✓ TestedWorks with CSV, Excel, Google Sheets → JSON-LD Schema
By dataclean.to team · 2026-02-05
B2B data formatting applies consistent structure to business information. Automated rules handle dates, phone numbers, addresses, currencies, and identifiers. This process transforms inconsistent spreadsheet data into uniformly formatted records that meet business requirements.
The Problem
B2B organizations receive data from partners, vendors, and legacy systems with inconsistent formatting. Dates mix MM/DD/YYYY and DD/MM/YYYY, phone numbers vary with parentheses and dashes, addresses lack standardization, and currency symbols differ across regions. Manual formatting of thousands of records takes excessive time and introduces errors. Automated formatting ensures consistent structure while maintaining data accuracy across all fields. Data Quality Management
How to Fix It
1
Upload B2B Data
Import CSV or Excel files containing business information. System analyzes data types, detects formatting patterns, and identifies inconsistencies.
2
Configure Format Rules
Set formatting preferences for dates, phone numbers, addresses, names, currencies, and identifiers. Choose regional formats and output standards.
3
Apply Formatting Rules
System processes each field with appropriate rules, standardizes formats, validates results, and flags entries that cannot be automatically formatted.
4
Review Formatted Data
Preview formatted results, inspect flagged entries requiring manual review, and adjust rules if formatting does not meet requirements.
5
Export Formatted Data
Download formatted dataset in CSV, JSON, or other formats with consistent structure ready for business system integration.
Frequently Asked Questions
What data types can be formatted?
Dates, times, phone numbers, addresses, postal codes, currencies, percentages, names, email addresses, URLs, company names, and business identifiers like DUNS or EIN.
How are ambiguous dates handled?
System detects date format from patterns in data. When ambiguous, you specify source format (MM/DD/YYYY vs DD/MM/YYYY) and target output format.
Can I preserve original data?
Yes, formatted data exports include both original and formatted values in separate columns for verification and audit trail purposes.
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.