The article by Yair Wand and Richard Y. Wang discusses the critical impact of poor data quality on organizational effectiveness, highlighting that over 60% of surveyed firms face data quality issues. The authors argue that data quality is a multidimensional concept, including dimensions such as accuracy, completeness, consistency, and timeliness, but note that there is no consensus on a universally accepted set of dimensions. They propose a system-oriented approach to defining data quality, anchored in ontological foundations, to provide guidance for system designers. The authors distinguish between the external and internal views of an information system, where the external view focuses on the use and effect of the system, while the internal view addresses its construction and operation. They define data quality in terms of the representation transformation, which maps the real-world system to the information system, and the interpretation transformation, which infers the real-world state from the information system. The article identifies four intrinsic data quality dimensions: completeness, ambiguity, meaningfulness, and correctness. These dimensions are derived from potential deficiencies in the representation mapping, such as incomplete, ambiguous, and meaningless states. The authors also analyze common data quality dimensions like accuracy, reliability, timeliness, and consistency, showing how they relate to their proposed system-oriented dimensions. Finally, the article suggests that these intrinsic dimensions can be used to guide data quality improvements and to develop data quality audit guidelines and metrics. The authors conclude by emphasizing the value of a rigorously defined set of data quality dimensions in supporting research and practice in the field.The article by Yair Wand and Richard Y. Wang discusses the critical impact of poor data quality on organizational effectiveness, highlighting that over 60% of surveyed firms face data quality issues. The authors argue that data quality is a multidimensional concept, including dimensions such as accuracy, completeness, consistency, and timeliness, but note that there is no consensus on a universally accepted set of dimensions. They propose a system-oriented approach to defining data quality, anchored in ontological foundations, to provide guidance for system designers. The authors distinguish between the external and internal views of an information system, where the external view focuses on the use and effect of the system, while the internal view addresses its construction and operation. They define data quality in terms of the representation transformation, which maps the real-world system to the information system, and the interpretation transformation, which infers the real-world state from the information system. The article identifies four intrinsic data quality dimensions: completeness, ambiguity, meaningfulness, and correctness. These dimensions are derived from potential deficiencies in the representation mapping, such as incomplete, ambiguous, and meaningless states. The authors also analyze common data quality dimensions like accuracy, reliability, timeliness, and consistency, showing how they relate to their proposed system-oriented dimensions. Finally, the article suggests that these intrinsic dimensions can be used to guide data quality improvements and to develop data quality audit guidelines and metrics. The authors conclude by emphasizing the value of a rigorously defined set of data quality dimensions in supporting research and practice in the field.