Abstract
The fact that Data Quality (DQ) depends on the context, in which data are produced, stored and used, is widely recognized in the research community. Data Warehouse Systems (DWS), whose main goal is to give support to decision making based on data, have had a huge growth in the last years, in research and industry. DQ in this kind of systems becomes essential. This work presents a proposal for identifying DQ problems in the domain of DWS, considering the different contexts that exist in each system component. This proposal may act as a first conceptual framework that guides the DQ-responsible in the management of DQ in DWS. The main contributions of this work are a thorough literature review about how contexts are used for evaluating DQ in DWS, and a proposal for assessing DQ in DWS through context-based DQ metrics.
Highlights
Data Quality (DQ) is a wide research area, which includes different aspects, issues and challenges
We present other results that are considered of interest to the general problem of the evaluation of DQ taking into account the context in Data Warehouse Systems (DWS)
This work proposes to assess DQ in DWS taking the context into account
Summary
Data Quality (DQ) is a wide research area, which includes different aspects, issues and challenges It is extremely relevant for industry due to its impact in information systems of all application domains. In [1] the authors present an analysis of the different definitions of DQ that were proposed since the 90’s, and they remark that a definition including the main characteristics considered by most of the existing proposals is needed. They conclude that DQ refers to a set of quality dimensions, which in general are defined as quality properties or characteristics, and which are grouped in four categories: accuracy, currency, completeness and consistency. The authors consider that is difficult to find a relevant definition that satisfies all disciplines and they mention that there are still few ideas about the relevant properties that should be considered when modeling context
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