How to structure data for data scraping (and leverage using AI model mapping)?

Have you ever found yourself drowning in a sea of unstructured data, unsure which pieces to keep and how to organize them?

Whether you're scraping data from websites, social media, or any other source, the key to success lies in how you structure that information. Let's dive into the world of data structuring for scraping, with a special focus on leveraging AI model mapping.

Table of Contents:

1. Introduction

2. The Importance of Structured Data

3. Simple Example: Company Information from LinkedIn

4. Medium Example: LinkedIn Companies with Employees

5. Advanced Example: LinkedIn Job Boards with Employees

6. Tips for Effective Data Structuring

7. Leverage Using AI

8. Conclusion

In this blog post, we'll explore how to structure data for effective scraping, using MongoDB as our example database. However, the principles we'll discuss apply equally to other databases like PostgreSQL or even spreadsheet tools like Google Sheets. We'll start with simple examples and progressively move to more complex scenarios, showing you how to handle various data relationships and maximize the value of your scraped information.

2. The Importance of Structured Data

Before we dive into specific examples, let's highlight why structured data is crucial for scraping:

• Consistency: Structured data ensures that all scraped information follows a predefined format, making it easier to process and analyze.

• Scalability: As your data grows, a well-structured database can handle increasing volumes without losing efficiency.

• Relationships: Proper structuring allows you to establish and maintain relationships between different data points, enhancing the overall value of your dataset.

• AI Integration: Well-structured data is essential for effective AI model mapping, enabling more sophisticated analysis and predictions.

3. Simple Example: Company Information from LinkedIn

Let's start with a basic scenario: scraping company information from LinkedIn. Here's how we might structure this data:

{
  "company_name": "TechCorp",
  "website_url": "https://www.techcorp.com",
  "linkedin_page": "https://www.linkedin.com/company/techcorp",
  "linkedin_company_id": "12345678"
}

In this case, the linkedin_company_id serves as our unique identifier. This is crucial because company names can change or be ambiguous, but the LinkedIn ID remains constant.

4. Medium Example: LinkedIn Companies with Employees

Now, let's expand our structure to include employee information:

Company:

{
  "company_name": "TechCorp",
  "website_url": "https://www.techcorp.com",
  "linkedin_page": "https://www.linkedin.com/company/techcorp",
  "linkedin_company_id": "12345678"
}

Employees:

{
  "profile_id": "87654321",
  "first_name": "John",
  "last_name": "Doe",
  "linkedin_company_id": "12345678"
}

Notice how we've included the linkedin_company_id in the employee data. This allows us to maintain the relationship between employees and their companies, even if we store this information in separate collections or tables.

To enhance our data structure, we can use tools like n8n to create workflows that check and enrich our data. The image above shows an example workflow that uses a webhook to receive input data, checks if the company already exists in our CRM, and retrieves additional information like the latest associated deal. This type of automation ensures our data remains consistent and up-to-date across different systems.

5. Advanced Example: LinkedIn Job Boards with Employees

For our most complex example, let's look at how we might structure data from LinkedIn job boards along with employee information:

Company:

{
  "company_name": "TechCorp",
  "website_url": "https://www.techcorp.com",
  "linkedin_page": "https://www.linkedin.com/company/techcorp",
  "linkedin_company_id": "12345678"
}

Employees:

{
  "profile_id": "87654321",
  "first_name": "John",
  "last_name": "Doe",
  "linkedin_company_id": "12345678"
}

Jobs:

{
  "job_id": "JOB123456",
  "company_name": "TechCorp",
  "linkedin_page": "https://www.linkedin.com/company/techcorp",
  "domain": "techcorp.com"
}

In this scenario, our final employee data structure might look something like this:

{
  "_id": "MONGO_ID_12345",
  "linkedin_id": "87654321",
  "executionId": "EXEC_98765",
  "firstname": "John",
  "image": "https://example.com/profile.jpg",
  "lastname": "Doe",
  "linkedin": "https://www.linkedin.com/in/johndoe",
  "linkedin_company_id": "12345678",
  "localisation": "San Francisco, CA",
  "started_position_month": "January",
  "started_position_year": "2022",
  "tenure_at_company_month": 18,
  "tenure_at_company_year": 1,
  "tenure_at_position": "1 year 6 months",
  "title_original": "Senior Software Engineer",
  "titles_clean": ["Software Engineer", "Senior"],
  "website": "https://www.techcorp.com",
  "domain": "techcorp.com",
  "industry": "Information Technology",
  "number_of_employees": "501-1000",
  "companySlug": "techcorp",
  "priority": "High",
  "email": "john.doe@techcorp.com"
}

6. Tips for Effective Data Structuring

• Think Big: Always structure your data with future needs in mind. It's easier to exclude unnecessary fields later than to add missing information.

• Use a Backup Object: Consider including a backup or json field in your structure to store the full original data. This can be invaluable if you need to access raw data later.

• Maintain Relationships: Always include identifiers (like linkedin_company_id) that allow you to link different data entities together.

• Leverage Automation: Use tools like n8n to create workflows that enrich and validate your data as it's ingested.

• Keep Source of Truth: Designate a primary system (like your CRM) as the source of truth, and ensure all other systems sync with it.

7. Leverage Using AI

As we dive deeper into data structuring, we can harness the power of AI to help us optimize our data models. Here's a prompt you can use with an AI model to assist in structuring your scraped data:

You are an expert in Data Scraping and Modeling. Your task is to keep the data structured and ensure it can be pushed to a CRM with all relevant data associated. 

Below is an example of the data format that I scraped, represented in JSON:
You data in input:
{
  "company": {
    "name": "Company Name",
    "website_url": "Website URL",
    "linkedin_page": "LinkedIn Page",
    "linkedin_company_id": "LinkedIn Company ID"
  },
  "employees": [
    {
      "profile_id": "Profile ID",
      "first_name": "First Name",
      "last_name": "Last Name",
      "linkedin_company_id": "LinkedIn Company ID"
    }
  ],
  "jobs": [
    {
      "job_id": "Job ID",
      "company_name": "Company Name",
      "linkedin_page": "LinkedIn Page",
      "domain": "Domain"
    }
  ]
}

In the CRM, the goal is to consolidate the data into two objects: companies and contacts. All relevant information from other objects should be associated with contacts to facilitate data pushing into outbound tools.

Please explain which unique key will help synchronize each object. Additionally, create a mermaid graph to visualize the data structure.

By using this prompt with an AI model, you can get valuable insights into how to structure your data for optimal use in your CRM and outbound tools. The AI will analyze your input data and provide recommendations on unique keys for synchronization, as well as a visual representation of the data structure

This AI-generated output can serve as a starting point for your data structuring efforts. It can help you identify the best unique keys for synchronization and visualize how different data objects relate to each other. This approach combines human expertise with AI capabilities to create more robust and efficient data structures.

By leveraging AI in this way, you can:

1. Quickly identify potential issues in your data structure

2. Get suggestions for optimizing your data model

3. Visualize complex data relationships more easily

4. Ensure that your data structure aligns with your CRM and outbound tool requirements

8. Conclusion

Effective data structuring is the cornerstone of successful scraping and AI mapping. By embracing the principles outlined here, you'll craft robust, scalable structures that unlock the full potential of your data.

Remember: think ahead, maintain clear relationships, and keep your end goals in focus.