✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-12
Notion databases are increasingly used as lightweight CRMs, contact trackers, and lead management systems. Teams store email addresses in database properties alongside notes, tags, and relationships. Because Notion's email property has minimal validation and many teams use rich text or text properties instead, exported email data ranges from properly formatted to embedded in sentences or mixed with other contact details. dataclean.to extracts and validates email addresses from your Notion export, regardless of how they were stored.
The Problem
Notion's strength as a flexible workspace creates email data chaos. The dedicated email property type enforces basic format checking, but many teams use a text or rich text property for contact info, which accepts anything: 'email: john@company.com (preferred)' or 'John - john@company.com / jane@company.com (assistant)'. Notion database exports to CSV may strip rich text formatting, leaving behind Markdown artifacts mixed with email addresses. Relations between databases can result in email data being stored in the related record rather than the main contact entry, requiring joins that the CSV export does not perform automatically. Notion's API exports may include email addresses embedded in page content (not database properties), which gets exported as raw text. Teams that use Notion for both personal notes and shared contacts may have test entries, placeholder addresses, and draft entries mixed with real contacts. Notion content export documentation
How to Fix It
1
Export your Notion database
Open the Notion database containing contact data, click the three-dot menu, and select Export as CSV. Choose 'Current View' to respect any filters, or 'All Views' for the complete dataset.
2
Upload to dataclean.to
Import the Notion CSV. The platform scans all columns for email content, including text properties where emails may be embedded alongside other information. It extracts and validates each address found.
3
Extract emails from unstructured fields
Parse text and rich text property exports to extract email addresses from sentences, notes, and mixed-content cells. Separate multiple addresses stored in a single cell into individual entries.
4
Remove placeholders and test entries
Identify entries that are clearly not real contacts: placeholder addresses, test@example.com entries, and draft records without valid email data. Flag Notion page links or URLs that were mistakenly entered in email fields.
5
Export clean contact data
Download the validated contacts with one clean email per row. Import into a dedicated CRM, email marketing platform, or back into a structured Notion database with proper email property types.
Frequently Asked Questions
Why is Notion email data so inconsistent?
Notion is a general workspace, not a CRM. Teams repurpose it for contact management without enforcing data entry standards. Different team members enter emails in different formats, in different property types, and with varying levels of additional context mixed in.
Can dataclean.to extract emails from Notion page content?
If your Notion export includes page content as text (via the 'Include content' option), dataclean.to can detect email addresses embedded in that text. However, the most reliable approach is to ensure emails are stored in dedicated database properties.
Does the export handle Notion relations and rollups?
Notion CSV exports do not automatically resolve relations. If email addresses are stored in a related database, you need to export that database separately and upload it to dataclean.to. Rollup properties that reference emails will appear as plain text in the export.
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.