Data quality management and governance are issues of growing importance to the academic and professional communities. Today there is great concern for the quality of corporate data, as data of poor quality means inaccurate information, which in turn means wasting of resources and harming the organizations, in particular the regulatory compliance and the relationships with their customers. To get an idea of poor data quality costs, The Data Warehousing Institute [1] estimated that current data quality problems cost U.S. businesses more than USD 600 billion a year. Furthermore, literature reveals multiple disasters whose causes are, among others, data quality problems, such as the explosion of the space shuttle Challenger and the shooting down of an Iranian Airbus by the USS Vincennes [2]. This work presents the exploratory phase of a research project whose main objective is to define a meta-framework made of organizational, methodological, technical and social "components", along with contingency factors, capable of being used by organizations in creating their own data quality frameworks.
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