Characteristics of Data Quality

Comprehensiveness:

All required data items are included. Ensure that the entire scope of the data is collected, and document intentional limitations.

Definition:

Clear definitions should be provided so that current and future data users will know what the data mean. Each data element should have clear meaning and acceptable values.

Accuracy:

Data are the correct values and are valid.

Integrity:

Data are true to the source and have not been altered or destroyed.

Accessibility:

Data items should be easily obtainable and legal to collect.

Precision:

Data values should be just large enough to support the application or process.

Granularity:

The attributes and values of data should be defined at the correct level of detail.

Relevancy:

The data are meaningful tot he performance of the process or application for which they are collected.

Currency:

The data should be up to date. A datum value is up to date if it is current for a specific point in time. It is outdated if it was current at some preceding time yet incorrect at a later time.

Consistency:

The value of the data should be reliable and the same across applications.

Timeliness:

Timeliness is determined by how the data are being used and their context.