Data quality (DQ) assessment can be significantly enhanced with the use of the right DQ assessment
methods, which provide automated solutions to assess DQ. The range of DQ assessment methods is very broad:
from data profiling and semantic profiling to data matching and data validation. This paper gives an overview of
current methods for DQ assessment and classifies the DQ assessment methods into an existing taxonomy of DQ
problems. Specific examples of the placement of each DQ method in the taxonomy are provided and illustrate why
the method is relevant to the particular taxonomy position. The gaps in the taxonomy, where no current DQ methods
exist, show where new methods are required and can guide future research and DQ tool development.