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# Data Transform

> Reshapes and renames values into clean, named variables for later steps.

The Data Transform node builds a tidy set of named values that later steps can
read. You give each value a name and tell it where to come from, whether that is
a field from an earlier step, a combination of several fields, or a fixed value.
It is the go-to node for reshaping and relabeling data as it moves through a
workflow.

## When to use it

* You want to pull a few fields out of a larger result and give them clear,
  simple names.
* You want to combine values, such as joining a first and last name into a full
  name.
* You want a clean snapshot of key values in the run history so it is easy to
  see what the workflow was working with.

## Inputs

| Field             | What it's for                                                                         | Example                                                |
| ----------------- | ------------------------------------------------------------------------------------- | ------------------------------------------------------ |
| Variable mappings | A set of named values. Each has a name and an expression that says what the value is. | `customer` comes from the start data's `customer_name` |

Each value can be a fixed entry you type in, a reference to an earlier step, or
a template that stitches several values together. See
[expressions](/platform/workflows/core-concepts/expressions/using-expressions-in-nodes) for how to
reference earlier steps.

## Outputs

| Field        | What you get back                                                                           |
| ------------ | ------------------------------------------------------------------------------------------- |
| Named values | Each name you defined becomes available to later steps, holding the value you mapped to it. |

## Example

An underwriting workflow has gathered an applicant's details and a decision
across several earlier steps. A Data Transform node pulls the important pieces
into one clean record for Marcus Johnson: `customer` from the start data,
`decision` from the earlier [Check](/platform/workflows/node-reference/logic-and-flow/check)
step, and a fixed `reviewed_at` label. Later steps and the run history now read
these three tidy values instead of digging through raw step output.

![The Data Transform node configuration panel, showing named variable mappings.](/_fern-img/91515dc816815192d02481846364644f57dee102754d96d5f1cda63b0c7f3a52.webp)

> **Note**
>
> Names you choose here become how later steps refer to the values, so keep them
> short and descriptive. This node does not change any earlier step's output, it
> just creates a fresh, well-named copy to work from.

## Related nodes

#### [Map](/platform/workflows/node-reference/logic-and-flow/map)

Reshape every item in a list, rather than a single set of values.

#### [Check](/platform/workflows/node-reference/logic-and-flow/check)

Turn several conditions into one labeled outcome.

> Documentation for Gail, the AI platform for financial services. Learn how to set up GailGPT and Gail Agent to automate customer communications for insurance, banking, and finance.