Cloud BI Migration & 30% Snowflake Processing Reduction for a Wealth Management Fund

100%

Daily data integration pipelines automated

500

Analytical programs migrated from on-prem to cloud

30%

Reduction in night processing time in Snowflake

Background

Our client is one of the largest insurance and wealth management organizations in Canada, with operations across North America. As a publicly traded financial services leader, our client supports complex business operations through a large ecosystem of data, analytics, and BI applications. As part of its strategic evolution and corporate restructuring, they launched major initiatives to modernize its technology landscape and ensure its data platforms could support long-term growth, scalability, and modern governance requirements.

Challenge

One of our client's key lines of business relied on an aging, on-premise BI solution that was no longer capable of meeting evolving analytical demands, performance expectations, and security standards. Several core technologies were approaching or had reached end-of-life, including legacy Oracle and SAS components. This environment created limitations in scalability, governance, and operational agility, while introducing risk in terms of compliance and future system sustainability. The organization required a secure, modern cloud-based BI platform capable of supporting present and future data integration needs while facilitating the controlled decommissioning of legacy systems.

Approach

Keyrus supported the client through a structured two-phase transformation strategy designed to modernize its BI capabilities while minimizing operational risk: Phase 1 – Lift and Shift Migration Migrated 182 BI front-end products from legacy Oracle on-prem architecture to the cloud BI and analytical platform. Phase 2 – Legacy Decommissioning & Data Modernization Leveraged migrated assets to drive further modernization and enable full retirement of outdated BI technologies. Implemented Snowflake as the cloud data warehouse foundation, utilizing its scalable storage and compute architecture Deployed Azure Data Factory to orchestrate daily data integration and pipeline scheduling Enabled real-time replication and Change Data Capture using HVR (Fivetran) for seamless data flow Established dual integration architecture: Real-time replication from PostgreSQL to Snowflake CDC data staging through Azure Storage before Snowflake ingestion Supported end-user enablement through SAS Viya and secure file-sharing for unstructured exports This methodical approach ensured continuity of operations while accelerating modernization.

Key results

Clear Modernization Roadmap Established: The client received a structured transformation plan defining roles, milestones, technical architecture, and sequencing for data warehouse modernization.

Future-State Architecture Defined: A scalable, governed, and analytics-ready architecture was designed to replace legacy systems and improve long-term performance and flexibility.

Foundation for Advanced Analytics Expansion: The modern environment now supports the launch of new initiatives and broader data value creation across business units.

Benefits

'The project enabled our client to transition from a legacy, high-risk BI environment to a secure, scalable, and cloud-native analytics ecosystem. This transformation strengthened governance, improved performance, and significantly increased the organization’s ability to leverage data as a strategic asset. By modernizing its data architecture, our client gained improved operational efficiency, enhanced scalability, and a future-ready analytics platform that supports innovation, agility, and smarter decision-making across the enterprise.