Data Cleaning + Normalization

Data Cleaning + Normalization

Clean, normalized data is essential for automation, reporting, and CRM integration. Learn the key techniques to transform messy data into high-quality, actionable records.

Common Data Quality Issues

• Inconsistent Formatting: Different date formats, phone number styles, capitalization
• Duplicate Records: Same company or person with slight variations
• Missing Data: Incomplete records with null or empty fields
• Invalid Data: Incorrect emails, phone numbers, or URLs
• Inconsistent Values: "CEO" vs "Chief Executive Officer"

Data Cleaning Techniques

Essential cleaning operations:

1. Trim Whitespace: Remove leading/trailing spaces
2. Standardize Capitalization: Title case for names, uppercase for states
3. Format Phone Numbers: Consistent format like +1-555-555-5555
4. Validate Emails: Check syntax and domain validity
5. Parse Addresses: Separate into street, city, state, zip
6. Remove Duplicates: Fuzzy matching to find similar records

Normalization Best Practices

Ensure consistency across your dataset:

• Use standard country codes (ISO 3166)
• Normalize industry classifications
• Standardize job titles to common categories
• Convert currencies to a single standard
• Use consistent date formats (ISO 8601)

AI-Powered Cleaning

Leverage AI for advanced cleaning:

• Use AI to standardize company names
• Extract structured data from unstructured text
• Categorize and tag records automatically
• Identify and merge duplicate records intelligently