What is field mapping?
Field mapping is the process of matching columns in source data to fields in the target system, handling differences in naming, structure, and format.
Understanding field mapping
When importing data, source columns rarely match target fields exactly. Field mapping bridges this gap by defining how source data maps to your system.
Mapping challenges include: - Different names: Source has "First Name", target expects "firstName" - Combined fields: Source has "Full Name", target needs separate first/last - Split fields: Source has "Address", target needs street/city/state/zip - Missing fields: Source doesn't have a required field - Extra fields: Source has columns you don't need
Good mapping solutions auto-detect obvious matches, let users manually map ambiguous columns, and remember mappings for future imports.
Key points
Matches source columns to target fields
Handles naming differences and structural mismatches
Can be automatic (AI-detected) or manual
Should remember mappings for repeat imports
Critical for handling varied source data
Frequently asked questions
How does automatic field mapping work?
Automatic mapping uses techniques like: exact name matching, fuzzy name matching (handling typos), header analysis, sample data analysis, and machine learning. It suggests likely matches that users can confirm or override.
What happens when a required field has no mapping?
Options include: require the user to map it manually, use a default value, compute it from other fields, or reject the import. The right choice depends on the field and your business rules.
Can I save field mappings for reuse?
Yes, good import tools let you save mapping configurations as templates. This is especially useful for repeat imports from the same source (e.g., monthly reports from a partner).