

Inconsistent master data maintenance, missing mandatory fields, duplicates and contradictory structures create high manual review effort and error-prone downstream processes. As a result, planning, reporting, ERP-related workflows and the reliability of operational decisions all suffer.
Artificial intelligence automates the validation of master data, transactional data and structured files for completeness, plausibility, consistency and duplicates. In master data management and data quality management, an AI system detects anomalies early, prioritises correction needs and supports data cleansing with concrete guidance for master data maintenance. This makes it possible to improve data quality measurably before faulty information flows into planning, reporting or operational processes.
Process cost reduction is strongest where recurring review and cleansing steps in data quality management are still handled manually. Artificial intelligence takes over duplicate detection, plausibility checks and the pre-prioritisation of anomalies, reducing the cost per record and cutting coordination effort across master data management, reporting and ERP-related processes.




















Zukunft beginnt, wenn menschliche Intelligenz künstliche Intelligenz entwickelt. Der erste Schritt ist nur ein Klick.
Zukunft beginnt, wenn menschliche Intelligenz künstliche Intelligenz entwickelt. Der erste Schritt ist nur ein Klick.