Food Manufacturing

SunButter builds a unified data warehouse to forecast business performance

How North Labs helped a growing food manufacturer replace 21+ hours of monthly manual reporting with a centralized data warehouse, automated pipelines, and predictive analytics.

The Challenge

SunButter is a leading brand of sunflower seed butter, serving consumers with nut allergies across the country. As the business grew, their existing data program couldn't keep pace. Reporting was fragmented, slow, and entirely manual.

Sales leadership spent 21+ hours a month combining sales reports, industry trends, retail data, and wholesale inventory spreadsheets — relying on pivot tables and manual extraction for analysis.
Inventory monitoring at the store and wholesale levels was based on "gut feeling" and trailing monthly reports, leaving the team reactive rather than predictive.

SunButter needed to evolve from spreadsheets and intuition to a single source of truth — one that could centralize internal and external data, automate reporting, and power forward-looking decisions.

Business Impact

21+ Hours Reclaimed Monthly

Eliminated days of manual data preparation, spreadsheet combining, and pivot table analysis — freeing sales leadership to focus on strategy.

Single Source of Truth

All internal and external data centralized in one place — enabling informed decisions on order fulfillment and region-specific sales performance.

Predictive Forecasting

Machine learning insights replaced "gut feeling" with data-driven predictions for inventory levels and sales trends — both horizontally over time and vertically across product lines.

The Solution

North Labs evolved SunButter's data culture by designing and implementing a modern data warehouse backed by advanced cloud and data infrastructure. The solution addressed every layer of the data lifecycle — from automated ingestion through transformation, reporting, and predictive analytics.

Goals

Eliminate manual intervention and data preparation entirely
Create a centralized source of truth combining internal and external data for reporting and ad-hoc analysis
Leverage data sharing for industry trends, retail data, wholesale inventory, and sales performance
Deploy machine learning for future inventory and sales predictions — replacing intuition with evidence

What We Built

North Labs designed a comprehensive data platform using Snowflake as the central warehouse, with automated ingestion pipelines, serverless compute for data processing, and modern ETL tooling to transform raw data into queryable, reportable assets. The reporting layer was migrated from spreadsheets to Power BI, giving stakeholders interactive dashboards for the first time.

Predictive models were layered on top of the warehouse, providing ML-powered trend analysis and forecasting across both time-series and dimensional data — enabling the team to anticipate demand rather than react to it.

The Outcome

Files are automatically uploaded and processed on a weekly basis, transformed into a usable form for querying, and mirrored in Snowflake to be leveraged in the BI tool of preference — completely hands-off.
With all data in one place, business stakeholders are able to make informed decisions regarding future order fulfillment and region- or store-specific sales, while eliminating days of work formatting and combining spreadsheets.
ML-powered insights and forecasting provide trend analysis and predictions across horizontal analytics (changes over a period of time) and vertical analytics (changes to a particular column or set of data) — turning raw data into forward-looking intelligence.