What is meant by the Data Quality Dimension of Uniqueness?

What is meant by the Data Quality Dimension of Uniqueness?

• A. No entry exists more than once unless expected
• B. All values in the dataset must be logically correct
• C. Data values must align with predetermined standards
• D. Each data entry must hold value significance

Answer: (A)

The Data Quality Dimension of Uniqueness refers to the principle that no entry in the dataset should appear more than once unless such duplication is intentional or expected within the context of the data. This concept is crucial for maintaining data integrity and ensuring that each record is distinct, especially in datasets where uniqueness is necessary for accurate identification, such as customer records or product IDs. When data is unique, it allows organizations to effectively manage and analyze the data without the complications that arise from duplicate entries. This is particularly important in operations like data retrieval and reporting, where duplicates can skew results and lead to incorrect conclusions. By adhering to the uniqueness dimension, organizations can ensure that each data entry provides reliable and valid information. The other options, while speaking to different aspects of data quality, do not specifically address the principle of uniqueness. For instance, logical correctness and alignment with standards relate to data consistency and conformity, whereas significance of value relates more to the relevance and contextual importance of the data rather than its uniqueness. Thus, focusing on uniqueness is essential for effective data management practices, ensuring clarity and precision in data handling.

The Data Quality Dimension of Uniqueness refers to the principle that no entry in the dataset should appear more than once unless such duplication is intentional or expected within the context of the data. This concept is crucial for maintaining data integrity and ensuring that each record is distinct, especially in datasets where uniqueness is necessary for accurate identification, such as customer records or product IDs.

When data is unique, it allows organizations to effectively manage and analyze the data without the complications that arise from duplicate entries. This is particularly important in operations like data retrieval and reporting, where duplicates can skew results and lead to incorrect conclusions. By adhering to the uniqueness dimension, organizations can ensure that each data entry provides reliable and valid information.

The other options, while speaking to different aspects of data quality, do not specifically address the principle of uniqueness. For instance, logical correctness and alignment with standards relate to data consistency and conformity, whereas significance of value relates more to the relevance and contextual importance of the data rather than its uniqueness. Thus, focusing on uniqueness is essential for effective data management practices, ensuring clarity and precision in data handling.