Training an LLM with diverse "data" instead of traditional "language" tokens could open up new use cases beyond traditional ML. Such a Large Data Model could aid in privacy-preserving data generation, augmenting datasets, filling in missing values, and executing complex data-related tasks with minimal feature engineering. It leverages the vast pattern recognition abilities of LLMs across varied domains.