1. How do we track the historical context of decommissioned metrics?

Include decommissioned metrics in lifecycle management and clearly indicate status, replacements, and when changes occurred.

2. How do we prevent LLMs from hallucinating answers?

Use a verification layer between the LLM and the output.

Do rigorous testing with real questions.

If the semantic layer and BI tools cannot handle a complex question reliably, do not force GenBI onto it.

3. Who is the customer for the semantic layer?

The primary customer is the business, especially decision-makers who rely on accurate metrics. Analysts benefit because they can define and manage metrics effectively.

4. Will GenBI answer all business questions in the future?

Generative BI is the “cherry on top.” It depends on whether the organization has done the foundational work in data infrastructure and governance.

5. How do we manage governance across different metric complexity levels?

Use a tiered governance model with different controls for corporate KPIs, functional metrics, and localized metrics, with ownership and approval at each level.

6. How do we transition from cataloging to a semantic layer?

Smaller organizations may leverage BI tool integrations. Larger organizations may need a separate tool or custom solution. The goal is interoperability and making definitions usable where people actually work.

7. What is the most common semantic layer failure mode?

Treating it as a one-time modeling project.

If there is no ownership, change workflow, and lifecycle (create, evolve, retire), the semantic layer will drift and teams will route around it.