Data Quality

Data quality is the level of reliability, accuracy, completeness and consistency of a company’s data. In a PIM, DAM and MDM context, it is essential to guarantee accurate, usable product information across every sales channel.

The dimensions of data quality

Data quality is measured against several criteria: accuracy (correct data), completeness (no missing information), consistency (uniformity across systems), timeliness (up-to-date data), uniqueness (no duplicates) and validity (compliance with formats and business rules). Poor quality has a direct impact on sales performance.

Business challenges of data quality

Poor-quality product data generates order errors, customer returns, loss of trust and degraded rankings on marketplaces. Excellent data quality, on the other hand, improves the customer experience, streamlines operations and strengthens regulatory compliance, in particular for the Digital Product Passport.

A concrete example

An online retailer analyses the quality of its 12,000 product records. Findings: 35% of records have incomplete descriptions, 20% have incorrect dimensions and 15% have poor-quality visuals. Impact: an 18% return rate and negative customer reviews. Solution: deploy a PIM or an MDM with automated quality rules (mandatory fields, validated formats, enrichment workflow). After 6 months: 98% completeness, return rate down to 8%, Amazon quality score up from 3.2 to 4.6/5. The PIM becomes the guarantor of data quality.