Data Mapping
What is Data Mapping?
Mapping is the answer to the question 'when system A sends us a record, which of our fields does each of theirs go into?' Sometimes the mapping is trivial (their 'email' to our 'email'); often it's not (their 'company_size' is in employee count, ours is in revenue band; their lifecycle stages don't line up with ours). Good mapping includes transformation rules — value normalization, default values for missing fields, splitting one source field into two destination fields. Bad mapping silently corrupts data — records sync but the meaning shifts subtly, and the corruption isn't caught until a report shows numbers that don't tie out. Mapping is one of those tasks that looks bureaucratic until the day you skip it.
Why it matters
- Prevents the silent-corruption pattern where data syncs but meaning shifts.
- Documents the contract between systems — useful for debugging, audits, and onboarding.
- Required for any integration that goes beyond field-name-matches-field-name.
Use cases
- CRM-to-warehouse mapping. Every CRM field mapped to its warehouse equivalent with type conversion.
- Form-to-CRM mapping. Form fields mapped to CRM fields with default values for the blanks.
- Source-to-canonical mapping. Multiple data sources mapped to a single canonical schema.
How turgo helps
turgo's integration UI makes mapping explicit and editable — every connected source's fields are visually mapped to turgo's canonical schema, with transformations and defaults configurable per field.
See turgo in action →
