Towards a practical "state reconstruction" for data quality methodologies: A customized list of dimensions

Abstract

Data quality (DQ) has been defined as "fitness for use" of the data (also called Information Quality). A single aspect of data quality is defined as a "dimension" such as "consistency", "accuracy", "completeness", or "timeliness". In order to assess and improve data quality, "methodologies" have been defined. Data quality methodologies are a set of guidelines and techniques that are designed for assessing, and perhaps, improving data quality in a given application or organization. Most data quality methodologies use a pre-defined list of dimensions to assess the quality of data. This pre-defined list is usually based on previous research and may not be related to the specific application at hand. As a prelude (or state reconstruction phase) for methodologies, a useful list of dimensions specific to the current application or organization must be collected. In this paper we propose a state reconstruction phase in order to achieve that. © 2012 Springer-Verlag GmbH.