CompTIA Data+ (DA0-002)Data Concepts and EnvironmentsHard
A data scientist is preparing a dataset for machine learning. The dataset contains customer information including 'CustomerID' (unique identifier), 'FirstName', 'LastName', 'EmailAddress', 'OrderCount' (number of orders), and 'LastOrderDate'. The scientist observes that 'OrderCount' is stored as text, which causes errors during numerical operations. To resolve this, 'OrderCount' needs to be converted to an integer. Which data quality dimension is primarily being addressed by this conversion?
- AConsistency
- BCompleteness
- CTimeliness
- DValidity
Show answer & explanationAnswer & explanation
Correct answer: D. Validity
Validity refers to whether data conforms to a defined format, type, or range. Storing 'OrderCount' as text when it should be an integer violates its validity, and converting it addresses this issue.
Why the other options are wrong
- A. Consistency refers to whether data is uniform across different systems or over time, or adheres to business rules, but the primary issue here is the field's intrinsic type.
- B. Completeness refers to the absence of missing values, which is not the problem described.
- C. Timeliness refers to how current the data is, not its format.
Data Validity
A data quality dimension that refers to the extent to which data conforms to defined formats, types, or business rules.
- Ensures data is in the correct format (e.g., numbers are numerical, dates are date types).
- Prevents incorrect or nonsensical entries (e.g., age cannot be negative).
- Crucial for data processing, analysis, and system interoperability.
Memory trick: Data quality is like a diamond's facets: each one (accuracy, completeness, validity, etc.) contributes to its overall brilliance and value.