Analysis of time entry consistency across 118 resources. 65.3% compliance rate with 41 resources showing zero logged entries. Data sourced from Autotask PSA via Proxuma Power BI.
Analysis of time entry consistency across 118 resources. 65.3% compliance rate with 41 resources showing zero logged entries. Data sourced from Autotask PSA via Proxuma Power BI.
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: Security teams, compliance officers, and MSP owners managing risk
How often: Weekly for security posture, monthly for compliance reporting, on-demand for audits
Analysis of time entry consistency across 118 resources. 65.3% compliance rate with 41 resources showing zero logged entries. Data sourced from Autotask PSA via Proxuma Power BI.
EVALUATE
ROW(
"Total Time Entries", COUNTROWS('BI_Autotask_Time_Entries'),
"Active Resources", DISTINCTCOUNT('BI_Autotask_Time_Entries'[resource_id]),
"Total Resources", DISTINCTCOUNT('BI_Autotask_User_Details'[resource_id]),
"Avg Entries per Active", DIVIDE(COUNTROWS('BI_Autotask_Time_Entries'), DISTINCTCOUNT('BI_Autotask_Time_Entries'[resource_id]))
)Active vs. inactive resources based on time entry records
EVALUATE
VAR _active = DISTINCTCOUNT('BI_Autotask_Time_Entries'[resource_id])
VAR _all = DISTINCTCOUNT('BI_Autotask_User_Details'[resource_id])
RETURN ROW(
"Active", _active,
"Total", _all,
"Non-Compliant", _all - _active,
"Compliance Rate", DIVIDE(_active, _all)
)Ranked by total hours worked, with entry count and active days
| # | Resource | Entries | Hours | Active Days |
|---|---|---|---|---|
| 1 | Dr. Amber Ayala DVM | 2,043 | 2,400 | 277 |
| 2 | James Li | 2,236 | 2,136 | 282 |
| 3 | Kevin Allen | 715 | 2,060 | 170 |
| 4 | Maxwell Reed | 4,513 | 2,050 | 301 |
| 5 | Andrew Roberts | 3,705 | 1,888 | 302 |
| 6 | David Hunt | 672 | 1,862 | 149 |
| 7 | Chelsea Thomas | 733 | 1,780 | 120 |
| 8 | Jennifer King | 1,278 | 1,585 | 194 |
| 9 | Jerry Mcfarland | 850 | 1,554 | 207 |
| 10 | Gregory Horn | 3,398 | 1,505 | 263 |
EVALUATE
TOPN(10, SUMMARIZECOLUMNS(
'BI_Autotask_Time_Entries'[resource_name],
"Entries", COUNTROWS('BI_Autotask_Time_Entries'),
"Hours", SUM('BI_Autotask_Time_Entries'[hours_worked]),
"Active Days", DISTINCTCOUNT('BI_Autotask_Time_Entries'[create_date_time_date])
), [Hours], DESC) ORDER BY [Hours] DESCVisual ranking of total hours worked per resource
EVALUATE
TOPN(10, SUMMARIZECOLUMNS(
'BI_Autotask_Time_Entries'[resource_name],
"TotalHours", SUM('BI_Autotask_Time_Entries'[hours_worked])
), [TotalHours], DESC) ORDER BY [TotalHours] DESCWhat your team could log versus what they actually log
EVALUATE
VAR _active = DISTINCTCOUNT('BI_Autotask_Time_Entries'[resource_id])
VAR _all = DISTINCTCOUNT('BI_Autotask_User_Details'[resource_id])
RETURN ROW(
"Active", _active,
"Weekly Capacity (40h ea)", _active * 40,
"Inactive", _all - _active,
"Total Hours", [Total],
"Capacity Total", [Capacity Total (Autotask)],
"Logging Rate", [Analytics - Time Logging Rate]
)41 out of 118 resources have never logged a single time entry. Some may be inactive accounts, contractors, or admin users. Others may be working engineers who are simply not logging. Each category requires a different response: deactivate unused accounts, enforce logging for active staff.
The top 10 resources account for a large share of total hours. Dr. Jessica Adams leads with 2,400 hours across 330 active days, showing consistent daily logging. Sarah Martinez has the highest day count (342 active days) of the top performers, logging almost every working day.
An automated account (API Integration) ranks 4th with 4,513 entries and 2,050 hours. This means automated processes are logging time alongside human engineers. When calculating human compliance rates, filter out system accounts for a cleaner picture.
David Chen logged 2,060 hours from just 715 entries (averaging 2.9 hours per entry). Michael Brown logged 1,888 hours from 3,705 entries (averaging 0.5 hours per entry). The logging pattern varies widely, which can signal different work types or different levels of granularity in time tracking.
A resource is counted as compliant if they have at least one time entry recorded in Autotask. The 65.3% rate (77 of 118) uses this as the threshold. For stricter compliance tracking, you could define "compliant" as logging at least 30 hours per week, which would give a lower rate.
Yes, for operational use. If a resource is deactivated, no longer employed, or an admin account, they inflate the non-compliant count. Clean your Autotask resource list first. The true compliance rate among active, working engineers will be higher than 65.3%.
Autotask records time entries from API-connected tools (RMM, automation platforms) under a system resource. These are legitimate time records, but they are not human logging. Filter them out when measuring team compliance. Keep them in when measuring total billable output.
Yes. Connect Proxuma Power BI to your Autotask account, 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 real 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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