Data validation is the process of checking data against defined rules before anyone stores or uses it. Only accurate, complete, and usable values get through. Common checks confirm data type, format, range, consistency, and uniqueness.<\/p>\n\n\n\n
Bad data is expensive in quiet ways. A wrong date of birth breaks an age analysis. A duplicate customer inflates revenue, and a ZIP code typo sends a package to the wrong state. Validation stops those errors at the door instead of after a decision goes wrong.<\/p>\n\n\n\n
In this guide, we cover the main types of data validation and the techniques teams use. You will also see how to validate survey data and which tools help.<\/p>\n\n\n\n\n\n
Data validation is a set of rules and automated checks that test whether each value meets the requirements for its field. These checks are often called check routines. The system rejects, flags, or returns any value that fails a rule.<\/p>\n\n\n\n
Checks range from simple to complex. A simple rule confirms that a date of birth contains only a valid date. A complex rule compares several fields, such as confirming that a delivery date falls after the order date.<\/p>\n\n\n\n
Here is how one record moves through validation:<\/p>\n\n\n\n
Only validated data should be imported, saved, or used. Otherwise, programs can fail, reports can mislead, and machine learning models trained on bad data produce wrong predictions.<\/p>\n\n\n\n
Data validation checks whether data follows the rules, while data verification checks whether data matches the truth or its source. A five-digit ZIP code passes validation. It still fails verification if it is not the customer’s real ZIP code.<\/p>\n\n\n\n
Both terms often get mixed up with data cleansing, which fixes errors after they enter a system.<\/p>\n\n\n\n