Data Migration Is a Business Transformation Workstream
Data migration is often underestimated as a technical extraction and loading exercise. In reality it requires business ownership, policy decisions, cleansing, reconciliation and repeated validation.
A strong strategy defines scope, quality expectations, ownership and execution waves early enough that data readiness does not become the final barrier to go-live.
21.1 Define Migration Scope by Business Need
Decide which master data, open transactions, historical balances and reference data are required in the new solution.
Do not migrate history merely because it exists. Consider legal retention, operational need, analytics and archive alternatives.
21.2 Assign Data Ownership
Business owners should define correctness and approve transformed data. Technical teams can move data but cannot decide what a valid customer, supplier or project record means.
Create ownership for each major data domain.
21.3 Profile Source Data Early
Measure duplicates, missing values, invalid codes, orphan relationships and inconsistent formats before finalizing effort.
Data profiling replaces optimistic assumptions with evidence.
21.4 Define Mapping and Transformation Rules
Document source-to-target mapping, default values, derivations, code translations and handling of invalid records.
Complex transformation rules should be reviewed by both business and technical owners.
21.5 Plan Cleansing at the Right Source
Where practical, correct master data in the source so operational users benefit before migration and repeated extracts remain consistent.
Where source correction is impossible, define controlled transformation rules.
21.6 Choose Migration Waves
Use mock migrations and staged rehearsals to test volume, mapping, timing and reconciliation. Separate technical proof from business validation.
Plan dependencies between domains; for example, transactions may depend on customers, products, projects or accounting structures.
21.7 Define Reconciliation Before Loading
Agree record counts, financial totals, control totals, exception thresholds and approval evidence in advance.
A successful load is not proof that the migrated data is correct.
21.8 Plan Cutover and Legacy Access
Define final extract timing, transaction freeze, delta migration, legacy read access, rollback assumptions and post-go-live responsibilities.
Migration strategy must connect directly to cutover planning.
From the Delivery Floor
A program planned to migrate all historical transactional data because users wanted familiar reporting. Profiling revealed years of inconsistent codes and incomplete relationships that would require major transformation.
The business chose to migrate open transactions and selected history while preserving older information in a governed archive. The decision reduced risk and allowed teams to focus cleansing effort on data that would actively drive the new system.
21.9 The Delivery Leader's View
Data readiness should appear in governance long before cutover. Watch data ownership, cleansing progress, mock-load quality and unresolved reconciliation differences as leading indicators of go-live risk.
Chapter 21 Implementation Checklist
- Is migration scope tied to business need?
- Are data-domain owners assigned?
- Has source data been profiled?
- Are mapping and transformation rules documented?
- Is cleansing ownership clear?
- Are domain dependencies understood?
- Are mock migrations planned?
- Are reconciliation rules agreed in advance?
- Is legacy access defined?
- Does migration strategy connect to cutover?
Key Takeaways
Migration is a business-owned workstream enabled by technology.
Profile data early instead of estimating from assumptions.
Migrate only what the future operating model needs.
Reconciliation criteria must be defined before the load.
Cutover and legacy-access decisions belong in migration strategy.
Closing Thought
The objective of migration is not to move every record. It is to make the new business process trustworthy on day one.