How To Fix SCPR433 - Field values could not be enhanced for all data records

SAP Error Message - Details

• Message type: E = Error

• Message class: SCPR - BC Sets

• Message number: 433

• Message text: Field values could not be enhanced for all data records

What causes this issue?

The BC Set contains data records which are no longer in the database.
Data records which are ONLY in the BC Set could not be written when
field values were read from the database.

System Response

These fields now have initial values and could lead to database
inconsistencies at activation.

How to fix this error?

Delete these data records from the BC Set to prevent these
inconsistencies.
Note: You can find these data records by showing a status icon in the
data record table (by choosing Utilities -> Settings... -> Data Record
Settings -> Show Status Icon).

Procedure for System Administrators

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

Error message extract from SAP system. Copyright SAP SE.

Smart SAP Assistant

• What is the cause and solution for SAP error message SCPR433 - Field values could not be enhanced for all data records ?

  The SAP error message SCPR433, which states "Field values could not be enhanced for all data records," typically occurs in the context of data enhancement processes, such as when using the SAP Data Services or during data migration activities. This error indicates that the system was unable to enhance certain field values for all records in the dataset.
  Causes:
  
  Missing Data: The fields that are supposed to be enhanced may not have the necessary data available in the source records.
  Data Type Mismatch: There may be a mismatch between the expected data type of the field and the actual data type present in the records.
  
  Configuration Issues: Incorrect configuration in the enhancement settings or rules can lead to this error.
  Business Logic Errors: The logic defined for enhancing the fields may not be correctly implemented, leading to failures in processing certain records.
  Data Quality Issues: Poor data quality, such as null values or invalid formats, can