Master data acts as the cornerstone for a multitude of business processes. Its accuracy has far-reaching implications, from the glaringly obvious – such as merchandise being blocked at customs – to the less visible, like recurring stock-outs caused by an underestimated distribution lead-time in Distribution Requirements Planning (DRP). Erroneous master data can disrupt operations, degrade performance, and affect a company’s bottom line.
However, relying solely on the implementation of procedures and best practices for managing master data is not sufficient: the reality of any complex system is that anomalies will eventually appear. A more proactive approach is required: establishing a robust measure of master data quality and creating an effective feedback loop. This goes beyond wishful thinking and into actionable territory where master data quality is continuously monitored and improved.
