Turning Data Into Decisions

Unified Policy, Risk, and Exposure Data to Enable Faster Insights and Stronger Governance

A mid-sized insurer offering commercial auto and property coverage needed to modernize its data foundation to keep pace with growing analytics demands, regulatory pressure, and increased reliance on near real-time reporting. Despite having a centralized on-prem data warehouse, the organization struggled with fragmented data models, slow reporting cycles, and inconsistent definitions across lines of business.

PremiumIQ partnered with the insurer to design and implement a scalable, domain-driven insurance data model on Snowflake. The result was a unified, governed analytics foundation that improved data trust, accelerated reporting, and positioned the organization for AI-driven insights.

The Challenges: Fragmented Data and Slow Time to Insight

While the insurer had access to significant volumes of data, its structure and delivery limited business value.

Key challenges included:

Disparate data sources across Hit Ratio, Management Liability, and Builder’s Risk
A legacy on-prem warehouse unable to support real-time or high-frequency reporting
Manual, multi-week monthly reporting processes dependent on spreadsheets
Inconsistent metric definitions and unclear data ownership
Limited scalability for advanced analytics, automation, and AI initiatives
Increasing regulatory and governance pressure without centralized controls

Leadership needed a modern data model that could scale with the business while improving reliability and transparency.

The Solution: A Domain-Driven Insurance Data Model on Snowflake

PremiumIQ designed and delivered a cloud-native insurance data model built on Snowflake, grounded in insurance-specific domains and governed analytics principles.

Key elements of the solution included:

Domain-based modeling for Policy, Coverage, Endorsements, Rating Factors, Parties, and Geography
Star schema design with Type 2 slowly changing dimensions to support historical analysis
Reference entity modeling using stable keys and minimal grain for consistent joins and reuse
Consolidation of 1,000+ attributes into efficient Snowflake semi-structured data types
Metadata-driven ELT pipelines built with dbt for automated, scalable ingestion
Governed semantic views to ensure consistent business logic and metric definitions
Power BI models built on certified views with clear join paths and calculations

The architecture emphasized reuse, performance, and governance without sacrificing flexibility.

The Impact: Faster Reporting, Stronger Governance, Scalable Analytics

The modernized data model transformed how the insurer accessed and used data across the organization.

Key outcomes included:

A unified 360-degree view of commercial policy data across lines of business
Replacement of manual monthly processes with fully automated daily reporting pipelines
Improved confidence in reporting through standardized definitions and governed views
Faster access to Hit Ratio, Risk, and Exposure insights for underwriting and management
Reduced operational effort and reporting cycle time
A scalable foundation to support AI, automation, and advanced analytics initiatives

Why It Matters

For insurers, analytics speed and data trust directly impact underwriting quality, regulatory compliance, and operational efficiency. By modernizing its data model and embedding governance into the architecture, this insurer moved from reactive reporting to proactive insight generation.

The result is a data platform that supports confident decision-making today and provides a durable foundation for future innovation.

“Modern insurance analytics start with a data model that reflects how the business actually operates. By organizing data around core insurance domains and embedding governance into the design, we helped the client move faster, trust their numbers, and build a platform that will scale with their analytics and AI ambitions.”