N8n automation has been eye-opening to me and one of the most fundamental concepts I’ve had to grasp is understanding data types. If you’re new to workflow automation, knowing your data types will save you hours of troubleshooting and make your automations much more powerful.
Why Data Types Matter in N8n
In N8n, data flows from one node to another, transforming and moving between different applications. Understanding data types is crucial because:
- Different nodes expect specific data types as input
- API calls require properly formatted data
- Incorrect data types cause workflow errors
- Data transformations depend on knowing what you’re working with
Think of data types as the “language” your automation speaks. Just like you wouldn’t use numbers when someone asks for your name, your workflow needs the right type of data for each operation.
The Main Data Types in N8n
1. String (Text)
Strings are sequences of characters wrapped in quotes. They’re the most common data type you’ll encounter.
Examples:
"Hello, World!""nancy@email.com""2025-10-17"(yes, even dates can be strings!)
Common use cases:
- Names, email addresses, descriptions
- Status labels like “Completed” or “In Progress”
- Any text content from forms or databases
N8n tip: In expressions, you can concatenate strings using the plus operator: {{$json.firstName + " " + $json.lastName}}
2. Number
Numbers represent numeric values and come in two forms: integers (whole numbers) and decimals (floating-point numbers).
Examples:
42(integer)3.14(decimal)-100(negative number)
Common use cases:
- Prices, quantities, counts
- IDs and reference numbers
- Mathematical calculations
- Age, duration, measurements
N8n tip: Numbers don’t need quotes. If you put quotes around them, they become strings!
3. Boolean
Booleans represent true or false values. They’re essential for conditional logic in your workflows.
Examples:
truefalse
Common use cases:
- Status flags (isActive, isCompleted, hasAccess)
- Conditional routing in IF nodes
- Feature toggles
- Checkbox values
N8n tip: Use booleans in IF nodes to create branches in your workflow based on conditions.
4. Array (List)
Arrays are ordered collections of values. Think of them as lists where each item has a position (index starting at 0).
Examples:
["Marketing", "Sales", "Development"]
[1, 2, 3, 4, 5]Common use cases:
- Lists of email recipients
- Multiple tags or categories
- Collection of items to process
- Results from database queries
N8n tip: Access array items using bracket notation: {{$json.tags[0]}} gets the first tag.
5. Object
Objects store data in key-value pairs. They’re like containers that hold related information together.
Example:
{
"name": "Nancy",
"department": "Automation",
"skills": ["N8n", "Monday.com"],
"yearsExperience": 0
}
Common use cases:
- User profiles
- Product information
- API responses
- Complex data structures
N8n tip: Access object properties using dot notation: {{$json.user.name}}
6. Null and Undefined
These represent the absence of a value, though they’re slightly different:
- Null: Intentionally empty value
- Undefined: Value hasn’t been set yet
Common use cases:
- Optional fields that weren’t filled out
- Missing data in API responses
- Placeholder for values to be added later
N8n tip: Check for null/undefined values to prevent errors: {{$json.email ?? "no-email@example.com"}}
Binary Data (Special Type in N8n)
N8n also handles binary data for files, images, PDFs, etc. This is stored separately from JSON data.
Common use cases:
- File uploads and downloads
- Image processing
- PDF generation
- Attachments in emails
Practical Example: Putting It All Together
Here’s what a typical data object might look like in an N8n workflow pulling from Monday.com:
{
"itemId": 123456,
"itemName": "Website Redesign Project",
"status": "In Progress",
"priority": "High",
"assignees": ["Nancy", "John", "Sarah"],
"budget": 15000.50,
"isUrgent": true,
"completionDate": null,
"tags": ["design", "client-work", "Q4"],
"metadata": {
"createdBy": "Nancy",
"lastModified": "2025-10-17",
"department": "Marketing"
}
}
In this example, we have:
- Numbers: itemId, budget
- Strings: itemName, status, priority, completionDate (when filled)
- Boolean: isUrgent
- Arrays: assignees, tags
- Object: metadata
- Null: completionDate (not yet set)
Common Data Type Mistakes to Avoid
1. String vs Number confusion "123" is not the same as 123. The first is text, the second is a number you can do math with.
2. Forgetting array indexes start at 0 The first item in an array is [0], not [1].
3. Missing quotes on strings N8n expressions need proper syntax: "text" not text
4. Not checking for null values Always handle cases where data might be missing to prevent workflow failures.
Tips for Working with Data Types in N8n
Use the data inspector: N8n shows you exactly what data type each field is. Click on any node execution to see the actual data.
Test with sample data: Before building complex workflows, test with simple, known data to understand how types transform.
Use Set node for conversions: When you need to change data types, the Set node is your friend. You can convert strings to numbers, split strings into arrays, etc.
Read node documentation: Each N8n node’s documentation tells you what data types it expects and returns.
My Learning Process
As I work through my automation classes, I’ve found that the best way to learn data types is by looking at real examples. Every time I connect a new app or run a workflow, I examine the data that comes through and identify each type.
Making mistakes has been incredibly valuable too. When a workflow breaks, understanding data types helps me quickly identify whether I’m passing a string when a number is expected, or trying to access an array item that doesn’t exist.
What data type challenges have you faced in your automation journey? How do you handle data type conversions in your workflows?
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