Forget Dimensions: Define Your Information Quality Using Quality View Patterns

Suzanne M. Embury*, Paolo Missier

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

When creating software components that aim to alleviate information quality problems, it is necessary to elicit the requirements that the problem holders have, as well as the details of the existing technical infrastructure that will form the basis of the solution. In the literature, standard sets of IQ dimensions have been proposed as a means of initiating and structuring the information gathering and design processes involved. Over the past decade, we have been involved in several projects to develop IQ assessment components. In the earlier projects, we tried hard to make use of the standard IQ dimensions in this way, but found that we derived little benefit from this approach. In some cases, the IQ problem we were focussed on could not be assigned cleanly to one dimension or another. In others, the dimension was clear, but we found that that knowledge saved us very little of the work we had to do when the dimension was not identified up front. However, IQ problems are typically very challenging, and some sort of guiding principles are needed. In this paper, we propose our earlier notion of the Quality View (QV) as an alternative (or additional) technique to IQ dimensions for developing IQ management components. We reflect on our experiences in using QVs in three quite different IQ-related projects, and show how our initial basic pattern turned out to be a good starting point for the information gathering and design tasks involved, replacing IQ dimensions in the role originally envisaged for them.

Original languageEnglish
Title of host publicationSynthese Library
PublisherSpringer Science and Business Media B.V.
Pages25-41
Number of pages17
DOIs
Publication statusPublished - 2014

Publication series

NameSynthese Library
Volume358
ISSN (Print)0166-6991
ISSN (Electronic)2542-8292

Bibliographical note

Funding Information:
The work reported in this paper on Quality Views was supported by a grant from the EPSRC. The opinions of the authors have been greatly improved by discussions with their colleagues on the Qurator project team, in the Information Management Group at Manchester and the Informatics research group at the University of Cardiff.

Funding Information:
Acknowledgements The work reported in this paper on Quality Views was supported by a grant from the EPSRC. The opinions of the authors have been greatly improved by discussions with their colleagues on the Qurator project team, in the Information Management Group at Manchester and the Informatics research group at the University of Cardiff.

Publisher Copyright:
© 2014, Springer International Publishing Switzerland.

Keywords

  • Address Data
  • Data Duplication
  • Information Quality
  • Information Quality Dimension
  • Quality View

ASJC Scopus subject areas

  • Language and Linguistics
  • History
  • Logic
  • History and Philosophy of Science

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