What is data mapping?
Data mapping is the translation layer between systems that name and structure their data differently.
It underpins every data movement - imports, migrations, integrations - and a mistake in it silently misplaces data.
Short answer
Data mapping is defining how data elements in one system correspond to those in another - which source field goes to which destination field, and how values translate between them. It is essential for imports, migrations, and integrations, where data moves between systems that structure it differently. Correct data mapping is what makes data land in the right place with the right meaning.
Key takeaways
- Defines how fields in one system correspond to another.
- Includes translating value formats between systems.
- Essential for imports, migrations, and integrations.
- Errors misplace data silently, without an error.
Why it matters
Whenever data moves between systems, mapping decides whether it lands correctly. A wrong mapping does not throw an error - it silently puts values in the wrong field or fails to translate them. Because the failure is invisible, correct mapping and testing are essential to data movement.
How Ardovo handles it
Ardovo auto-maps common fields and translates values for imports, migrations, and integrations, previewing the result before committing. Rook flags likely mismaps, so data moving into Ardovo lands in the right field with the right meaning rather than failing silently.
Frequently asked questions
Where is data mapping used?
In imports (file columns to CRM fields), migrations (one system's model to another's), and integrations (syncing fields between connected tools). Anywhere data moves between systems that structure it differently, mapping defines the correspondence, making it essential to all data movement.
Why is data mapping error-prone?
Because a wrong mapping fails silently - no error, just values in the wrong field or untranslated picklist values. The failure is invisible until someone notices misplaced data. This is why correct mapping, careful attention to near-match fields, and testing on a sample are essential to any data movement.
What does data mapping include besides pairing fields?
Translating value formats and picklist options between systems, splitting or combining fields, and deciding where unmatched fields go. Two systems might both have a status field but use different values, or format dates differently. Good mapping handles the value translation, not just the field-to-field pairing.