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How are logged hours distributed month-to-month across the largest client accounts?

Hours Logged per Company per Month

A data-driven analysis of hours logged per company per month from your Power BI environment, with breakdowns and actionable findings.

Built from: Autotask PSA

How this report was made

Autotask PSA

Multiple data sources combined

Proxuma Power BI

Pre-built MSP semantic model, 50+ measures

AI via MCP

Claude or ChatGPT writes DAX queries, executes them, formats output

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This Report
KPIs, breakdowns, trends, recommendations

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Hours Logged per Company per Month

This report analyzes hours logged per company per month 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: Operations managers, service delivery leads, and MSP owners managing capacity

How often: Weekly for scheduling, monthly for utilization reviews, quarterly for staffing decisions

Time saved

Calculating utilization from time entries and ticket data manually is tedious. This report does it automatically.

Capacity insight

See who is overloaded, who has bandwidth, and where bottlenecks form.

Staffing data

Evidence-based decisions about hiring, scheduling, and workload distribution.

Report categoryResource & Capacity

Data sourceAutotask PSA · Datto RMM · Datto Backup · Microsoft 365 · SmileBack · HubSpot · IT Glue

RefreshReal-time via Power BI

Generation timeUnder 15 minutes

AI requiredClaude, ChatGPT or Copilot

AudienceOperations managers, service delivery leads

Where to find this in Proxuma

Power BI › Resources › Hours Logged per Company per Month

Get this report for your MSP

What you can measure in this report

Summary Metrics

Hours by Resource

Hours by Company

Billable vs Non-Billable

Monthly Hours Trend

Analysis

Recommended Actions

Frequently Asked Questions

TOTAL HOURS

AI-Generated Power BI Report

Hours Logged per Company per Month

A data-driven analysis of hours logged per company per month from your Power BI environment, with breakdowns and actionable findings.

Demo Report: This report uses synthetic data to demonstrate AI-generated insights from Proxuma Power BI. The structure, DAX queries, and analysis reflect real MSP data patterns.

1.0 Summary Metrics

TOTAL HOURS

25,868

15 resources logged

View DAX Query — Summary query

-- Combined summary metrics from Power BI dataset

What are these DAX queries? DAX (Data Analysis Expressions) is the formula language Power BI uses to query data. Each collapsible section below shows the exact query the AI wrote and ran. You can copy any query and run it in Power BI Desktop against your own dataset.

1.0 Hours by Resource

Hours logged per resource from the demo dataset

Brandon Lynn

1,343

Brandon Bishop

1,361

Daniel Daniels

1,418

Gregory Horn

1,504

Elizabeth Ortega

1,433

Jennifer King

1,584

Jeremy White

1,492

Dr. Amber Ayala DVM

2,399

Kevin Allen

2,060

James Li

2,135

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

View DAX Query — Hours by Resource query

EVALUATE TOPN(15, SUMMARIZECOLUMNS('BI_Autotask_Time_Entries'[resource_name], "Hours", SUM('BI_Autotask_Time_Entries'[hours_worked])), [Hours], DESC)

2.0 Hours by Company

Total hours logged per company

Richards, Bell and Christ

782

Wu-Jackson

962

Price-Gomez

864

Martin Group

2,217

Thompson, Contreras and R

1,006

Doyle-Contreras

961

Clements, Pham and Garcia

866

None

7,264

Lewis LLC

2,801

Little Group

3,791

Company	Aug-25	Sep-25	Oct-25	Nov-25	Dec-25	Jan-26	6-mo Total
Craig-Huynh	125	560	613	267	243	166	1,974
Little Group	265	179	190	182	213	153	1,183
Martin Group	143	154	173	117	161	68	816
Rivers, Rogers and Mitchell	137	167	258	180	91	87	920
Lewis LLC	106	115	91	71	75	40	498
Ramos Group	101	158	116	136	117	41	669
Wall PLC	91	106	83	108	91	51	531
Burke, Armstrong and Morgan	45	53	71	62	140	42	413

View DAX Query — Hours by Company query

EVALUATE VAR TopCos = TOPN(8, ADDCOLUMNS(SUMMARIZE('BI_Autotask_Companies','BI_Autotask_Companies'[company_name]), "Worked", [Company - Hours Worked]), [Worked], DESC) VAR Last6Months = {"2025-08","2025-09","2025-10","2025-11","2025-12","2026-01"} RETURN ADDCOLUMNS(CROSSJOIN(SELECTCOLUMNS(TopCos,"company_name",'BI_Autotask_Companies'[company_name]), SELECTCOLUMNS(Last6Months,"YM",[Value])), "Hours", CALCULATE(SUM('BI_Autotask_Time_Entries'[hours_worked]), 'BI_Autotask_Companies'[company_name]=EARLIER([company_name]), FORMAT('BI_Autotask_Time_Entries'[date_worked],"YYYY-MM")=EARLIER([YM])))

3.0 Billable vs Non-Billable

Split between billable and non-billable hours

Billable (38,363h)

Non-Billable (12,387h)

Non-Billable	Hours
-	38,363.8
True	12,387.8

View DAX Query — Billable vs Non-Billable query

EVALUATE SUMMARIZECOLUMNS('BI_Autotask_Time_Entries'[is_non_billable], "Hours", SUM('BI_Autotask_Time_Entries'[hours_worked]))

4.0 Monthly Hours Trend

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

View DAX Query — Monthly Hours Trend query

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)

6.0 Analysis

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.

7.0 Recommended Actions

✓

1. Schedule Recurring Review

Set up a weekly or monthly review of hours logged per company per month metrics. Trends matter more than snapshots. Use the DAX queries in this report as your starting point.

✓

2. Connect Your Own Data

This report uses demo data. Connect Proxuma Power BI to your own Autotask PSA to generate this analysis from your real numbers.

8.0 Frequently Asked Questions

What data sources does the Hours Logged per Company per Month report use?

This report pulls data from PSA through the Proxuma Power BI integration, using DAX queries against the live data model.

How often is this data refreshed?

The underlying Power BI dataset refreshes daily. Reports can be regenerated at any time for the latest figures.

Can I customize this hours logged per company per month report?

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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