According to recent research, enterprises would probably be losing approximately $406 million every year due to low-quality data, which prevents their AI applications from working efficiently [1][2][3]. Research shows that the accumulated losses will be a staggering amount, reaching $745 billion by the end of 2025. Data quality is not an option or recommendation for developers and data engineers; it is a technical requirement.
This article describes the gateways, sources, and methods for creating AI systems that depend on a flow of quality information.