A data-driven analysis of project budget variance from your Power BI environment, with breakdowns and actionable findings.
This report analyzes project budget variance 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: Project managers, operations leads, and MSP owners tracking delivery
How often: Weekly for status updates, milestone dates for escalation, monthly for portfolio review
A data-driven analysis of project budget variance from your Power BI environment, with breakdowns and actionable findings.
-- Combined summary metrics from Power BI datasetActive projects ranked by logged hours
| # | Project | Client | Status | Estimate (h) | Worked (h) | Overrun (h) | Variance % |
|---|---|---|---|---|---|---|---|
| 1 | Project Environment | Lewis LLC | Complete | 1205.0 | 1586.7 | +381.6 | 32% |
| 2 | Project Along | Doyle-Contreras | Complete | 238.0 | 536.3 | +298.3 | 125% |
| 3 | Project Number | Little Group | Complete | 24.0 | 302.8 | +278.8 | 1162% |
| 4 | Project Leave | Clements, Pham and Garcia | Complete | 219.0 | 417.3 | +198.3 | 91% |
| 5 | Project Not | Martin Group | Complete | 5.0 | 155.8 | +150.8 | 3017% |
| 6 | Project Feel | Wu-Jackson | In progress | 161.0 | 276.8 | +115.8 | 72% |
| 7 | Project Return | Thomas-Watson | Complete | 36.0 | 135.8 | +99.8 | 277% |
| 8 | Project Set | Smith and Sons | In progress | 24.0 | 101.5 | +77.5 | 323% |
| 9 | Project Though | Burke, Armstrong and Morgan | In progress | 269.0 | 338.8 | +69.8 | 26% |
| 10 | Project Paper | Green PLC | Live | 90.0 | 158.2 | +68.2 | 76% |
| 11 | Project Safe | George Ltd | Complete | 117.2 | 185.2 | +68.0 | 58% |
| 12 | Project Set | Conway Ltd | Complete | 107.0 | 171.2 | +64.2 | 60% |
| 13 | Project Mr | Rivers, Rogers and Mitchell | In progress | 160.0 | 208.1 | +48.1 | 30% |
| 14 | Project Plant | Welch Inc | Complete | 8.0 | 54.5 | +46.5 | 581% |
| 15 | Project Machine | Conway Ltd | Complete | 43.0 | 80.8 | +37.8 | 88% |
EVALUATE
TOPN(15,
SELECTCOLUMNS(
FILTER('BI_Autotask_Projects', 'BI_Autotask_Projects'[estimated_hours] > 0),
"Project", 'BI_Autotask_Projects'[project_name],
"Company", 'BI_Autotask_Projects'[company_name],
"Status", 'BI_Autotask_Projects'[project_status_name],
"EstHours", 'BI_Autotask_Projects'[estimated_hours],
"WorkedHours", 'BI_Autotask_Projects'[worked_hours],
"VarianceHours", 'BI_Autotask_Projects'[worked_hours] - 'BI_Autotask_Projects'[estimated_hours],
"VariancePct", DIVIDE('BI_Autotask_Projects'[worked_hours] - 'BI_Autotask_Projects'[estimated_hours], 'BI_Autotask_Projects'[estimated_hours])
),
[VarianceHours], DESC
)Hours logged per resource from the demo dataset
| Resource | Hours |
|---|---|
| Brandon Lynn | 1,343.7 |
| Brandon Bishop | 1,361.5 |
| Daniel Daniels | 1,418.4 |
| Gregory Horn | 1,504.5 |
| Elizabeth Ortega | 1,433.4 |
| Jennifer King | 1,584.5 |
| Jeremy White | 1,492.5 |
| Dr. Amber Ayala DVM | 2,399.8 |
| Kevin Allen | 2,060.1 |
| James Li | 2,136.0 |
| Maxwell Reed | 2,050.3 |
| Chelsea Thomas | 1,779.6 |
| David Hunt | 1,862.2 |
| Andrew Roberts | 1,887.7 |
| Jerry Mcfarland | 1,554.0 |
EVALUATE TOPN(15, SUMMARIZECOLUMNS('BI_Autotask_Time_Entries'[resource_name], "Hours", SUM('BI_Autotask_Time_Entries'[hours_worked])), [Hours], DESC)Monthly hours trend over the observed period
| Month | Hours |
|---|---|
| 202502 | 2,534.3 |
| 202503 | 3,330.5 |
| 202504 | 3,588.0 |
| 202505 | 3,314.9 |
| 202506 | 3,198.0 |
| 202507 | 3,536.6 |
| 202508 | 2,686.4 |
| 202509 | 3,864.6 |
| 202510 | 4,003.3 |
| 202511 | 3,314.2 |
| 202512 | 3,247.4 |
| 202601 | 2,115.7 |
EVALUATE TOPN(12, SUMMARIZECOLUMNS('BI_Common_Dim_Date'[year_month], "Hours", SUM('BI_Autotask_Time_Entries'[hours_worked])), 'BI_Common_Dim_Date'[year_month], DESC)What the data is telling us
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.
Set up a weekly or monthly review of project budget variance 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.
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