Crossing N-able RMM device inventory with HiBob team structure to reveal which technician teams carry a disproportionate endpoint load - and where capacity planning needs attention.
Crossing N-able RMM device inventory with HiBob team structure to reveal which technician teams carry a disproportionate endpoint load - and where capacity planning needs attention.
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
Crossing N-able RMM device inventory with HiBob team structure to reveal which technician teams carry a disproportionate endpoint load - and where capacity planning needs attention.
EVALUATE ROW("TotalDevices", COUNTROWS('BI_Datto_Rmm_Devices'), "Resources", DISTINCTCOUNT('BI_Autotask_Time_Entries'[resource_name]), "DevicePerTech", DIVIDE(COUNTROWS('BI_Datto_Rmm_Devices'), DISTINCTCOUNT('BI_Autotask_Time_Entries'[resource_name])))| Team | Techs | Devices | Devices / Tech | Status |
|---|---|---|---|---|
| Infrastructure | 3 | 487 | 162.3 | Over Capacity |
| Service Desk L2 | 4 | 512 | 128.0 | Over Capacity |
| Field Engineering | 3 | 341 | 113.7 | Over Capacity |
| Cloud Operations | 4 | 298 | 74.5 | Healthy |
| Service Desk L1 | 4 | 209 | 52.3 | Healthy |
Three of the five teams exceed the 100-device threshold. Infrastructure carries the heaviest burden at 162.3 devices per technician - over 60% above the recommended maximum. Service Desk L2 and Field Engineering also run above safe levels, suggesting that workload distribution across teams is uneven.
Devices Per Tech By Team =
VAR _team = SELECTEDVALUE ( BI_HiBob_Employees[team] )
VAR _techCount =
COUNTROWS (
FILTER (
BI_HiBob_Employees,
BI_HiBob_Employees[team] = _team
)
)
VAR _deviceCount =
COUNTROWS (
FILTER (
BI_NAble_Device_Statistic,
RELATED ( BI_HiBob_Employees[team] ) = _team
)
)
RETURN
DIVIDE ( _deviceCount, _techCount, 0 )The chart above makes the gap clear. Infrastructure and Service Desk L2 both sit deep in the red zone, while Cloud Operations and Service Desk L1 operate well within safe boundaries. The difference between the most loaded team (162.3) and the least loaded (52.3) is a factor of 3x - a signal that device assignments may not be following headcount changes.
Team Device Summary =
SUMMARIZE (
BI_HiBob_Employees,
BI_HiBob_Employees[team],
"Techs", COUNTROWS ( BI_HiBob_Employees ),
"Devices",
COUNTROWS (
FILTER (
BI_NAble_Device_Statistic,
RELATED ( BI_HiBob_Employees[team] )
= EARLIER ( BI_HiBob_Employees[team] )
)
)
)| Technician | Team | Devices | Direct Reports | Load |
|---|---|---|---|---|
| J. van den Berg | Infrastructure | 198 | 2 | Critical |
| R. Bakker | Infrastructure | 172 | 0 | Critical |
| M. de Vries | Service Desk L2 | 156 | 3 | Critical |
| K. Jansen | Service Desk L2 | 141 | 0 | High |
| T. Meijer | Field Engineering | 134 | 1 | High |
| A. Smit | Field Engineering | 121 | 0 | High |
| P. Hendriks | Infrastructure | 117 | 4 | High |
| S. Vermeer | Service Desk L2 | 108 | 0 | High |
| L. Dekker | Service Desk L2 | 107 | 0 | High |
| D. Visser | Field Engineering | 86 | 0 | Normal |
J. van den Berg in Infrastructure manages 198 devices while also supervising 2 direct reports - nearly double the recommended load. Three technicians sit at critical levels (150+), and another five fall in the high-load bracket (100 - 150). Only 10 of the 18 total technicians are shown here; the remaining eight all fall below 85 devices.
Device complexity matters as much as raw count. Infrastructure not only manages the most devices per tech, but also handles a heavier server mix (35%). Servers typically require more hands-on maintenance than workstations, which compounds the overload. Cloud Operations has a similar server share (52%) but benefits from a lower device-to-tech ratio overall.
Nearly a third of all technicians operate at critical load levels. Combined with the 45% in the high-load bracket, that means 75% of the technical workforce manages more devices than recommended. Only a quarter of technicians fall within healthy parameters. This skew puts service quality and response times at risk across the board.
With only 3 technicians managing 487 devices - including a high proportion of servers - the Infrastructure team faces the most severe overload. This creates a single point of failure risk if any team member is absent or leaves the organization.
Only 25% of the workforce operates within the recommended 100-device limit. The majority is stretched thin, which correlates with longer ticket response times and higher rates of missed patch windows in other Proxuma reports.
These two teams demonstrate that balanced workloads are possible within the current organization. Their device-to-tech ratios (52.3 and 74.5 respectively) leave room for growth without immediate hiring.
1. Redistribute devices from Infrastructure to Cloud Operations. Cloud Operations already handles a similar device profile (servers and workstations) but operates at 74.5 devices per tech. Moving 80 - 100 devices from Infrastructure would bring both teams closer to the 100-device target without new hires.
2. Add one technician to Service Desk L2. At 128 devices per tech across 4 people, adding a fifth team member would reduce the ratio to 102.4 - just above threshold. Combined with better device routing, this could bring L2 into the healthy range within one quarter.
3. Implement automated device assignment reviews. Build a monthly Power BI alert that flags any team exceeding 110 devices per technician. This early warning allows the Service Manager to rebalance before teams reach critical load. The DAX measures in sections 1.0 and 2.0 of this report provide the foundation for that alert.
Industry benchmarks suggest 75 to 100 managed devices per technician for a mixed environment of workstations and servers. The exact number depends on device complexity, automation maturity, and the share of proactive versus reactive work. This report uses 100 as the upper boundary for "healthy."
Device counts are pulled from the BI_NAble_Device_Statistic table, which syncs daily from N-able RMM. Team and technician information comes from BI_HiBob_Employees. The two datasets are joined through BI_Autotask_Companies to map N-able sites to technician assignments.
Monthly reviews are recommended for stable teams. If a team is going through onboarding, offboarding, or client transitions, a bi-weekly cadence helps catch ratio spikes before they affect service delivery. Pair this report with ticket volume data for a fuller picture of technician workload.
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