3,551 of 6,953 devices are not checking in. This report ranks every site by offline count, breaks down device types, and flags where unresolved alerts overlap with missing hardware.
3,551 of 6,953 devices are not checking in. This report ranks every site by offline count, breaks down device types, and flags where unresolved alerts overlap with missing hardware.
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, asset managers, and service delivery leads
How often: Weekly for fleet reviews, monthly for lifecycle planning, quarterly for budgeting
3,551 of 6,953 devices are not checking in. This report ranks every site by offline count, breaks down device types, and flags where unresolved alerts overlap with missing hardware.
EVALUATE ROW("TotalDevices", COUNTROWS('BI_Datto_Rmm_Devices'), "Online", CALCULATE(COUNTROWS('BI_Datto_Rmm_Devices'), 'BI_Datto_Rmm_Devices'[Online] = TRUE()), "Offline", CALCULATE(COUNTROWS('BI_Datto_Rmm_Devices'), 'BI_Datto_Rmm_Devices'[Online] = FALSE()))All sites ranked by number of offline devices. Offline % color-coded: red (>60%), amber (40–60%), green (<40%).
| # | Site | Total | Online | Offline | Offline % | Unresolved Alerts |
|---|---|---|---|---|---|---|
| 1 | Foster Inc | 1,355 | 515 | 840 | 62.0% | 979 |
| 2 | Client A | 715 | 225 | 490 | 68.5% | 699 |
| 3 | Wall PLC | 320 | 114 | 206 | 64.4% | 34 |
| 4 | Client B | 350 | 188 | 162 | 46.3% | 83 |
| 5 | Clements Group | 103 | 30 | 73 | 70.9% | 19 |
EVALUATE
ADDCOLUMNS(
VALUES('BI_Datto_Rmm_Devices'[site_name]),
"TotalDevices", CALCULATE(COUNTROWS('BI_Datto_Rmm_Devices')),
"Online", CALCULATE(
COUNTROWS('BI_Datto_Rmm_Devices'),
'BI_Datto_Rmm_Devices'[online] = TRUE()),
"Offline", CALCULATE(
COUNTROWS('BI_Datto_Rmm_Devices'),
'BI_Datto_Rmm_Devices'[online] = FALSE()),
"UnresolvedAlerts", CALCULATE(
COUNTROWS('BI_Datto_Rmm_Alerts'),
'BI_Datto_Rmm_Alerts'[resolved] = FALSE())
)
ORDER BY [Offline] DESCSegmented bars showing how each device type splits between online and offline. Notebooks account for the majority of offline devices.
EVALUATE
ADDCOLUMNS(
VALUES('BI_Datto_Rmm_Devices'[type]),
"Count", CALCULATE(COUNTROWS('BI_Datto_Rmm_Devices')),
"Online", CALCULATE(
COUNTROWS('BI_Datto_Rmm_Devices'),
'BI_Datto_Rmm_Devices'[online] = TRUE()),
"Offline", CALCULATE(
COUNTROWS('BI_Datto_Rmm_Devices'),
'BI_Datto_Rmm_Devices'[online] = FALSE())
)
ORDER BY [Count] DESCHorizontal bar chart showing the five sites with the most offline devices, scaled against the highest count (Foster Inc at 840).
EVALUATE
TOPN(
5,
ADDCOLUMNS(
VALUES('BI_Datto_Rmm_Devices'[site_name]),
"Offline", CALCULATE(
COUNTROWS('BI_Datto_Rmm_Devices'),
'BI_Datto_Rmm_Devices'[online] = FALSE()),
"Total", CALCULATE(COUNTROWS('BI_Datto_Rmm_Devices')),
"OfflinePct", DIVIDE(
CALCULATE(COUNTROWS('BI_Datto_Rmm_Devices'),
'BI_Datto_Rmm_Devices'[online] = FALSE()),
CALCULATE(COUNTROWS('BI_Datto_Rmm_Devices')))
),
[Offline], DESC
)
ORDER BY [Offline] DESC3,551 devices are offline out of 6,953 total. That is a 51% offline rate across the entire managed fleet. Some of this is expected: notebooks get closed and put in bags, seasonal workers return hardware, employees leave without IT reclaiming the device. But the scale of the problem points to something more systemic.
