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Granular data is detailed data, or the lowest level that data can be in a target set. It refers to the size that data fields are divided into, in short how detail-oriented a single field is. A good example of data granularity is how a name field is subdivided, if it is contained in a single field or subdivided into its constituents such as first name, middle name and last name. As the data becomes more subdivided and specific, it is also considered more granular.
Granular data, as the name suggests, is data that is in pieces, as small as possible, in order to be more defined and detailed. The advantage of granular data is that it can be molded in any way that the data scientist or analyst requires, just like granules of sand that conform to their container. Granular data can be aggregated and disaggregated to meet the needs of different situations.
If data is not granulated, such as a name or address field being saved as a whole, then it is very difficult for analysts to mine and analyze data because they are in large chunks. Granular data can be easily merged with data from external sources and can be effectively integrated and managed.