What does the DQ intrinsic attribute "accuracy" refer to?

What does the DQ intrinsic attribute "accuracy" refer to?

• A. The relevance of data to a business context
• B. The correctness of the data
• C. The speed at which data is processed
• D. The authority of the data source

Answer: (B)

The "accuracy" intrinsic attribute of data quality refers specifically to the correctness of the data. It measures whether the data values accurately represent what they are intended to describe, thereby reflecting the true situation or facts that they are supposed to represent. Accuracy is critical for maintaining the integrity of data and ensuring that decisions based on that data are reliable. In the context of data quality, accuracy serves as a benchmark for assessing how well the data serves its purpose. For instance, if customer records in a database contain incorrect names or addresses, those inaccuracies can lead to poor decision-making, ineffective marketing efforts, and an overall breakdown in business processes. Therefore, understanding and measuring accuracy is essential for organizations striving for high-quality data management. The other options reflect different aspects of data quality but do not define accuracy. Relevance pertains to how pertinent data is in a specific business context, speed relates to how quickly data can be processed, and authority involves the credibility and trustworthiness of the source where the data originates. Each of these attributes contributes to overall data quality but does not directly define the accuracy attribute.

The "accuracy" intrinsic attribute of data quality refers specifically to the correctness of the data. It measures whether the data values accurately represent what they are intended to describe, thereby reflecting the true situation or facts that they are supposed to represent. Accuracy is critical for maintaining the integrity of data and ensuring that decisions based on that data are reliable.

In the context of data quality, accuracy serves as a benchmark for assessing how well the data serves its purpose. For instance, if customer records in a database contain incorrect names or addresses, those inaccuracies can lead to poor decision-making, ineffective marketing efforts, and an overall breakdown in business processes. Therefore, understanding and measuring accuracy is essential for organizations striving for high-quality data management.

The other options reflect different aspects of data quality but do not define accuracy. Relevance pertains to how pertinent data is in a specific business context, speed relates to how quickly data can be processed, and authority involves the credibility and trustworthiness of the source where the data originates. Each of these attributes contributes to overall data quality but does not directly define the accuracy attribute.