Master data governance: controls and evidence

Before accepting Data migration, use a versioned scope, representative records, normal and exception scenarios, permission checks, reconciliation where applicable, and recorded unresolved risks. The evidence should demonstrate that business owners approve meaning and reconciliation while technical staff control repeatable extraction and loading. Product labels and configured screens are not acceptance evidence by themselves. For Master data governance: controls and evidence, apply that guidance to source ownership, profiling, mapping, cleansing, access, rehearsal, reconciliation, and retention, then record what moves, what stays, who corrects, how meaning maps, and how results reconcile in the acceptance evidence.

To test Data migration, choose one bounded workflow, a small authoritative data set, named roles, explicit success and stop conditions, and a reversible release path. Include moving every historical row without deciding what is authoritative, useful, lawful, and reconcilable as a failure scenario. Keep maturity and limitations visible, then expand only after the agreed evidence is complete. For Master data governance: controls and evidence, apply that guidance to source ownership, profiling, mapping, cleansing, access, rehearsal, reconciliation, and retention, then record what moves, what stays, who corrects, how meaning maps, and how results reconcile in the acceptance evidence.

For Data migration, select a small set of measures tied to the intended decision, define their source and timing, and record the baseline before change. Include an exception or quality measure, then verify that business owners approve meaning and reconciliation while technical staff control repeatable extraction and loading. This prevents faster processing from being mistaken for a better controlled outcome. For Master data governance: controls and evidence, apply that guidance to source ownership, profiling, mapping, cleansing, access, rehearsal, reconciliation, and retention, then record what moves, what stays, who corrects, how meaning maps, and how results reconcile in the acceptance evidence.

A common risk is moving every historical row without deciding what is authoritative, useful, lawful, and reconcilable. Make the assumption visible, assign an owner, test the highest consequence exception, and prevent the workflow from advancing when required evidence is missing. For Master data governance: controls and evidence, apply that guidance to source ownership, profiling, mapping, cleansing, access, rehearsal, reconciliation, and retention, then record what moves, what stays, who corrects, how meaning maps, and how results reconcile in the acceptance evidence.