If you’ve ever opened your monthly financials in CargoWise and thought,
“Why does this margin look… off?”
—you are already seeing the effects of dirty data.
Dirty data is the silent profit killer in logistics.
It slips into shipments, spreads through job files, and eventually lands in your P&L—distorting margins, confusing leadership, and forcing your finance team into constant cleanup mode.
The good news:
You can catch it early.
Long before it becomes a financial problem.
Here are the biggest sources of dirty data in CargoWise—and how to detect (and prevent) them before they impact your bottom line.
1️⃣ Charge Codes: The First Line of Defense (or Disaster)
CargoWise lives and dies by its charge codes.
If the codes aren’t clean, nothing else will be.
What to look for:
- Multiple charge codes that mean the same thing
- “MISC” or “OTHER” codes used as a catch-all
- Revenue and cost codes not clearly separated
- Similar-sounding codes that confuse staff
- Charge codes mapped incorrectly to the GL
Why it matters:
Charge codes drive revenue, cost, margin, and profitability reporting.
If this foundation is dirty, everything downstream becomes a guessing game.
Quick Fix:
Review your top 25 most-used charge codes monthly.
You’ll instantly see which ones cause confusion or misuse.
2️⃣ Jobs Closed with Missing Costs — But Don’t Chase Your Tails
Closed jobs with zero or incomplete costs are one of the biggest signs of dirty data.
But they’re also the area where companies waste the most time “fixing” symptoms instead of fixing the process.
What to look for:
- Jobs closed before all vendor invoices arrive
- Jobs closed with no vendor costs
- Teams closing jobs just to clear their worklist
- Chronically late vendor billing
- Inconsistent accrual processes
This creates margin volatility that finance sees too late.
But here’s the key:
Don’t chase your tails.
If you don’t have a standardized accounting process—especially for consolidations—you will end up:
- reopening jobs unnecessarily
- manually reassigning costs
- relying on guesswork rather than structure
My preferred method (as a former Controller):
Book all costs at the consolidation file and push them down to the house files.
Why this works:
- The consol shows the complete financial picture
- House files show accurate profit
- No shipment shows false 80% margins
- You stop manually chasing down missing costs
- Your P&L reflects true profitability
With a disciplined consolidation process, margin becomes predictable—not chaotic.
Quick Fix:
Run weekly:
Jobs Closed Without Costs (last 14 days)
Review exceptions through the lens of your consolidation model.
3️⃣ Inconsistent Customer Billing — The Hidden Trouble Zone
Inconsistent billing is one of the most frequent (and costly) sources of dirty revenue data.
What to look for:
- Wrong bill-to party selected
- Duplicate customer profiles
- Manual overrides of defaults
- Jobs invoiced under the wrong entity
- Missing credit terms, tax profiles, or billing defaults
- Staff using “whatever worked last time” instead of the correct profile
And here’s the uncomfortable truth:
Wrong bill-to selection happens more than you think.
Why?
Because real operations often look like this:
- Someone accidentally chooses a duplicate customer profile
- A customer wasn’t set up yet, so Ops creates a dummy bill-to to move the shipment along
- A branch maintains a different version of the customer record
- A team member selects the wrong entity because the names are similar
Once a dummy or incorrect bill-to hits the file,
your revenue, AR, and cash flow are already compromised.
This is where companies need controls, not corrections:
- Tight customer creation workflow
- Approval gates
- Duplicate customer cleanup
- Mandatory customer selection rules
- No dummy records allowed—ever
Why it matters:
Wrong bill-to = wrong revenue = wrong AR = wrong cash flow.
It also creates:
- rebilling
- customer disputes
- delayed collections
- inaccurate customer profitability
- audit issues
Quick Fix:
Audit 50 recent invoices for your top 5 customers.
Patterns (and problems) appear fast.
4️⃣ Costs Posted to the Wrong Job or Wrong Department
If your costs are landing in the wrong place, your P&L becomes a work of fiction.
What to look for:
- Vendor invoices attached to the wrong job
- Costs posted to closed jobs
- Costs without job numbers
- Blanket invoices allocated incorrectly
- Teams not reviewing job cost screens consistently
Why it matters:
Misallocated costs destroy the credibility of customer, lane, and branch profitability reporting.
Quick Fix:
Run weekly exception reports:
- Vendor Costs With No Job Number
- Vendor Costs Posted to Closed Jobs
These two reports alone catch the majority of misallocations.
5️⃣ Incorrect or Missing Dimensions (Branch, Department, Mode, Service)
Dimensions are the backbone of financial segmentation.
What to look for:
- Jobs missing branch or department
- Wrong office selected
- Wrong mode or service code
- Incorrect product or trade lane identifiers
Why it matters:
Your P&L segments become incorrect, misleading leadership and skewing decision-making.
Quick Fix:
Daily report:
Jobs Missing Required Operational Fields
Clean data begins before the job progresses.
6️⃣ Duplicate Revenue or Duplicate Costs
One of the quietest killers of financial accuracy.
What to look for:
- Duplicate charge code entries
- Staff billing the same revenue twice
- Same vendor cost entered twice
- Accrual posted and cost posted again
- Vendor invoices duplicated during manual entry
Why it matters:
Duplicate revenue inflates profit artificially.
Duplicate costs destroy margin.
Both erode trust in the financials.
Quick Fix:
Enable duplicate line checks or set alert rules for repeated:
charge code + vendor + amount combinations.
7️⃣ Manual Entry — The Root of Most Dirty Data
If it’s manual, it’s vulnerable.
If it’s repeated, it’s dangerous.
What to look for:
- Manual invoice creation
- Manual cost allocation
- Manual accrual logs
- Manual non-integrated milestones
- Staff manually selecting customer or vendor fields every time
Why it matters:
Manual = inconsistent.
Inconsistent = unreliable.
Unreliable = unusable P&L.
Quick Fix:
Ask two questions for every manual task:
- Why is this manual?
- Can CargoWise do this automatically?
Usually the answer is: yes, it can.
The Bottom Line
Dirty data doesn’t start in finance.
It ends there.
It starts in operations:
- inconsistent entries
- unclear processes
- incorrect templates
- missing fields
- duplicate customer records
- closed jobs with missing costs
- dummy bill-to records
- manual workarounds
But you can catch it long before it shows up on your P&L.
Build a clean data pipeline:
- strong templates
- mandatory fields
- automated controls
- exception reporting
- consolidation accounting discipline
- customer creation controls
- cross-functional alignment
When you do this, your P&L stops being a crime scene investigation and becomes what it should be:
A tool for decision-making, truth, and margin protection.
If Your CargoWise Data Isn’t Telling the Truth, I Can Help
I work with freight forwarders, finance teams, and logistics leaders to:
• eliminate dirty data at the source
• implement consolidation accounting that works
• create customer master data controls
• align operations and finance inside CargoWise
• restore margin accuracy
• build strong month-end and year-end processes
If your P&L feels off—or if you’re constantly cleaning up data instead of using it—
👉 Send me a message.
👉 Book a micro-consulting session.
👉 Or invite me to audit your CargoWise workflows.
Clean data isn’t just accuracy—
it’s trust, profit, and operational clarity.
ALL2S CONSULTING LLC 