Insurance data migrations fail. The numbers are stark: 54% are late, 74% go over budget. When dealing with 10 million+ records, 61% go over time. So data migration in the insurance industry is big and requires planning to ensure data integrity, system compatibility and minimal downtime.
What is Data Migration
Data migration is the process of moving data from one system, storage location or format to another. This complex and tricky process involves several key steps: analyzing the existing data, selecting the right migration method, testing and validating the migrated data and deploying to the new system.
For insurance companies data migration is key to managing and utilizing their vast amounts of sensitive data. By understanding the complexities of data migration they can ensure their data remains accurate, secure and accessible during the migration process.
The Size
Insurance companies have millions of sensitive data spread across multiple legacy systems. Policy info, claims records, customer data has been building up over decades. Moving this data requires planning and execution to keep the business running while keeping the data intact.
Most insurance companies run on systems that are 20-30 years old. These legacy systems use old storage methods like DB2 databases and mainframe systems. To move data from these ancient formats into modern structures you need special tools.
This is a technical nightmare due to data quality issues, legacy system compatibility and the need for rigorous testing. A well planned migration strategy is key to successful data migration with minimal downtime and disruption.
Insurance Migration Challenges
Legacy system complexity is the first big hurdle. Insurance databases have incompatible data formats spread across multiple platforms. A single customer’s info might be in 4 different systems, each with different storage methods and data structures. And data breaches during data migration require robust security to protect sensitive data.
Data quality issues are common in most migration projects. Missing values, duplicate entries and non-standardized formats have built up over years of operation. When combining data from multiple sources accuracy gets exponentially harder. Data mapping is key to linking data fields between legacy systems and new platforms to ensure data transfer and alignment to target system structures. Insurance companies need to keep all historical records while keeping data accurate and accessible.
Business has to run during migration. Insurance can’t stop - claims have to be processed, customer service requests have to be handled and policy info has to be maintained during the transition. This creates the tension between thorough data transfer and minimal disruption.
Data Quality Issues
Data quality issues can kill a data migration project. Poor data quality can cause errors, delays and data loss which can be disastrous for insurance companies that rely on their data for their business. Organizations lose an average of $15 million per year due to poor data quality, which includes direct losses from revenue and indirect costs associated with inefficiencies and missed opportunities. The U.S. economy suffers approximately $3.1 trillion annually from poor data quality, reflecting the widespread nature of this issue across various sectors.
Common data quality issues are incomplete or inaccurate data, duplicate entries and non-standardized formats. You need to invest time and resources to identify and fix these data quality issues before you start the migration. By doing so, you can improve the accuracy and reliability of your migrated data and have a smoother transition and better operational efficiency.
System Compatibility and Integration
System compatibility and integration is key during data migration. Ensuring the source and target systems are compatible and can integrate seamlessly is crucial for a successful migration.
Incompatibility issues arise when moving data from older legacy systems to newer more modern systems. These issues occur because the new system may not support some of the features or data structures used by the old system.
According to a report from Clearwater Analytics, as much as 74% of insurance companies still use legacy systems for core functions, indicating a widespread reliance on outdated technology within the industry. The 2024 Industry Trends Report by Earnix highlights that 49% of insurers admit they are behind schedule in their modernization efforts. On average, insurers allocate about 70% of their annual IT budgets to maintaining legacy systems.
A customized migration plan should be developed that takes into account the specific needs and requirements of both systems. This will ensure the migration process is smooth and the migrated data is fully functional in the new system.
How to Plan a Data Migration Project
Planning a data migration project requires considering data quality, system compatibility and security. A data migration plan should include a project timeline, a list of deliverables and milestones.
It should also outline the roles and responsibilities of each team member, communication channels and a contingency plan for any issues that may arise during the migration. By planning the data migration thoroughly insurance companies can mitigate risks, data integrity and a successful migration.
Answers
Pre-migration assessment is the foundation of a successful transition. Analysis of source and target systems reveals incompatibility issues early. Companies should document existing business rules, map data structures and identify data quality issues before you start the actual migration.
Data cleansing focuses on critical business data first. This means standardizing customer records, removing duplicates and validating claims history. Modern ETL (Extract, Transform, Load) tools make this process easier with automated data profiling and built-in validation rules.
Testing is key to data integrity. Companies should do pilot migrations with sample data to verify completeness and accuracy. End-to-end process testing ensures business runs smoothly after migration. Real-time monitoring helps to track progress and flag issues before they impact business.
Also, implementing data security measures such as encryption and access controls is crucial to protect sensitive data during migration. This will increase migration success rates and reduce downtime. Insurance companies that invest time and use the right tools will have smoother transitions and better operational efficiency post-migration.
Execution and Testing
Execution and testing is the critical phase of the data migration process. During execution phase the data is migrated from source system to target system. This phase requires attention to detail to ensure the data is migrated accurately and securely.
After execution, the testing phase involves validating the migrated data to ensure its accuracy and integrity. Testing and validation is critical to confirm the data is migrated correctly and the target system is working as expected. By testing thoroughly insurance companies can identify and fix issues before they impact business, a successful data migration.
ETL Tools and Automation Framework
Modern ETL tools turn insurance data migration from manual processes into automated operations. Decerto’s Data Migrator automates complex transformations while keeping data intact. It handles policy info, claims records and customer data through predefined validation rules.
Data Migrator’s parallel processing allows migrating multiple data streams in parallel, reducing overall project duration.
Regulatory Compliance and Security
Insurance data migration has to comply with GDPR, HIPAA and local regulations. Decerto’s framework has built-in compliance checks throughout the migration process. The system will flag sensitive customer info and apply the right security protocols.
Data is encrypted during transfer between systems. Access controls and audit trails will track every change, so you have transparent compliance records for the regulators.
Generali Case Study: Migration Success
Generali Group Poland worked with Decerto to migrate their legacy insurance systems. The project involved:
Complex data structures over multiple decades Millions of customer records and policy documents Integration with existing business processes
Data Migrator:
- Automated source data validation
- Standardized transformation rules
- Real-time migration monitoring
- Business continuity during transition
The migration was completed early, 0 data loss and 99.9% accuracy of the records.
Decerto’s Data Migration Expertise
Decerto is an insurance software company, we bring industry knowledge to data migration projects. Our approach combines technical expertise with insurance specific requirements:
Data profiling identifies patterns and anomalies before migration starts. Transformation rules standardize info across different systems. Validation checks data accuracy throughout the process.
The Data Migrator solution:
- Automated data cleansing
- Pre-built insurance industry mappings
- Real-time monitoring
- Reconciliation tools.
