Building Trusted Executive Analytics on Databricks

Executive analytics fails in the definitions. Databricks SQL, AI/BI dashboards, and Genie can all query the same lakehouse. If they do not share a semantic foundation, you have accelerated an argument.

Write metric contracts

For each executive KPI, record grain, owner, source tables, time logic, and the known exclusions. Put that contract in the data platform—comments, metric views, governed gold tables—not only in a slide.

Gold tables are the product

Databricks describes gold as enriched, often aggregated data aligned to business needs and optimized for query performance. That is the correct layer for executive consumption. If leadership SQL still joins three silver tables with a filter nobody remembers, the model is not done.

One warehouse pattern, many surfaces

Dashboards, spreadsheets that must die, embedded analytics, and Genie spaces should read the same gold. Genie is useful when a space is built on modeled, documented, permissioned data. It is not a way to skip modeling.

Trust is also latency and access

A correct number that arrives after the meeting is not used. A number that the wrong people can export is not governed. Warehouse sizing, incremental refresh, and Unity Catalog grants are part of the analytics architecture.

The test is simple: can two leaders, an operator, and an auditor get the same answer for the same period without a reconciliation thread? If not, keep working on the model. The chart can wait.