Data Migration Without Regret
Finance & Banking • ~7–9 min read • Published Oct 1, 2024
ERP programs don’t fail on configuration; they fail on data. The antidote is a migration approach that treats data as a product—owned, measured, and proven in dress rehearsals before cutover.
Why this matters now
Financial institutions carry complex master data and high-stakes transactional accuracy. Errors in balances, sub-ledger integrity, or reference data can cascade into customer impact and regulatory exposure.
Teams often under-budget data work and leave fallbacks vague. We recommend a migration plan with explicit quality gates, reconciliation packs, and reversible cutover patterns.
Our point of view
Run data migration like a controlled, auditable supply chain. Define ownership, track defects, prove reconciliations at increasing volumes, and commit to a go/no-go only on objective evidence.
The migration playbook
1) Data posture & ownership
- Golden sources & lineage: Document authoritative systems and transformations end-to-end.
- Owners: Name data product owners for each domain (customers, suppliers, chart of accounts, items).
- Quality thresholds: Define target % for completeness, duplicates, and validation rules per domain.
2) Waves & trial loads
- Incremental waves: Start with masters, then open balances, then recent transactions.
- Dress rehearsals: Execute at least two full-volume loads with timed runbooks.
- Defect burn-down: Track by severity and domain; block promotion on unresolved Class-A defects.
3) Reconciliation & sign-off
- Financial reconciliations: Trial balance parity, sub-ledger to GL, AR/AP aging alignment.
- Operational reconciliations: Inventory valuation, open POs/SOs, tax codes, and pricing conditions.
- Evidence pack: Snapshots, counts, checksums, and exception logs—signed by business owners.
4) Cutover patterns & fallbacks
- Delta strategy: Freeze window + deltas captured; replay deltas post-cutover.
- Rollback triggers: Pre-defined thresholds (e.g., unreconciled variance > X bp).
- Blue-green option: Parallel run for high-risk processes, with switchback capability.
Common failure modes—& controls that prevent them
- Late discovery of dirty data: Start profiling in week 1; publish weekly quality trends.
- Scope creep on historicals: Migrate what is needed to run; archive the rest with retrieval SLAs.
- Manual reconciliations at 2am: Automate core checks; practice the runbook at scale.
- Ambiguous ownership: CFO/CIO jointly enforce data product owners and sign-off criteria.
Day-1 and the run-state
- Master data stewardship: RACI, change controls, and SLAs for ongoing quality.
- Monitoring: Drift detection on key reference tables and high-volume interfaces.
- Backlog: Post-go-live data fixes and enhancements triaged weekly with business owners.
Closing
“No regret” migration is not zero defects—it’s zero surprises. Own the data, prove it with rehearsals, reconcile with evidence, and cut over with fallbacks you’re confident to use.