Field Mapping – How to Map a field

Estimated reading time: 4 min
Field Mapping banner.

In Wiresk, Field Mapping is the process of connecting data fields from one app or step in your Flow to the expected fields in another step or destination app. It defines what data goes where.

Field Mapping happens when:

  • You receive data from a Trigger (like Notion, a CRM, or an email)
  • You pass that data into another Method (like Google Sheets, Slack, or Airtable)
  • You match each source field to the correct target field

This ensures that the correct values are sent to the right places.

If your Flow pulls pages from Notion and sends them to Google Sheets:

Notion FieldMapped To Google Sheets Column
Page TitleTitle
Created TimeDate Added
StatusTask Status

You choose these mappings manually in Wiresk when setting up the Method.

  • In each Method configuration screen, Wiresk displays a list of required or optional fields for the app you are sending data to.
  • You may see tooltips that describe what kind of data each target field expects.
Screenshot of Wiresk Flow builder showing the setting of the Airtable Method "Create a new record". A red  rectangle highlight the map fields.
  • Prevents data mismatches (e.g., sending text to a date field)
  • Ensures your Flow runs correctly without errors
  • Lets you transform or reformat values before inserting
  • Makes your Flow readable and maintainable

In most cases, Wiresk will create the mapping for you based on common field names. However, you can personalize your field mapping as you see fit: 

  1. Simply click on the field you want to map in your destination app. 
  1. Choose your mapping method: 

Click on the input field, here you can select preloaded data from your Trigger or your Previous Method. 

You can Map the “Input“ fields, by just following the branch of the metadata as in the table below, and select the last metadata name, for example: Data > customer > first_name (select “ first_name “if you want to map this metadata) 

Example of field mapping table:  

Metadata Description 
Data >  order_Number Order number 
Data >  processed_at Processed order date and time 
Data > Customer > first_name Customer first name 
Data > Customer > last_name Customer last name 

Example: Mapping the email received date to a Spreadsheet.

  • Click on “Input” field, a screen pops up, in the list of Input tab (dataset from the Trigger), select “Date” to Map the email received date in header 1.
screenshot of Wiresk Method setting from Google sheets called Insert New Row. its shows how to map data from Gmail Trigger, Receive new mail.

If the data is pre-defined, like a status or category, you can choose from a list of options displayed dynamically.

Need to set a specific value for the field? You can enter it directly in the manual input field by clicking on the lightning bolt icon, or couple it with a flow input (preloaded data from previous steps) by typing “@”. In the list that appeared, just select the wanted data field to map.

A screenshot of a configuration panel in Wiresk, highlighting the "Manual Input" option for the "Name" field.

Explanation of “+ Element” and “+ Map” Buttons:

This button allows you to manually add a single element to an array.

When mapping data, arrays often contain multiple elements (e.g., multiple invoices, invoice lines, or items in an order). However, if you need to add just one specific element manually instead of mapping a dynamic array, you can use the “+Element” button to do so.

This is useful when:

  • You need to insert a fixed value into an array.
  • The array should always contain at least one predefined item.
  • You are manually configuring an array without relying on dynamic data input.

This button is used when you have an array in the input data, and you need to generate one corresponding output element for each item in the array.

This allows Wiresk to dynamically iterate over the input array and create structured output data, regardless of whether the array contains one, ten, or a thousand elements.

When to use it:

  • When dealing with complex data structures, such as groups of modifiers, options, or add-ons, where each group contains multiple attributes (e.g., name, type…).
  • It allows you to map a collection or group of related fields in a structured manner.

Example: mapping option prices from an array:

  • Click on the button and select the option_prices array in the input data.
  • For every option price element in the input array, one corresponding output element is created.
  • Each field (external_key, name, price, active, default) can be mapped manually or dynamically from the array.
Screenshot of Field mapping setting.  Its shows different field mapped from Option prices array from a data source.

You can manually Map a field with an Object key-value, with this method, you can specify the order of the Object in an Array from the current step or previous steps.

Requires you to Map an array field first, refer to + Map.

1 – Input manually with the lightning bold icon (refer to the step above):

{{local.0.fieldName }}

  • The local keyword refer to the Mapped dataset within the current step.
  • The 0 represents the depth level of the locally mapped data structure.
  • The fieldName represents a specific field within the indexed array object.

Suppose you receive an object with the following structure:

firstname  
lastname  
invoices (array of invoices)  
   ├── invoice_date  
   ├── amount  
   ├── invoice_lines (array of line items)  
       ├── productid  
       ├── name  

If you map the invoices array in a step, you’ll be working with an object that contains invoice_date, amount, and another array (invoice_lines).

  • To access invoice_date within the invoice mapping, you use: local.0.invoice_date This means “invoice_date from the first level of mapped array (invoices)”.
  • Now, inside the mapping of invoice_lines, you’re at a deeper level (inside invoice_lines which is an array inside invoices).
    • To access productid inside invoice_lines, you use: local.1.productid
    • Here, local.1 refers to the second level of array mapping (invoice_lines).
Summary:
local.0 → Refers to the first level of mapped data (e.g., fields inside invoices).
local.1 → Refers to the second level of mapped data (e.g., fields inside invoice_lines).
The numbers in local.X represent the depth of the mapping in the hierarchy.

2 – You will need to set each mapped field Calculation type as a String Interpolation.

  1. Select the Ellipsis icon (…)
  2. Select String Interpolation
Screenshot of the Wiresk Flow builder. it shows the field settings when user Map a field manually, within a structured dataset. Red arrows point to the ellipsis icon and to String interpolation choice.

Enter manually via the Lightning bold icon:

{{context_data.stepx.x.fieldName}} or {{context_data.stepx.fieldName}}

where:

  • context_data → The top-level object containing workflow-related data.
  • stepx → Represents a specific step in your Flow, where x is the placeholder for the step. 0 is the first step in your Flow, 1 is the second etc…
  • x (represent a number after stepx) indicates the numerical order within the array. 0 represent the first object, 1 the second etc…
  • fieldName → The specific field within the object that needs to be mapped.

Don’t forget to set each mapped field Calculation type as a String Interpolation.


Explore our User Guide here.
For a complete tutorial on how to create a Flow, see “How to create a Flow in Wiresk
Certified by TAC Security