# Infor M3 Data Conversion Mapping Worksheet > Source: https://netray.co/tools/m3-data-conversion-mapping-worksheet > Publisher: Netray (netray.co), AI and integration engineering for ERP and manufacturing systems. > Section: Free Tools ## Short answer Track every legacy field to M3 target mapping with transformation rule, owner, record counts and cleansing status in a single table. ## Category erp-migration ## Erp Infor M3 ## Audience - M3 consultant - Data migration lead - Business analyst ## Tool **Kind:** worksheet **Mode:** editable **Currency:** $ **Columns:** #### Data object **Key:** object **Type:** select **Options:** - Customer - Supplier - Item - Product structure - Routing - Open customer orders - Open purchase orders - Inventory balance - Open AR - Open AP - Other #### Legacy field **Key:** legacy_field **Type:** text #### M3 target field or API **Key:** m3_target **Type:** text #### Transformation rule **Key:** rule **Type:** text #### Data owner **Key:** owner **Type:** text #### Records **Key:** records **Type:** number **Format:** integer #### Records failing validation **Key:** bad **Type:** number **Format:** integer #### Clean rate **Key:** pass_pct **Type:** number **Expr:** if(gt(records,0),(records - bad) / records * 100,0) **Format:** percent #### Status **Key:** status **Type:** select **Options:** - Not started - Mapped - Cleansing - Test loaded - Signed off - Loaded to production **Rows:** **Object:** Customer **Legacy field:** CUST_NAME **M3 target:** Customer name (CRS610 customer record) **Rule:** Trim, title case, max length per M3 field **Owner:** Sales admin **Records:** 4200 **Bad:** 130 **Status:** Cleansing **Object:** Item **Legacy field:** PART_NO **M3 target:** Item number (MMS001 item record) **Rule:** Remove spaces, apply new item type mapping **Owner:** Engineering **Records:** 18500 **Bad:** 940 **Status:** Mapped **Object:** Item **Legacy field:** UOM **M3 target:** Basic unit of measure **Rule:** Map legacy codes to M3 unit codes **Owner:** Engineering **Records:** 18500 **Bad:** 60 **Status:** Mapped **Object:** Supplier **Legacy field:** VENDOR_TERMS **M3 target:** Payment terms **Rule:** Lookup table legacy to M3 terms code **Owner:** Purchasing **Records:** 960 **Bad:** 12 **Status:** Test loaded **Object:** Inventory balance **Legacy field:** ON_HAND **M3 target:** Balance ID by warehouse and location **Rule:** Cut off at freeze, reconcile to GL **Owner:** Warehouse **Records:** 31000 **Bad:** 410 **Status:** Not started **Object:** Open customer orders **Legacy field:** ORDER_LINE **M3 target:** Order line via MI transaction **Rule:** Only lines with open quantity above zero **Owner:** Customer service **Records:** 2300 **Bad:** 85 **Status:** Not started **Totals:** #### Total records in scope **Key:** records **Agg:** sum **Format:** integer #### Total failing validation **Key:** bad **Agg:** sum **Format:** integer #### Mapping rows **Key:** object **Agg:** count **Format:** integer **Summary:** #### Overall clean rate **Key:** overall_clean **Expr:** if(gt(sum_records,0),(sum_records - sum_bad) / sum_records * 100,0) **Format:** percent **Notes:** Record counts count each mapped field once per row, so total records is a workload indicator, not a unique record count. Confirm M3 target field names in your own version before building the load. ## Content **Intro:** Conversion is where M3 projects slip. This worksheet gives you one place to map each legacy field to its M3 target, state the rule, name the owner, and see the clean rate before each mock load. **Sections:** #### How to use it **Body:** Start by listing the data objects in scope, then add one row per field or field group that needs a rule. Fill in the legacy source, the M3 target, and the transformation. Record counts and failing records come from your profiling queries and mock loads. The clean rate is calculated for each row and for the whole sheet. Update the status after every mock conversion so the steering group can see how many fields are signed off. Export to Excel to hand to the data owners for review. **Bullets:** - Map to the M3 target as the load tool sees it: field name or API transaction - Write rules in plain language that the data owner can approve - Run at least two full mock conversions before cutover - Keep unsigned mappings in a separate filter view #### Choosing a load approach **Body:** Master data such as customers, suppliers and items is usually loaded using M3 MI program transactions or Infor supplied load tooling, with the choice made per volume and per team skill. Transactional data such as open orders and balances needs sequence control. Load the structures first: facilities, warehouses, item groups, customers and items. Then load stock balances, then open purchase and sales documents. Opening financial balances are usually loaded as journal entries or open items, depending on how finance wants to reconcile. #### Reading the clean rate **Body:** A clean rate below 95 percent on a master data object is a signal to stop and fix the source rather than carry errors into M3. Group failing records by cause: missing mandatory field, invalid code, duplicate key, length overflow. Most failures are one of these four, and each has a cheap fix at the source. A rising clean rate across mock loads is the metric to show the steering group, not the number of records loaded. #### Common mistakes **Body:** Teams map fields without confirming whether the M3 target is mandatory, and discover it in the first load. Others forget that item, warehouse and facility data are linked, so one wrong warehouse code fails thousands of balance lines. Freeze dates are also commonly missed, which leaves inventory and open orders out of balance on cutover day. Reconcile counts and values at every load, not just at the end. **Cta:** If you want legacy data profiled against M3 rules before the first mock load, Netray can discuss a private, on-prem approach that keeps the data inside your network. **Faq:** #### How many mock conversions should an M3 project run? **Answer:** Most projects plan at least two or three full mocks, with the final one timed like the real cutover. Each mock should include reconciliation and business validation, and each should show a higher clean rate than the one before. #### Do I need a row for every field? **Answer:** No. Map every field that needs a rule, lookup or decision. Straight copy fields can be grouped in one row per object. The goal is to make the rules reviewable, not to produce a thousand row file nobody reads. #### Who should own the mapping? **Answer:** The business data owner approves the rule and the cleansed values. The consultant or data lead owns the technical mapping and load. Put both names on the row if responsibility is shared, so sign-off cannot be missed. #### Can this replace my migration tool? **Answer:** No, it is a planning and tracking sheet. Use it to design and approve the mapping, then build the actual loads with the tool you use. Keep the sheet updated so it stays the single record of what was agreed. ## Search terms infor m3 data migration template, m3 data conversion mapping excel, m3 field mapping template, erp data migration mapping worksheet, m3 conversion load plan, legacy to m3 data mapping --- Talk to an engineer: https://netray.co/contact?intent=erp_ai | Machine catalog: https://netray.co/agent/catalog.json