Data Migration in Insurance: Moving Data Safely

Our data migration consultant for insurance worked closely with the client’s IT and compliance departments. Detailed planning and pre-migration testing were completed to reduce error potential during execution.

The project team included a database architect, two data engineers, a systems administrator, and a QA analyst.

The migration followed five structured stages: discovery, preparation, migration, repair, and verification.

• Discovery: Identified tenant boundaries and related records.
• Preparation: Created mapping tables and planned enum transformations.
• Migration: Loaded parent tables first, followed by dependent tables.
• Repair: Resolved orphaned references and data inconsistencies.
• Verification: Conducted record counts, foreign key checks, and file audits.

This structured cooperation reflected data migration best practices, ensuring that each step was controlled, documented, and reversible.

We implemented a batch-based SQL and shell-driven process specialized for insurance data migration. The operation started with selecting tenant data, temporarily disabling triggers to avoid callbacks during bulk inserts. Parent records such as accounts and profiles were migrated first, followed by contacts, notes, events, and history data.

Sequences were reset to prevent ID collisions, and foreign keys were reconnected using shadow columns from the old system. Legacy enums for statuses, sources, and carriers were mapped using deterministic SQL updates. Files and attachments were transferred via shell scripts based on collected IDs.

To minimize risk, all steps were executed in reversible batches — a key method in data migration best practices. Each stage included “data” and “data final” checkpoints to ensure recovery options. Orphaned records and inconsistent UUIDs were corrected or flagged for review.

Key features:

• Parent-child mapping using old_* columns
• Enum and code-set remapping
• Bulk-safe inserts with conflict handling
• File and attachment transfer via shell automation
• Reversible batch stages for redo safety

Key engineering decisions:

• Disable triggers during bulk operations for speed and stability
• Map all legacy IDs and enums deterministically to prevent drift
• Use shadow columns to reconnect foreign keys and repair orphans
• Batch load large datasets with retry-safe scripts
• Verify data integrity at every stage

This approach represented a practical example of enterprise data migration adapted for the insurance sector, following established data migration best practices.