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When data is imported from another system, you might discover that some values are missing in it. In some cases, these values simply contain no content. In other cases, these values are non-existent. Depending on how the missing values entered the data, you may end up processing them in different ways. This section describes how to identify and manage missing data in your datasets.


NOTE: If you are unsure of the meaning of a column of data that contains missing values, you should attempt to review the source data or contact the individual who generated the data to identify why values may be missing and how to effectively manage them in

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and downstream systems.



Tip: You can also use the IFMISSING function to test for empty values. Unlike the above construction, however, IFMISSING does not support an else clause when the value is present. For more information, see IFMISSING Function.


Copy values from another column



NOTE: If missing metadata is not supported as part of the value in the target system, you can insert the metadata as a separate column and then apply the metadata to the data inside the target system.



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label((label = "validation_tasks"data_type") OR (label = "missing") OR (label = "transformation_ui"))