How To Fix CNV_20100235 - Accts are unique in row &1, which is not the case in most likely scen. &2


CNV_20100235 - Overview

  • Message type: E = Error

  • Message class: CNV_20100 - Messageclass for chart of account conversion

  • Message number: 235

  • Message text: Accts are unique in row &1, which is not the case in most likely scen. &2

  • Show details Hide details
  • What causes this issue?

    Uniqueness means that <ZH>none </>of the accounts that are converted in
    a given chart of accounts exists in any other chart of accounts.
    According to the mapping table (row &V1&), all accounts are unique.
    However this is not the case in the most likely scenario &V2&.

    System Response

    No suitable scenario was found during the check for possible scenarios.

    The system issues an error message and will not allow you to continue with this transaction until the error is resolved.

    How to fix this error?

    Find out if another scenario can be applied.

    Error message extract from SAP system. Copyright SAP SE.


CNV_20100235 - Details

  • The SAP error message CNV_20100235 typically occurs during data migration or conversion processes, particularly when using the SAP S/4HANA Migration Cockpit or similar tools. This error indicates that there is a uniqueness violation in the account assignments for a specific row in your data. 
    
    Cause: The error message suggests that the system has detected that the accounts specified in the data row are expected to be unique, but they are not. This can happen in scenarios such as: Duplicate Entries: There are multiple entries in the source data that have the same account assignment for the same entity or transaction. Incorrect Mapping: The mapping of accounts from the legacy system to the target system may not be set up correctly, leading to conflicts. Data Quality Issues: The source data may contain inconsistencies or errors, such as typos or incorrect account numbers.
    Solution: To resolve this error, you can take the following steps: Identify Duplicates: Review the data

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