Normalization transforms tightly clustered data points or reels in skewed points, helping to create clearer visualizations and allow you to read data quickly.

For example, if you have a few outliers in your data, the significant trends in the bulk of the data might be obscured before applying a normalization. This is very common with data quality errors. Normalization is also very helpful when you have data that is expected to be heavily skewed, and it allows you to obtain insights from data that would otherwise be difficult to interpret.

Using Normalization

Applying Normalization can be done in 3 ways:

• Option 1: Right-click on the plot, then select Axes Normalization. This will also apply the same method to all three axes at once.
  

  Note: If a feature does not meet Normalization specifications, the Normalization will not apply to that feature but will still apply to other features that do meet the specifications.

• Option 2: Click on the desired Dimension Icon in the Mapping Panel. Select a Normalization from the Norm drop down options, which will only apply the selected method to selected feature.
• Option 3: Create a new normalized feature by right-clicking a numerical feature and selecting an option from the Normalize section.

Note: Options 1 and 2 are visual normalizations only (data values are not being changed). Option 3 creates a new column in the dataset with normalized values.

Additionally, if a feature has been applied to the Color dimension in the Mapping Panel, you can apply Normalization to the gradient to more easily see the separation between points.

• Related Article: Setting Key Parameters in the Mapping Panel

Working with Normalization

There are four types of normalization techniques that you can use to improve visualization of the data: