Faster Decisions, Better Data

As a mid-sized commercial P&C insurer advanced its multi-year digital transformation, the organization modernized core systems and migrated to the cloud, including Guidewire Cloud InsuranceSuite and supporting platforms. A centralized enterprise data warehouse (EDW) was established to support system migration and data integration.

While this foundation enabled progress in consolidating data, it was not designed to support scalable, business-ready analytics. As market conditions evolved and demand for timely, data-driven insights increased, limitations in how data was structured, governed, and delivered became more pronounced.

What initially appeared to be a technology challenge revealed a broader issue: analytics delivery was not aligned to how the business consumed and used data. To address this, the organization needed to transition from an EDW-centric delivery model to a data product strategy aligned to business domains.

The Challenge: EDW-Centric Delivery Limits Scale

Despite significant investment in a centralized data platform, the organization faced persistent challenges that limited the effectiveness of its analytics capabilities:

Data silos continued to emerge across business areas, creating multiple versions of the truth and driving reconciliation cycles
Increasing complexity across pipelines and data stores created a long and difficult “last mile” to delivering usable insights
Governance processes were inconsistently applied, with backlog and workarounds reducing data quality and trust
Poor EDW SLA performance impacted reliability and delayed access to critical data
Redundant and conflicting engineering efforts increased delivery timelines and operational costs
Analytics delivery remained project-based and disconnected from business needs, limiting self-service and innovation

The EDW provided a centralized repository, but it did not provide a scalable way to deliver trusted, domain-aligned data to the business. Leadership recognized that a new operating model was required.

The Solution: Transitioning to a Data Analytics Product Strategy

The organization partnered with PremiumIQ to define and implement a Data Analytics Product (DAP) strategy and operating model, shifting from centralized EDW delivery to domain-oriented data products aligned to business needs.

The engagement began with a 10-week strategic discovery and assessment across business and IT stakeholders. Based on these findings, PremiumIQ recommended a data product approach centered on ownership, alignment, and scalability.

Key elements of the solution included:

Defining commercial P&C insurance data domains and associated data products, including Billing Financial, Agency, Enterprise Dimensions, and Policy Financial
Establishing a business-led product management model to ensure data products aligned with business priorities and outcomes
Implementing agile, cross-functional teams responsible for the end-to-end lifecycle of each data product
Embedding data governance, business rules, and reference data directly into each data product to ensure consistency and trust
Designing a scalable architecture supported by shared services, including governance, infrastructure, data management, engineering, and operations
Developing a 4-year data product roadmap, supported by a financial business case and resource planning. Rather than treating governance and quality as downstream activities, they were built directly into the platform and operating model.

The approach prioritized early execution through the launch of a pilot data product, demonstrating value while establishing the foundation for broader adoption.

The Results: Scalable, Trusted Data Products

The transition to a data product strategy delivered measurable improvements across both business and technology dimensions:

Establishment of domain-aligned data products that provided a consistent, trusted “single source of truth” across business areas
Reduction in reconciliation effort and conflicting definitions through embedded governance and standardized business logic
Simplification of data pipelines and architecture, reducing complexity and lowering operational costs
Successful delivery of a pilot (Billing Financial data product), followed by three additional data products across key domains
Improved SLA performance and faster delivery of analytics through product-oriented teams
Increased business adoption and understanding of data through consistent, reusable data products
A scalable foundation for future analytics and AI initiatives built on trusted, domain-driven data

By shifting to a data product operating model, the organization transformed analytics from a fragmented, project-based function into a scalable, business-aligned capability.

Why It Matters

Centralized data platforms alone do not deliver business value. When analytics is delivered through disconnected pipelines and projects, complexity increases and trust declines.

A data product strategy changes this by aligning data to business domains, embedding governance into delivery, and creating reusable, scalable assets that serve the organization consistently.

By moving beyond an EDW-centric model to domain-driven data products, this organization established a foundation that improves trust, reduces cost, and enables faster, more confident decision-making at scale.

“This transformation enabled the organization to move from centralized data management to domain-aligned data products. By embedding governance, simplifying delivery, and aligning to business outcomes, they created a scalable foundation that improves trust and accelerates decision-making.”
— PremiumIQ Consulting Lead