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.
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03
Solution
Kickstart your Data Science Projects
Spend your time on the results, rather than the data preparation.