Many companies are struggling with unstructured, incomplete, or erroneous data sets that have accumulated in systems over years. This inadequate data quality leads to inefficient processes, makes analyses difficult and reduces the basis for well-founded decisions. Manual cleaning is usually impracticable because data volume and complexity are too large.
Together with the customer, Liquam has developed an AI-based solution that automatically analyses, purifies and transforms unstructured data into a consistent form.
A practical example:
A manufacturing company had heterogeneous data sets from various systems and departments, which urgently needed to be structured and cleaned up in order to set up digital sales and production management. The aim was to significantly improve data quality and create the basis for automated, data-driven processes.
In this project, Liquam developed an AI-based platform for an industrial customer that automatically checks, purifies and provides unstructured data in a structured manner. After an analysis of the data sources, test rules and structuring criteria were defined together with the customer.
The AI analyses the data sets, detects inconsistencies, duplicates or missing information and — depending on the configuration — automatically makes corrections or provides targeted suggestions for action. The cleaned and structured data is provided centrally and can be integrated directly into existing ERP, CRM or BI systems.
A dashboard visualizes the progress of the cleanup, the data status, and the level of automation. The solution can be extended, e.g. with specific test rules, industry standards or connections to PIM or DMS systems.
