PartnerRe is a leading global reinsurer with a diligent underwriting culture, strong balance sheet, and disciplined investment decisions—reflected in its high financial strength ratings. With over US$ 11.2 billion in total capital, PartnerRe plays a critical role in supporting insurance companies and communities after risk events. Headquartered in Bermuda, the company operates in 15 office locations and employs over 1,300 professionals worldwide, serving clients across a diverse range of sectors.
The challenge: Increasing complexity and pressure
As a leading global reinsurer, data has always been central to PartnerRe’s operations—powering pricing decisions, regulatory reporting, and risk assessments. However, evolving demands were beginning to stretch the limits of the company’s traditional data infrastructure:
Legacy integration methods
PartnerRe’s reliance on on-premises databases and ETL tools such as Informatica made it difficult to respond to increasing data variety, growing complexity, and ever-evolving regulatory requirements.
Increasingly complex regulatory landscape
Global reporting standards such as IFRS 17, Solvency II, and LDTI—alongside regional compliance mandates across multiple jurisdictions—required PartnerRe to adopt a data architecture capable of rapid adaptation, scalable enforcement of business rules, and efficient rollout to avoid operational disruption.
Disconnected systems
To meet growing business demand for faster time-to-market, PartnerRe adopted several SaaS solutions. While individually effective, these systems operated in silos and lacked native interoperability, making it difficult to combine data and generate unified insights.
Rapid data volume growth
As new systems were implemented, data volumes expanded significantly. Business users needed granular, client-specific data to assess individual risks, often requiring access to historical datasets. This led to exponential data growth, compounding the challenge of managing and delivering timely insights.
Diverse and demanding user base
PartnerRe’s data environment needed to serve a growing and varied group of users—from analysts and business stakeholders to data scientists—each leveraging different tools and languages like SQL and R. However, the high demand for data access placed increasing pressure on already stretched IT and data engineering teams. Without a scalable, self-service solution, routine data requests risked becoming bottlenecks, slowing down innovation and decision-making across the organization.
The solution: A modern data architecture powered by data virtualization
To overcome growing complexity and fragmentation in its data ecosystem, PartnerRe set out to design a modern architecture that could unify all data—regardless of location or format—while delivering fast, secure, and flexible access to both business and technical users.
After exploring various integration strategies, the team concluded that data virtualization offered the most effective and future-proof solution. By creating a logical data layer across all systems—on-premises and in the cloud—PartnerRe could accelerate data delivery, reduce integration costs, and support a wide range of use cases without disrupting existing infrastructure.
At the core of this new architecture is a centralized data hub, bringing together diverse data sources—structured and semi-structured, legacy and modern—into a unified, governed access point. This ensures consistency across the organization while simplifying connectivity and data management.
Complementing the hub, modular data products were introduced—curated, reusable datasets tailored to serve specific regulatory, analytical, and operational needs. These data products became the interface through which business teams could explore and consume relevant insights, without waiting on IT.
“We needed something that could bridge our existing systems with modern cloud tools, support multiple user personas, and handle rapid regulatory changes. Advanced data virtualization gave us that flexibility.”
— Thomas Mager, Head of Data and Analytics, PartnerRe
PartnerRe chose CData Virtuality
CData Virtuality was implemented as the foundation of its new architecture, serving as the logical layer that abstracts and connects all data sources.
Solution highlights:
- Logical data layer: CData Virtuality provides a central virtual layer for business logic, enabling SQL-based access to any data source, regardless of location or format.
- Unified data access: Snowflake provides scalable cloud compute and storage, while CData Virtuality virtualizes and intelligently pushes down queries for optimal performance.
- Data governance and cost allocation: Role-based access ensures users only see what they’re authorized to view. At the same time, data usage is tracked and assigned to the appropriate departments, enabling transparency and financial accountability.
- Self-service and innovation sandboxes: Analysts and data scientists now access curated data products in governed sandboxes, accelerating prototyping and reducing IT dependency.
The outcome: Competitive advantage through faster data access
By implementing CData Virtuality as the foundation of its modern data architecture, PartnerRe successfully broke free from the constraints of its legacy environment. The organization now operates within a unified, agile, and scalable data landscape, enabling faster decision-making, stronger collaboration, and long-term adaptability.
Development cycles have significantly shortened, allowing dashboards and reports to be launched faster and insights to reach decision-makers more efficiently. This acceleration in time-to-insight has become a competitive advantage, particularly in a market where timely data can directly influence risk assessment and profitability.
Teams across underwriting, finance, and risk management now collaborate on a shared, consistent data foundation, eliminating the inefficiencies of siloed data and fostering cross-functional alignment. A wide spectrum of users—including analysts, data scientists, and business users—can securely access and work with data using familiar tools and languages like SQL and R, enabling democratized data access.
The platform has also led to optimized resource use: IT and data engineering teams are no longer bogged down by repetitive provisioning tasks, freeing them to focus on strategic initiatives and innovation.
And the architecture is designed for the long haul. With CData Virtuality as the logical backbone and Snowflake delivering elastic compute, PartnerRe now operates on an infrastructure built for future-proof flexibility, capable of integrating new SaaS systems, BI tools, or even advanced AI and machine learning use cases as the business evolves.
“The implementation of CData Virtuality has been a game-changer for us. It’s not just about the technology—it’s about bringing our teams together, streamlining processes, and enabling true data democratization. Our journey towards a modern data architecture wouldn’t have been possible without this innovative platform.”
— Thomas Mager, Head of Data and Analytics, PartnerRe
Looking ahead: AI, ML, and beyond
PartnerRe continues to build on this foundation, exploring advanced analytics, AI, and machine learning. The partnership with CData ensures the company remains agile, compliant, and competitive in an increasingly data-driven industry.
