The Data Quality Audit Nobody Has Run Yet
A $20M natural foods brand closes the quarter with a net margin of 4.2%. Retailer deductions total $310K — from Walmart, UNFI, and KeHE combined. Annualized, that is $1.24M. Net income is $840K. The deductions are larger than the profit.
The CFO asks where the money went. The controller pulls up a spreadsheet: hundreds of line items, dozens of reason codes. Some are trade spend. Some are fines. Some are errors nobody disputed in time. The spreadsheet shows what was deducted. It does not show why — because the answer is buried in the product master, where a handful of wrong numbers have been quietly generating fines for months.
Deductions eat more than the profit
The scale of the problem is well documented. According to Inmar, deductions run 5–15% of gross sales across the CPG sector. Net margins sit at 3–5%. At a $20M brand, 5% in deductions is $1M — more than the company earns if margins land at the low end. The deduction line is not a cost of doing business. It is the largest unmanaged cost on the P&L.
The fines themselves are only part of it. Inmar reports that finance teams spend 30–50% of their time on deduction paperwork — pulling payment stubs, matching codes to invoices, hunting for documentation. Disputing a $200 deduction costs $300–$500 in staff time. It often costs more to fight the fine than to eat it. And 10–20% of deductions go unchallenged entirely — not because they are correct, but because nobody had the time or the paperwork to push back before the window closed.
The brand budgets for trade spend. It budgets for freight. It does not budget for the cost of its own data being wrong.
A few wrong fields, repeated across every shipment
Chargebacks from Walmart look different from chargebacks from UNFI, which look different from chargebacks from KeHE. Different codes, different schedules, different portals. They appear to be separate problems. Usually, they are the same problem.
A brand's product data lives in at least three places: wherever the brand keeps its product master (NetSuite if the brand has outgrown spreadsheets, an Excel workbook if it has not), the syndication platform (now Syndigo, after the 1WorldSync acquisition), and each retailer's own system (Walmart Item 360, UNFI Connect, KeHE CONNECT). The twelve fields that matter most — case dimensions, weights, barcodes, pack sizes — need to match across all three. When they do not, every shipment of that SKU triggers a mismatch. The mismatch triggers a fine. The fine repeats until someone fixes the number.
The culprits are usually the SKUs that changed. New packaging altered the case dimensions. A reformulation changed the weight. A brand refresh meant a new UPC. Someone updated the internal records. Nobody updated the syndication platform or the retailer portal. The product on the pallet is correct. The data describing it is not. And the automated systems that compare the two do not care about intent — they care about whether the numbers match.
In almost every audit, a small handful of SKUs generate a disproportionate share of the fines. A data quality audit finds them.
What the audit covers
The scope is narrow by design: twelve fields, every active SKU, three systems. Not a governance program. Not a software implementation. A diagnostic.
Pull the data. Export the product master — whether that is an ERP, a spreadsheet, or something in between. Download the published records from Syndigo. Pull the item setup from each retailer portal. Line them up: same SKU, same field, three sources.
Find the disagreements. Compare field by field. Where do the numbers differ? A case-cube discrepancy of four hundredths of a cubic foot sounds trivial. Spread across a year of shipments, it is a recurring fine that nobody has connected to a data entry field because everyone assumed it was a shipping problem.
Put a dollar figure on each one. Match each mismatch to historical deductions. The reason codes retailers use — ASN weight error, GTIN not found, pack count mismatch — map back to specific fields. The audit turns data errors into dollar amounts: this SKU, this field, this much per quarter.
Rank by cost. The most expensive mismatches almost always cluster around a handful of SKUs — the ones that changed without the change reaching every system. Fix those first. The correction is a data entry task. It takes hours. The problem it resolves has been running up charges for months.
The work is measured in weeks. The output is a report that tells you exactly what to fix and in what order.
Why it hasn't been done — and why that's fixable
Everyone at the brand knows the data is messy. Operations knows. Finance knows. The sales team hears about it from buyers. Nobody has fixed it because nobody owns it, and nobody owns it because the work crosses three systems, two departments, and at least three retailer portals. It falls between the cracks — not because it is hard, but because it is nobody's single job.
It does not need to be anyone's single job. It needs to be done once, correctly, and then maintained as products change. A brand can assign it internally or hire someone to do it. The deliverable is the same either way: a reconciliation report that turns data mismatches into dollar amounts, ranked by cost. The top corrections pay for the work almost immediately. Everything after that is recovered margin.
Find out what your data disagreements cost
Lailara runs a scoped data quality audit: twelve fields, every active SKU, every system — your product master, your syndication platform, and your retailer portals. The deliverable is a reconciliation report showing exactly where your systems disagree and what each disagreement costs you. If your deduction line keeps growing and nobody can explain why, book a 30-minute scoping call.
See the methodology behind this post. The worked example — 50 SKUs, 5 product lines, 6 contracted retailers, $458,000/year in traced chargebacks — is a live demo you can open and explore. Product Data Health Audit →
The Ten Decisions is the hub for everything in this blog. Every data problem maps to one of ten decisions a $25M specialty food brand makes without adequate information. See the full picture →