Data Validation

Data validation checks whether data meets predefined rules for format, type, range, and accuracy before it enters your system or gets used in a process. It catches problems at the point of entry: Is this a valid email format? Does this ZIP code exist? Is this phone number the right length? Validation is your first line of defense against bad data getting into your database.

Why It Matters

Cleaning data after it's in your system is 10x more expensive than catching it on the way in. A validation rule that rejects "[email protected]" saves the downstream work of identifying the bounce, flagging the record, researching the correct address, and updating it. Multiply that by thousands of records and validation becomes one of the highest-ROI data quality investments you can make.

Types of Data Validation

• Format validation: Check that emails have an @ and domain, phone numbers have correct digit counts, ZIP codes are 5 or 9 digits
• Range validation: Employee count should be positive. Revenue should be within reasonable bounds. Dates should be in the past for founding years
• Referential validation: Does this state+ZIP combination actually exist? Does the area code match the stated location?
• Cross-field validation: If country is 'US,' the phone should start with +1. If the role is 'CEO,' seniority shouldn't be 'Junior'
• Real-time verification: Check email deliverability, phone connectivity, and address existence against live databases

Example

A company adds validation rules to their web forms: email format check, phone length check, and company name required. They also run nightly validation on CRM imports. Invalid records go to a review queue instead of entering the database directly. After three months, the percentage of records requiring manual cleanup drops from 25% to 4%.

Related Terms

• Email Validation
• Address Validation
• Data Quality Management

Related Resources

• Data Validation Service
• Pre-Import Validation
• Batch Validation