Data quality is crucial for the effectiveness of an organization. Many organizations face challenges due to poor data quality, which can lead to failed business process reengineering initiatives. Data quality is a multidimensional concept, with dimensions such as accuracy, completeness, consistency, and timeliness. However, there is no general agreement on these dimensions. The article proposes an ontological approach to define data quality, anchoring it in the real-world system's representation. This approach considers the real-world system's state and how it is represented in the information system. The article identifies four intrinsic data quality dimensions: completeness, unambiguity, meaningfulness, and correctness. These dimensions are derived from the analysis of representation mapping between the real-world system and the information system. The article also discusses the implications of these dimensions for information systems design, emphasizing the need for rigorous definitions and guidelines to ensure data quality. The study highlights the importance of understanding data quality in the context of system design and the need for a common set of terms to support the development of a cumulative body of work in data quality research.Data quality is crucial for the effectiveness of an organization. Many organizations face challenges due to poor data quality, which can lead to failed business process reengineering initiatives. Data quality is a multidimensional concept, with dimensions such as accuracy, completeness, consistency, and timeliness. However, there is no general agreement on these dimensions. The article proposes an ontological approach to define data quality, anchoring it in the real-world system's representation. This approach considers the real-world system's state and how it is represented in the information system. The article identifies four intrinsic data quality dimensions: completeness, unambiguity, meaningfulness, and correctness. These dimensions are derived from the analysis of representation mapping between the real-world system and the information system. The article also discusses the implications of these dimensions for information systems design, emphasizing the need for rigorous definitions and guidelines to ensure data quality. The study highlights the importance of understanding data quality in the context of system design and the need for a common set of terms to support the development of a cumulative body of work in data quality research.