  Data preparation, especially data cleaning, is very important to ensure data
quality and to improve the output of automated decision systems. Since there is
no single tool that covers all steps required, a combination of tools -- namely
a data preparation pipeline -- is required. Such process comes with a number of
challenges. We outline the challenges and describe the different tasks we want
to analyze in our future research to address these. A test data generator which
we implemented to constitute the basis for our future work will also be
introduced in detail.
