The Great Data Divide` highlights the limitations of traditional organizational data management practices, where transactional and analytical data are handled by separate teams. This divide can hinder AI goals due to the increasing demand for real-time and historical data from machine learning models. The article proposes a new approach, treating data as a product with characteristics like state, age, and context, and unifying teams through Domain-Driven Design to manage data more efficiently. By erasing the line between transactional and analytics teams, organizations can increase their overall data processing proficiency and unlock the full potential of AI models.