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Data Quality and Decision Making

  • Rosanne Price
  • Graeme Shanks
Part of the International Handbooks Information System book series (INFOSYS)

Abstract

Decision-makers often rely on data to support their decision-making processes. There is strong evidence, however, that data quality problems are widespread in practice and that reliance on data of poor or uncertain quality leads to less-effective decision-making. Addressing this issue requires first a means of understanding data quality and then techniques both for improving data quality and for improving decision-making based on data quality information. This paper presents a semiotic-based framework for understanding data quality that consists of three categories: syntactic (form), semantic (meaning) and pragmatic (use). This framework is then used as a basis for discussing data quality problems, improvement, and tags, where tags are used to provide data quality information to decisionmakers.

Keywords

Data Quality Quality Category Improve Data Quality Quality Framework Integrity Rule 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Rosanne Price
    • 1
  • Graeme Shanks
    • 1
  1. 1.Faculty of Information TechnologyMonash UniversityClaytonAustralia

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