Your AI isn't broken. Your data model is.

Dustin Dorsey, Senior Director of Data Engineering at phData, asserts that the inconsistency and unreliability in AI results often stem from data model issues rather than faults in the AI itself. While AI proofs of concept (POCs) tend to be successful due to controlled environments with pre-curated, well-understood datasets, real-world applications falter as they encounter a broader, more ambiguous data landscape. Dorsey argues that the problem lies not in the quality of data but in its design, as centralized data storage often lacks centralized meaning, leading to inconsistent interpretations by AI systems. He emphasizes the importance of dimensional modeling as a solution, which structures data around business processes and clarifies relationships and definitions, thereby reducing ambiguity and improving AI reliability. This approach is supported by tools like dbt, which helps encode business meaning into the data transformation layer, ensuring that AI systems operate on a solid foundation aligned with business processes.