How To Fix RSANA_UMM_API456 - Keys of training dataset &1 must match keys of Predictive Scenario.

SAP Error Message - Details

• Message type: E = Error

• Message class: RSANA_UMM_API - UMM API Messages

• Message number: 456

• Message text: Keys of training dataset &1 must match keys of Predictive Scenario.

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 RSANA_UMM_API456 - Keys of training dataset &1 must match keys of Predictive Scenario. ?

  The SAP error message RSANA_UMM_API456 indicates that there is a mismatch between the keys of the training dataset and the keys defined in the predictive scenario when using the SAP Predictive Analytics or SAP Analytics Cloud. This error typically arises when you are trying to use a dataset for training a predictive model, but the keys (or identifiers) in the dataset do not align with what the predictive scenario expects.
  Cause:
  
  
  Key Mismatch: The keys (e.g., ID fields) in your training dataset do not match the keys defined in the predictive scenario. This could be due to:
  
  Missing keys in the training dataset.
  Extra keys in the training dataset that are not defined in the predictive scenario.
  Different data types or formats for the keys.
  Changes in the structure of the dataset after the predictive scenario was created.
  
  
  
  Data Preparation Issues: The dataset may not have been properly prepared or transformed to meet the requirements of the predictive scenario.
  
  
  Versioning Issues: If the predictive scenario was modified after the training dataset was created, the keys may no longer align.
  
  
  Solution:
  
  
  Check Key Definitions: Review the keys defined in the predictive scenario and ensure that the training dataset contains the same keys. This includes checking for:
  
  Correct naming conventions.
  Data types (e.g., string vs. integer).
  Presence of all required keys.
  
  
  
  Modify the Training Dataset: If there are discrepancies, you may need to modify the training dataset to ensure it matches the keys expected by the predictive scenario. This could involve:
  
  Adding missing keys.
  Removing unnecessary keys.
  Renaming keys to match.
  
  
  
  Recreate the Predictive Scenario: If the predictive scenario is outdated or no longer matches the current dataset structure, consider recreating the predictive scenario based on the current training dataset.
  
  
  Data Validation: Validate the data in the training dataset to ensure it is clean and formatted correctly. This includes checking for null values, duplicates, and ensuring that the data types are consistent.
  
  
  Documentation and Logs: Check SAP documentation for any specific requirements regarding key definitions and predictive scenarios. Additionally, review any logs or error messages for more context on the issue.
  
  
  Related Information:
  
  SAP Predictive Analytics Documentation: Refer to the official SAP documentation for guidelines on setting up predictive scenarios and training datasets.
  Data Preparation Best Practices: Familiarize yourself with best practices for data preparation in SAP, as this can help prevent similar issues in the future.
  Community Forums: Engage with SAP community forums or support channels for additional insights and solutions from other users who may have encountered similar issues.
  
  By following these steps, you should be able to resolve the RSANA_UMM_API456 error and successfully align your training dataset with the predictive scenario.