Accelerating Cloud Transformation

Building a scalable, cost-efficient, and future-ready data environment

As data volumes grew and analytics and AI demands surged, one North American insurer faced a critical decision: modernize or fall behind. Its enterprise data warehouse (EDW) supported thousands of users and 20,000 reports but was constrained by 6,000 aging Informatica jobs and 700 terabytes of data on a rigid, on prem database appliance.

Partnering with PremiumIQ, the insurer launched a comprehensive migration to Snowflake, reducing cost, improving performance, and establishing a cloud-native foundation for long-term analytics innovation.

Key Challenges

• Complex legacy environment – Thousands of ETL processes built over a decade made modernization daunting. Of the 6000+ jobs, over 350 were highly complex hand-coded programs, limiting the usefulness of automated conversion tools.

• Scalability and performance limits – The on-prem system struggled to meet SLA expectations for expanding workloads and new analytics use cases.

• Rising cost of ownership – High licensing, infrastructure, and maintenance costs hindered innovation and flexibility.

• Aggressive delivery timeline – The business required completion from planning to cut-over within 2.5 years, without using incumbent systems integrators.

Strategic Approach

Recognizing that modernization required both technical precision and strategic alignment, PremiumIQ led the end-to-end planning and architecture effort through a multi-phase transformation roadmap.

1. Business Case & Platform Selection
PremiumIQ began by conducting a comprehensive cost-benefit analysis (CBA) to quantify labor, licensing, and infrastructure costs and define the 2.5-year execution plan. The analysis projected a two-year payback on a $10M investment, driven by reduced licensing, lower labor costs, and improved scalability. Snowflake and dbt were selected as the core modernization platforms.

2. Phased Migration Strategy
PremiumIQ developed a phased plan to ensure stability and minimize disruption:

• Phase 1: Migrate the Gold Layer with 42 data marts supporting more than      20,000 Business Objects reports while establishing a new orchestration      framework.

• Phase 2: Re-engineer backend logic and more than 6,000 Informatica      pipelines into dbt, modernizing the ELT layer.

• Phase 3: Execute, validate, and deploy each wave with parallel testing      to ensure business continuity.

• Phase 4: Decommission legacy ETL and database infrastructure.

3. Stakeholder and Vendor Alignment
PremiumIQ facilitated collaboration across business and IT, securing alignment on priorities and ensuring the right vendor and resource structure to deliver the program on schedule.

Transformational Outcomes

• Data Modernization with Measurable ROI
  The CBA delivered compelling results, so compelling that the business approved $10M in funding out of cycle, validating confidence in the approach and leadership.

• Scalable, Cloud-Ready Architecture
  The Snowflake and dbt architecture provides a flexible, future-ready environment that supports new analytics use cases, AI, and self-service capabilities.

• Operational Efficiency and Cost Savings
  The new platform will reduce infrastructure and maintenance costs, simplify development, and accelerate delivery cycles through automation and modular design.

• Enterprise Alignment
  The transformation unified stakeholders around a shared vision for analytics modernization with clear ownership and measurable business outcomes.

“The key to this program’s success isn’t just technology, it’s alignment. By defining the business case first and structuring the migration around measurable ROI and clear accountability, the organization will gain business buy-in and long-term confidence in its data strategy.”

— Tyler Sierzega, Engagement Lead

Looking Ahead

With planning complete and the modernization program underway, the insurer is on track to transition its full analytics ecosystem to Snowflake within 2.5 years. Once live, the new environment will unlock faster data access, stronger governance, and an adaptable foundation for advanced analytics, machine learning, and AI.

This transformation represents more than a move to the cloud. It is a step toward a smarter, leaner, and more innovation-ready data future.