Data quality: definition and role in PIM

In PIM practice, data quality is measured on six dimensions: completeness, accuracy, consistency, timeliness, conformity to standards and uniqueness. Each dimension needs a formula, a target per channel and an owner. GS1's Data Quality Framework for brand owners measures quality on completeness, timeliness and accuracy, and checks accuracy against physical products.

Illustrative example: a distributor reports 96 percent completeness for its webshop. A sample of 100 products checked against datasheets then shows wrong values in 9 percent of technical attributes. The accuracy gap leads to new validation rules and a supplier data review.

In a selection, buyers should define quality targets before demos and test how each PIM enforces them. Validation rules, completeness gates, duplicate detection and quality dashboards are the main capabilities. A quality requirement without a metric cannot be tested.