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Adding Calculated Columns into Your Dataset
Create calculated columns in your dataset to extract more useful information, such as a ratio or aggregation. A calculated column uses a formula to derive its value from other data (such as fields, expressions, and values).
Value of Calculated Columns
Calculated columns can provide a succinct, single representation of meaningful but more complex data relationships. For example:
- Fields that precisely describe the outcome you’re analyzing or predicting can improve pattern detection and enable more actionable insights to be found.
- Calculated columns usually results in better analysis and higher model accuracy than any single-variable transformation.
For your use case, consider ways in which you can use calculated columns to boost your analysis and models.
Types of Calculated Columns
| Type | Description |
|---|---|
| Aggregations | Examples of commonly computed aggregated fields include: mean (average), most recent, minimum, maximum, sum, multiplying two variables together, and ratios made by dividing one variable by another. |
| Ratios | Ratios can communicate more complex concepts, such as a price-to-earnings ratio, in which price or earnings alone can deliver this insight. |
| Transformations | Transformation refers to the replacement of a variable by a function. For instance, replacing a variable by its square or cube root or logarithm is a transformation. You transform variables when you want to change the scale of a variable or standardize the values of a variable for better understanding. Variable transformation can also be done using categories or bins to create variables: for example, binning continuous Lead Age into Lead Age Groups or Price into Price Categories, such as Discount, Retail, and OEM. |
Einstein Discovery converts a numeric field with 10 or fewer distinct values to a text field.
Ways to Calculate Column Values
CRM Analytics provides several approaches for preparing data.
| Approach | To Learn More |
|---|---|
| Data Prep and transformations | Clean, Transform, and Load Data with Data Prep |
| dataflows and transformations | Design Datasets with Dataflows and the Dataset Builder |
| Data Prep Classic (recipes) and calculated fields | Clean, Transform, and Load Data with Data Prep Classic |
To determine the best way to calculate values for your use case, see Why Should I Use Recipes Instead of Dataflows?
