Having an analytical insight is one thing, but actually doing something about it is something else entirely. Today’s fast-changing business climate demands real-time visibility and up-to-the-minute recommendations from data and analytics. This in turn depends on (a) clearly defined projects, (b) data acquisition, blending, exploration, (c) feature engineering, model training, evaluation, (d) model deployment, monitoring and rebasing. In this presentation, I provide a tour of this end-end data science lifecycle and key components, including an agile data fabric and catalog, a data science modeling environment, model operations and interactive visual analytics on streaming and accumulating data. I will illustrate this flow with case studies in manufacturing and financial services, and describe the emerging world of human-centered AI in context.