Part 15/18:
A hypothetical example discussed is personalized lending. Data components like credit profiles, income, and transaction histories underpin eligibility decisions. To foster transparency, a semantic layer on top of the data stack should explain why a person qualifies for a particular loan—sharing metadata, visualizations, and workflow explanations.
Such explanations bolster trust and facilitate regulatory compliance, ensuring borrowers and internal teams understand the rationale behind decisions. A semantic layer acts as the bridge between raw data and human understanding, weaving the story behind the figures.