Granular data

What is granular data?
Granular data is detailed data or the lowest level that data can be in a target set. It refers to the size into which data fields are divided, in short, how detailed a single field is. A good example of data granularity is how a name field is broken down when it is contained in a single field or broken into its constituent parts such as first name, middle name, and last name. As the data becomes more subdivided and specific, it is also considered in more detail.

Granular data, as the name suggests, is data that is as small as possible to be more precise and detailed. The advantage of granular data is that it can be shaped in any way the data scientist or analyst needs, just like grains of sand that conform to their container. Granular data can be aggregated and disaggregated to meet the needs of different situations.

Unless data is granulated, such as a name or address field, stored as a whole, it is very difficult for analysts to analyze and analyze data because it comes in large numbers. Granular data can easily be merged with data from external sources and effectively integrated and managed.

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