Bad data costs more than the software you bought to fix it.
Lailara LLC finds the product master errors, reconciliation failures, and silent data defects that compound into six-figure losses — and delivers a remediation plan your team can execute without my involvement.
What I Do
Data Hygiene & Validation
Fourteen fields in your product master drive 80% of retailer chargebacks. I audit transaction data, incentive payments, and product records (UPC/GTIN, GDSN, 1WorldSync) end to end — tracing every downstream failure to the specific field that caused it, calculating the annualized cost, and delivering a prioritized fix roadmap.
Decision Frameworks
Velocity analysis, SKU rationalization, launch diagnostics, distribution strategy. I build interactive tools that connect your raw data to the specific decision someone needs to make on Monday morning. FastAPI services, React dashboards, Quarto reports, Shiny tools — the format fits the user and the question.
Data Quality Engineering
Automated validation that catches problems before they enter your systems. Mixed formats, phantom duplicates, placeholder floods, silent type coercion. GS1/GTIN validation against retailer-specific requirements. Exception-handling protocols that give your team a clear path from defect to resolution.
Systems & Infrastructure
The audit pipeline, the validation layer, the reporting infrastructure that runs without manual intervention. I build the tooling — Python CLI tools, automated scoring scripts, reproducible analysis pipelines — so the data quality work doesn't depend on one person remembering to check.
The Quiet Cost
The average specialty food brand spends 15–20% of revenue on trade promotions and cannot say which ones made money. Chargebacks run 5–15% of gross sales while net margins sit at 3–5%. Most of that loss traces to twelve product master fields that disagree across three systems — and nobody has audited them.
These problems don’t announce themselves. They compound in reports that are slightly off, reconciliations that don’t tie, and submissions that get kicked back. By the time someone notices, nine months of automatic deductions have already hit the P&L.
I work upstream — on the data layer between your raw records and the systems that depend on them. The goal is simple: what flows through is already right.
How I Work
Scoping Conversation
Thirty minutes. You describe the data problem; I tell you whether it's a fit and what a scoped engagement looks like.
Frame the Problem
We define the scope together — what data, what systems, what question needs answering. I send a written proposal with the deliverable, the timeline, and a fixed fee. No ambiguity about what you're getting.
Deliver
I do the work and hand you the deliverable — a written report, an interactive tool, a triage workbook, whatever the scope calls for. If the data needs ongoing attention, we talk about a retainer. If not, you have everything you need.
Thirty minutes. No obligation.
Tell me about the data problem. I’ll tell you where the impact is hiding.
Get in Touch