We run member-data migration as a discipline, not a project task. The migration is proven through an iterative cycle of trials, a full dress rehearsal and a controlled go-live — and validated at every step by customer-led reconciliation that demonstrates the ETL is moving each record correctly.
A successful migration is earned through repetition. Each trial proves a little more of the ETL build and the target platform configuration. We iterate until reconciliation tolerances are met, simulate the live event once through a dress rehearsal, then execute a controlled go-live.
Systems, products, source of truth, depth of history and the reconciliation approach.
Source-to-target mapping, ETL and the reconciliation engine, on a baseline configuration.
Iterative data loads (Trial 0, 1, 2, 3…) that prove mapping and configuration and surface data quality early.
A single full simulation of the live cutover — timing, outage window and reconciliation, end to end.
Controlled cutover to a runsheet, with a stage-gate sign-off on reconciliation before services reopen.
The build-and-trial cycle repeats: each trial feeds fixes back into mapping and ETL, and the next load proves them — improving the build measurably load on load. The dress rehearsal then runs once, by which point exceptions are well understood and have defined remediation paths before the real cutover.
Source data is transformed to the target platform's terminology and compared on like terms by an independent reconciliation engine. Migration and reconciliation teams operate separately so validation stays unbiased — and the client owns and signs off the result.
Extract baseline reports from the source platform(s).
Extract equivalent reports from the target platform.
Convert to target terminology to compare on like terms.
Run the independent comparison engine, source vs target.
Investigate, classify and agree exceptions; feed the next cycle.
Every reconciliation domain — Critical Success Factors, financial, non-financial, insurance, defined benefits and pensions — is rated against agreed thresholds and a defined go-live tolerance. Exceptions are graded by severity, assigned an owner, and quarantined where a member could otherwise transact incorrectly.
Deep, repeated experience on the platforms that run Australian super means we don't start from a blank page. We migrate the bulk of a platform from well-understood standard data domains with minimal upfront requirements — so the requirements effort concentrates where it actually earns its keep.
Members, accounts, balances, transactions, addresses, banking and reference data move on established patterns.
These domains carry fund-specific design and calculation rules that cannot be assumed. We treat them as dedicated workstreams, with their own mapping, reconciliation and product-team validation.
Accurate, complete and consistent source data is a precursor to a clean migration. Our process surfaces quality issues quickly — through trials — then drives a deliberate decision on where each issue is resolved.
Corrected in the originating system before the next load — the cleanest outcome where time allows.
Handled by transform logic or a controlled script, proven in the following trial.
A defined, tracked remediation after cutover — used where the issue is contained and low-risk.
Experience across the superannuation sector, including complex defined benefit and insurance products, for some of the largest funds in the Australian market. Examples are presented anonymously; client details available on request.
Member books converted from legacy registries to contemporary administration platforms as part of major fund mergers, with Critical Success Factors reconciled before sign-off.
Fund balances reconciled to within fully-explained exceptions — proven first through dress rehearsal, then at the live cutover.
Multi-tranche migration to a new registry platform delivered under independent program delivery assurance, with reconciliation evidence at each tranche.