Data Excellence
Set the stage for digitalization by taking your data quality to the next level
Set the stage for digitalization by taking your data quality to the next level
Often, there is no central approach or clear responsibility for identifying, controlling, and eliminating data quality issues.
Complex business processes - often across system boundaries - make it everything but easy to identify quality issues systematically.
It's tedious and resource-intensive to keep your data up to standard constantly.
Using various interfaces to external data providers, the Data Quality Navigator can automatically validate, enrich, and standardize your master data, e.g., address or bank information, to allow fully automated processes.
Data Quality Navigator detects duplicates, misspellings, inconsistencies, formating issues, and enriches and validates your data using a portfolio of state-of-the-art data providers. Your customer data is corrected, updated, enriched and validated with minimal manual effort in your organization.
The Data Quality Navigator offers extensive checks to validate packaging data, identifying mismatches between ordering and delivery packaging and verifying packaging hierarchies and the right quantities.
The Data Quality Navigator contextualizes MES with ERP data to derive the risk of potential material shortages by systematically analyzing line supply parameters. Inappropriate settings are indicated and permanently monitored to support stable and efficient material flows.
Data Quality Navigator's comprehensive repository of validation rules covering a wide range of material-related processes identifies potential process disruptions and enables proactive countermeasures.
Integrating data from different source systems, the Data Quality Navigator supports the validation of the consistency between material demand requirements from program planning and the required supplier capacity.
Data Quality Navigator enhances master data integrity for GS1 GDSN, ensuring comprehensive compliance with the latest German regulations. Avoid the costly consequences of data errors such as supply chain delays, incorrect product listings, and potential regulatory fines.
