The McPherson Companies — From JD Edwards Complexity to Governed Snowflake Analytics

Challenge

McPherson didn’t need one more report. Across fuel, lubricants, fleet-card, and equipment-service lines, operational and financial logic lived in JD Edwards, spreadsheets, source-system conventions, and the knowledge of the people who run the business. Teams needed visibility into volume, margin, customer activity, sales performance, financial statements, planning data, and inventory — so every team could work from shared definitions and the same numbers.

They needed one place where definitions, grain, lineage, and access were managed deliberately — not a new spreadsheet every time the business asked a question.

Solution

Cloud Data Consulting started with a Discovery & Foundation engagement, partnering closely with McPherson’s finance and operations teams, whose subject-matter expertise shaped the work throughout.

Evaluate, Then Build

CDC didn’t arrive with a fixed toolset. The Discovery & Foundation sprint surveyed the options and chose deliberately: Fivetran and Coalesce for ingestion and transformation. To make the business-intelligence decision on evidence instead of opinion, CDC stood up a working dimensional model in dbt — real data, fast — so a structured BI evaluation could run against the actual business. That evaluation selected Omni.

A Snowflake Source of Truth

Operational and financial source data — JD Edwards included — flows through Fivetran into Snowflake, the single source of truth across operating and financial subject areas. The first subject area was McPherson Oil Products volume and margin. Reusable models translate JD Edwards conventions and subject-matter expertise into business definitions built once and used across domains.

Self-Service Analytics

Omni gives business users self-service access while definitions stay close to the source of truth. Drill-through lets someone move from a summary tile straight into the actuals behind the number — useful for real business review, not just presentation.

Finance & Planning

The same Snowflake data extends into finance, where Planful connects automated financial reporting and planning back to operational detail.

AI-Assisted Delivery

CDC delivered with AI-assisted engineering under senior human review. That is what compressed the timeline — without giving up the business-logic validation a data platform has to have.

Results

The full team kicked off in early January 2026. By April 6 — about 90 days later — power-user training was complete, first-wave sales users were coming online, and customer and sales dashboards were ready for business review. A year’s worth of traditional data-platform work, delivered in roughly a quarter.

Metric	Result
First-phase delivery	A year’s worth of work in ~90 days (Jan–Apr 2026)
Source of truth	Snowflake across operating + financial domains
Self-service analytics	Omni model with drill-through to actuals
Subject areas	MOP volume/margin live; Finance/Planning and Supply Chain following
Manual reporting	Moving onto the platform, domain by domain
Reuse	One platform pattern carried into each new subject area

A Foundation, Not a Dashboard

The win wasn’t a new data stack. It was turning business logic into reusable assets — a Snowflake source of truth, self-service analytics, and finance/planning integration — that keeps expanding as McPherson adds subject areas. After the first release, CDC stabilized the initial subject area and moved into Finance and Supply Chain.

From Compiling Reports to Building Them

The platform changed individual jobs, too. One analyst had spent part of every day assembling a recurring management report by hand. That report is moving onto the platform — and the same analyst is now building dashboards in Omni himself, a shift from compiling numbers to working with them.

A Foundation for Future AI

AI is only useful when the data underneath it is strong enough to trust. The same AI-assisted approach that compressed this build is why CDC treats a well-modeled Snowflake foundation as the prerequisite for whatever AI McPherson builds next — not an afterthought.

Client Testimonial

“We just accomplished in 90 days what traditionally takes a year or more to do. This is twelve months of work.”

— Kevin Griffin, VP of Financial Planning & Analysis, The McPherson Companies

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That’s the CDC pattern: senior data architecture, hands-on implementation, and delivery focused on business outcomes.

If your operating and financial numbers live in an ERP, spreadsheets, and a few people’s heads — and every team answers the same question a little differently — that’s where McPherson started.