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 It is outdated if it was current at some preceding time yet incorrect at a later time.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.