What is the low code solution when importing problematic data?

What is the low code solution when importing problematic data?

• A. Fix Scripts
• B. Auto close case on work order closure
• C. Enable state flows
• D. Source Transformation

Answer: (D)

When importing data that doesn’t quite fit what the target table expects, adjusting the values as part of the import process with a source transformation is the most efficient, low-code approach. A transform map can include a source transformation for a field, letting you derive the exact value you want before it’s written to the target. This lets you handle messy data formats—like different date formats, spaces or dashes in phone numbers, or a single source column that should map to multiple target fields—without building separate scripts or post-import fixes. The transformation can use simple logic and built-in functions to clean, parse, or reformat values on the fly, which keeps the data clean right at the point of entry. Other options don’t address data quality during import: one would involve fixing data after it’s imported, which is less efficient; others relate to processes or lifecycles (like closing cases or running flows) rather than shaping the incoming data itself.

When importing data that doesn’t quite fit what the target table expects, adjusting the values as part of the import process with a source transformation is the most efficient, low-code approach. A transform map can include a source transformation for a field, letting you derive the exact value you want before it’s written to the target. This lets you handle messy data formats—like different date formats, spaces or dashes in phone numbers, or a single source column that should map to multiple target fields—without building separate scripts or post-import fixes. The transformation can use simple logic and built-in functions to clean, parse, or reformat values on the fly, which keeps the data clean right at the point of entry.

Other options don’t address data quality during import: one would involve fixing data after it’s imported, which is less efficient; others relate to processes or lifecycles (like closing cases or running flows) rather than shaping the incoming data itself.