Identify if the column values are required.
Check the target system to determine if the field must have a value. If values are not required, don't worry about it. Consider dropping the column.
Remember that null values imported into
are exported as missing values, which are easier to consume in most systems.
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Check the column header and data type to determine if values are required. For example, in transactional data, a field called
coupon_coderequires data only if every transaction is processed with one.
- If it's available, check the source system to see if it requires entry into the field. If an entry is required and your data contains missing values, then there is an issue in how the data was exported from the source system.
Insert a constant value. You can replace a missing value with a constant, which may make it easier to locate more important issues in the application.
Use a function. Particularly if the missing data can be computed, you can use one of the available functions to populate the missing values.
Copy values from another column. If a value from another column or a modified form of it can be used for the missing value, you can use the
settransform to overwrite the missing values.
Delete rows. Select the missing values bar and use the
deletetransform to remove the problematic rows.
NOTE: Since missing data may not be an explicit problem, you should avoid deleting rows or the column itself until other options have been reviewed.
- Hide the column for now. You can remove the column from display if you want to focus on other things. Select Hide from the column drop-down. Note that hidden rows columns still appear in any generated output.
Drop the column. If the column data is unnecessary or otherwise unusable, you can drop the entire column from your dataset. Select Drop from the column drop-down.
Tip: Drop unnecessary columns as early as possible. Less data is easier to work with in the application and increases job execution performance.