Agile software development and elastic cloud foundations have enabled on-demand expansion of compute functions from real-time processing to Machine Learning at scale but Data has been left behind. Data Gravity presents a very real and practical challenge of moving from the pilot phase to scaled production where huge amounts of data must be modeled, managed and migrated just as seamlessly as software code and ML models. In this session we will explore the relationships between DataOps, Data Gravity, Cloud-native data services and Data Mesh to build agile data architectures that power massive scale digital twins of complex real-world environments such as muti-site manufacturing programs and entire power grid networks. In this talk, Stuart Sim (McKinsey & Company) will offer best practices, real world case examples, and strategies to build agile data architectures.