Data is the basis of every digital transformation — but incorrect, duplicate, or contradictory master data often leads to process inefficiencies, wrong decisions and unnecessary costs. Especially in established system landscapes with many data sources, unnoticed inconsistencies arise, which have a negative impact on sales, purchasing, logistics and controlling. Manual cleaning is time-consuming, error-prone and barely scalable.
Together with the customer, Liquam has developed an AI-based solution that automatically monitors and optimizes the quality of master data.
A practical example:
An industrial company was faced with the challenge of efficiently cleaning master data records with tens of thousands of articles, customer and supplier data that had grown over the years. The aim was to automatically identify and correct incorrect and duplicate data sets and thus create the basis for continuous digital processes and precise analyses.
In this project, Liquam developed an AI-based platform for an industrial customer that automatically checks and purifies master data. After an analysis of the existing data structures, central data sources were brought together and the test rules were defined together with the customer.
The AI analyses master data sets for duplicates, incorrect values or contradictory information. It recognizes patterns, anomalies, and recurring problems and either suggests corrections or — depending on the configuration — suggests automatic fixes.
A management dashboard visualizes data quality in real time and shows where action is needed. The solution can be flexibly extended, for example with industry-specific audit rules, connection to PIM or ERP systems, or integration into existing data governance strategies.
