A data migration plan is the governing document for the structured movement of data from legacy source systems into a target system — covering the full lifecycle from extraction and profiling through cleansing, transformation, loading, and validation. In ERP and EPM implementations, data migration is not a technical task performed near go-live; it is a programme workstream that begins in the design phase, runs in parallel with system configuration, and concludes only when reconciled data in the target system matches the agreed migration scope and quality thresholds. A data migration plan that is treated as a late-stage activity is one of the most common causes of implementation delays and post-go-live data quality issues.
Structure and Content
A complete data migration plan addresses six workstreams:
| Workstream | Content |
|---|---|
| Migration scope | Which data domains are in scope (chart of accounts, open balances, fixed assets, open AP/AR, historical transactions), at what level of detail, and for what historical period |
| Data profiling | Analysis of source data quality — completeness, consistency, referential integrity — and the volume of records requiring cleansing before migration |
| Data cleansing approach | Who is responsible for cleansing (business or IT), what the cleansing rules are, and how cleansing completion is verified |
| Transformation design | Mapping from source to target schema, including account mapping, entity mapping, and dimension value translation |
| Migration runs | The sequence of trial migrations, their purpose (data volume test, reconciliation test, performance test), and the acceptance criteria for each |
| Reconciliation controls | How migrated data will be validated against source — record counts, total values, spot checks — and what the tolerance is for migration differences |
Common Gaps and Failure Modes
The specific failure that most frequently derails cutover is the data migration plan that schedules only one full trial migration — typically two weeks before go-live — and uses it to discover both the data quality issues and the performance of the migration scripts simultaneously. The trial reveals that 15% of fixed asset records cannot be loaded because of legacy data quality issues that require business review; the business review takes three weeks; and the go-live date moves while the project team waits. The correct approach is to run data profiling in the design phase, identify data quality issues early enough for the business to resolve them, and use the final trial migrations to validate a clean dataset against a defined reconciliation threshold — not to discover that the dataset is not yet clean.
How Loop Wise Solutions Produces This
Loop Wise Solutions initiates the data migration workstream at programme kick-off — not at the start of testing. Data profiling runs in parallel with system design, giving the business team the maximum available time to resolve quality issues before they become critical path risks. We run a minimum of three full trial migrations before the go-live cutover: a first trial to establish the baseline, a second to validate cleansed data, and a third to confirm cutover timing and reconciliation. Each trial produces a reconciliation report that is signed off by the finance team lead before the next migration run proceeds.
