Digital processes are scaling, and master data cleansing should too.
Natural language processing and machine learning make it possible to scale master data cleansing.
Step: 1
How good is your master data?
The first step in any cleansing process is an awareness of data quality. The quality analysis creates transparency in no time at all.
Step: 2
Get the most out of your data!
A lot of information ends up in designation texts: stresses, materials, dimensions ... get this data back and clear the way for seamless information utilisation in your company!
Step: 3
Harmonise your data world!
Designations are as varied as the names people give them. Finding parts again becomes a project, reuse decreases. Use AI to get back to standardised terms for parts and find what you are looking for.
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These include essential cookies that are necessary for the operation of the site, as well as others that are used only for anonymous statistical purposes, for comfort settings or to display personalized content. You can decide for yourself which categories you want to allow. Please note that based on your settings, not all functions of the website may be available.