In an era where data is pivotal, effective data migration is critical for insurance companies aiming to stay competitive. With global data volume expected to reach roughly 180 zettabytes by 2025, insurers face unprecedented growth and challenges in managing and governing data. According to Experian, only 46% of data migration projects are delivered on time, and 74% face budget overruns. Consequently, insurance firms must adopt a robust data and analytics strategy to derive real value and achieve successful data migration.
A data-driven business is nearly twice as likely to grow revenue and reduce costs, according to Forrester Research. Companies must capture and utilize data effectively to become insights-driven organizations. Data migration allows insurers to leverage new technologies, improve data analytics and reporting capabilities, and comply with industry standards and regulations. Whether it involves storage, database, application, business, or cloud migration, insurers need to stay ahead of the technological curve. Regular updates to homegrown applications or the adoption of suitable off-the-shelf solutions tailored to insurance can significantly boost data retrieval speeds, security, processing, and analytics capabilities while reducing costs.
Why and When You Need Data Migration in Insurance
Data migration is crucial for insurance companies for several reasons. Storage migration helps transfer customer records or policy information from outdated systems to more modern ones. Database migration involves moving data between different databases, while application migration shifts data from old policy management systems to new platforms, integrating automated claims processing, customer relationship management, and risk assessment. Business migration involves transferring data related to underwriting processes, claims management, business rules, or customer service operations. Cloud migration moves data, applications, and other systems to cloud environments, enhancing flexibility, scalability, and cost-efficiency.
The adoption of the latest cloud-enabled applications is essential for insurers to remain competitive. Technology is constantly evolving, with new solutions such as artificial intelligence reshaping the industry. Insurers need to keep pace with these advancements by regularly updating their systems and adopting applications tailored to their specific needs. This approach improves data accessibility, security, processing, and analytics capabilities while reducing operational costs and enhancing overall efficiency.
Common Risks and Challenges in Data Migration Projects
Migrating vast volumes of data from old policy or claims systems to new ones presents significant challenges for insurance companies. Identifying risks early in the data migration process and devising mitigation plans is essential for success. Data quality issues can be particularly difficult to address unless a thorough analysis is conducted before migration begins. Addressing missing values, incorrect and incomplete values, and duplicate and non-standard values during the migration process is paramount.
Data breaches and regulatory violations are substantial risks that insurers must manage during migration. Implementing proper governance mechanisms and security protocols is essential for handling sensitive personally identifiable information (PII) while migrating customer and policy data. Ensuring compliance with data protection regulations, such as the General Data Protection Regulation (GDPR) or the Health Insurance Portability and Accountability Act (HIPAA), is crucial to avoid legal repercussions and maintain customer trust. Additionally, minimizing business disruption through initial analysis of data mapping and quality can significantly reduce the impact on operations.
Best Practices
To increase the success rate of data migration, insurers should follow these eight best practices. First, understanding data context ensures data quality and avoids misinterpretation of values. Proper planning involves drawing a detailed plan covering the need for migration, the scope of data to be migrated, available time, and potential risks and challenges. A comprehensive testing strategy includes reconciliation, validation, and ensuring overall data accuracy in the new system. Execution strategy—whether big bang, phased, or hybrid—depends on the specific use case and requirements.
Choosing between manual and automated tools for data migration is critical. Data migration activities like profiling, extraction, transformation, cleansing, loading, testing, and validation can be error-prone without automation. Using automated tools reduces the chances of mistakes and reworking. Backing up all data securely before starting the migration process is a simple yet crucial step. Conducting thorough data assessment and classification involves studying source and target data structures and formats and categorizing data correctly to build a detailed mapping document. Finally, data cleansing is essential for addressing data quality issues during the transformation process.
The Importance of Migration Strategies, Processes, and Tools
In an era where data is crucial, efficient data migration is essential for insurance companies to stay competitive. With global data volume projected to reach approximately 180 zettabytes by 2025, insurers face significant growth and challenges in data management and governance. Experian reports that only 46% of data migration projects meet deadlines, and 74% exceed budget. Therefore, insurance firms need a strong data and analytics strategy to gain real value and ensure successful data migration.
A data-driven business is nearly twice as likely to increase revenue and cut costs, according to Forrester Research. Companies must capture and utilize data effectively to become insights-driven organizations. Data migration enables insurers to adopt new technologies, enhance data analytics and reporting, and comply with industry regulations. Whether it’s storage, database, application, business, or cloud migration, insurers must stay ahead technologically. Regular updates to custom applications or adopting suitable off-the-shelf solutions can notably improve data retrieval speeds, security, processing, and analytics, while also reducing costs.
