Data is generated in almost every digital interaction, and using it effectively requires high quality. But what exactly is data quality? The literature offers many, widely diverging answers. This article presents a systematic literature review that screened more than 8,700 records on data quality and identified 35 providing original, dimension-based definitions. Using feature-oriented domain analysis, a feature model of these heterogeneous definitions is derived and used to classify all identified publications. The classification reveals a heterogeneous landscape: despite an ISO standard and a few highly cited quasi-standards, no consensus exists on which quality dimensions to consider or how to define them. As definitions increasingly emerge for specific domains, contexts of use, and new types of data, the respective communities need to deliberately converge on how data quality is defined in their field.

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