Data Migration Services

Big bang moves everything in one planned window. It is simpler but concentrates risk, so it suits smaller, well-understood datasets that can tolerate downtime. A phased or trickle approach moves data in batches while both systems stay live, which spreads risk but requires synchronization. A parallel run is the safest for regulated or high-stakes data but costs more. We recommend an approach based on your downtime tolerance, volumes and risk.

We define acceptance criteria before moving anything, then automate checks after every load: record counts per entity, sums of financial values, checksums of key columns and comparison of sampled records. Business users validate that reports and balances match in the target. All results are logged and packaged as evidence for sign-off and audit.

It depends on volumes, the approach and how much data can be loaded in advance. By loading history early and using incremental loads or change data capture to keep the target current, the final window often covers only the last changes, validation and switching users. Rehearsals measure the real duration so the business can plan around it.

Every cutover has go/no-go checkpoints with clear criteria and a rehearsed rollback plan that keeps the source system recoverable. If validation fails, we either fix the issue within the window or roll back in a controlled way. Because rehearsals have exercised the runbook, the team knows what to do at each step.

We need data owners or subject matter experts who understand what the data means, to review mappings and resolve quality issues, plus access to source and target environments and a business lead for acceptance testing. Their time is concentrated during mapping and testing. We send weekly written updates and run demos so stakeholders see progress without attending every meeting.

Data moves over encrypted connections and is stored encrypted at rest. Development and test runs use masked or subset data wherever possible, access is limited to named team members and logged, and temporary staging areas are cleaned up after cutover. We follow your security and data residency policies and document all data flows for review.

Yes, this is common with legacy applications. We reverse-engineer data structures by analyzing database schemas, application screens, reports and actual values, and we interview long-time users who understand how fields are really used. Where vendors no longer support the system, we extract data directly from the database or exports. Findings are documented as we go, so knowledge that existed only in people’s heads becomes part of the migration specification.

After a successful cutover and a hypercare period, the old system is typically set to read-only, archived or decommissioned according to your retention and compliance requirements. We help define what must be retained, in what format and for how long, and whether a searchable archive is needed for audits or legal holds. Decommissioning only happens once business owners confirm that nothing still depends on the old system.