Glossar

Data quality describes the degree to which data is accurate, complete, consistent, up to date, and suitable for its intended purpose. High-quality data is essential for reliable processes, efficient automation, and informed decision-making.

In logistics, accurate data on dimensions, weight, and volume is particularly important for storage, packaging, freight calculation, and transport planning. Automated data capture and validation help identify incorrect or outdated information and ensure reliable master data for connected ERP, WMS, and shipping systems.

Practical example:
A logistics company automatically captures the dimensions and weight of incoming products and compares the measured values with existing master data. Deviations can be identified and corrected before they affect downstream logistics processes.

See also: Data Capture accuracy, Data Integrity, Dimension Recording, DWS, Master Data, Measurement Accuracy, Valid Data, Weight Recording