What does data quality ensure regarding data suitability?

What does data quality ensure regarding data suitability?

• A. It guarantees data is always accurate
• B. It assures data is fit for consumption
• C. It confirms data is never duplicated
• D. It validates data only after it’s used

Answer: (B)

Data quality plays a crucial role in ensuring that data is suitable for its intended use, and the emphasis is on the fitness of data for consumption. This means that the data meets specific criteria that allow it to be utilized effectively in various contexts, such as decision-making, analysis, and reporting. When data is described as being "fit for consumption," it implies that it is reliable, relevant, timely, and appropriate for the specific purpose it is being used for. The concept of fitness for consumption encompasses several dimensions of data quality, including accuracy, completeness, consistency, and relevance. It ensures that users can trust and use the data in their operations without encountering significant issues. Other options do not accurately capture the essence of data quality in relation to suitability. For instance, while it’s important for data to be accurate, data quality does not guarantee that data is always accurate, as there can still be errors or inconsistencies. Similarly, while avoiding duplication is a critical aspect of managing data, ensuring that data is free of duplicates is just one facet of overall data quality and does not encapsulate the broader idea of suitability for use. Validating data only after it’s been used does not align with proactive data quality practices; rather, it is essential to ensure high

Data quality plays a crucial role in ensuring that data is suitable for its intended use, and the emphasis is on the fitness of data for consumption. This means that the data meets specific criteria that allow it to be utilized effectively in various contexts, such as decision-making, analysis, and reporting. When data is described as being "fit for consumption," it implies that it is reliable, relevant, timely, and appropriate for the specific purpose it is being used for.

The concept of fitness for consumption encompasses several dimensions of data quality, including accuracy, completeness, consistency, and relevance. It ensures that users can trust and use the data in their operations without encountering significant issues.

Other options do not accurately capture the essence of data quality in relation to suitability. For instance, while it’s important for data to be accurate, data quality does not guarantee that data is always accurate, as there can still be errors or inconsistencies. Similarly, while avoiding duplication is a critical aspect of managing data, ensuring that data is free of duplicates is just one facet of overall data quality and does not encapsulate the broader idea of suitability for use. Validating data only after it’s been used does not align with proactive data quality practices; rather, it is essential to ensure high