What best describes the term "granularity" in the context of data quality?

Granularity refers to the level of detail or specificity within data. In the context of data quality, having the right level of granularity is crucial because it affects how well data can be utilized for analysis, decision-making, and reporting. For instance, a dataset that records patient information at a granular level, such as individual symptoms or medication dosages, enables more precise analysis and insights compared to aggregated data that might summarize the information.

When considering data quality, granularity ensures that the data captured is sufficiently detailed to meet the needs of users and applications without overwhelming them with excessive information that could lead to complexity and misunderstanding. Higher granularity allows for more nuanced analyses and can highlight patterns and trends that may be missed in less detailed datasets.