D365 Finance and Supply Chain Data Migration Mapping Worksheet
Most D365 go-lives that slip do so on data. A mapping sheet puts every source field, target entity, rule and owner in one place, so cleansing work is visible long before the first mock load.
| Migration object | Source field | Target data entity | Target field | Transformation rule | Owner | Status | Source records | Rejected records | Reject rate | |
|---|---|---|---|---|---|---|---|---|---|---|
1.5% | ||||||||||
0% | ||||||||||
1% | ||||||||||
0% | ||||||||||
0% |
Entity names above are examples. Confirm the exact data entity and field names in the Data management workspace of your environment and version before loading.
Profile your source data before mock load one
Optional: a private data profiling and mapping assistant that runs on your own infrastructure.
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How to use the worksheet
Add a row for each field mapping or each migration object, depending on how detailed you need to be. Name the target data entity as it appears in the Data management workspace, record the transformation rule in plain words, and give every row an owner from the business. Update status after each mock load and enter source and rejected record counts to see reject rates calculate automatically.
- Map to data entities, not tables
- Write rules in plain words a business owner can check
- Record counts after every mock load
- Keep a separate row for each load sequence dependency
Load order and dependencies
Entity load order matters: legal entities, number sequences, main accounts and dimensions precede customers, vendors and released products, which precede open orders and opening balances. Use the data management framework's execution units and sequencing in data projects to enforce order. Staging tables let you validate before copying to target, which keeps errors out of production tables.
Reading the reject rate
A reject rate that stays above a few percent after several mock loads usually points to a rule problem, not random bad data. Look at the most common error message in the execution log and fix the rule or the source cleansing. Aim to reach a stable, explainable rate before cutover rehearsal, and have the business sign off each object on the final mock.
Common mistakes
Teams map to the legacy structure instead of the D365 data model, forget financial dimensions and default values, and migrate far more history than the business will use. Also avoid loading open transactions without a cutoff plan; document how in-flight orders are handled at the freeze. Keep the sheet under version control, because decisions here become audit evidence.
Frequently Asked Questions
Should I map every field?
Map every field you intend to load, and explicitly record fields you decided not to migrate with the reason. A short list of excluded fields prevents later debates about missing data and gives auditors a clear decision trail.
How many mock loads are typical?
Most projects run at least two or three full mock loads before cutover, plus a final rehearsal in a production-like environment. The right number depends on data volume and quality, and each mock should reduce rejects and timings.
Can I use Excel to load data?
Yes, for small volumes. The Excel add-in and the data management import can load templates, but for large volumes use package imports with data entities. Always load into a test environment first.
Who should own each object?
A named business owner who can judge correctness, not only a technical consultant. Finance owns balances and customers, procurement owns vendors and open purchase orders, and so on. Sign-off should come from them.
If you are mapping a large legacy ERP, Netray can help build private tooling that proposes field mappings and spots profile anomalies in your source data, with every mapping reviewed by your team.
Related Tools
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