Build a Complete Data Enrichment Workflow in Naiden

Key Points:

• The workflow uses a form trigger as the entry point
• Data enrichment happens via the People Data Lab API
• Error handling ensures workflow reliability
• Only European companies are added to Google Sheets
• A summary message is sent to Slack with a link to the sheet

My Take:

This workflow combines several essential automation techniques into one practical use case. It’s perfect for lead generation teams or market research that need to quickly process and enrich company information from just their website URLs.

Key Points:

• Add a form trigger node with a single field called “URL”
• The form accepts multiple URLs separated by commas
• For testing purposes, you can pin the submitted data
• Test example: URLs for Google, Naiden, Facebook, Pulan, and Spendesk

My Take:

Using a form as an entry point makes your workflow accessible to non-technical team members. It’s a great way to democratize data processes in your organization without requiring everyone to understand the underlying automation.

Key Points:

• Use an Edit Fields node to convert the comma-separated string into an array
• Configure the field name as “URL list” with type “array”
• Use the Split Out node to convert the array into separate workflow items
• Add a Loop Over Items node with batch size set to 1

// Within Edit Fields node
// Field: URL list
// Type: Array
// Value: Split input by comma
{
  name: "URL list",
  type: "array",
  value: "{{$json.URL.split(',')}}"
}
    

My Take:

This pattern of converting strings to arrays and then splitting them into individual workflow items is extremely useful. It lets you accept bulk input from users while processing each item individually with proper error handling.

Key Points:

• Add an HTTP node configured for a GET request
• Set up authentication using your People Data Lab API key
• Configure the query to search for company data by website
• Use dynamic expressions to insert each URL from the loop
• Process one URL at a time for better error handling

// HTTP Node Configuration
{
  "method": "GET",
  "url": "https://api.peopledatalab.com/v5/company/enrich",
  "headers": {
    "X-API-Key": "YOUR_PDL_API_KEY"
  },
  "body": {
    "size": 1,
    "sql": "SELECT * FROM company WHERE website = '" + $json.URL list + "'"
  }
}
    

My Take:

The People Data Lab API is powerful for company enrichment, but notice how this pattern works for any API. Just replace the endpoint and adjust the query format, and you could use this same workflow structure with Clearbit, Hunter.io, or any other data enrichment service.

Key Points:

• Add an If node after the HTTP request
• Check if the status code is not equal to 200
• If there’s an error, stop execution and throw a descriptive error
• If successful, continue processing the next item

// If Node Condition
$json.status !== 200

// True path: Error handling
// Action: Stop and throw error
"Error on PDL enrichment"
    

My Take:

Never skip error handling in production workflows. This approach lets you gracefully fail on individual items without crashing the entire workflow. For even more robust error handling, you could log the errors to a separate Google Sheet for later review.

Key Points:

• Use Edit Fields to extract only needed data from the API response
• Focus on company name, employee count, country, continent, and funding
• Add an If node to filter for companies where continent equals “Europe”
• Connect a Google Sheets node to append the filtered data
• Map the relevant fields to your sheet columns

// Edit Fields for data simplification
{
  "name": "{{$json.data[0].name}}",
  "employee_count": {{$json.data[0].employee_count}},
  "country": "{{$json.data[0].location.country}}",
  "continent": "{{$json.data[0].location.continent}}",
  "funding": {{$json.data[0].total_funding_raised}}
}

// If Node Condition for filtering
$json.continent === "Europe"
    

My Take:

Simplifying complex API responses before further processing is a best practice. It makes your workflow more readable and easier to debug. The filtering step also shows how you can create targeted workflows that only process specific data matching your business criteria.

Key Points:

• Use the Summarize node to count the number of processed companies
• Add a Slack node configured to send a message to a specific user
• Implement Slack Blocks for advanced message formatting
• Include dynamic data with the count of added companies
• Add a button that links directly to the Google Sheet

// Slack Blocks Format
[
  {
    "type": "section",
    "text": {
      "type": "mrkdwn",
      "text": "Your Naiden workflow just added {{$json.counter_name}} companies to your Google sheet!"
    }
  },
  {
    "type": "section",
    "text": {
      "type": "mrkdwn",
      "text": "Here is a link to access your Google sheet:"
    },
    "accessory": {
      "type": "button",
      "text": {
        "type": "plain_text",
        "text": "Google Sheet",
        "emoji": true
      },
      "value": "click_me_123",
      "url": "https://docs.google.com/spreadsheets/d/YOUR_SHEET_ID/edit",
      "action_id": "button-action"
    }
  }
]
    

My Take:

The Slack notification is what transforms this from a simple data pipeline into a true business workflow. By notifying users when the process completes and providing a direct link to the results, you close the loop and make the automation immediately actionable for your team.