Data Migration

Data migration must preserve meaning, integrity, access, and auditability. We define transformation and reconciliation rules before transfer begins.

When this service is needed

• Data is moving to new storage, applications, or analytics platforms.
• Formats, structures, or data quality require adjustment.
• Data volume requires staged synchronisation.
• The business needs evidence of completeness and integrity.

Scope

• Source-data profiling, sensitivity classification, and ownership.
• Field, format, encoding, metadata, and permission mapping.
• Cleansing, deduplication, transformation, and exception handling.
• Initial load, incremental sync, freeze windows, and final cutover.
• Checksums, record counts, sampling, and business reconciliation.
• Encryption, audit trails, rollback, and source-data retention.

Migration stages

1. Discovery: inventory assets, dependencies, owners, and criticality.
2. Design: define the target, migration method, acceptance criteria, and rollback.
3. Rehearsal: test tools, timings, data, and procedures within a controlled scope.
4. Cutover: execute the move through checklists and escalation paths.
5. Validation: prove function, integrity, security, and performance.
6. Stabilisation: monitor outcomes, remediate findings, and retire legacy systems.

Deliverables

• Inventory and dependency map.
• Migration runbook, cutover plan, and rollback plan.
• Test and reconciliation evidence.
• Target-environment documentation and decommission plan.

Intended outcomes

Data becomes available on the target with an agreed structure, verifiable integrity, and documented exceptions.