Types of data validation

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This text outlines the purpose, examples, and limitations of six data validation types. The first five are linked to the data itself: type, range, constraint, consistency, and structure. The sixth type pertains to application code validation that accepts user input.

As a junior data analyst, you may not conduct all these validations. However, it's helpful to ask about the validation process before working with a dataset. Data validation ensures data integrity and confidence in its cleanliness. Here's a breakdown of the six data validation types, along with their purposes, examples, and limitations.

Data type

• Purpose: Check that the data matches the data type defined for a field.
• Example: Data values for school grades 1-12 must be a numeric data type.
• Limitations: The data value 13 would pass the data type validation but would be an unacceptable value. For this case, data range validation is also needed.

Data range

• Purpose: Check that the data falls within an acceptable range of values defined for the field.
• Example: Data values for school grades should be values between 1 and 12.
• Limitations: The data value 11.5 would be in the data range and would also pass as a numeric data type. But, it would be unacceptable because there aren't half grades. For this case, data constraint validation is also needed.

Data constrains

• Purpose: Check that the data meets certain conditions or criteria for a field. This includes the type of data entered as well as other attributes of the field, such as number of characters.
• Example: Content constraint: Data values for school grades 1-12 must be whole numbers.
• Limitations: The data value 13 is a whole number and would pass the content constraint validation. But, it would be unacceptable since 13 isn’t a recognized school grade. For this case, data range validation is also needed.

Data consistency

• Purpose: Check that the data makes sense in the context of other related data.
• Example: Data values for product shipping dates can’t be earlier than product production dates.
• Limitations: Data might be consistent but still incorrect or inaccurate. A shipping date could be later than a production date and still be wrong.

Data structure

• Purpose: Check that the data follows or conforms to a set structure.
• Example: Web pages must follow a prescribed structure to be displayed properly.
• Limitations: A data structure might be correct with the data still incorrect or inaccurate. Content on a web page could be displayed properly and still contain the wrong information.

Code validation

• Purpose: Check that the application code systematically performs any of the previously mentioned validations during user data input.
• Example: Common problems discovered during code validation include: more than one data type allowed, data range checking not done, or ending of text strings not well defined.
• Limitations: Code validation might not validate all possible variations with data input.