[GTM glossary](/glossary.md) Field mapping

Data & Enrichment

# Field mapping

Also known as: data mapping

In short

Field mapping is the process of defining which field in a source system corresponds to which field in a destination system. For example, mapping "Company Name" in a spreadsheet to the "Account Name" field in a CRM.

Good mapping is the foundation of imports, integrations, and enrichment. When it is wrong, data lands in the wrong field, overwrites good values, or is silently dropped.

## What a mapping specifies

* **Source and destination:** Which field feeds which field.
* **Format rules:** How to convert values, such as date formats, phone formats, or country names to codes.
* **Direction:** One-way or two-way sync, and which side wins on conflict.
* **Defaults and blanks:** What to do when the source is empty.

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## How to build a mapping

### 1. List both field sets

Export the field list from each system with types and allowed values.

### 2. Match by meaning

Match by what a field means, not just its name. "Status" in two tools may hold different values.

### 3. Handle picklists

Map each source value to a destination value, for example "Mid-market" to "MM". Unmapped values are a common cause of errors.

### 4. Test with a sample

Run a small batch and check results by hand before syncing everything.

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## Example

Say an enrichment tool returns employee count as a number, but your CRM uses size bands like 51-200. The mapping needs a rule that converts the number into the right band, otherwise the field stays empty or errors out.

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## Common mistakes

* **Mapping to custom fields no one owns:** Unused fields clutter the CRM.
* **Ignoring overwrite rules:** A sync can replace rep-entered data.
* **No documentation:** Keep a record so changes are traceable.
* **Skipping tests:** A bad mapping can corrupt thousands of records fast.

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## Field mapping FAQs

**What is field mapping?**

Field mapping defines which field in a source system matches which field in a destination system, such as a spreadsheet column to a CRM field. It also covers format conversions and which side wins when values conflict.

**Why is field mapping important for CRM integrations?**

Without correct mapping, synced data can land in the wrong field, overwrite good values, or be dropped. Clear mapping keeps imports, enrichment, and integrations reliable, so reps and reports can trust what they see in the CRM.

**How do you map picklist values between systems?**

List each value from the source and assign the matching value in the destination, for example Mid-market to MM. Decide a default for unmapped values and test with a sample, since unmapped picklist values are a common source of sync errors.

**What are common field mapping mistakes?**

Common mistakes include matching fields by name rather than meaning, ignoring overwrite rules, skipping format conversions, leaving picklists unmapped, and not testing on a small batch first. Documenting the mapping also helps later troubleshooting.

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## More related phrases

* [CRM data enrichment](/glossary/crm-data-enrichment.md)
* [Data pipeline](/glossary/data-pipeline.md)
* [Department mapping](/glossary/department-mapping.md)

## Similar phrases

* [Bi-directional sync](/glossary/bi-directional-sync.md)  
Bi-directional sync keeps data consistent between two systems by passing changes in both directions, so an update in either tool shows up in the other.
* [Native integration](/glossary/native-integration.md)  
A native integration is a built-in, vendor-maintained connection between two software products that syncs data without custom code or a third-party middleware tool.
* [CRM integration](/glossary/crm-integration.md)  
CRM integration connects your customer relationship management system with other tools, such as marketing automation, data providers, and sales engagement, so records and activity sync automatically between them.
* [Data normalization](/glossary/data-normalization.md)  
Data normalization is the process of converting values into a consistent, standard format so records can be matched, compared, filtered, and reported on reliably.
* [Data standardization](/glossary/data-standardization.md)  
Data standardization converts data into one agreed format, such as consistent country names, date styles, and job title spellings, so records can be compared, filtered, and combined reliably.