Overview of the Data Quality Framework
Data Quality Management aims at ensuring Data is fit for purpose based on data producers and consumers requirements. It is central to data management. Low quality data represents cost and risk as well as a negative impact on value.
Managing quality of data is challenging because data is often created as a by- product of operational processes and explicit standards for data quality are not set.
Quality must be planned for as part of the data lifecycle because the it can be impacted by a range of lifecycle events.
The Data Quality Framework has been designed to :
enforce best practices,
define and share common language,
avoid fragmented approach generating repetitive tasks and misalignments,
set unified requirements and measurement mechanisms,
provide pre-built components, libraries, and modules that abstract away repetitive tasks and common functionalities.
Contents of the framework can be found here :