Title: A classification of data quality assessment and improvement methods
Authors: Philip Woodall; Martin Oberhofer; Alexander Borek
Addresses: Department of Engineering, Institute for Manufacturing, University of Cambridge, 17 Charles Babbage Road, Cambridge, UK ' IBM Germany Research and Development, Schoenaicherstrasse 220, 71032, Boeblingen, Germany ' IBM Global Business Services, Hollerithstraße 1, 81829 Munich, Germany
Abstract: 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.
Keywords: information quality; data quality assessment; data quality improvement; software tools; automated data quality software; information systems.
International Journal of Information Quality, 2014 Vol.3 No.4, pp.298 - 321
Received: 24 May 2013
Accepted: 11 Feb 2014
Published online: 17 Apr 2015 *