Data Quality

Data quality is not a one-time project but an operational state that has to be actively maintained. The moment data is in motion (new products, changed suppliers, updated prices), new quality issues appear. The question is not whether data quality drifts, but how fast it is detected and corrected.

Classic quality dimensions are completeness (are required fields filled?), correctness (does the value match reality?), consistency (does the value fit related data?), currency (is the value still valid?), and uniqueness (no duplicates?). Each dimension needs its own measurement and correction mechanisms.

Pimcore PIM makes data quality operational. Validation rules prevent bad data from being created in the first place, a completeness score shows the per-product state against channel requirements, workflows route gaps to responsible owners, and AI-driven anomaly detection flags suspicious values. Data quality becomes an ongoing process, not a periodic cleanup project.