Legacy data variations
Profile nulls, duplicates, obsolete codes and incompatible formats before defining transformation rules.
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SOLUTIONSData Migration
Data Migration and Reconciliation
Move records between systems using mapping rules, controlled cutover and source-to-target checks.
A migration is not complete when counts match. Keys, relationships, calculated fields, history and downstream users also need to remain consistent with the approved mapping and retention rules.
dataData Migration
BUSINESS CONTEXT
When this solution is relevant
Useful during application replacement, data platform migration, system consolidation and major schema changes.
SOLUTION DETAILS
Where this helps
Illustrative situations and business dependencies.
Dependent records
Identify parent-child relationships, historical records and downstream consumers affected by cutover.
SOLUTION DETAILS
Delivery activities
How a delivery engagement can be structured.
01
Agree migration scope
Define eligible records, field mappings, reconciliation measures and accepted exclusions.
02
Rehearse cutover
Use repeatable extracts, transformations and load scripts with a documented rollback approach.
03
Review exceptions
Track rejects and mismatches to agreed resolution before final transition.
SOLUTION DETAILS
Validation and controls
Checks to consider as part of solution acceptance.
Beyond record counts
Check primary keys, joins, reference integrity, calculations, history and representative business cases.
Post-cutover checks
Reconcile target data and verify critical application, API and reporting workflows.
SOLUTION DETAILS
Potential benefit
Possible value, subject to scope, implementation and actual results.
Potential benefit
A documented transition with visible data exceptions and defined acceptance evidence.
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