A mid-size freight carrier had TAI, NetSuite, and Gusto living in three separate systems. Finance was spending 16 hours a week manually reconciling them. A 10–14 day reporting lag created margin blind spots and over $200K in aging receivables with no automated follow-up. We built the data warehouse first — and the dashboard paid for itself before go-live.
The carrier’s operational data was trapped in silos. TAI handled dispatch and load management. NetSuite owned cost and GL detail. Gusto held payroll and driver hours. None of them talked to each other.
Every Monday, a finance analyst spent the better part of two days exporting CSVs, reconciling carrier codes, and building a weekly report that was already stale by the time it hit the CFO’s inbox. Decision-making was reactive by design — there was no other option.
Before touching Power BI, we ran a structured KPI alignment session with the CFO, Controller, and Regional Ops Manager. The rule: if a metric doesn’t change a weekly action, it doesn’t go on Page 1.
| KPI | Business Question It Answers | Owner | Source | Refresh |
|---|---|---|---|---|
| Cost per Shipment | Are we profitable by lane, carrier, and customer? | Finance + Ops | NetSuite GL + TAI | Daily |
| On-Time Delivery (OTD) | Are we meeting SLAs — and who’s at fault when we miss? | Regional Ops | TAI dispatch data | Hourly |
| AR Aging / DSO | Which invoices are at risk — and has anyone followed up? | Controller | NetSuite AR | Daily |
| Asset Utilization | How much of our loaded-mile capacity are we actually using? | Fleet/Dispatch | TAI telematics | Hourly |
| Freight Cost Variance | Where are we over plan — and is it a lane, carrier, or fuel issue? | Finance | NetSuite vs. TAI budget | Daily |
| Accessorials per Shipment | Are hidden costs (detention, layover, lumper) eating our margin? | Finance + Ops | NetSuite line items | Daily |
Executives get clarity. Controllers get levers. Analysts get receipts. Every tab was designed around the decisions that persona actually makes on a weekly basis.
We built the data warehouse before writing a single DAX measure. The reason: a beautiful dashboard on unreliable data destroys trust and kills adoption. The ETL investment pays for itself — and then the dashboard pays for the engagement.
These aren’t projected savings. They were measured at 12 weeks post-launch against baseline. The $60K engagement paid for itself before the 90-day review.
| Benefit Category | How It Was Measured | 90-Day Value | Annualized |
|---|---|---|---|
| Reporting time savings | 16 hrs → 2 hrs/wk × blended analyst rate | $30K | $120K |
| Margin recapture | Cost/shipment reduction on targeted lanes × volume | $80K | $320K |
| DSO improvement (AR) | 12-day DSO improvement × avg receivables balance | $50K | $200K |
| Payroll efficiency | Gusto integration reduced manual hours-verification | $35K | $140K |
| Total verified benefit | $195K | $780K |
Five engagements fail for every one that succeeds — not because of the tool, but because of the order of operations. We flipped it.
Every engagement starts with a free Power BI Precision Audit™ — a clear map of what to fix before spending a dollar on build. We’ll show you exactly where your reporting is bleeding time and margin.