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Data is of high quality if it is fit for its intended use. The quality ofdata is influenced by the underlying data model and its quality. One majorquality problem is the heterogeneity of data as quality aspects such asunderstandability and interoperability are impaired. This heterogeneity may becaused by quality problems in the data model. Data heterogeneity can occur inparticular when the information given is not structured enough and justcaptured in data values, often due to missing or non-suitable structure in theunderlying data model. We propose a bottom-up approach to detecting qualityproblems in data models that manifest in heterogeneous data values. It supportsan explorative analysis of the existing data and can be configured by domainexperts according to their domain knowledge. All values of a selected datafield are clustered by syntactic similarity. Thereby an overview of the datavalues' diversity in syntax is provided. It shall help domain experts tounderstand how the data model is used in practice and to derive potentialquality problems of the data model. We outline a proof-of-conceptimplementation and evaluate our approach using cultural heritage data.

Detecting Quality Problems in Data Models by Clustering Heterogeneous Data Values