  Data is of high quality if it is fit for its intended use. The quality of
data is influenced by the underlying data model and its quality. One major
quality problem is the heterogeneity of data as quality aspects such as
understandability and interoperability are impaired. This heterogeneity may be
caused by quality problems in the data model. Data heterogeneity can occur in
particular when the information given is not structured enough and just
captured in data values, often due to missing or non-suitable structure in the
underlying data model. We propose a bottom-up approach to detecting quality
problems in data models that manifest in heterogeneous data values. It supports
an explorative analysis of the existing data and can be configured by domain
experts according to their domain knowledge. All values of a selected data
field are clustered by syntactic similarity. Thereby an overview of the data
values' diversity in syntax is provided. It shall help domain experts to
understand how the data model is used in practice and to derive potential
quality problems of the data model. We outline a proof-of-concept
implementation and evaluate our approach using cultural heritage data.
