Data quality (DQ) assessment and improvement in larger
information systems would often not be feasible without using suitable “DQ
methods”, which are algorithms that can be automatically executed by
computer systems to detect and/or correct problems in datasets. Currently, these
methods are already essential, and they will be of even greater importance as
the quantity of data in organisational systems grows. This paper provides a
review of existing methods for both DQ assessment and improvement and
classifies them according to the DQ problem and problem context. Six gaps
have been identified in the classification, where no current DQ methods exist,
and these show where new methods are required as a guide for future research
and DQ tool development.This is the accepted manuscript. It's currently embargoed pending publication by Inderscience
