In a data mapping, you might need to identify missing or unwanted data when a source or
target field contains compound data. For example, consider a
long_address field that has this format:
number street apartment city state zip zip+4 country
Suppose that you want to separate the long_address field into discrete
fields for number, street, city, state, and zip. To do this,
you select long_address as the source field and then select
the target fields. You then add padding fields at the locations for the
parts of the source field that you do not want. In this example, the
unwanted parts are apartment, zip+4, and country.
To identify the unwanted parts, you need to know the order
of the parts. The order indicates an index for
each part of the content in the compound field. For example, the long_address
field has 8 ordered parts. Starting at 1, the index of each part is:
1 |
number |
2 |
street |
3 |
apartment |
4 |
city |
5 |
state |
6 |
zip |
7 |
zip+4 |
8 |
country |
In the data mapper, to identify apartment, zip+4, and country as missing, you add padding fields at indexes 3, 7, and 8. See [separate-one-source-field-into-multiple-target-fields].
Now suppose that you want to combine source fields for
number, street, city, state, and zip into a long_address
target field. Further suppose that there are no source fields to provide content for
apartment, zip+4, and country. In the data mapper, you need to
identify these fields as missing. Again, you add padding fields
at indexes 3, 7, and 8. See [combine-multiple-source-fields-into-one-target-field].