Wholesale Distribution · PE-Backed
$4.1M in revenue opportunities hiding in plain sight
National Wholesale Distributor — Green Industry
Unlocking a Dormant Data Model to Drive $4.1M in Revenue Opportunities
Industry: Wholesale Distribution — Horticulture, Lawn & Garden, Greenhouse Supplies Structure: PE-backed, 500+ employees, national footprint Engagement: 2 months
The Problem
Revenue wasn't moving. Category managers were running historical sales data in Excel files that crashed on open. Finance had a Power BI model that took four hours to load and four hours to refresh — so they ran it once a day, sent screenshots when sales asked questions, and called it reporting.
A new Chief Merchandising and Pricing Officer came in to fix it, and brought me in as part of a small team built to cut through the bureaucracy fast.
On day two, sitting with the data team and the DBA, we found the real story: the company had built a complete, well-structured data model of its entire business over ten years ago. Products, sales, geography, customers, pricing history — all of it. No one in the business was using it. No one knew how. It had been sitting untouched for a decade while the rest of the organization worked around it with crashing spreadsheets and daily screenshots.
The DBA nearly jumped out of his chair when I told him we were going to start using it.
The Work
The data model didn't need to be built. It needed to be translated. Nobody had ever bridged the gap between what the business needed to know and what the data team had already built — so I worked directly with the DBA to map the structure, identify the tables relevant to pricing and category management, and build the queries to answer the questions actually being asked.
Within two weeks of engagement start, category managers were getting item-level answers in minutes instead of waiting for a screenshot.
With clean data access established, I ran a full revenue opportunity analysis across the company's $65M product portfolio — examining purchase patterns, geographic distribution, customer segments, and assortment gaps. That analysis surfaced $4.1M in revenue opportunities, all from data that had been sitting unused in the existing model.
I also mapped the current state of reporting across the organization and designed a roadmap for ETL and reporting automation — a structured path from the manual, screenshot-driven workflow to a scalable self-service environment.
Outcomes
$4.1M in revenue opportunities identified across a $65M portfolio — sourced entirely from existing data nobody was querying.
Category managers went from crashing Excel files to answers in minutes. A data model untouched for ten years became the operational analytics layer within two weeks.
A reporting automation roadmap delivered — a clear path from daily screenshots to a self-service environment.
The engagement was cut short by a PE-driven leadership change before the roadmap could be fully executed. The analytical foundation and revenue opportunity work were delivered and documented before close.
The Pattern
The data wasn't missing. A decade of transaction history was sitting in a well-structured database while the business ran manual workarounds around it. The problem was never technical — it was translation. No one had ever connected what the business needed to know with what the data team had already built.
Two weeks of focused work on that gap produced more analytical capability than years of workarounds had.
Engagement Lead: Michael Cohen (solo, reporting to Chief Merchandising Officer) Stack: Legacy SQL Data Model · Power BI · Excel
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