What is data quality and name three metrics used to measure it.

What is data quality and name three metrics used to measure it.

• A. Data quality is the accuracy, completeness, and consistency of data; metrics include accuracy, completeness, timeliness, and consistency.
• B. Data quality is about data size; metrics include volume, velocity, variety.
• C. Data quality equals only data accuracy; metrics include accuracy only.
• D. Data quality is about metadata richness; metrics include schema count, documentation completeness, and lineage length.

Answer: (A)

Data quality is about how well data serves its intended purpose—whether it can be trusted to support the decisions or processes it’s used for. The strongest answer reflects the main dimensions that matter in practice: accuracy, completeness, and consistency, with concrete metrics that measure those aspects (and often timeliness as well). Accuracy means the data reflects the real world data it’s supposed to represent. Completeness means all required data is present, with no missing values in essential fields. Consistency means data is uniform across records and systems, following common formats and rules. Timeliness is also important because data that is accurate and complete but out of date may mislead decisions. By naming accuracy, completeness, timeliness, and consistency as the metrics, this option aligns with how data quality is evaluated in real scenarios. The other choices aren’t a good fit because they focus on aspects that aren’t the quality of the data itself. One centers on data size and uses the 3 Vs (volume, velocity, variety), which describe data characteristics, not quality. Another narrows quality down to accuracy alone, ignoring other essential dimensions like completeness and consistency. The last emphasizes metadata richness, which relates to metadata quality rather than the data values themselves.

Data quality is about how well data serves its intended purpose—whether it can be trusted to support the decisions or processes it’s used for. The strongest answer reflects the main dimensions that matter in practice: accuracy, completeness, and consistency, with concrete metrics that measure those aspects (and often timeliness as well).

Accuracy means the data reflects the real world data it’s supposed to represent. Completeness means all required data is present, with no missing values in essential fields. Consistency means data is uniform across records and systems, following common formats and rules. Timeliness is also important because data that is accurate and complete but out of date may mislead decisions. By naming accuracy, completeness, timeliness, and consistency as the metrics, this option aligns with how data quality is evaluated in real scenarios.

The other choices aren’t a good fit because they focus on aspects that aren’t the quality of the data itself. One centers on data size and uses the 3 Vs (volume, velocity, variety), which describe data characteristics, not quality. Another narrows quality down to accuracy alone, ignoring other essential dimensions like completeness and consistency. The last emphasizes metadata richness, which relates to metadata quality rather than the data values themselves.