How To Fix TW206 - Garbage character & found in &.&


TW206 - Overview

  • Message type: E = Error

  • Message class: TW - R3trans - Messages

  • Message number: 206

  • Message text: Garbage character & found in &.&

  • Show details Hide details
  • Scrap character & found in &.<(>&<)>

    What causes this issue?

    The table contains scrap characters.

    System Response

    These are not exported by transport program R3trans.

    How to fix this error?

    Read SAP Note 718511. It contains the current recommendations for how to
    deal with scrap characters. It is usually only possible to solve the
    problem by correcting the destroyed data records in the export system
    and repeating the export.

    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 TW206 - Garbage character & found in &.& ?
    The SAP error message TW206, which states "Garbage character & found in &.&," typically indicates that there is an invalid or unexpected character in a field that is being processed. This can occur in various contexts, such as during data entry, file uploads, or when processing data in reports or transactions.
    Causes:
    
    Invalid Characters: The presence of non-printable or special characters in a text field that is expected to contain only alphanumeric characters.
    Data Corruption: Data may have been corrupted during transfer or storage, leading to unexpected characters.
    Encoding Issues: Mismatches in character encoding (e.g., UTF-8 vs. ASCII) can lead to garbage characters appearing in the data.
    User Input Errors: Users may inadvertently enter invalid characters when filling out forms or fields.
    
    Solutions:
    
    Data Validation: Implement validation checks to ensure that only valid characters are allowed in the input fields. This can be done through input masks or validation rules.
    Data Cleansing: Review and cleanse the data to

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