What are the best practices in data validation?

What are the best practices in data validation?

Best practices in data validation include defining clear validation rules, using automated validation tools, and regularly reviewing data quality.

Can you explain why data validation is important?

Data validation is crucial because it ensures the accuracy and quality of data, which is essential for making informed decisions.

What tools can I use for data validation?

Some popular tools for data validation include Talend, Apache Nifi, and Microsoft Excel's data validation features.

How often should I perform data validation?

Data validation should be performed regularly, ideally every time data is entered or updated, to maintain data integrity.

What are common mistakes to avoid in data validation?

Common mistakes include not validating data at the source, relying solely on automated tools, and failing to update validation rules.