Which of the following is a characteristic of good data quality?

Good data quality is fundamentally characterized by accuracy, completeness, and reliability. Accuracy ensures that the data is correct and precisely reflects the real-world phenomenon it is meant to represent. Completeness indicates that the data includes all necessary information required for analysis, without missing elements that could lead to misleading conclusions. Reliability refers to the consistency of the data, meaning that repeated measures or collections will yield similar results under similar conditions.

These attributes are crucial for making informed decisions based on the data. When data is accurate, complete, and reliable, it enhances the validity of analyses and resulting insights, ultimately leading to better decision-making processes in data-driven environments.

The other attributes listed in the other choices, while important in their own contexts, do not directly define the core aspects of good data quality like the selected option does. Standardization and formatting may enhance the usability of data, but they do not guarantee its inherent quality. Similarly, frequency, accessibility, and usability pertain more to data management rather than its quality. Lastly, size, shape, and distribution are more about the characteristics of data sets than about the quality of the data itself.