Here are a few best practices for validating your CRM data:

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Update your CRM when you receive new data: Knowing how to edit and bulk edit your CRM data is crucial for properly cleaning your data. As soon as you become aware of a change you need to make, go into your CRM and update the records. This ensures your team continually operates with updated data.
Manually remove disengaged contacts: Keeping contacts in your CRM as long as possible may seem logical, but hanging onto bad contacts could slow your team down. Manually delete no-longer- kuwait mobile number list useful contacts and make sure you unsubscribe contacts from your email campaigns who request to stop receiving them.
Automate data cleaning with a data cleaning tool: While manually cleaning data has its place, you might find automating certain aspects of data cleaning helpful. Plenty of third-party data scrubbing tools exist for you to use with your CRM. Your CRM may also detect and merge duplicate data for you.
Data validation
One way to ensure data quality and accuracy within your CRM is through data validation. Data validation is the process of reviewing customer data to confirm that it meets the standards and rules set forth by your business and industry.

Data validation is an important step in the customer data collection process because it helps ensure data accuracy and clarity. This process allows you to avoid any errors in your data and can help your teams better interpret the information.

This process verifies whether the data cleaning was successful and whether the information meets your company and industry standards. Essentially, data validation is the time to perform final checks before putting your data into use.

How to validate your data

Implement data validation rules: Before performing data validation, you need to set data validation rules. These criteria provide a set of standards by which you can judge your data to ensure its quality. Consider what rules might be appropriate for your company or industry, like a valid email format or specific date range.
Check for inaccuracies and redundancies: With your data validation rules in place, it’s time to perform data validation. Set up scheduled validation checks with specific datasets to keep from overwhelming your team. During this step in the process, you check whether the data is complete, accurate, and updated according to your rules.