Notebooks are the biggest problem category. Of 4,439 notebooks in the fleet, 2,506 are offline. That is 56% of all notebooks. They account for 70% of all offline devices. Many of these are likely stale records: devices that were reimaged, replaced, or returned without being removed from the RMM. Every one of them generates alerts, inflates device counts, and clutters dashboards.
Foster Inc alone accounts for 840 offline devices with 979 unresolved alerts. That combination means the NOC is buried in noise from this single site. Client A follows at 490 offline devices and 699 unresolved alerts. Together, these two sites represent 37% of all offline devices in the fleet.
Clements Group has the worst offline rate at 71%, though the absolute count (73) is smaller. Only 30 of their 103 devices are checking in. For a site this small, that likely means the RMM agent was deployed once and never maintained.
On the other end, servers and firewalls have relatively low offline rates (27% and 21% respectively). The 32 offline servers should still be investigated individually, since a server that stops checking in is a much higher-risk event than a notebook.
5 priorities based on the findings above
Foster Inc has 840 offline devices and Client A has 490. Start by filtering for devices that have not checked in for 90+ days. Export the list, cross-reference with Active Directory or the PSA asset list, and archive anything that no longer exists. This alone will cut your unresolved alert volume by a third.
A notebook offline is inconvenient. A server offline is a potential outage, failed backup, or security gap. Pull the list of 32 offline servers and check each one: is it decommissioned? Was the RMM agent removed during a migration? Is it actually down and nobody noticed? Offline servers should never be a background count.
With 71% of devices offline and only 30 out of 103 checking in, the RMM deployment at this site has effectively failed. Schedule a site visit or remote session to redeploy the agent. If this is a client you bill per-device, you are billing for 103 devices while monitoring 30.
Create a Datto RMM filter for devices offline for more than 90 days. Run it monthly. Auto-tag them as "Pending Removal" and send the list to the account manager for sign-off before deletion. This prevents the fleet from ballooning again after you clean it up once.
If you bill per managed device, your invoices should reflect the 3,402 devices that are actually checking in, not the 6,953 in the RMM. Run a reconciliation before the next billing cycle. Clients who discover the discrepancy themselves tend to lose trust fast. Better to bring it to them proactively.
A device is "offline" when the Datto RMM agent reports its online status as FALSE. This means the agent has not sent a heartbeat to the RMM platform within the expected interval. It could mean the device is powered off, disconnected from the network, had the agent uninstalled, or was physically removed from the environment.
Notebooks are portable by nature, so some will always show as offline when closed or disconnected. But a 56% offline rate across 4,439 devices points to stale records: machines that were replaced, reimaged without re-enrolling the RMM agent, or returned by employees who left. The RMM keeps the record until someone deletes it.
Not automatically. First cross-reference each device with Active Directory and the PSA asset records. A device offline for 90+ days with no matching AD object is safe to archive. A device offline for 14 days that belongs to a current employee might just need the agent reinstalled. Always export a list before bulk deletion.
Offline devices generate "device offline" alerts that pile up. Foster Inc has 979 unresolved alerts alongside 840 offline devices. Most of those alerts are likely stale device-offline notifications. Cleaning up the stale devices removes the alerts too, which makes the remaining alerts (the ones that actually matter) visible again.
Yes. Add a filter on 'BI_Datto_Rmm_Devices'[site_name] to any DAX query in this report. You can also filter by device type, last-seen date, or alert category. The queries are designed to work with Power BI's filter context, so slicing the data is straightforward.
Yes. Connect Proxuma Power BI to your Datto RMM instance, add an AI tool (Claude, ChatGPT, or Copilot) via MCP, and ask the same question. The AI writes the DAX queries, runs them against your live device data, and produces a report like this in under fifteen minutes.
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.
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