Granularity of data refers to the level of detail or specificity at which data is collected, stored, or analyzed. It determines the extent to which individual data points are disaggregated or aggregated.

For example:

In data analysis, LOD expressions are used to specify the desired level of detail for a calculation or aggregation. LOD expressions allow you to perform calculations at a different level of detail than the overall dataset. This means you can aggregate data at a higher level while still including specific details or vice versa.

LOD expressions typically involve keywords like “FIXED,” “INCLUDE,” or “EXCLUDE” to define the specific dimensions or fields at different levels of detail. These expressions enable analysts to perform calculations that are not limited to the default level of aggregation in the dataset, providing more flexibility in data analysis and visualization.

FIXED

Fixes the level of detail to the specified dimension, independent to other dimensions in the view.

INCLUDE

Includes the level of detail of the specified dimension (a more granular level) in addition to other dimensions in the view.

EXCLUDE

Excludes the level of detail of the specified dimension, even if it is in the view

Syntax will be like:

 

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Author: The Data School