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Data quality (DQ) has been studied in significant depth over the last two decades and has received attention from both the academic and the practitioner community. Over that period of time a large number of data quality dimensions have been identified in due course of research and practice. While it is important to embrace the diversity of views of data quality, it is equally important for the data quality research and practitioner community to be united in the consistent interpretation of this foundational concept. In this paper, we provide a step towards this consistent interpretation. Through a systematic review of research and practitioner literature, we identify previously published data quality dimensions and embark on the analysis and consolidation of the overlapping and inconsistent definitions. We stipulate that the shared understanding facilitated by this consolidation is a necessary prelude to generic and declarative forms of requirements modeling for data quality.