Forecasting the next major shifts in the data industry and what they mean for products

Give your point of view on how the data landscape is changing. What are the next big shifts, and why do you expect them? ```hint Reason from drivers Start from the forces changing what data is used for and how it gets processed, then reason forward to concrete shifts. For each shift, ask who feels it first and what new product or bottleneck it creates. ``` ### Clarifying Questions - Does "the data landscape" mean data infrastructure (storage, databases, processing engines), analytics and business intelligence, the data used to build AI models, or all three? - What horizon do you have in mind: the next couple of years, or a decade out? - Should I connect the view to where NVIDIA could play, or keep it industry-wide? ### What a Strong Answer Covers - A clear thesis supported by a small number of shifts, each tied to an underlying driver rather than a buzzword - Signals that each shift is already under way, with an honest level of confidence - Who each shift affects (data engineers, analysts, model builders, business users) and the products or bottlenecks it creates - What the view means for NVIDIA, for example where accelerated computing fits in data processing and in the pipelines that feed AI - Counter-trends and risks, such as regulation, cost pressure, or adoption lagging behind the hype ### Follow-up Questions - If you could fund only one of these bets next year, which would it be, and what early signal would tell you it is working? - Which prediction are you least sure of, and what evidence would change your mind? - Which existing product category does one of these shifts threaten, and how should the incumbent respond?