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Transform B2B Data into Organization Schema Markup

✓ Tested Works with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-05

B2B organization data converts into Schema.org Organization markup for company information. Structured business details help search engines understand corporate entities, locations, and relationships. This process transforms spreadsheet data containing company profiles into valid JSON-LD that meets schema requirements.

The Problem

B2B companies maintain organization data in CRM systems, vendor lists, and partner databases with inconsistent formatting. Company names, addresses, contact details, and identifiers vary across sources. Manual schema creation for hundreds of organizations takes excessive time. Automated conversion ensures consistent structured data while maintaining organizational hierarchies, subsidiary relationships, and business identifiers like DUNS numbers. Schema.org Organization Specification

How to Fix It

1
Upload Organization Data
Import CSV or Excel files containing company information including name, legal name, address, phone, email, URL, and business identifiers. System identifies organization-specific columns.
2
Map Organization Properties
Match spreadsheet columns to Schema.org Organization properties like name, legalName, address (PostalAddress), contactPoint, url, logo, and identifier fields.
3
Normalize Business Data
Standardize addresses to postal address format, validate phone numbers, ensure URLs are properly formatted, and structure contact points with contactType and telephone.
4
Generate Organization Schema
Create JSON-LD structured data for each organization with proper nesting of address, contact points, and organizational relationships like parentOrganization or subOrganization.
5
Export Schema Files
Download individual JSON-LD files or bulk export all organization schemas with validated structure ready for website implementation.

Frequently Asked Questions

What organization properties are required?
Only name is required. Recommended properties include address, telephone, url, logo, contactPoint, and sameAs links to social profiles for complete business representation.
How are addresses structured?
Addresses use PostalAddress type with streetAddress, addressLocality, addressRegion, postalCode, and addressCountry properties following schema specifications.
Can I represent subsidiary relationships?
Yes, use parentOrganization and subOrganization properties to link related entities. Each referenced organization should have its own Organization schema with unique identifier.

Example: Input → Output

nameaddresscityphonewebsite
City Plumbing Co789 Elm StDenver CO(720) 555-0200cityplumbing.com
city plumbing co789 elm stdenverco(720)555-0200www.cityplumbing.com

Red rows show common data quality issues. dataclean.to normalizes and generates JSON-LD automatically.

{
  "@context": "https://schema.org",
  "@type": "LocalBusiness",
  "name": "City Plumbing Co",
  "address": {"@type": "PostalAddress", "streetAddress": "789 Elm St", "addressLocality": "Denver", "addressRegion": "CO"},
  "telephone": "+17205550200",
  "url": "https://cityplumbing.com"
}
💡 How it works: LocalBusiness schema helps your listing appear in Google Maps and local search with structured contact info.

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