What does data quality encompass in terms of attributes?

What does data quality encompass in terms of attributes?

• A. Only consistency and uniqueness
• B. Accuracy, completeness, and integrity
• C. Timeliness and validity only
• D. Completeness and unavailability

Answer: (B)

Data quality is a critical concept that refers to the overall effectiveness of data in meeting its intended purpose. It encompasses a range of attributes that determine how well the data meets various business needs. Focusing on accuracy, completeness, and integrity provides a comprehensive view of data quality. - **Accuracy** ensures that the data correctly represents the real-world construct it is intended to model. This means that the information is truthful and reliable. - **Completeness** assesses whether all necessary data is present. For data to be useful, it must include all required information without missing elements. - **Integrity** refers to the data's consistency and reliability over its lifecycle. This attribute checks the validity of the data according to its defined rules and relationships. These three attributes work together to create a robust framework for evaluating data quality. Considering attributes like these is crucial for organizations that rely on data-driven decision-making, as high-quality data leads to better outcomes and efficiencies. The other options either limit the scope of data quality attributes or include terms that do not align with standard definitions, illustrating that thorough understanding includes a broader set of qualities beyond just a few selected aspects.

Data quality is a critical concept that refers to the overall effectiveness of data in meeting its intended purpose. It encompasses a range of attributes that determine how well the data meets various business needs.

Focusing on accuracy, completeness, and integrity provides a comprehensive view of data quality.

• Accuracy ensures that the data correctly represents the real-world construct it is intended to model. This means that the information is truthful and reliable.

• Completeness assesses whether all necessary data is present. For data to be useful, it must include all required information without missing elements.

• Integrity refers to the data's consistency and reliability over its lifecycle. This attribute checks the validity of the data according to its defined rules and relationships.

These three attributes work together to create a robust framework for evaluating data quality. Considering attributes like these is crucial for organizations that rely on data-driven decision-making, as high-quality data leads to better outcomes and efficiencies.

The other options either limit the scope of data quality attributes or include terms that do not align with standard definitions, illustrating that thorough understanding includes a broader set of qualities beyond just a few selected aspects.