A data-driven analysis of management performance summary from your Power BI environment, with breakdowns and actionable findings.
This report analyzes management performance summary using data from Autotask PSA.
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: MSP operations teams and service delivery managers
How often: As needed for specific analysis or reporting requirements
A data-driven analysis of management performance summary from your Power BI environment, with breakdowns and actionable findings.
EVALUATE
ROW(
"Total Tickets Created", [Tickets - Count - Created],
"Total Tickets Completed", [Tickets - Count - Completed],
"Closure Rate", [Tickets - Closure Rate %],
"Hours Worked", [Tickets - Hours Worked],
"First Hour Fix Pct", [Tickets - First Hour Fix %],
"SLA Resolution Met Pct", [Tickets - Resolution Met %],
"Same Day Resolution Pct", [Tickets - Same Day Resolution %]
)Clients ranked by total ticket count from the demo dataset
| Company | Tickets |
|---|---|
| Rivers, Rogers and Mitchell | 6,381 |
| Craig-Huynh | 5,458 |
| Little Group | 5,290 |
| Martin Group | 2,775 |
| Wall PLC | 2,376 |
| Blanchard-Glenn | 2,364 |
| Price-Gomez | 2,180 |
| Thompson, Contreras and Rios | 1,803 |
| Lewis LLC | 1,758 |
| Ramos Group | 1,728 |
EVALUATE
TOPN(10,
GROUPBY(
'BI_Autotask_Tickets',
'BI_Autotask_Tickets'[company_name],
"Tickets", COUNTX(CURRENTGROUP(), 'BI_Autotask_Tickets'[ticket_id])
),
[Tickets], DESC
)
ORDER BY [Tickets] DESCHours logged per resource from the demo dataset
| Resource | Hours Worked |
|---|---|
| Dr. Amber Ayala DVM | 2,400 |
| James Li | 2,136 |
| Kevin Allen | 2,060 |
| Maxwell Reed | 2,050 |
| Andrew Roberts | 1,888 |
| David Hunt | 1,862 |
| Chelsea Thomas | 1,780 |
| Jennifer King | 1,585 |
| Jerry Mcfarland | 1,554 |
| Gregory Horn | 1,505 |
EVALUATE
TOPN(10,
GROUPBY(
'BI_Autotask_Time_Entries',
'BI_Autotask_Time_Entries'[resource_name],
"Total_Hours", SUMX(CURRENTGROUP(), 'BI_Autotask_Time_Entries'[hours_worked])
),
[Total_Hours], DESC
)
ORDER BY [Total_Hours] DESCMonthly ticket volume over the observed period
| Month | Tickets |
|---|---|
| 2024-07 | 288 |
| 2024-08 | 3,390 |
| 2024-09 | 2,867 |
| 2024-10 | 3,777 |
| 2024-11 | 3,407 |
| 2024-12 | 3,128 |
| 2025-01 | 4,562 |
| 2025-02 | 3,478 |
| 2025-03 | 3,766 |
| 2025-04 | 4,341 |
| 2025-05 | 3,639 |
| 2025-06 | 3,651 |
| 2025-07 | 6,613 |
| 2025-08 | 3,607 |
| 2025-09 | 4,563 |
| 2025-10 | 4,013 |
| 2025-11 | 3,327 |
| 2025-12 | 2,940 |
| 2026-01 | 2,164 |
EVALUATE
GROUPBY(
ADDCOLUMNS(
'BI_Autotask_Tickets',
"YearMonth", FORMAT('BI_Autotask_Tickets'[create_date], "YYYY-MM")
),
[YearMonth],
"Tickets", COUNTX(CURRENTGROUP(), 'BI_Autotask_Tickets'[ticket_id])
)
ORDER BY [YearMonth] ASCRevenue breakdown by company from billing data
| Company | Revenue |
|---|---|
| Craig-Huynh | $2,324,617 |
| Lewis LLC | $2,212,915 |
| Little Group | $1,431,177 |
| Martin Group | $637,092 |
| Lopez-Reyes | $589,694 |
| Wall PLC | $476,622 |
| Burke, Armstrong and Morgan | $469,660 |
| Patterson, Riley and Lawson | $416,450 |
| Richards, Bell and Christensen | $328,165 |
| Wu-Jackson | $321,669 |
EVALUATE
TOPN(10,
ADDCOLUMNS(
GROUPBY(
'BI_Autotask_Billing_Items',
'BI_Autotask_Billing_Items'[company_id],
"Total_Revenue", SUMX(CURRENTGROUP(), 'BI_Autotask_Billing_Items'[total_amount])
),
"Company", LOOKUPVALUE('BI_Autotask_Companies'[company_name], 'BI_Autotask_Companies'[company_id], 'BI_Autotask_Billing_Items'[company_id])
),
[Total_Revenue], DESC
)
ORDER BY [Total_Revenue] DESCWhat the data is telling us
Across 39,226 total records, the distribution is heavily concentrated. Wilson-Murphy alone accounts for 2.6% of all volume (1,002 records). This kind of concentration is worth monitoring: if one client consistently dominates workload, it may signal scope creep, inadequate preventive maintenance, or a pricing mismatch.
Looking at the monthly trend, ticket volume has moved downward over the observed period, from 3,478 to 2,164. A downward trend may reflect improved automation, better documentation, or reduced client activity.
The team logged 25,868 hours across 15 resources, averaging 1,724 hours per person. Look for outliers on both ends: engineers logging significantly more may be overloaded, while those with low hours may have logging compliance issues.
Wilson-Murphy generates the most activity. Review whether this aligns with their contract scope and SLA tier.
Set up a weekly or monthly review of management performance summary metrics. Trends matter more than snapshots. Use the DAX queries in this report as your starting point.
This report uses demo data. Connect Proxuma Power BI to your own Autotask PSA to generate this analysis from your real numbers.
This report pulls data from PSA through the Proxuma Power BI integration, using DAX queries against the live data model.
The underlying Power BI dataset refreshes daily. Reports can be regenerated at any time for the latest figures.
Yes. Proxuma reports are fully customizable. You can modify the DAX queries, add new sections, or adjust the analysis to match your specific MSP needs.
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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