Phone number data is one of the most important contact details for businesses and applications, but it often arrives in inconsistent formats, with errors, duplicates, or missing information. Cleaning phone number data is essential to ensure accuracy, improve communication, and comply with regulations. Here’s a comprehensive approach to the best practices for cleaning phone number data effectively.
1. Understanding the Importance of Cleaning Phone Numbers
Raw phone number data can contain a wide variety of issues:
Different formatting styles (with or without country codes, spaces, dashes, parentheses)
Invalid or incomplete numbers
Duplicates
Non-numeric characters or extra symbols
Numbers from different countries mixed together
Cleaning this data improves deliverability of calls and messages, reduces costs, and enhances user experience.
2. Standardizing Phone Number Formats
The first step is to standardize all phone numbers into a consistent format. This often means:
Removing all non-numeric characters (spaces, dashes, parentheses, plus signs)
Ensuring all numbers include the country code (e.g., +1 for the U.S.)
Formatting numbers according to international standards such as E.164, which recent mobile phone number data formats numbers as [+][country code][subscriber number] with no spaces or symbols (e.g., +14155552671).
Standardization makes it easier to compare and validate numbers across datasets.
3. Validating Phone Numbers
Validation checks whether a phone number is structurally correct and possible:
Length check: Each country has specific phone number length ranges. Numbers too short or too long can be flagged.
Country code validation: Verify that the country code exists and matches the number format.
Format validation: Use libraries or APIs (like Google's libphonenumber) to validate number formats.
Carrier or service type validation: Some services can check if a number is mobile, landline, or VOIP, which may affect message delivery.
Invalid numbers should be flagged for review or removed.
4. De-duplication
Duplicate phone numbers can cause redundant communications and increased costs. After formatting and validation, remove exact duplicates.
For more advanced cleaning, consider:
Near-duplicate detection (e.g., numbers differing only by formatting)
Handling multiple numbers per contact by linking or merging records.
5. Enrichment and Correction
Where possible, enrich phone number data by:
Adding missing country codes based on user location or other data points
Correcting obvious errors such as transposed digits or common typos (if validation APIs provide suggestions)
Removing numbers on Do Not Call lists or known spam sources
6. Automating with Tools and Libraries
Manual cleaning is inefficient and error-prone. Use tools such as:
Google’s libphonenumber: An open-source library widely used for parsing, formatting, and validating international numbers.
Phone validation APIs: Services like Twilio Lookup, NumVerify, or others that provide real-time validation and information.
Data cleaning platforms: Some CRM or marketing platforms include built-in phone cleaning tools.
Automation helps keep data consistent and up-to-date.
7. Maintaining Clean Data
Cleaning is not a one-time task. To maintain clean phone number data:
Implement validation at data entry points (web forms, app sign-ups)
Periodically run cleaning scripts or services on existing databases
Monitor data quality and address errors promptly