Artificial Intelligence and its Generative version do not “solve” the problem of Data Quality, but they certainly change and broaden its boundaries.The rapid evolution in the world of Data Management and in particular the sudden explosion of these technologies have rightly shifted the focus to strategic and interesting “hype” topics.

Building the best AI model is a particularly difficult undertaking if we do not have large masses of data that are judged to be reliable. Indeed, on the one hand, the amount of data and information that models require to run amplifies the risks of poor quality of that information. On the other hand, Artificial Intelligence can be of great help in supporting data quality management processes (e.g., Augmented Data Quality) at different stages from design, to the execution of the controls themselves.

This white paper explains how to set up a system of data controls to create value in the enterprise, starting with the definition and types of controls and ending with the working model of a “Hub Spoke” system to govern data quality.