What provides a no-code solution when importing problematic data?

What provides a no-code solution when importing problematic data?

• A. Data Cleansing Scripts
• B. Import Wizard
• C. Field Normalization Rules
• D. Data Mapping Templates

Answer: (C)

Field Normalization Rules let you automatically transform incoming values into a standardized form during import, and you don’t need to write any code to do it. By setting up rules that map variants to a single, consistent value, you can handle messy data—like different spellings, abbreviations, or formats—as soon as it enters the system. For example, variations such as “CA,” “Calif,” and “California” can all be normalized to a single state name, or phone numbers and addresses can be standardized to a uniform format. This makes the import process resilient to problematic data and reduces the need for post-import cleanup. Other options either involve writing scripts (code) or focus on how data is mapped or imported rather than consistently correcting the data values themselves, so they don’t provide the same zero-code, automatic data quality solution.

Field Normalization Rules let you automatically transform incoming values into a standardized form during import, and you don’t need to write any code to do it. By setting up rules that map variants to a single, consistent value, you can handle messy data—like different spellings, abbreviations, or formats—as soon as it enters the system. For example, variations such as “CA,” “Calif,” and “California” can all be normalized to a single state name, or phone numbers and addresses can be standardized to a uniform format. This makes the import process resilient to problematic data and reduces the need for post-import cleanup. Other options either involve writing scripts (code) or focus on how data is mapped or imported rather than consistently correcting the data values themselves, so they don’t provide the same zero-code, automatic data quality solution.