Implementing Data Quality By Design For Improved Data Integrity
To begin, it is important to understand that data quality and data integrity are not the same thing.
Data quality may be defined as the general utility of a data set as a function of its ability to meet the requirements for its use. This definition includes relativity that may also be explained as bias, which simply means that context is necessary to fully interpret and understand the data. Data quality is very specific to the data set and the data itself and, if measured to be poor, may be improved through verification, transformation, and/or cleanup.
Data integrity is about trust and is as much about the supporting systems and processes as it is about the data set and the data itself. Data integrity relates to the state of the data or the sensitivity of data to external influence or change...
