ERP Data Migration Mapping Worksheet (Source to Target Fields)
A mapping worksheet is the single document every data migration depends on. It states, field by field, where a value comes from, where it lands in the new ERP and what happens to it on the way. This version computes reject rates per row so you can see which objects are hurting each trial load.
| Data object | Source field | Target field | Transformation rule | Records | Rejects in last load | Reject rate | Data owner | Status | Due date | |
|---|---|---|---|---|---|---|---|---|---|---|
1.3% | ||||||||||
1.9% | ||||||||||
1.3% | ||||||||||
2% | ||||||||||
1.2% |
Add one row per field, not per object, once mapping starts. Keep the rule column precise enough that a second person could rebuild the load script from it.
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How to use the worksheet
Start with one row per field of each data object you plan to migrate. Fill the source and target names exactly as they appear in the legacy table and the new system's field list, then write the transformation rule in plain language. After every trial load, update the records and rejects columns and move the status forward. The reject rate column is calculated for you and the summary shows the overall rate across all rows.
- One owner per row, a business person and not the consultant
- Write rules as lookups, splits, defaults or formulas
- Update rejects after every trial load, not only the last one
- Keep a separate tab for code translation tables
What to map and in what order
Load static master data first: units of measure, currencies, payment terms, tax codes, then customers, suppliers and items. Structures such as BOMs and routings come next because they depend on items and work centres. Open transactions, stock balances and finance opening balances are loaded last and reconciled against the legacy system. European projects also need legal fields mapped carefully, such as VAT IDs, IBAN and BIC, commodity codes for Intrastat and country of origin.
Reading the reject rate
A reject rate under one percent on master data is a reasonable target for the final dress rehearsal, but the number matters less than the reasons. Group rejects by cause: missing mandatory field, failed validation, duplicate key or unmapped code. Fix the cause in the source data where possible, not in the load script, so the clean data stays clean after go-live.
Common mistakes
Teams often map only the fields they know the legacy system has and forget that the target requires new mandatory fields. Others migrate everything, including a decade of inactive items, which inflates the effort and the reject count. Agree an inactivity cut-off early and archive the rest. Finally, do not let the mapping live only in the consultant's laptop; the customer must own and sign it.
Frequently Asked Questions
How granular should a mapping row be?
One row per target field is the safest. Object-level rows hide the transformation logic, and that logic is where most rejects come from. A BOM with fifteen fields should have fifteen rows, even if twelve of them are a straight copy.
Who should sign off the mapping?
The business data owner for each object signs the rows, for example purchasing for suppliers and engineering for BOMs. The project manager tracks status, and the technical lead confirms that each rule is implementable in the load tool.
Does this work for non-European ERPs?
Yes. The structure is system neutral. The European angle is only in the example rows, such as VAT ID validation and Intrastat related fields. Replace those with local equivalents for your own country.
How often should reject counts be updated?
After every trial load. Typical projects run three to five trial loads, and the trend across loads is the best indicator of readiness. A flat reject count between loads means the root causes are not being fixed.
If your migration source is Infor SyteLine, Baan or another system we work with, we can generate a first-draft mapping from the schema for you to review. Entirely optional.
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