Data Products for Analytics Agility

Five years into its digital overhaul, the insurer’s biggest challenge wasn’t transformation; it was trust in its own data. Despite modernizing core systems like Guidewire, Workday, and Duck Creek and building a centralized data warehouse, the mid-sized U.S. P&C insurer still struggled with fragmented data, inconsistent reporting, and limited business insight.

Key Challenges:

Data silos and trust issues – “Multiple versions of the truth” across business areas led to endless reconciliation cycles and conflicting reports.
Operational inefficiency – Complex data pipelines, poor SLA performance, governance gaps, and high delivery costs.
Limited business value – Difficulty enabling self-service analytics, data-driven decisions, and innovation.

To modernize its data foundation the insurer partnered with PremiumIQ. Recognizing that technology alone would not solve organizational and operational challenges, PremiumIQ brought industry-specific artifacts and best practices to fundamentally reimagine how the company approached data analytics.

Reimagining Data as a Product

PremiumIQ led a 10-week strategic discovery and assessment that resulted in a complete pivot to a Data Analytics Product (DAP) strategy and operating model. The approach included:

Defining commercial P&C insurance data domains and related products
Establishing business-led product management with agile delivery teams
Embedding data governance into the product development process
Designing the DAP architecture and shared services framework
Following the strategy phase, PremiumIQ guided the client through key implementation milestones:
Decision to adopt the DAP operating model and launch a pilot (Billing Financial domain)
Development of a four-year delivery roadmap, financial business case, and 2025 resource and budget plans
Inception and delivery of three additional DAPs (Agency, Enterprise Dimensions, and Policy Financial domains)

Transformational Impact

Building Trust Through Data Consistency

The product-centric model is establishing a single version of truth across the organization by:

Unifying legacy and modern data sources within each domain
Standardizing business definitions, rules, and reference data
Eliminating conflicting reports and reconciliation cycles
Driving consistent consumption of analytics products across business areas

Accelerating Business Value

With reliable, well-governed data products taking shape, the organization is now able to:

Reduce data complexity for easier user understanding
Increase productivity as teams shift from validation to insight generation
Build confidence in self-service analytics
Establish a foundation for advanced analytics and innovation initiatives

Optimizing Delivery and Operations

The DAP model is transforming how the organization delivers analytics capabilities:

Lower platform and resource costs through streamlined architecture
Accelerate delivery through product-oriented teams
Improve SLA performance with clear ownership and accountability
Integrate governance directly into the product development lifecycle

“Many insurers face the same challenge: investing heavily in modern technology and AI while their data remains fragmented and untrusted. The breakthrough here was not just better architecture or tools; it was a fundamental shift in mindset. By treating data as business products with clear ownership, consumers, and value, and combining that with P&C domain expertise, true transformation became possible.”

— Mark Hodson, Partner, PremiumIQ

Looking Forward

This transformation proves that sustainable analytics enablement requires more than technology modernization; it demands a strategic operating model that aligns data delivery with business needs. By treating data as products with clear ownership and embedded governance, organizations can break down silos, accelerate time-to-insight, and create a foundation for continuous innovation.

With a proven framework, working pilots, and a comprehensive four-year roadmap in place, the insurer is now positioned to scale this approach across additional data domains and compete more effectively in an increasingly data-driven insurance marketplace.