To sustain its international expansion, Toly required a shift from manual, siloed reporting to a modern, governed analytics platform. Eunoia partnered with Toly to transition its architecture from a legacy on-premise system to a unified Microsoft Azure data lakehouse. The goal was to establish a single, trustworthy source of truth for enterprise reporting that would improve operational decision-making today and pave the way for predictive AI capabilities tomorrow.
Toly is a global leader in packaging solutions. As the company rapidly expanded its global footprint, establishing scalable digital operations and ensuring seamless information flow became a primary focus.
Strategic Global Visibility – Ensuring leadership across different countries and departments had immediate, reliable access to performance metrics.
Operational Efficiency – Eliminating the heavy reliance on manual data compilation, hidden spreadsheet adjustments, and time-consuming weekly refresh cycles.
AI Readiness – Establishing a clean, governed, and secure foundation that could easily integrate with future AI tools and Microsoft Copilot.
With critical business data, specifically across Sales, Finance, and Leads & Opportunities, fragmented across Dynamics CRM, Dynamics 365 Business Central, and a legacy on-premise data warehouse, generating consistent reports was difficult.
This disjointed landscape forced the team to rely on manual data extraction, costing approximately 20 hours of manual effort per week. Furthermore, reporting was limited to weekly refresh cycles, meaning data availability covered only roughly 14% of the work week. The manual and fragmented nature of the reporting made it difficult for decision-makers to trust, explain, or securely drill down into the reported figures.
The solution involved successfully establishing a unified Microsoft Azure data lakehouse that combined data lake scalability with data warehouse reliability. This enabled:
API-Based Integration
Business Central access was modernised from direct database reads to an API-based integration, establishing a more sustainable extraction pattern.
Automated Data Pipelines
Data is ingested via controlled, incremental daily batch loads with built-in schema checks and duplicate detection to ensure high confidence across the estate.
Medallion Architecture
The platform utilises Azure Databricks to manage Landing/Bronze (raw), Silver (cleansed), and Gold (business-ready) data layers.
Robust Governance & Security
Unity Catalog provides data lineage and secure sharing, while Power BI enforces Row-Level Security through Entra Groups defined directly by Toly.
100% Daily Data Availability – Toly moved from weekly reporting to automated daily refreshes, increasing data availability from roughly 14% of the week to complete daily coverage.
20 Hours of Weekly Effort Reclaimed – The automated platform immediately eliminated an estimated 20 hours per week of manual data extraction. Long-term, the platform is projected to save Toly the equivalent of two full-time manual roles.
5-10 Million Records Centralised – The project successfully unified 5 to 10 million operational records into a single governed estate, giving 50 users across multiple countries access to cross-system data at the touch of a button.
Future-Proof AI Readiness – The secure, governed foundation prepares Toly for future AI use cases, including GenAI-powered insights and natural language querying over trusted business data.
“The data platform delivered with Eunoia has transformed how we access and trust our information, bringing data from multiple systems together into a single, reliable source of truth that supports faster and better decision-making. Eunoia’s deep understanding of the Microsoft Azure ecosystem, combined with a pragmatic and collaborative approach, made them a trusted partner in building a scalable foundation that will continue to deliver value as we expand our reporting and analytics capabilities.”
– Nick Xuereb, Chief Financial Officer at Toly
Get in touch with our team to see how we can help you on your data journey,
