[GTM glossary](/glossary.txt) Data hygiene

Data & Enrichment

# Data hygiene

In short

Data hygiene is the set of routines and rules that keep your customer and prospect data clean over time. It includes removing duplicates, fixing errors, standardizing formats, filling gaps, and retiring records that are no longer valid.

Hygiene is a habit, not a project. A one-off cleanup helps for a quarter, but new records, imports, and job changes dirty the database again. Data cleansing is the act of fixing data; data hygiene is the ongoing discipline that makes cleansing routine.

## What good data hygiene covers

* **Accuracy:** Values match reality, such as correct titles and working emails.
* **Consistency:** The same thing is written the same way, such as one format for country or industry.
* **Completeness:** Fields your team relies on are filled in.
* **Uniqueness:** Each person and company appears once.
* **Timeliness:** Records are reviewed or refreshed on a schedule.

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## A practical hygiene routine

### Set standards

Write down required fields, allowed values, and naming rules. Use picklists instead of free text where you can.

### Validate at the door

Check email syntax, require a domain on accounts, and match against existing records at form submit and import.

### Run recurring jobs

Schedule duplicate detection, email verification, and enrichment refresh. Weekly or monthly runs beat annual cleanups.

### Assign owners

Name a person or team accountable for each object, usually RevOps, and give reps a simple way to flag bad records.

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## Example

Say a RevOps lead finds that 12% of a sampled contact list bounces. She adds email verification to the import process, a monthly duplicate merge, and a required domain field on accounts. Next quarter she samples again and compares. The numbers are illustrative; the point is to measure before and after.

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## Common mistakes

* **Treating it as a one-time project:** Without a routine, quality slides back.
* **No ownership:** Everyone assumes someone else is responsible.
* **Over-cleaning:** Deleting records without checking value can remove useful history.
* **Ignoring the source of errors:** Fix the form or integration creating bad data, not only the bad data.

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## Data hygiene FAQs

**What is data hygiene?**

Data hygiene is the ongoing practice of keeping customer and prospect data clean and usable. It covers removing duplicates, correcting errors, standardizing formats, filling gaps, and retiring invalid records on a regular schedule.

**Why is data hygiene important in sales and marketing?**

Clean data means emails reach inboxes, leads route to the right rep, scoring and segmentation work as intended, and reports can be trusted. Poor hygiene wastes rep time and distorts decisions made from CRM numbers.

**What is the difference between data hygiene and data cleansing?**

Data cleansing is the act of fixing existing errors, such as merging duplicates or correcting values. Data hygiene is the ongoing set of habits, rules, and ownership that keeps data clean so cleansing stays routine.

**How do you improve data hygiene in a CRM?**

Set field standards, validate data at entry and import, schedule recurring duplicate checks and verification, assign clear owners, and fix the processes that create bad data instead of only cleaning the results.

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## More related phrases

* [B2B data erosion](/glossary/b2b-data-erosion.txt)
* [Data decay](/glossary/data-decay.txt)
* [Data normalization](/glossary/data-normalization.txt)

## Similar phrases

* [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.
* [Data deduplication](/glossary/data-deduplication.txt)  
Data deduplication is the process of finding and merging duplicate records, such as the same company or contact entered twice, so each real entity has one clean record.
* [Data quality](/glossary/data-quality.txt)  
Data quality describes how well a dataset is fit for its intended use, measured by traits such as accuracy, completeness, consistency, timeliness, and uniqueness.
* [Data standardization](/glossary/data-standardization.txt)  
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.
* [CRM hygiene](/glossary/crm-hygiene.txt)  
CRM hygiene is the ongoing practice of keeping CRM records accurate, complete, deduplicated, and consistent so that reports, routing, and forecasts can be trusted.