CIOs and CTOs are prioritizing the ability to turn enterprise data into actionable insights, as fragmented, siloed systems have limited effective decision-making for years. Many organizations suffer from inconsistent data, weak analytics, duplicated MIS efforts, and data integrity risks due to poor data design. Advances in storage and processing have renewed interest in data warehouses (DWHs), but success depends on sound design choices. Key decisions span data models, warehouse platforms, analytics approaches, and implementation partners. Businesses may adopt industry-standard data models, partner-provided models, or custom designs depending on the uniqueness of reporting needs. DWH platforms range from standard databases for lower-volume analytics to appliance-based solutions for real-time and big-data use cases. Analytics can be embedded within the DWH or delivered through stand-alone applications, depending on complexity and change frequency. Selecting a capable implementation partner is critical, as they own data integration, quality, performance, reporting, and change management. A clear DWH strategy is essential to build a reliable foundation for data-driven decisions.