How to move data to the core of insurance

Jun 30, 2026 Data Advisory, Data and Finance in Insurance, Data Migration

Data management: from bottleneck to competitive advantage

For decades, data was treated by insurance companies primarily as a supporting asset, necessary for reporting, compliance, and actuarial analysis, but rarely central to strategic decision-making. That reality has fundamentally changed. In the future, data will form the backbone of insurance operations, influencing underwriting, claims handling, pricing, customer experience, and risk management. But the industry still has a long way to go.

The volume, variety, and velocity of available data have increased dramatically. Insurers now leverage not only traditional policy and claims data, but also telematics, IoT inputs, third-party datasets, climate information, and behavioural signals. This has elevated data to a decisive competitive factor. But the heterogeneity of these data has turned out as a main blocker. Most insurers still lag behind in building truly data-driven business models. Legacy IT landscapes, fragmented architectures, and persistent data quality issues remain major obstacles, particularly when it comes to adopting advanced analytics and AI. Consequently, data management has become a top priority in insurance IT, with its importance steadily increasing.

Poor data quality, fragmented data models and lack of integration

Several structural challenges continue to limit the effective use of data in insurance. Chief among them is poor data quality, with many organisations relying on datasets that are inconsistent across systems, incomplete due to missing or poorly migrated information, and unreliable because of outdated or conflicting records. This undermines trust in analytics and often forces business users to rely on manual workarounds or legacy data sources. At the same time, fragmented data models and rules exacerbate the issue. Over years of system evolution, insurers have built complex landscapes with multiple policy, claims, and billing platforms, each with its own data structures, business logic, and processing rules. The resulting lack of consistency makes it difficult to harmonise data and introduces significant friction into reporting and transformation initiatives.

A further complication is the lack of integration across systems. Data silos limit end-to-end visibility, make cross-functional insights hard to generate, and prevent real-time analytics. Traditional “Extract, Transform, Load (ETL)” pipelines are often too rigid and struggle to keep pace with modern, cloud-based architectures. Governance is another weak point. Many insurers operate without robust, enterprise-wide data governance frameworks, resulting in unclear ownership, inconsistent metric definitions, and limited accountability for data quality. Coupled with this, data processes are frequently not designed to scale, making them unsuitable for modern analytics and AI use cases. Finally, performance constraints in legacy systems, such as slow processing, inefficient batch jobs, and limited scalability, further restrict the ability to generate timely insights and operate efficiently.

Creating a future-proof data management

Addressing these challenges requires a holistic transformation of data management approaches. A key starting point is improving data quality through systematic reconciliation and validation. By identifying inconsistencies across systems, validating critical financial and operational data, and embedding continuous quality checks, ideally automated, insurers can establish a reliable data foundation. Equally important is the modernisation of data models. Standardised definitions, support for schema evolution, and alignment with modern platforms enable consistency and scalability. A unified data model significantly simplifies integration and analytics.

Strong data governance frameworks provide the structure needed to sustain these improvements. Clearly defined roles such as data owners and stewards, along with formalised processes and enforcement mechanisms, ensure that data is actively managed as a strategic asset rather than passively generated. Integration must also evolve. Moving from rigid ETL approaches to more flexible models, such as cloud-based “Extract, Load, Transform” (ELT), API-driven access, and event-based architectures, enables scalability and responsiveness to changing business needs. Cloud-based platforms play a central role in this transformation. Solutions such as Snowflake, Databricks, and Azure services offer elastic scalability, high-performance processing, and native support for advanced analytics and AI, enabling insurers to build resilient data ecosystems. Underlying all these changes is the need for strong alignment between business and IT. Close collaboration ensures that data initiatives are driven by business value, governance is practical and adopted, and transformation efforts deliver measurable outcomes.

Real-world use cases: addressing data quality issues

A leading London Market insurer, for example, faced a major challenge during its migration to Guidewire Cloud, where legacy ETL-based BI processes were no longer compatible. By implementing a cloud-aligned ELT approach using Cloud Data Access and Snowflake, the organisation introduced governed and scalable data processing with built-in historisation, schema evolution, and quality checks. The shift of transformation logic into Snowflake enabled a modern, high-performance analytics environment without disrupting operations.

In another case, a global insurer upgrading its claim system had to manage major data model changes and new financial rules while maintaining stable downstream integrations. A controlled, data-focused upgrade approach ensured accurate financial reconciliation, preserved reporting continuity, and adapted ETL processes to regulatory needs. The upgrade was completed without business disruption, with validated data and stable integrations.

A U.S. regional insurer, meanwhile, uncovered serious data quality issues during a cloud migration. Its existing data warehouse produced inconsistent and unreliable outputs, limiting usability. By redesigning the data pipeline using Databricks and Azure Data Factory and implementing a Medallion architecture, the insurer introduced clear data layers, automated validation, and simplified transformations. This restored trust in the data and significantly improved performance and scalability.

Finally, a large Central and Eastern European insurer successfully implemented a comprehensive data governance model across multiple legal entities within just 8 weeks. Through tailored governance frameworks, a structured playbook, and strong business-IT alignment, the organisation established clear ownership and processes, creating a solid foundation for ongoing data transformation.

Turning data into a strategic asset

The insurance industry is at a turning point. As data continues to grow in both volume and complexity, success will depend on how effectively organisations manage and leverage it. While the challenges, legacy systems, poor data quality, and fragmented architectures, are significant, they are far from insurmountable. With modern technologies, robust governance, and strong organisational alignment, insurers can unlock the full value of their data. Those that succeed will innovate faster, operate more efficiently, and deliver superior customer experiences. In doing so, they will shift from being constrained by data to being truly powered by it.