Data Validation Definition
Data validation is the process of checking data against a set of rules to make sure it is correct, complete, and in the expected format before it is saved, used, or shared. For example, a validation rule might reject an email address without an "@" sign or a product weight entered as text instead of a number.
What kinds of checks are there?
- Format checks confirm that data follows the expected pattern, such as a date written as DD/MM/YYYY or a barcode with the right number of digits
- Range checks make sure values fall within allowed limits, such as a price above zero or a discount below 100%
- Required field checks make sure mandatory information is not left empty
- Allowed value checks confirm that an entry comes from a fixed list, such as a color chosen from a set of approved options
- Consistency checks make sure related values fit together, such as a sale end date that falls after the start date
How is it different from data cleansing?
Validation checks data and flags or blocks anything that breaks the rules, ideally at the moment it is entered or imported. Data cleansing fixes problems in data that already exists. Validation prevents new errors, while cleansing corrects old ones, so the two are usually used together.
How does PIM use data validation?
A Product Information Management (PIM) system applies validation rules when product data is entered by hand, imported from suppliers, or prepared for a sales channel. It can block invalid values from being saved, flag incomplete products, and stop a product from being published until it meets each channel's requirements, such as a marketplace that asks for a GTIN (Global Trade Item Number) and images in a specific size.
