Clean Data for Schema Markup

✓ Tested Works with CSV, Excel, Google Sheets → JSON-LD Schema

By dataclean.to team · 2026-02-13

Schema markup only works when the underlying data is clean, consistent, and formatted to match schema.org property requirements. Most organizations have the data they need for structured markup scattered across spreadsheets and databases, but that data contains formatting inconsistencies that produce validation errors, incomplete markup, and missed SEO opportunities. dataclean.to is purpose-built to bridge the gap between raw business data and the precision that schema.org demands.

The Problem

Schema.org defines over 800 types with thousands of properties, each expecting specific data formats. A LocalBusiness needs telephone in E.164 format, openingHours as day-time specifications, and geo as decimal coordinates. A Product needs offers with numeric prices, ISO 4217 currency codes, and ItemAvailability enumerations. An Event needs dates in ISO 8601 and locations as nested PostalAddress objects. Your source data almost certainly doesn't match these specifications — it uses local phone formats, natural language hours, informal price notations, and flat address strings. Manually converting all of this is tedious and error-prone, especially across large datasets. Schema.org vocabulary and type specifications

How to Fix It

Upload your source data

Import your CSV, Excel, or Airtable data into dataclean.to. The platform accepts data in any column layout and naming convention — you don't need to pre-format anything to match schema.org property names.

Auto-detect data types across all columns

dataclean.to analyzes the actual values in each column (not just headers) to detect data types: prices, dates, phone numbers, emails, URLs, addresses, coordinates, ratings, and identifiers like GTINs and ISBNs. This detection drives the cleaning rules applied in the next step.

Apply type-specific cleaning rules

Each detected data type receives appropriate normalization. Dates convert to ISO 8601. Phone numbers adopt E.164 format. Prices become decimal numbers with currency codes. URLs are validated and normalized. Addresses are parsed into structured components. Text fields are trimmed and sanitized.

Select schema type and map properties

Choose the schema.org type that matches your data from 19+ supported types. Map your cleaned columns to schema properties. The platform shows required versus optional properties for each type and validates that your data types match what each property expects.

Export validated schema markup

Generate JSON-LD for your entire dataset. Each record becomes a properly structured schema.org object. Export as downloadable files or deploy via a hosted API endpoint that serves the markup on your website through a single-line embed script.

Frequently Asked Questions

Which schema.org types does dataclean.to support?

dataclean.to supports 19+ schema types including Product, LocalBusiness, Event, FAQPage, Article, Recipe, JobPosting, Course, Restaurant, Organization, Person, HowTo, Book, MusicRecording, Movie, SoftwareApplication, and more. Each type has its own set of mappable properties.

What if my data has columns that don't map to any schema property?

Not every column needs to map to a schema property. Columns like internal IDs, notes, or operational data can be left unmapped. They remain in your project data but are excluded from the schema markup output. Only mapped columns appear in the generated JSON-LD.

How do I handle nested schema types like PostalAddress within LocalBusiness?

dataclean.to handles nesting automatically. When you map address-related columns (street, city, state, zip, country) to address properties, they are output as a nested PostalAddress object within the parent schema type. Similarly, price and availability columns become nested Offer objects.

Example: Input → Output

name	email	phone	city	status
Alice Johnson	alice@example.com	+1-555-0101	New York	active
alice johnson	ALICE@EXAMPLE.COM	5550101	new york	Active

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

{
  "@context": "https://schema.org",
  "@type": "Dataset",
  "name": "Cleaned Customer Data",
  "description": "Normalized customer records with standardized fields",
  "keywords": ["customer data", "CRM", "contact list"]
}

💡 How it works: Consistent data formatting reduces import errors and makes your dataset compatible with downstream tools.

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