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.
Leader @Build by McKinsey
Stuart is a leader of Build by McKinsey, the firm’s community of technologists and designers who help our clients turn game-changing opportunities into reality. In particular, he leads Build by McKinsey’s labs—a unit that focuses on developing next-generation horizontal tools and assets to accelerate impact, improve quality, and reduce risk. He also leads our global team of data engineers and architects who help organizations undergo complex, data-driven digital transformations in all manner of legacy and modern cloud-based environments. He is a passionate advocate and agent for creating sustainable data foundations for new digital opportunities, analytics transformations, and bringing new uses to old data through thoughtful data architecture.
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