The article discusses the increasing prevalence of data quality (DQ) problems, particularly in organizational databases, and their significant social and economic impacts. It argues that DQ issues should be understood within the broader context of information systems (IS), where data is collected from multiple sources and used for decision-making. The authors propose a more comprehensive conceptualization of DQ that includes the context in which data is produced and used, rather than treating DQ as an intrinsic concept independent of this context. The research examines DQ projects from three leading-edge organizations—GoldenAir, BetterCare, and HyCare—to identify common patterns of DQ problems. These patterns are categorized into intrinsic, accessibility, and contextual dimensions. Intrinsic DQ problems arise from mismatches among data sources, leading to reduced data credibility and reputation. Accessibility DQ problems include technical, representational, and volume issues that hinder data manipulation and analysis. Contextual DQ problems involve data that is incomplete, inadequately defined, or not appropriately aggregated, failing to meet the evolving needs of data consumers. The authors recommend that IS professionals address DQ problems by considering the broader concerns of data consumers, including the ease of access and understanding, and the relevance of data to specific tasks. They suggest that solutions should involve both technical improvements and process-oriented techniques, such as IS auditing, to ensure data quality across the entire data lifecycle. The findings provide a basis for building new theories about organizational DQ problems and their solutions, emphasizing the importance of a holistic approach that considers the task context and evolving data requirements of users.The article discusses the increasing prevalence of data quality (DQ) problems, particularly in organizational databases, and their significant social and economic impacts. It argues that DQ issues should be understood within the broader context of information systems (IS), where data is collected from multiple sources and used for decision-making. The authors propose a more comprehensive conceptualization of DQ that includes the context in which data is produced and used, rather than treating DQ as an intrinsic concept independent of this context. The research examines DQ projects from three leading-edge organizations—GoldenAir, BetterCare, and HyCare—to identify common patterns of DQ problems. These patterns are categorized into intrinsic, accessibility, and contextual dimensions. Intrinsic DQ problems arise from mismatches among data sources, leading to reduced data credibility and reputation. Accessibility DQ problems include technical, representational, and volume issues that hinder data manipulation and analysis. Contextual DQ problems involve data that is incomplete, inadequately defined, or not appropriately aggregated, failing to meet the evolving needs of data consumers. The authors recommend that IS professionals address DQ problems by considering the broader concerns of data consumers, including the ease of access and understanding, and the relevance of data to specific tasks. They suggest that solutions should involve both technical improvements and process-oriented techniques, such as IS auditing, to ensure data quality across the entire data lifecycle. The findings provide a basis for building new theories about organizational DQ problems and their solutions, emphasizing the importance of a holistic approach that considers the task context and evolving data requirements of users.