Granularity is the term in which CDI is separated into single risk packets, so that value may be "risk encapsulated" and incrementally attached to the packet data.
It is the "extent to which CDI is measured and subdivided into singular transmission(s) or data packets that give CloudCover unique qualification to insure information on the move.
The term granularity is relative, when used to compare data risk, data valuation, data classification and the description of the Internet transmission protocol.
The terms “fine” and “coarse” are used consistently across fields, but the term “granularity” itself is not. For example, in data risk "more granularity" refers to more risk segmentation of smaller size (data) payloads, while data valuation that is "more granular" has fewer and larger numbers of data value or "dava".
The granularity of data refers to the re-fineness or subdivided data fields.
For example, a postal address can be recorded, with low granularity, as a single field:
1. address = 100 Main Street, Anytown, MN 55000 USA
or with high granularity, as multiple fields:
2. street address = 100 Main Street
3. city = Anytown
4. postal code = MN 55000
5. country = USA
In data risk modeling, granularity refers to the number of exposures identified in the IP data transmission. The higher the granularity, the more positions are in a data transmission - time period, providing higher degree of risk diversification, which in turn reduce concentration risk. This is colloquially known as “not putting all your eggs in one basket”.
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