All of these answers are correct. The hallmarks of data quality include completeness, consistency, and accuracy.

1. Completeness: This refers to having all the necessary data. Incomplete data can lead to inaccurate conclusions or decisions.

2. Consistency: This refers to data being consistent throughout the dataset. Inconsistent data can cause confusion and lead to incorrect results.

3. Accuracy: This refers to the data being correct. Inaccurate data can lead to false conclusions and poor decision making.

Question

All of these answers are correct. The hallmarks of data quality include completeness, consistency, and accuracy.

1. Completeness: This refers to having all the necessary data. Incomplete data can lead to inaccurate conclusions or decisions.

2. Consistency: This refers to data being consistent throughout the dataset. Inconsistent data can cause confusion and lead to incorrect results.

3. Accuracy: This refers to the data being correct. Inaccurate data can lead to false conclusions and poor decision making.

Knowee AI · Accepted Answer

All of these answers are correct. The hallmarks of data quality include completeness, consistency, and accuracy.

1. Completeness: This refers to having all the necessary data. Incomplete data can lead to inaccurate conclusions or decisions.

2. Consistency: This refers to data being consistent throughout the dataset. Inconsistent data can cause confusion and lead to incorrect results.

3. Accuracy: This refers to the data being correct. Inaccurate data can lead to false conclusions and poor decision making.

What are hallmarks of data quality?completenessconsistencyall of these answersaccuracy