Modernizing a data ecosystem after rapid growth

A major US P&C and group benefits insurer with over 200 years of experience and operations across the United States recently grew significantly by acquiring smaller companies. This resulted in a fragmented landscape of on-site legacy systems for claims, underwriting, and customer data. Each state the organization operated in had its own regulatory requirements, making system integration and data consistency even more complicated.

This patchwork environment resulted in data silos, preventing the insurer from making the most of its vast data assets. The issues spanned three major data categories: customer and risk, claims, and internal risk and pricing. What’s more, these datasets lived in different systems and sometimes varied by product or state. All of these factors led to a bottleneck, inhibiting fast, efficient, and scalable data and analytics operations.

For example, claims data was stored in an on-site, siloed database, while legacy sales and distribution data marts were tied to aging SQL data warehouse. These systems were difficult to scale and maintain, hindering enterprise-wide data initiatives.

To address these challenges and enable modern data and analytics operations, the insurer decided to transition its claims, sales, and distribution data to cloud-based systems for standardization and scalability.

A unified approach to data

Capgemini collaborated with the insurer to migrate data processing and analytics workloads from on-site platforms to cloud-native environments without disrupting business continuity. This approach let the company scale its data infrastructure enterprise-wide while significantly reducing the total cost of ownership. To do so, Capgemini used its proprietary assets and accelerators to identify, extract, and migrate data from a wide array of legacy systems. At the center of this effort was an Extract, Transform, Load (ETL) framework built to handle the scale and complexity of the insurer’s data environment. This framework ensured that large, diverse datasets could be moved efficiently and reliably to the cloud, regardless of format or source.

Legacy data systems were migrated and consolidated into a unified cloud platform, guided by a reference framework that defined the target state for all data. This provided a consistent foundation for enterprise-wide data operations and supported long-term scalability.

The transformation culminated in a data marketplace that enabled teams across the organization to request access, share insights, and contribute new datasets in a structured, secure way. By choosing Snowflake as the cloud data platform, the project team delivered a solution with built-in capabilities in advanced analytics, AI readiness, and elastic data storage.

To support this transformation, the insurer set up Standard Operational Procedures (SOPs) and best practices for data access, management, and integration. These SOPs clearly defined access protocols and authentication mechanisms, drastically reducing wait times from days to minutes. The standardized framework let the company onboard new entities seamlessly and ensured repeatable, compliant processes across business units.

Recognizing the unique demands of the insurance industry, the transformation was closely aligned with core business functions like distribution, policy administration, underwriting, and claims. By tailoring the solution to the insurer’s operational structure, Capgemini embedded the new systems smoothly within day-to-day workflows, ensuring relevance, usability, and long-term value.

A leap towards a data-driven future

By uniting multiple data initiatives under a single, enterprise-wide transformation, the insurance firm underwent a large-scale modernization effort that was aligned with its most important business goals. This initiative established a modern data foundation that now powers advanced analytics, AI and machine learning (ML) experimentation, and real-time insights into risk and product performance.

By creating a unified data marketplace, teams can now discover and access data assets in less than 30 minutes – a process that once took days. At the same time, integrated governance tools display clear ownership, definitions, and usage metrics, which has significantly improved data stewardship and trust.

This modern ecosystem has reduced data-processing latency by 50% and accelerated data-querying by 90%, enabling faster, more agile operations. Improved on-demand analytics capabilities have accelerated time to insight by 70% and reduced analytics cycle times by 60%, empowering business users to make informed decisions more quickly.

With 60% faster time to market for benefits products and 40% shorter servicing times, the transformation has unlocked a new level of business agility. The organization can also scale rapidly, integrating new data sources 70% faster than before.

By eliminating data silos and inconsistencies, the insurer has opened enterprise-wide access to AI and ML – capabilities that were once out of reach. Now, it’s actively running proofs of concept and scaling AI initiatives, supported by advanced technologies like lakehouse architecture, Iceberg tables, and GenAI.

With this future-ready data foundation, the insurer has positioned itself as one of the most data-mature organizations in the US. Together with Capgemini, the company continues to expand its AI and ML capabilities, driving enterprise innovation and long-term transformation.