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Data Quality Measures Quiz

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What are the factors comprising data quality?

Completeness, accuracy, relevance, timeliness, and consistency

Validity, reliability, precision, consistency, and availability

Accuracy, completeness, consistency, timeliness, believability, and interpretability (correct)

Correctness, efficiency, relevance, security, and usability

What does completeness refer to in data quality?

Data that were inconsistent with other recorded data may have been deleted

The fact that the month-end data are not updated in a timely fashion

Attributes of interest may not always be available (correct)

Data that are accurate or not (i.e., having incorrect attribute values)

What does accuracy refer to in data quality?

Timely update

Correct or wrong, accurate or not (i.e., having incorrect attribute values) (correct)

Not recorded, unavailable

Some modified but some not

What is consistency in the context of data quality?

<p>Data that were inconsistent with other recorded data may have been deleted</p> Signup and view all the answers

What factor of data quality is impacted by the month-end data not being updated in a timely fashion?

<p>Timeliness</p> Signup and view all the answers

Study Notes

Data Quality Factors

Data quality comprises several factors, including completeness, accuracy, consistency, and others.

Completeness

Completeness refers to the extent to which all required data is present and available.

Accuracy

Accuracy refers to the degree to which data correctly reflects the real-world object or event it is intended to represent.

Consistency

Consistency refers to the uniformity and coherence of data in terms of its format, structure, and content across different data sources and systems.

Timeliness

Timeliness is a factor of data quality that is impacted when data is not updated in a timely fashion, such as month-end data not being updated promptly.

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Description

Test your knowledge of data quality measures including accuracy, completeness, consistency, timeliness, believability, and interpretability. This quiz covers topics such as data cleaning, data integration, data reduction, and data transformation.