Where should a semantic layer be built?

A semantic layer is crucial in a data ecosystem, even with a well-built data model. It ensures consistency by making metric definitions explicit, reducing errors that can occur when writing SQL queries. The abstraction offered by the semantic layer simplifies data retrieval and speeds up development, as users don't need to be proficient in SQL. Semantic layers also improve AI readiness by allowing complex queries to be expressed in plain language, making it easier for AI systems to interact with and analyze data accurately. Security is enhanced through well-defined entities that provide a checkpoint for enforcing access policies. Performance and cost efficiencies are significant benefits of adopting semantic layers, as they increase cache hit probabilities and enable sophisticated caching mechanisms. Overall, the semantic layer offers substantial advantages in consistency, user interface, AI readiness, security, and performance, making it an indispensable component for data-driven organizations.