Supported Validations & Mapping Logic

Supported Validation Types

Foxentry Data Cleaner supports five core validation types, directly corresponding to our REST API endpoints:

• Names (name): Validates and corrects first names and surnames. Fixes typos, missing diacritics, and determines gender.
• Locations (location): Validates full postal addresses. Verifies if the combination of street, city, and ZIP code actually exists.
• Emails (email): Checks syntax, verifies domain MX records, and detects disposable or temporary email addresses.
• Phones (phone): Validates international phone formats and verifies network carrier details or live number reachability.
• Companies (company): Validates business details using corporate registration numbers (IČO) or VAT IDs (DIČ).

Column Mapping Interface

In the data preview screen, every column from your uploaded file features a dropdown menu at the top. Your task is to assign each column to its corresponding data type.

• Ignoring Columns: Any column that you do not want to validate (e.g., internal database IDs, registration dates, order prices) should be left as — ignore —. The application will completely skip these fields during the API request, but it will preserve them unchanged in the final output file.
• Header Row: Ensure the First row is header checkbox is toggled correctly. If your file contains column names in the first row, keep it checked so the application doesn't try to validate the header names as real customer data.

Understanding Data Groups

When mapping columns, you will notice that certain options have numeric indices (e.g., Address 1, Address 2 or Company 1, Company 2). This is a fundamental concept called Grouping.

A single Foxentry API request often requires multiple fields to evaluate a single record. Groups tell the Data Cleaner which columns in your spreadsheet logically belong together to form a single API payload.

Example Scenario: Multiple Addresses on One Row

Imagine your Excel sheet contains both a Billing Address and a Shipping Address side by side on the same row:

To process this correctly without mixing the data, you must group them separately using the indices:

• Column A $\rightarrow$ Map to Address 1: Street
• Column B $\rightarrow$ Map to Address 1: City
• Column C $\rightarrow$ Map to Address 1: ZIP
• Column D $\rightarrow$ Map to Address 2: Street
• Column E $\rightarrow$ Map to Address 2: City
• Column F $\rightarrow$ Map to Address 2: ZIP

By doing this, the Data Cleaner understands that Columns A, B, and C form one complete address validation request, while Columns D, E, and F form a completely separate request.

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Independent Processing

Grouping is not limited to addresses. If your database has multiple phone numbers (e.g., Phone 1 for primary, Phone 2 for secondary), use separate group indices to ensure they are validated independently.

Next step: After mapping your columns, you can fine-tune the strictness and depth of each validator. Learn how to configure these in the Datascopes & Response Formats section.