Data Warehousing Reimagined:
Fueling AI-powered BI at Scale

This presentation explores data warehousing not merely as a technical solution, but as a critical business capability for achieving strategic goals. It addresses the common pitfalls of data warehousing projects, arguing that most failures are not due to technology, but to poor planning, lack of organizational data literacy, and a failure to address the underlying data quality. The core message is that "garbage in, garbage out" is always true, and that a successful data warehouse is built on a foundation of clean, organized data.  The presentation outlines good data warehousing practices that prioritize:

• Engineering and Architecture: Emphasizing that effective data warehousing requires skilled engineering talent and a holistic, adaptive architectural approach, rather than a prescriptive, cookie-cutter method.

• Data Preparation and Quality: Highlighting the need to separate "the wheat from the chaff," with a focus on eliminating redundant, obsolete, or trivial (ROT) data before it enters the warehouse.

• Iterative Development: Advocating for a continuous improvement cycle (plan, do, check, act) to iteratively refine the data warehouse in alignment with strategic business direction.

• Business Alignment: Stressing that data warehousing must be driven by business needs and a clear strategy, with a focus on using warehousing capabilities to solve specific business challenges rather than just building a large repository.

The ultimate goal is to reframe data warehousing as a means to increase organizational capabilities, improve operations, and create new strategic opportunities, thereby providing a clear, measurable return on investment.