Methodology

Data Quality controls can be implemented by adopting either a rule-based or a statistical / ML based approach. The following figure presents the alternatives:

Controls alternatives

Approaches to implementing DQ Controls

The specification and implementation of Data Quality controls involves various personas:

  • Data Owners who are accountable for the Datasets that are under their scope

  • Data Stewards, with potential help from Data Quality analysts, who are responsible for the identification and specification of controls

  • Technical stakeholders such as R&D Teams, Data Engineers, Data Quality teams who are responsible for the implementation of the controls

Controls sequence diagram

Process to implement Data Quality Controls

The following table presents the detailed steps involved in the specification, implementation and review of Data Quality controls:

Controls methodology

Data Quality Controls activities


(*)Data Quality Controls templates

Please refer to the Data Quality Framework documentation center.