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Message type: E = Error
Message class: CNV_20100 - Messageclass for chart of account conversion
Message number: 429
Message text: Masked value &1 was not converted
Masked value &V1& is contained in table &V2&, but is <ZH>not</> not
contained in the mapping table and was consequently not converted.
If you want to convert the value &V1& during the next conversion,
include it in the mapping (CNV_COA_OTHERS) in the phase <ZK>Mapping
Tables</>, activity <ZK> Specify Mapping Values (Chart of Accounts) </>,
in the mapping for 'Assignment of old/new further accounts'.
Note that relevant fields in other tables may contain the same value,
which will then be converted as well.
Error message extract from SAP system. Copyright SAP SE.
CNV_20100429
- Masked value &1 was not converted ?The SAP error message CNV_20100429 indicates that a masked value (represented by
&1
) was not converted during a data migration or conversion process. This error typically arises in the context of SAP's data migration tools, particularly when using the SAP S/4HANA Migration Cockpit or other data conversion tools.Cause:
- Data Format Issues: The value may not conform to the expected format or data type required by the target system.
- Mapping Issues: There may be a problem with the mapping of the source data to the target fields, leading to the inability to convert certain values.
- Missing Configuration: The conversion rules or configurations may not be set up correctly, causing certain values to be skipped.
- Data Quality Issues: The source data may contain invalid or unexpected values that cannot be processed.
Solution:
- Check Data Format: Review the source data for the masked value and ensure it matches the expected format for the target system. Correct any discrepancies.
- Review Mapping Configuration: Verify the mapping settings in the migration tool to ensure that all necessary fields are correctly mapped and that there are no missing mappings.
- Adjust Conversion Rules: If applicable, modify the conversion rules to handle the specific masked value appropriately.
- Data Cleansing: Perform data cleansing on the source data to address any quality issues. This may involve correcting invalid entries or removing duplicates.
- Log Analysis: Check the detailed logs generated during the migration process for additional context on why the conversion failed. This can provide insights into specific issues with the data.
- Testing: After making adjustments, run a test migration with a smaller dataset to ensure that the changes resolve the issue before attempting a full migration.
Related Information:
By following these steps, you should be able to identify the root cause of the error and implement a solution to ensure successful data conversion.
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