I’ll provide a comprehensive solution covering email regex validation, data cleansing before import, and error log analysis.
Understanding SAP CX Email Validation
SAP CX 2111 uses strict RFC 5322 email validation with these key rules:
- Local part (before @): Letters, digits, and special characters (. _ % + -)
- Domain part: Letters, digits, hyphens (not at start/end), and dots
- Must contain exactly one @ symbol
- TLD must be 2+ characters
- No spaces, control characters, or Unicode in standard mode
Step 1: Error Log Analysis
First, export the complete error log from the Data Import Tool and categorize failures:
import pandas as pd
import re
errors = pd.read_csv('import_errors.log')
email_pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
errors['error_type'] = errors['email'].apply(lambda x: 'trailing_space' if x != x.strip() else 'other')
This categorizes issues for targeted fixing.
Step 2: Data Cleansing Before Import
Create a multi-stage cleansing pipeline:
Phase 1: Auto-Fixable Issues
- Trim whitespace: `email.strip()
- Normalize case: Convert domain to lowercase (local part is case-sensitive per RFC but SAP CX typically lowercases)
- Replace common substitutions: “(at)” → “@”, “[dot]” → “.”
- Remove duplicate @ symbols (keep first occurrence)
Phase 2: Character Encoding
- Convert smart quotes to straight quotes
- Replace en-dash/em-dash with standard hyphen
- Remove non-breaking spaces (\xa0)
- Validate UTF-8 encoding and convert to ASCII where possible
Phase 3: Business Logic Decisions
For problematic emails that can’t be auto-fixed:
- Internal/test accounts (admin@localhost): Create a placeholder domain like “@internal.yourcompany.com” or flag for manual review
- Completely malformed: Mark as “requires_contact_update” and import with a temporary valid email like “update-needed@yourcompany.com”
- Duplicates: SAP CX requires unique emails per account - resolve conflicts before import
Step 4: Validation Script
Before re-importing, validate all emails against SAP CX’s expected pattern:
def validate_email(email):
pattern = r'^[a-zA-Z0-9._%+-]+@[a-zA-Z0-9.-]+\.[a-zA-Z]{2,}$'
return bool(re.match(pattern, email))
df['is_valid'] = df['email'].apply(validate_email)
invalid_emails = df[~df['is_valid']]
Review the invalid_emails dataframe before import.
Step 5: Import Configuration
In the Data Import Tool settings:
- Enable “Skip invalid records” mode to log errors without halting the entire import
- Set batch size to 1,000 records for easier error tracking
- Enable detailed logging to capture row numbers and specific validation failures
- Use “Update existing records” mode if re-importing after cleansing
Step 6: Post-Import Reconciliation
After import:
- Compare imported count vs source count
- Export accounts with placeholder emails (“update-needed@…”) for manual correction
- Create a workflow in SAP CX to flag accounts needing email updates
- Schedule follow-up data quality checks
Specific Fixes for Your Issues
- Trailing spaces:
UPDATE accounts SET email = TRIM(email) before export
- Case inconsistencies: Not typically a validation failure, but normalize for consistency
- localhost domains: Replace with valid domain or use a dedicated import domain
- “(at)” substitutions: Regex replace before import: `email.replace(‘(at)’, ‘@’)
Hyphen Issue Investigation
For emails like “john.doe@company-name.co.uk” being rejected, verify:
- The hyphen is ASCII 45 (0x2D), not Unicode dash variants
- No hyphens at the start or end of domain parts
- Domain doesn’t have consecutive hyphens (–)
Use a hex editor to inspect the actual character bytes in your CSV.
Recommended Approach
- Create a cleansing script that processes your CSV before import
- Generate two output files: “clean_import.csv” (auto-fixed) and “manual_review.csv” (requires decisions)
- Import the clean file first
- Work with business users to resolve manual review cases
- Import the manually corrected records in a second batch
This systematic approach ensures data quality while minimizing manual effort and preventing import failures.