How To Fix RSDME349 - Nonnumeric value assigned to continuous field &1 - values ignored

RSDME349 - Overview

• Message type: E = Error

• Message class: RSDME -

• Message number: 349

• Message text: Nonnumeric value assigned to continuous field &1 - values ignored

Self-Explanatory Message

Since SAP believes that this specific error message is 'self-explanatory,' no more information has been given.The majority of messages in the SAP system have a message text, however this is frequently insufficient to comprehend or resolve the problem.

To make things easier, more detailed information is frequently added to describe the issue, how to fix it, and the necessary steps or configuration modifications.

Unfortunately, there isn't any extra information in this error notice.

What else can you do?

First, use our AnswerBot below to get a possible cause and solution (requires a premium subscription).

Also, review the in-depth Common Questions & Answers listed below; you could discover a solution there or be able to connect with others who have faced similar challenges.

You can also try searching the SAP support portal (support.sap.com) but you need a special user ID to access it. It is possible that an SAP support note exists that provides additional details about the mistake or even steps for fixing it.

Smart SAP Assistant

• What is the cause and solution for SAP error message RSDME349 - Nonnumeric value assigned to continuous field &1 - values ignored ?

  The SAP error message RSDME349 indicates that a non-numeric value has been assigned to a continuous field in a data model, typically in the context of SAP BW (Business Warehouse) or SAP HANA. This error usually occurs during data loading or transformation processes when the system encounters a value that it cannot interpret as a number in a field that is expected to contain numeric data.
  
  Cause:
  
  Data Type Mismatch: The field in question is defined as a numeric type (e.g., integer, decimal), but the incoming data contains non-numeric characters (e.g., letters, special characters).
  Data Quality Issues: The source data may have inconsistencies or errors, such as missing values, incorrect formatting, or unexpected characters.
  Transformation Logic: If there are transformations applied to the data (e.g., during ETL processes), they may inadvertently introduce non-numeric values into numeric fields.
  
  
  Solution:
  
  Data Validation: Check the source data for any non-numeric values in the field that is causing the error.