As the use of machine learning (ML) becomes ubiquitous, there is a growing understanding and appreciation for the role that data plays for building successful ML solutions. Classical ML research has been primarily focused on learning algorithms and their guarantees. Recent progress has shown that data is playing an increasingly central role in creating ML solutions, such as the massive text data used for training powerful language models, (semi-)automatic engineering of weak supervision data that enables applications in few-labels settings, and various data augmentation and manipulation techniques that lead to performance boosts on many real world tasks. On the other hand, data is one of the main sources of security, privacy, and bias issues in deploying ML solutions in the real world. This workshop will focus on the new perspective of machine learning for data --- specifically how ML techniques can be used to facilitate and automate a range of data operations (e.g. ML-assisted labeling, synthesis, selection, augmentation), and the associated challenges of quality, security, privacy and fairness for which ML techniques can also enable solutions.