  High-quality data is key to interpretable and trustworthy data analytics and
the basis for meaningful data-driven decisions. In practical scenarios, data
quality is typically associated with data preprocessing, profiling, and
cleansing for subsequent tasks like data integration or data analytics.
However, from a scientific perspective, a lot of research has been published
about the measurement (i.e., the detection) of data quality issues and
different generally applicable data quality dimensions and metrics have been
discussed. In this work, we close the gap between research into data quality
measurement and practical implementations by investigating the functional scope
of current data quality tools. With a systematic search, we identified 667
software tools dedicated to "data quality", from which we evaluated 13 tools
with respect to three functionality areas: (1) data profiling, (2) data quality
measurement in terms of metrics, and (3) continuous data quality monitoring. We
selected the evaluated tools with regard to pre-defined exclusion criteria to
ensure that they are domain-independent, provide the investigated functions,
and are evaluable freely or as trial. This survey aims at a comprehensive
overview on state-of-the-art data quality tools and reveals potential for their
functional enhancement. Additionally, the results allow a critical discussion
on concepts, which are widely accepted in research, but hardly implemented in
any tool observed, for example, generally applicable data quality metrics.
