02
Problems solved
Common Problems in Data Science
Data science is a must have – yet projects often fail because of hurdles in data quality, interpretability, scalability and alignment with business goals.
Data access & data preparation
Locating the necessary source data within SAP & combining it in a meaningful way can become a hurdle.
01
Interpretability
Black-box AI models hinder decision-making; a lack of model transparency is impacting user trust.
02
Gap between IT & business
There is a gap between rather technical insights vs. the business understanding necessitates.
03
03
Solution
Kickstart your Data Science Projects
Spend your time on the results, rather than the data preparation.
Automated Prep