What are hallmarks of data quality?completenessconsistencyall of these answersaccuracy

When assessing data quality, it’s essential to consider several key hallmarks that ensure the data is reliable, usable, and effective for decision-making. Here are the primary hallmarks of data quality:

1. Completeness: This aspect refers to the extent to which all necessary data is present. Missing data can significantly impact analysis and decision-making. Ensuring completeness means that all expected fields or values are filled in and no critical information is left out.

2. Consistency: Consistency measures whether the data is the same across different datasets and over time. Inconsistent data can create confusion and lead to erroneous conclusions. For example, if a customer's name is spelled differently in two different tables, this inconsistency needs to be resolved.

3. Accuracy: This hallmark assesses whether the data is correct and reliable. Accurate data reflects the true situation without errors or distortions. Ensuring accuracy may involve verifying data against trusted sources or conducting validations.

4. All of these answers: Since completeness, consistency, and accuracy are all essential components of data quality, this option encapsulates the significance of evaluating all these aspects cohesively to achieve high data quality.

In summary, understanding these hallmarks—completeness, consistency, and accuracy—are vital for maintaining high data quality, ultimately empowering organizations to make informed decisions based on reliable information.