The term refers to data that has been broken down into detailed subcategories or subgroups. Rather than presenting information as a single aggregate number, it is separated into its constituent parts. This separation allows for the examination of underlying patterns and variations that would otherwise be masked. For instance, instead of presenting overall sales figures, one might present sales figures categorized by region, customer demographic, or product type.
The utility of granular information lies in its ability to reveal disparities and nuances within a larger dataset. This facilitates more targeted analysis, informed decision-making, and effective interventions. Historically, limitations in data collection and processing often necessitated the use of aggregated figures. However, advancements in technology have made the capture, storage, and analysis of detailed information more feasible and cost-effective, leading to increased emphasis on this type of examination.
