Turn scattered datasets into trusted, reusable data products that power AI, analytics, and measurable business results. See why so many expensive data initiatives stall after go-live, and then what works when you treat data like a real product with real users. With the surge of generative and agentic AI, know that the models aren't the bottleneck; it's the data. The winners are the organizations that can deliver data that's well designed, trusted, easy to find, and consistent across the business. You'll get a clear, practical definition of a data product as a "well-defined, reusable, governed, and user-oriented data asset," plus identify the core properties that separate a true data product from a database table, one-off extract, or dashboard. Be able to explain the keywords teams actually wrestle with, such as data governance, metadata management, data catalog, data quality, interoperability, stewardship, and discoverability. Next, thinking operationally, see how to apply an end-to-end data product lifecycle (planning through demise), including reusable templates to move from idea to execution, covering project charter, business requirements, design blueprints, and ROI. Finally, you'll learn how to scale beyond one heroic team: architectural blueprints (including a reference architecture and an AWS mapping), certification criteria ("compliance by design", catalog and metadata, incident response, and adoption and impact), and the people side (roles like data product owner and governance specialists), and how to drive adoption with a structured change approach (including Kotter's 8-step model). If you're building a data product strategy, modern data platform, data mesh-style operating model, or a portfolio roadmap