What is the key characteristic of data quality?

What is the key characteristic of data quality?

• A. Variability in data formats
• B. Single source of truth
• C. Accuracy, completeness, and consistency of data
• D. Volume of data collected

Answer: (C)

The key characteristic of data quality hinges on the concepts of accuracy, completeness, and consistency of data. These attributes are critical as they determine how well the data serves its intended purpose in decision-making and analysis. Accuracy refers to the correctness of the data, ensuring that it reflects the real-world scenarios it is supposed to represent. Completeness indicates that all necessary data is present and available for use, meaning that there are no missing elements that could lead to misleading conclusions. Consistency entails that the data is uniform across different datasets and over time, which is vital for maintaining reliability when integrating or comparing multiple data sources. In contrast, the other options, while related to data management, do not encapsulate the essence of data quality as effectively. Variability in data formats introduces complexities and potential errors rather than defining quality. A single source of truth, while important for governance and reducing discrepancies, does not address the intrinsic properties of the data itself. Finally, the volume of data collected pertains more to the quantity rather than the quality of the data, as high volumes can still contain inaccuracies or inconsistencies. Thus, the correct understanding of data quality is inherently tied to the accuracy, completeness, and consistency of the data involved.

The key characteristic of data quality hinges on the concepts of accuracy, completeness, and consistency of data. These attributes are critical as they determine how well the data serves its intended purpose in decision-making and analysis.

Accuracy refers to the correctness of the data, ensuring that it reflects the real-world scenarios it is supposed to represent. Completeness indicates that all necessary data is present and available for use, meaning that there are no missing elements that could lead to misleading conclusions. Consistency entails that the data is uniform across different datasets and over time, which is vital for maintaining reliability when integrating or comparing multiple data sources.

In contrast, the other options, while related to data management, do not encapsulate the essence of data quality as effectively. Variability in data formats introduces complexities and potential errors rather than defining quality. A single source of truth, while important for governance and reducing discrepancies, does not address the intrinsic properties of the data itself. Finally, the volume of data collected pertains more to the quantity rather than the quality of the data, as high volumes can still contain inaccuracies or inconsistencies.

Thus, the correct understanding of data quality is inherently tied to the accuracy, completeness, and consistency of the data involved.