Accuracy

Completeness

Consistency

Timeliness

Relevance

Data profiling

Data cleansing

Data validation

Data governance

Data integration

Missing values

Inconsistent data

Duplicate data

Outdated data

All of the above

To identify data quality issues

To understand the distribution of data

To identify outliers

To identify duplicate data

All of the above

Accuracy

Completeness

Consistency

Timeliness

All of the above

Accuracy is the degree to which data is correct, while precision is the degree to which data is repeatable.

Accuracy is the degree to which data is consistent, while precision is the degree to which data is reliable.

Accuracy is the degree to which data is complete, while precision is the degree to which data is timely.

Accuracy is the degree to which data is relevant, while precision is the degree to which data is useful.

None of the above

To ensure that data is accurate

To ensure that data is complete

To ensure that data is consistent

To ensure that data is timely

All of the above

Use data profiling to identify data quality issues

Use data cleansing to correct data quality issues

Use data validation to ensure that data is accurate

Use data governance to manage data quality

All of the above

Poor data quality can lead to incorrect decisions

Poor data quality can lead to wasted resources

Poor data quality can lead to lost revenue

Poor data quality can lead to all of the above

None of the above

Data governance provides a framework for managing data quality

Data governance establishes data quality standards

Data governance monitors data quality

Data governance enforces data quality

All of the above

Data quality is the degree to which data is accurate, complete, consistent, and timely, while data integrity is the degree to which data is protected from unauthorized access, use, or disclosure.

Data quality is the degree to which data is relevant, while data integrity is the degree to which data is reliable.

Data quality is the degree to which data is useful, while data integrity is the degree to which data is secure.

Data quality is the degree to which data is valuable, while data integrity is the degree to which data is trustworthy.

None of the above

Accuracy, completeness, and consistency

Timeliness, relevance, and usability

Security, privacy, and compliance

All of the above

None of the above

Data quality is the degree to which data is accurate, complete, consistent, and timely, while data governance is the process of managing data quality.

Data quality is the degree to which data is relevant, while data governance is the process of ensuring that data is used for its intended purpose.

Data quality is the degree to which data is useful, while data governance is the process of making data available to those who need it.

Data quality is the degree to which data is valuable, while data governance is the process of protecting data from unauthorized access.

None of the above

Accuracy, completeness, consistency, and timeliness

Relevance, usability, value, and trustworthiness

Security, privacy, compliance, and auditability

All of the above

None of the above

Data quality is the degree to which data is accurate, complete, consistent, and timely, while data integrity is the degree to which data is protected from unauthorized access, use, or disclosure.

Data quality is the degree to which data is relevant, while data integrity is the degree to which data is reliable.

Data quality is the degree to which data is useful, while data integrity is the degree to which data is secure.

Data quality is the degree to which data is valuable, while data integrity is the degree to which data is trustworthy.

None of the above