[GTM glossary](/glossary.md) Data accuracy

Data & Enrichment

# Data accuracy

In short

Data accuracy is the degree to which a stored value matches the true, real-world value. An email address is accurate if it reaches the person it claims to, and a job title is accurate if the person still holds it.

Accuracy is one dimension of data quality, alongside completeness, freshness, and consistency. A field can be filled in and still be wrong, which is why accuracy has to be checked separately.

## Why accuracy matters in GTM

* **Deliverability:** Wrong emails bounce, which can damage sender reputation.
* **Targeting:** Wrong industry or size puts accounts into the wrong segment or score.
* **Rep trust:** Reps stop using a CRM they do not believe.
* **Reporting:** Inaccurate fields distort pipeline and conversion analysis.

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## How to measure it

You rarely know the truth for every record, so measure with samples and proxies.

* **Sample audit:** Pull a random set of records, check each field by hand against a reliable source, and calculate the share that are correct.
* **Bounce rate:** A rising email bounce rate points to inaccurate addresses.
* **Returned mail and wrong-number outcomes:** Tag these outcomes in the CRM so you can count them.
* **Rep feedback:** Give reps a one-click way to flag a bad record.

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## An example

Say you audit 100 contacts and find 82 have the correct title and email. Your sample accuracy is 82 percent. If most errors are old job titles, the fix is more frequent refreshes, not a bigger database.

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## How to improve it

* **Verify at entry:** Validate emails and formats when a record is created.
* **Refresh regularly:** Re-check fast-decaying fields such as titles and emails.
* **Cross-check sources:** Agreement between independent sources raises confidence.
* **Close the loop:** Feed bounces and rep corrections back into the data.

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## Accuracy versus freshness

Accuracy asks whether the value is correct now. Freshness asks how recently it was verified. Old data tends to be less accurate, but a recent record can still be wrong.

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## Data accuracy FAQs

**What is data accuracy?**

Data accuracy is how closely a stored value matches the real-world truth. An accurate email reaches the intended person, and an accurate job title reflects the role the person holds today.

**How do you measure data accuracy?**

Audit a random sample of records, check each field against a reliable source, and calculate the share that are correct. Supporting signals include email bounce rate, wrong-number outcomes, returned mail, and flags raised by reps.

**What is the difference between data accuracy and data quality?**

Data quality is the overall fitness of data for use and includes accuracy, completeness, consistency, and freshness. Data accuracy is one of those dimensions and covers only whether values are correct.

**How can you improve data accuracy in a CRM?**

Validate data when records are created, refresh fast-changing fields regularly, cross-check against independent sources, deduplicate records, and feed bounce results and rep corrections back into the database.

Share term

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## More related phrases

* [B2B data erosion](/glossary/b2b-data-erosion.md)
* [Data coverage](/glossary/data-coverage.md)
* [Fill rate](/glossary/fill-rate.md)

## Similar phrases

* [Data freshness](/glossary/data-freshness.md)  
Data freshness describes how recently the values in a record were collected or verified, and whether they still reflect the current reality of a company or contact.
* [Data quality](/glossary/data-quality.md)  
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 decay](/glossary/data-decay.md)  
Data decay is the gradual loss of accuracy in contact and company records as people change jobs, companies change details, and information goes out of date over time.
* [Data hygiene](/glossary/data-hygiene.md)  
Data hygiene is the ongoing practice of keeping CRM and marketing data clean, accurate, consistent, and usable through regular checks, standards, and ownership.
* [Email validation](/glossary/email-validation.md)  
Email validation checks whether an email address is correctly formatted and technically able to exist, catching typos and invalid domains before they reach your CRM or sending tool.