Providing Support for Automated Product Data Quality Assurance: A Case Study

Abstract

Data quality is of utmost importance in large product databases. This is especially true for food products, since potentially health-critical data is contained. With growing database size, manual quality assurance becomes infeasible. GS1 Sync, governed by GS1 Austria, is rapidly becoming the largest national food product database. In order to support manual quality assurance, we have conceptualized a process to conduct product data quality assurance in an automatic way, based on defining rules and classifying product data. In order to evaluate our approach, we have implemented a prototype and performed a proof-of-concept. Although our research is still a work-in-progress, we were able to show that our approach is able to find a substantial number of issues that did not appear during manual control.