This research paper focuses on the data problems that are addressed by data quality tools. Specific questions of the data can elicit information that will determine which features of data quality tools are appropriate in which circumstances. The primary objective of the effort is to develop a tool to support the identification of data quality issues and the selection of tools for addressing tho...

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Overall, the quality of recent cancer registration data in Scotland appears to be high. It is evident from reviewing the literature in this field that relatively few other cancer registries undertake specific studies of data quality and publish their results. However, where such studies are available, the quality of Scottish cancer registration data does seem to compare favourably to that repor...

The ultimate goal of data quality management (DQM) is to improve the data quality (DQ) to facilitate enterprises decision-making, and the data quality assessment (DQA) is an important aspect in the process of DQM. Existing research in DQA focuses on the establishment of evaluation indicators and quantified methods in specific areas of application, but does not take into account the evolution of...

Abstract Implementing a Total Data Quality Management (TDQM) program is not a trivial undertaking. Two key steps are (1) to clearly define what an organization means by data quality and (2) to develop metrics that measure data quality dimensions and that are linked to the organization’s goals and objectives. This paper presents the case of Information Resources, Inc., which exemplifies how a co...

REQUIRED) In recent years, several process models for data quality management have been proposed. As data quality problems are highly application-specific, these models have to remain abstract. This leaves the question of what to do exactly in a given situation unanswered. The task of implementing a data quality process is usually delegated to data quality experts. To do so, they rely heavily o...