---
schema_version: "1"
id: 035134938d19bcda01eea58fe081f37b32c3bdf827054a4f4a17689d4e9da968
title: Adding Calculated Columns into Your Dataset
collection: admin
version: current
locale: en-us
portal_url: https://docs.salesforce.com/admin/current/en-us/analytics/264-0-0/bi_edd_prep_fields.md
canonical_url: https://help.salesforce.com/s/articleView?id=analytics.bi_edd_prep_fields.htm&release=264.0.0&type=5
source_site: help.salesforce.com
content_type: documentation
content_hash: f64bbc3cd1d3d81c00fae0245f7893e5baec8248744bef0d07d153f219b2ccf6
description: 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).
products:
  - CRM Analytics
  - Cross Cloud
release: "264"
---
# 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. |

![Note](https://sf-zdocs-cdn-prod.zoominsoftware.com/tdta-analytics-bi-264-0-0-production-enus/ec791473-f5b4-4f64-a656-9104f455d966/images/icon_note.png)

Note Einstein Discovery doesn’t support fields that contain multiple values, such as a list or array. To learn more, see this KB article: [Limitations of MultiValue Fields in CRM Analytics](https://help.salesforce.com/articleView?id=000319818&language=en_US).

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](https://help.salesforce.com/s/articleView?id=analytics.bi_integrate_data_prep_overview.htm&release=264.0.0&type=5 "Data Prep provides an intuitive, visual interface in CRM Analytics that allows you to easily point-and-click your way to build recipes that prepare data and load it into a target. Use the graph of a recipe to see at a glance where the data comes from and how it flows through the recipe to the target. To validate the recipe as you build, preview how raw data is transformed at every step of the way.") |
| dataflows and transformations | [Design Datasets with Dataflows and the Dataset Builder](https://help.salesforce.com/s/articleView?id=analytics.bi_integrate_design_datasets_with_dataflows_and_builder.htm&release=264.0.0&type=5 "Use a dataflow to create one or more datasets based on source data from existing datasets or synced data. A dataflow is a set of instructions that specifies what input data to include, how to transform that data, and which datasets to load the transformed data into.") |
| Data Prep Classic (recipes) and calculated fields | [Clean, Transform, and Load Data with Data Prep Classic](https://help.salesforce.com/s/articleView?id=analytics.bi_integrate_data_prep_create_recipe.htm&release=264.0.0&type=5 "Use a recipe to clean and combine data from multiple datasets or connected objects. Add bucket and formula fields, filter rows, transform field values, convert field types, and standardize date formats. You can then output the results to a new target dataset.") |

To determine the best way to calculate values for your use case, see [Why Should I Use Recipes Instead of Dataflows?](https://help.salesforce.com/s/articleView?id=analytics.bi_integrate_prepare.htm&release=264.0.0&type=5 "Recipes and dataflows both prepare data. Compared to dataflows, recipes are newer and are recommended for functionality and ease of use. Recipes allow you to preview the data as you transform it, while dataflows only show your node schema. For example, recipes have more join types and transformations with built-in machine learning - such as Predict Missing Values and Detect Sentiment - that aren’t available in dataflows. Recipes can also aggregate data to a higher level.")

#### See Also

- [Collect, Cleanse, and Optimize Your Data](https://help.salesforce.com/s/articleView?id=analytics.bi_edd_prep_cleanse.htm&release=264.0.0&type=5 "All data prep efforts are oriented toward providing the best data you can for the business outcome you want to examine or predict. CRM Analytics provides the tools you need to consolidate, clean, and prepare your data for analysis. This topic describes approaches to consider when preparing data for your solution.")