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Data Verification vs. Data Validation vs. Data Validity
Data verification - the technical accuracy of the data; data validation - traditional accuracy; and data validity - its contextual application.
Data integrity and relevance are non-negotiable in data analysis and data science because they are prerequisites for generating good insights and making informed decisions.
Poor-quality data means poor-quality insights, and organizations risk making decisions based on flawed information.
To ensure data integrity, analysts should master three critical concepts: data validation, data verification, and data validity.
Although these terms are often used interchangeably, they address different aspects of data quality. They play distinct but complementary roles in achieving a common goal.
I’ll explain their distinctions, provide actionable steps to implement them, and offer tips on communicating these processes effectively.
Data Validation
In the traditional sense, data validation ensures that data is correct, complete, and meaningful before it is accepted into a system or used for analysis. It checks whether the data meets predefined rules or business requirements at the point of entry or ingestion.
