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This section describes techniques to normalize numeric values in your datasets. Ideally, your source systems are configured to capture and deliver data using a consistent set of units in a standardized structure and format. In practice, data from multiple systems can illuminate differences in the level of precision used in numeric data or differences in text entries that reference the same thing. Within

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, you can use the following techniques to address some of the issues you might encounter in the standardization of units and values for numeric types.

## Numeric precision

In

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, mathematical computations are performed using 64-bit floating point operations to 15 decimals of precision. However, due to rounding off, truncation, and other technical factors, small discrepancies in outputs can be expected. Example:

 -636074 -2.46509e+06

Suppose you apply the following transformation:

D trans
RawWrangle true MySum step derive type: single value: (-636074.22 + -2465086.34) as: 'MySum' Formula type Single row formula Formula (-636074.22 + -2465086.34) New column name New formula

The expected output in the MySum column: -3101160.56

The actual output for in the MySum column: -3101160.5599999996

Info

NOTE: For 64-bit floating point mathematical operations, deviations like the above are intrinsic to the Decimal data type and how the platform performs computations.

Depending on your precision requirements, you can manage precision across your columns using a transformation like the following, which rounds off MySum to three digits:

D trans
RawWrangle true step set col: WM_Week value: round(\$col, 3) Columns MySum Formula ROUND(\$col,3) Edit column with formula

For more information on floating point computations, see https://en.wikipedia.org/wiki/Numeric_precision_in_Microsoft_Excel.

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