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Data Migration & Data Services

Migration at scale, data governance and lineage, and the data foundations for analytics and AI.

Data migration moves an organisation's records, balances and history from legacy systems to new ones, accurately and in a way that can be proven. Transformativ plans and runs migrations for core platform, separation and wealth platform programs, from mapping and cleansing through mock migrations to reconciliation and sign-off. Our people have transitioned $8B in client assets between wealth platforms with 100% accuracy.

THE TRIGGERS

Four situations where migration expertise changes the outcome

  1. 01

    A core platform replacement depends on moving millions of records without error.

  2. 02

    A separation or merger means customer and account data must move between organisations.

  3. 03

    Data quality in the legacy estate is unknown, and cutover is getting closer.

  4. 04

    The organisation wants its data ready for analytics and AI, and doesn't trust it yet.

WHAT WE DO

Every stage of a migration, and the data foundations behind it

Migration strategy and blueprint

The scope, sequence and approach for moving the data, from pilot to waves, designed around the program's cutover plan.

Mapping and transformation

Every record, balance, history, mandate and instrument mapped from source to target, with the business rules made explicit.

Profiling and cleansing

Data quality measured early and fixed at source, so problems surface in rehearsal and not on the cutover weekend.

Mock migrations and reconciliation

Repeated mock migrations with automated reconciliation and exception handling, so every account is proven before it moves.

Data governance and lineage

Master data governance, ownership and lineage embedded, so the data stays clean after go-live.

Data foundations for analytics and AI

Data pipelines, models and quality controls that give analytics and AI a reliable base to work from.

HOW IT WORKS

How we run a migration

  1. 1
    ASSESS

    Profile the source data, agree scope and target, and set the reconciliation standard the migration must meet.

  2. 2
    DESIGN

    Build the mapping, transformation rules and migration blueprint, with the business signing off the rules.

  3. 3
    REHEARSE

    Run mock migrations with automated validation and reconciliation, cleansing and fixing until the results hold.

  4. 4
    MIGRATE

    Move the data in the planned waves, reconcile every account, and hand over governed data to business as usual.

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CASE STUDY

$8B moved with 100% accuracy

100%

accuracy on $8B in client assets moved between wealth platforms: Australia's largest wrap platform transition at the time, with zero penalties and full client retention

Read the case study →
HOW WE ENGAGE

Three ways to work with us on data migration

Compare engagement models →

Advisory & Assessment

A data readiness and migration risk review, with a plan to close the gaps before cutover.

Specialist Squads

A migration squad, with a migration lead, analysts and engineers, for the migration phase of the program.

Managed Capability Service

Ongoing data services, such as data governance or data quality management, run as a managed service.

QUESTIONS

Common questions about data migration

Data migration is moving an organisation's records, balances and history from legacy systems to new ones. In a transformation program it covers the migration strategy, mapping and transformation, cleansing, mock migrations, reconciliation and the final migration at cutover.

Map every data element from source to target with explicit business rules, cleanse at source, run repeated mock migrations with automated validation, and reconcile every account before and after each move. In one wealth platform transition, $8B in client assets moved with 100% accuracy.

A mock migration is a full rehearsal of the data migration using real data in a test environment. Each one is measured for accuracy, completeness and timing, and the results drive the fixes needed before the real migration.

It depends on the volume and quality of the data, the number of source systems, and how many migration waves the program needs. Profiling the data early is the fastest way to a realistic timeline.

Data lineage records where each data element came from and how it was transformed. It lets auditors and regulators trace any figure back to its source, and it keeps data trustworthy for reporting, analytics and AI after the migration.

NEXT STEP5 MINUTES

Know where your program stands before the next dollar goes in.