What occurs to values that Analytics does not recognize as dates when using the To Date transformation?

When utilizing the To Date transformation in analytics, values that are not recognized as valid dates are replaced with null. This is important as it helps maintain the integrity of the data set by ensuring that only accurate and recognizable date values are processed. If an input value does not meet the criteria for a valid date format, it cannot be converted, leading to its replacement with a null.

This handling allows analysts to identify which values were problematic for date conversion during their data processing. As a result, any subsequent analysis can be performed without the risk of erroneous date values affecting data outcomes. Recognizing these nulls can subsequently prompt further investigation or clean-up of the data set for better accuracy in analytics work.

Other options do not apply in this context; for instance, default values or the current date might mask underlying issues in the data, leading to potential inaccuracies. Logging values for review is not typically part of the immediate data transformation process, as the primary goal is to create a clean and usable data set right from the start.