A parallel run is more than opening two systems at once. It is a planned comparison intended to show whether the target’s data, rules and outcomes hold under real conditions. Without clear ownership, it instead creates duplicate work, conflicting records and a transition that never ends.
Define the system of record
For every data category, specify which system is authoritative at every stage. Allowing users to write freely in both creates conflict. Prefer one defined write path with replicated data, or tightly limited double entry for selected comparison scenarios.
Also state which decisions may already be made in the target system and which remain binding only in the legacy application during validation.
Define comparison in business terms
Technical equality is not always necessary. Rounding, timing or an intentionally improved workflow may produce differences. Every critical output needs a tolerance, classification and owner responsible for evaluating variance.
Use representative scenarios including exceptions and failure conditions. A system that handles only the happy path is not ready for migration.
Rehearse migration repeatedly
The first data transfer is for discovery, not go-live. Rehearsals uncover invalid values, broken references, ambiguous history and performance limits. Each run should be reproducible and produce reconciliation across counts, totals and domain samples.
Write cutover and rollback as a runbook
The cutover plan names the window, freeze, final migration, checks, approval, communication and responsible people. The abort condition matters equally: until when can the team fall back, which data must return and who makes the decision?
Google SRE frames reliable launches as cross-functional work with explicit readiness review. Migration likewise requires operations, support, domain owners, security and engineering to participate before cutover.
End the transition deliberately
Define exit criteria before parallel operation begins: accepted parity scenarios, adequate performance, completed training, verified backups and resolved material differences. Then archive or retire the old system under control.
Parallel migration reduces risk through verifiable learning, not duration. It succeeds when it enables an evidence-based go-live decision and then actually ends.