Data quality is a crucial yet often vaguely defined concept that extends beyond common descriptors like timeliness, accuracy, and completeness, requiring a more nuanced understanding tailored to specific business needs. This exploration introduces a framework for evaluating data quality through eight dimensions: accuracy, completeness, consistency, reliability, timeliness, uniqueness, usefulness, and differences. These dimensions highlight how different problems necessitate varying levels of data quality, emphasizing that the data must be not only accurate and complete but also relevant and timely for effective decision-making. Issues in data accuracy can arise from errors in data collection or transformation, making it vital to continually assess accuracy through confidence levels, while completeness involves ensuring that all necessary data points are present to address specific business questions or problems. By focusing on these dimensions, businesses can better articulate their data quality requirements, ensuring they gather just the necessary information to solve their challenges without unnecessary data accumulation.