[GTM glossary](/glossary.txt) Enriching data

Data & Enrichment

# Enriching data

In short

Enriching data is the act of adding missing or updated information to records you already have, using a second source. A contact with only an email address, for example, might be enriched with a job title, company, and location.

This page covers the practice and workflow. Enrichment, as a concept, is the broader capability and the output; enriching data is the step-by-step work of deciding what to add, from where, and how to write it back safely.

## A practical process

### 1. Decide what you need

List the fields that drive decisions: for example industry, employee count, job title, and region for routing and scoring. Skip fields nobody uses.

### 2. Audit what you have

Measure how many records are missing each field. This shows where enrichment will help most and gives you a baseline.

### 3. Pick sources and match keys

Records match on keys such as email, domain, or LinkedIn URL. Clean keys give better matches, so fix those first.

### 4. Write back with rules

Decide whether new data overwrites old values or only fills blanks. Protect fields that reps edit by hand.

### 5. Re-check regularly

Data changes. Schedule refreshes for fields that go stale, like titles.

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## What you can add

* **Company fields:** Industry, size, revenue range, location, and tech stack.
* **Contact fields:** Title, seniority, department, and verified email or phone.
* **Context:** Recent funding or hiring events that may change priority.

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## Example

Say a team has 2,000 leads, and half lack a job title. They enrich by email, fill only blank fields, and then route leads by seniority. Leads still missing a title go to a manual review queue rather than being guessed.

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## Common mistakes

* **Overwriting good data:** A blanket overwrite can replace correct values with worse ones.
* **No baseline:** Without before-and-after fill rates you cannot judge results.
* **Enriching everything:** Enrich the records you will act on, to control cost.

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## Enriching data FAQs

**What does enriching data mean?**

Enriching data means adding missing or updated details to records you already hold, using another source. For example, adding job title, company size, and industry to a contact that only has an email address.

**How do you enrich data in your CRM?**

Decide which fields you need, measure how many are missing, match records to a data source on a key like email or domain, and write results back with rules about overwriting. Then schedule refreshes for fields that change often.

**What data can you enrich?**

Common additions include company details such as industry, size, and location, and contact details such as title, seniority, and verified email or phone. Some teams also add context such as tech stack or recent funding.

**What is the difference between enriching data and cleaning data?**

Enriching adds new information to a record, while cleaning fixes or removes what is already wrong, such as duplicates and typos. Teams usually clean first so enrichment matches records correctly.

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## More related phrases

* [CRM enrichment](/glossary/crm-enrichment.txt)
* [Account enrichment](/glossary/account-enrichment.txt)
* [B2B data enrichment](/glossary/b2b-data-enrichment.txt)

## Similar phrases

* [CRM data enrichment](/glossary/crm-data-enrichment.txt)  
CRM data enrichment is the practice of adding missing company and contact information to existing CRM records so teams can segment, route, score, and personalize with confidence.
* [Data enrichment](/glossary/data-enrichment.txt)  
Data enrichment is the process of adding missing or updated information, such as firmographics, contact details, and technographics, to records you already have from internal or external sources.
* [Fill rate](/glossary/fill-rate.txt)  
Fill rate is the percentage of records in a dataset that have a value for a given field, and it shows how complete your CRM or enriched data really is.
* [Lead enrichment](/glossary/lead-enrichment.txt)  
Lead enrichment adds missing company and contact details to a lead record, such as industry, company size, and job title, so teams can qualify, score, and route leads faster.
* [Data cleansing](/glossary/data-cleansing.txt)  
Data cleansing is the process of finding and fixing incorrect, duplicate, incomplete, or badly formatted records in a database so that the data can be trusted for outreach, reporting, and routing.