Shawn Phillips

Principal Consultant · Lailara LLC

Twenty-five years cleaning, validating, and building the reporting layer for transaction and operational data. Incentive fulfillment, rebate processing, product data syndication, retail supply chain. Currently Director of Operations and Chief Solutions Architect at Incentive Insights, where I’ve spent twenty years building the data quality standards a production platform depends on — and Principal Consultant at Lailara LLC, where I do the same work for specialty food brands scaling into national retail.

How I Got Here

I started in bioinformatics in 1999 and moved into incentive fulfillment operations in 2001. I’ve spent over twenty years with the same data infrastructure — designing it, expanding it, adapting it as the company grew and the technology changed. Small team, real constraints, no one to hand things off to. When a data error hit, it was a client escalation, not a ticket.

That means I’ve seen what survives and what doesn’t — across tool changes, team changes, growth, and every edge case production can throw at a system. Most consultants deliver and move on. The work I do is shaped by what happens in the years after the delivery.

Lailara LLC is the consulting practice I built around that experience — product data audits, decision tools, quality engineering for specialty food brands and CPG companies scaling faster than their data infrastructure can keep up.

Education & Training

Harvard Business School Online

Certificates in Strategy Execution and Digital Marketing Strategy

University of Kentucky

M.S. Vocational Education · B.S. Agriculture-Animal Sciences

Tools

PythonSQLRPower BIFastAPIQuartoShinySQLiteExcelClaude CodePlotlypandas

Portfolio

Different problems, different tools. The common thread: data that hasn’t been cleaned, validated, or interpreted yet — and a deliverable built for someone who needs to act on what’s in it.

Product Data Health Audit

Data readiness audit for a specialty food brand scaling into national retail. Finds $296,000/year in retailer chargebacks from product data defects, traces every chargeback to the specific field that caused it. Five artifacts from one reproducible pipeline.

RQuartoSQLiteShiny

Trade Spend & Deduction Recovery

Interactive decision tool that makes retailer deduction losses visible and actionable. Ten connected views including Sankey flow, recovery simulation, and retailer scorecards.

ReactPython

Fulfillment & OTIF Diagnostic

Reconciles internal fulfillment metrics against retailer scorecards, quantifies the gap, and produces a remediation plan prioritized by cost of inaction.

ReactPython

Channel Profitability & Capital Allocation

Which channels earn after all costs, where the revenue-to-cash leakage concentrates, and whether capital is flowing to the right shelf.

ReactPython

SKU Portfolio Audit

A scored kill list with quantified annual savings, a fix-or-kill action plan with one specific lever per SKU, and the methodology to re-run it quarterly.

Python

Retail Readiness & Launch

Readiness across eight dimensions for a specific retailer launch. Cash-flow modeling, GTIN validation, EDI preflight, and a gap-closing action plan.

FastAPIvanilla JSFly.io

Customer Analysis — Due Diligence

Acquisition due diligence from transaction data — customer concentration, cohort retention, CLV modeling, risk flags, and a post-close action plan.

RQuarto

GTIN Validator

Product data validation against GS1 standards with retailer-specific context. Generates branded PDF reports with a prioritized fix roadmap.

ReactFastAPIFly.io

Thirty minutes. No obligation.

Tell me about the data problem. I’ll tell you where the impact is hiding.

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