What kind of data does the compute transformation work with?

The compute transformation is specifically designed to work with numerical data that can be derived from referencing or manipulating other fields within the dataset. This allows users to perform calculations, create new numerical variables, and apply mathematical operations based on existing field values.

In practice, compute transformations are integral in analytics to derive insights from data. For instance, if you have a dataset that includes sales data and you want to calculate the total revenue generated from multiple product fields, the compute transformation can access these numerical fields to perform the necessary calculations.

This specificity of the compute transformation to numerical data is what distinguishes it from other categories, such as categorical data, which cannot be meaningfully aggregated in the same way. Moreover, unstructured data such as text or images doesn’t lend itself readily to numerical computation without significant preprocessing, which is outside the scope of what the compute transformation directly handles. Thus, by focusing on numerical data derived from referencing other fields, the compute transformation effectively enables users to unlock new analytical possibilities.