Our pre-acquisition data assessment gives you a clear picture of what integration will actually require — before you sign.
How the target’s systems are actually used — the business logic and institutional knowledge that don’t appear in a platform inventory
What the data actually contains at depth: volume, quality, consistency, and the interdependencies that determine true migration scope
Where the target’s business practices don’t translate directly to your architecture — the use cases that must be understood before cost and timeline can be honestly estimated
The deliverable is a data landscape assessment that belongs in the deal model.
Post-acquisition Migration
Successful migration doesn’t happen by accident. Each phase informs the next — and where the work allows, they can overlap.
Scoping Discovery and Data Landscape Mapping — Defining what moves, what gets archived, and what the migration will actually require. Everything else builds on this foundation.
Data Quality Assessment and Remediation Planning — Establishing the true condition of the source data and building a realistic remediation plan before the timeline is locked.
Migration Architecture and Sequencing — Designing transformation logic, selecting tools against understood requirements, and sequencing workstreams to minimize business disruption.
Trial Migrations — Iterative, structured tests against real data. The number of trials is discovered through the process — production cutover happens when the evidence earns it, not when the calendar says so.
Cutover Validation and Decommissioning — Go/no-go requires both technical validation and business sign-off. A structured hypercare period follows go-live to catch and resolve what slipped through.
