81.8% | 9 | 1 | Medium |
| Client S | 122 | 50.0% | 1 | 0 | High |
Client S stands out with a 50% backup success rate on a single device. Combined with 122 active RMM alerts, this account has the worst backup health in the dataset. Client Q and Client R both sit at around 80% backup success rates with 800+ alerts each.
Among the higher-alert companies, Client K deserves attention: 1,792 alerts with 4 devices showing backup issues. That is the highest count of problematic backup devices for any single account.
EVALUATE
TOPN(10,
FILTER(
SUMMARIZECOLUMNS(
BI_Autotask_Companies[company_name],
"Alerts", COUNTROWS(BI_Datto_Rmm_Alerts),
"BackupRate", [NAble - Backup Success Rate %]
),
[Alerts] > 100
),
[Alerts], DESC
)The overall backup picture is not bad at 92.9% success. But that number masks the real problem: the majority of high-alert companies have no backup data at all. The 92.9% only measures companies that actually have backup deployed. It says nothing about the companies that should have backup but do not.
EVALUATE ROW(
"TotalAlerts", COUNTROWS(BI_Datto_Rmm_Alerts),
"BackupSuccessRate", [NAble - Backup Success Rate %],
"DevicesWithIssues", [NAble - Devices with Backup Issues],
"DevicesWithBackup", [NAble - Devices with Recent Backup],
"TotalActiveDevices", [NAble - Total Active Devices]
)Seven of the top ten alert-generating companies show zero backup data in N-able. These 7 accounts produce 59,294 RMM alerts combined, representing 43.8% of total alert volume. If any of these devices suffers a hardware failure or ransomware event, there is no documented recovery path.
With only 1 device backed up and a 50% success rate, Client S has the worst backup health in the dataset. Combined with 122 active RMM alerts, this account is one failed backup away from data loss with no recovery option.
Despite a 100% success rate on the devices that work, Client K has the highest number of devices with backup issues (4 out of 11). At 1,792 alerts, this account is generating noise and burning through backup reliability at the same time.
Client Q (80.0%), Client R (81.8%), and Client P (81.8%) all fall under the 85% threshold. These accounts carry 800-900 alerts each. The combination of elevated alert counts and sub-par backup rates puts them in the "needs immediate review" category.
Client F (3,838 alerts, 100% backup, 26 devices) and Client J (2,033 alerts, 100%, 6 devices) prove that high alert volume does not automatically mean poor backup health. The alerts and the backup are separate problems. These companies have solved the backup side.
1. Audit the top 7 alert-generating companies with no backup data. Start with Client A (26,873 alerts) and Client B (9,307 alerts). Determine whether backup is genuinely absent or whether the N-able agent is deployed but not linked to the correct company in the data model. If backup is truly missing, these accounts should be at the top of the deployment queue.
2. Fix Client S immediately. A single device at 50% backup success with 122 active alerts is a data loss incident waiting to happen. This is the smallest fix in the report, only one device, and it should be resolved within a week.
3. Review Client K's 4 backup-issue devices. Client K generates 1,792 alerts and has the highest count of problematic backup devices. Investigate what is causing the backup failures: storage capacity, agent misconfiguration, or connectivity problems.
4. Set a minimum backup coverage threshold per company. Any company generating more than 500 RMM alerts should have at least 90% of its managed devices covered by backup. Build a Power BI page that flags companies falling below this threshold. The DAX queries in this report give you the building blocks.
5. Schedule this cross-source report monthly. The value of correlating RMM alerts with backup status grows over time. Monthly tracking would reveal whether the backup gaps are being closed and whether new high-alert companies are emerging without backup protection.
A company shows "No Data" when it has RMM alerts in Datto but zero records in the N-able backup tables (BI_NAble_Device_Statistic). This can mean backup is not deployed for that client, or the backup agent is deployed but not mapped to the correct company_name in the data model. Both scenarios need investigation.
The N-able Devices with Backup Issues measure counts devices where the most recent backup job failed or is overdue. This includes devices where the backup agent is installed but the last job did not complete successfully, or where the backup schedule has not run within the expected time window.
The NAble - Backup Success Rate % measure divides devices with a recent successful backup by the total active devices with backup configured. A 92.9% rate means 157 of 169 active devices completed their last backup job successfully. The remaining 12 either failed or are overdue.
Not necessarily. RMM alert volume depends on monitoring policy configuration. Some companies have aggressive monitoring policies that generate alerts for minor events (disk space warnings, patch availability, service restarts). Others only alert on critical failures. High alert counts indicate more monitoring activity, not always worse device health. That said, a company with thousands of unresolved alerts is clearly not keeping up with alert triage.
Yes. The 7 high-alert companies with no backup data are natural upsell candidates. You can use the alert volume as evidence of device instability and position backup as a risk mitigation measure. The DAX queries in this report let you pull real numbers per client for QBR conversations.
Monthly is the recommended cadence. RMM alerts accumulate daily, and backup status changes as agents are deployed or fail. A monthly check ensures new high-risk accounts are caught before they become incidents. The queries run in under 30 seconds through the Power BI MCP server.
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