Data warehousing is a data analytics strategy for collecting, storing, and managing large volumes of structured data into a centralized repository where advanced analytics processing and aggregation can be performed. Ad hoc and other complex reporting is then served to business intelligence applications. Traditionally, data warehouses were used like data lakes—to cleanse and transform the data—but data lakes and lakehouses have largely assumed these responsibilities. Data warehouses now adopt a more narrow scope, but one that is critical for many enterprises. Data warehouses should be utilized for strategic, top-down use cases. For example, senior leaders need KPI reporting on the performance of the business to make high-value decisions. That’s one of the more common justifications for a data warehouse. Another might include the reporting features in a customer-facing product. Today, report and query development is one of the largest costs associated with data warehousing solutions. With generative and agentic AI, it's possible for data warehouses to deliver greater value at faster speeds and lower cost.