Perspective Chapter: The Role of Data Quality Dimensions in Data-Driven Decision-Making

This chapter explores the role of data quality dimensions in enabling effective data-driven decision-making. After defining the concept of data quality through established frameworks such as ISO 8000 and DAMA-DMBOK2, the chapter provides a detailed analysis of seventeen key dimensions, including accuracy, completeness, timeliness, relevance, accessibility, credibility, and others. Each dimension is discussed in terms of definition, significance, common challenges, and improvement strategies. The chapter also examines how these dimensions interact, sometimes reinforcing and sometimes conflicting with one another, and emphasizes the need for a holistic approach to data quality management. Practical methods for measuring and monitoring data quality are introduced, including both objective and subjective metrics, data profiling techniques, and the use of automated tools and key performance indicators. Finally, case studies from healthcare, finance, and e-commerce illustrate the real-world consequences of poor data quality and the benefits of systematic quality improvement. By combining theoretical foundations, practical tools, and applied examples, the chapter provides a comprehensive educational resource on how data quality supports reliable and trustworthy decision-making.