✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-13
JSON-LD is Google's preferred format for structured data markup, but generating valid JSON-LD requires clean, consistently formatted source data. When your data comes from spreadsheets, databases, or content management systems, it inevitably contains the kind of formatting inconsistencies that produce invalid or incomplete JSON-LD. dataclean.to takes your raw data, identifies every field that would cause JSON-LD issues, and normalizes it into a format that outputs valid, schema.org-compliant structured data.
The Problem
JSON-LD has strict requirements that human-entered data rarely meets. Strings containing unescaped quotes break the JSON syntax entirely. Dates in natural language ("March 5th, 2026") are invalid — JSON-LD needs ISO 8601 ("2026-03-05"). Numeric fields with thousand separators, currency symbols, or unit labels cause type mismatches. Empty fields generate null values that may violate required property constraints. URLs with spaces or special characters produce invalid link references. Each of these issues, trivial on its own, can cascade through a dataset of hundreds of records to produce JSON-LD output that search engines either reject or misinterpret. JSON-LD 1.1 specification (W3C)
How to Fix It
1
Upload your source data
Import the CSV, Excel, or Airtable data that you want to convert to JSON-LD. dataclean.to works with any tabular data source regardless of how the columns are named or formatted.
2
Identify JSON-LD compatibility issues
The platform scans each column for values that would produce invalid JSON-LD: unescaped special characters, non-ISO date formats, mixed numeric formats, empty required fields, malformed URLs, and text with embedded HTML or control characters.
3
Normalize data types for JSON-LD compliance
Apply automatic cleaning rules: dates convert to ISO 8601, prices become plain numbers, URLs are encoded properly, special characters in text are escaped, and empty values are handled according to whether the target schema property is required or optional.
4
Map to schema.org properties
Select the schema type for your data and map your cleaned columns to schema.org properties. dataclean.to validates that the data types of your cleaned values match what each property expects — Text, Number, Date, URL, or nested type.
5
Generate validated JSON-LD output
Export the JSON-LD for your entire dataset. Each row becomes a properly structured JSON-LD object with the correct @context, @type, and property values. The output is validated against schema.org specifications before export.
Frequently Asked Questions
What data issues most commonly break JSON-LD output?
The most frequent problems are unescaped double quotes in text fields (which break JSON syntax), dates not in ISO 8601 format (which Google cannot parse), prices with currency symbols or commas (which create string/number type mismatches), and URLs with spaces or special characters (which produce invalid references).
Does dataclean.to validate the JSON-LD output structure?
Yes. The generated JSON-LD is validated for correct JSON syntax, proper @context and @type declarations, and compliance with schema.org property requirements for the selected type. This catches structural issues before you deploy the markup to your site.
Can I clean data once and generate multiple JSON-LD schema types?
Yes. After cleaning your source data, you can create multiple schema mappings from the same dataset. For example, a restaurant database could generate both Restaurant schema (for the business listing) and Menu/MenuItem schema (for menu items) without re-cleaning the data.
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.