Data Quality & Validation Flashcards

Which data quality dimension refers to the degree to which data correctly represents the real-world construct it is intended to model?

Answer: Accuracy

Accuracy is the dimension that measures how closely data values reflect the true, real-world values they are intended to represent.

A dataset is considered 'complete' when it:

Answer: Has all required fields populated with valid values

Completeness means all mandatory attributes are present and contain meaningful, non-null values rather than being empty or missing.

Which validation technique checks that a data value falls within a predefined acceptable range?

Answer: Range check

A range check validates that a value lies between a minimum and maximum threshold, such as ensuring a percentage field is between 0 and 100.

What is the primary purpose of a checksum in data validation?

Answer: To detect errors introduced during data transmission or storage

A checksum is a calculated value used to verify data integrity by detecting accidental changes or corruption during transmission or storage.

Which of the following best describes 'data consistency'?

Answer: The same data element holds the same value across all related datasets and systems

Consistency requires that the same data item has the same value wherever it appears across different tables, databases, or systems.

A 'uniqueness' constraint in data quality is violated when:

Answer: Two records share a value that should be exclusive to one record

Uniqueness violations occur when a value that must be unique—such as a social security number or primary key—appears in more than one record.

Which data profiling technique is used to understand the distribution and frequency of values in a data column?

Answer: Value frequency analysis

Value frequency analysis examines how often each distinct value appears in a column, revealing patterns, outliers, and potential data quality issues.