Data is more crucial than ever, enabling the first generation of deep learning models to the new generation of foundation models. These foundation models are rapidly incorporating into several safety critical applications of human life. Thus, the large volume of data they rely on must be high-quality for safe model development. Due to the sheer volume of raw data, it is necessary to obtain a scalable ability to rank and select data by its inherent quality and value for both generic and specific tasks. Recently, foundation models themselves are used to discover even more data to feed into more foundation model training. This cyclic relationship between data and foundation models introduces another layer of complexity and biases to consider. Overall, this enormous challenge to discover the next generation of data requires several considerations: definition of quality data, bias-free data, scalability, generating data, ethical data gathering, continuous data gathering, and hallucination free foundation models for data mining. In this workshop, 7 leading experts across academia and industry will discuss how to tackle this large challenge together.