Defining and managing data quality rules

There are many definitions of data quality but data is generally considered high quality if, "they are fit for their intended uses in operations, decision making and planning." (Tom Redman<Redman, T.C. (2008). With Compose, the data must be "fit" for use in a data mart.

Compose provides two ways of ensuring data quality: Data validation and data cleansing. As opposed to data validation which usually results in data being rejected, data cleansing provides a means of replacing, modifying, or deleting incomplete, incorrect or inaccurate data.

Data that is rejected by a rule will be copied to Error Mart tables in the Error Mart schema defined in the Landing Zone database settings.

Details about rejected data can be viewed in the monitor's Error Mart tab. For more information, see Viewing information in the monitor.