  In the field of machine learning, data understanding is the practice of
getting initial insights in unknown datasets. Such knowledge-intensive tasks
require a lot of documentation, which is necessary for data scientists to grasp
the meaning of the data. Usually, documentation is separate from the data in
various external documents, diagrams, spreadsheets and tools which causes
considerable look up overhead. Moreover, other supporting applications are not
able to consume and utilize such unstructured data. That is why we propose a
methodology that uses a single semantic model that interlinks data with its
documentation. Hence, data scientists are able to directly look up the
connected information about the data by simply following links. Equally, they
can browse the documentation which always refers to the data. Furthermore, the
model can be used by other approaches providing additional support, like
searching, comparing, integrating or visualizing data. To showcase our approach
we also demonstrate an early prototype.
