From SAP Silos to Self-Service: How a Manufacturer Unlocked AI-Powered Insights with Snowflake

Overview 

A leading manufacturing company partnered with CloudEQS to replace fragmented SAP data, brittle pipelines, and sprawling BI tools with a governed, self-service analytics platform built on Snowflake. 

 In just 12 months, the CloudEQS team designed and delivered a complete cloud data architecture, migrated legacy ETL to Matillion, and rolled out Sigma Computing to enable self-service analytics across their business.  

The Challenge: Siloed SAP Data and Brittle Pipelines Were Slowing the Business 

The client, a mid-size manufacturing company, ran its core operations on SAP ECC alongside Salesforce, SharePoint, SAP SuccessFactors, and dozens of Excel workbooks. That architecture created three recurring problems: 

1. SAP’s rigid structure made it difficult to understand customers, analyze operations, or move quickly on new initiatives, which slowed time to market. 

2. Data pipelines connecting these systems were brittle and failure-prone, eroding trust in the numbers business teams relied on every day. 

3. Multiple BI platforms had proliferated across departments, creating data sprawl with no single source of truth for Operations, Product, Finance, Sales, or Customer Success. 

Leadership needed a modern, centralized data platform that could support self-service analytics without sacrificing governance or reliability. 

The Solution: A Cloud Data Platform Built on Snowflake, Matillion, and Sigma 

The CloudEQS team designed and implemented an end-to-end cloud analytics platform centered on Snowflake, Matillion, and Sigma, built around 5 pillars: 

• Enterprise readiness. CloudEQS prepared the environment for enterprise-scale operations. Including single sign-on (SSO), network policies, role-based access control (RBAC), and SCIM integrations for identity management. 

• Data Management: Snowflake became the client’s central source of truth, unifying data across Operations, Product, Finance, Sales, and Customer Success into one governed platform. 

• SAP Data Pipeline Stabilization and Modernizing ETL Platform: Implemented Matillion to stabilize data replication from SAP to Snowflake. Additionally, Matillion ingests data from SharePoint, Salesforce, Excel, and SAP SuccessFactors, feeding a structured Snowflake pipeline of staging, raw, transformation, and reporting layers. 

• Core Data Models: The CloudEQS team built foundational business data models covering Backlog, Booking, Shipping, and Material Cost, along with shared common dimensions for Customer, Time, Product, and Vendor Material.  

• Self-Service Enablement: Sigma Computing was deployed on top of Snowflake to give business users direct, self-service access to curated data without waiting on IT. 

Architecture at a Glance

In short, Matillion ingests and orchestrates data from core business systems into a Bronze-Silver-Gold Snowflake architecture, which then branches into two consumption paths: an AI path powered by Snowflake Cortex and surfaced through Snowflake Intelligence, and a reporting path delivered through Sigma’s self-service dashboards. 

The Results: Faster Delivery, More Trust, Fewer Platforms 

The Snowflake migration delivered measurable impact across delivery speed, infrastructure efficiency, and data reliability: 

• 30+ foundational data models built and delivered within the first three quarters of the engagement. 

• Stable, reliable SAP-to-Snowflake data replication, resolving one of the client’s most persistent data trust issues. 

• 6+ Operational AI Agents built for Sales, Finance, Operations, and Customer Success to self-service insights. 

• Reduced infrastructure management overhead following the move to Snowflake’s cloud-native platform. 

• AI-assisted conversion of legacy Excel VBA macros into Sigma-native logic, accelerating the retirement of brittle, spreadsheet-based workflows. 

Beyond the immediate delivery gains, the client now operates on a governed, cloud-native data platform, one that replaces fragmented SAP reporting and shadow spreadsheets with a single source of truth, and positions the organization to scale self-service analytics and future AI-driven initiatives. 

Why It Matters 

For manufacturers running on SAP, data fragmentation is a common bottleneck: critical information about customers, operations, and finances gets locked into rigid ERP structures and disconnected spreadsheets. This engagement shows what’s possible when a cloud data warehouse, a modern ETL tool, and a self-service BI layer are combined into a single, governed platform: faster model delivery, more reliable pipelines, and a genuine single source of truth across the business. 

Interested in modernizing your own data architecture? Connect with the CloudEQS team to discuss how a Snowflake-based data platform could work for your organization.