How can I give business users self-service analytics without sacrificing governance?

Summary

• Let business users ask questions of the data they are already authorized to see, so governance travels with the data rather than being bolted on afterward. Databricks AI/BI delivers this on the Data Intelligence Platform.
• AI/BI pairs two experiences: Genie for natural-language analytics and AI/BI Dashboards for low-code visualizations — both governed natively by Unity Catalog.
• One governance layer, enforced at the SQL engine: row-level security, column masks, and table grants apply identically whether a user asks Genie, opens a dashboard, or runs a notebook, so applications cannot bypass Unity Catalog.
• Consistent, trusted definitions: certified Metric Views define KPIs once so every user sees the same governed calculation instead of a locally recreated formula.
• Auditable by design: every question, answer, and executed query is captured for audit and lineage in Unity Catalog, and Databricks One gives business users a simplified, read-only door to dashboards and Genie Agents.

How can I give business users self-service analytics without sacrificing governance?

Self-service analytics stalls when every question has to route through an analyst, and it becomes risky when access is loosened to speed things up. The goal is to let business users ask questions in their own words and get trustworthy answers directly — while the same security policies and business definitions the organization already relies on continue to apply. Databricks AI/BI delivers this with two complementary experiences, Genie for conversational analytics and AI/BI Dashboards for low-code visual analytics, both native to the Databricks Data Intelligence Platform and governed by Unity Catalog. See What is AI/BI?.

Why Databricks AI/BI enables governed self-service

• One governance layer, enforced at the query engine. Unity Catalog is the single authorization layer for Databricks, and enforcement fires at the SQL engine. It does not matter which service issues the query — Genie, an AI/BI dashboard, or a notebook — the same row filters, column masks, and grants apply, and applications cannot bypass governance. See Governance and security for dashboards.
• Row- and column-level security. Row filter functions transparently restrict which rows a user sees based on their identity, and column mask functions transform or redact sensitive values such as PII at query runtime. Genie and dashboards can only query objects on which the user holds SELECT access, so users see only the data they are authorized to see.
• Ask questions in plain English with Genie. Rather than requiring SQL or an analyst, users type questions in natural language and receive answers as narrative explanations, tables, and visualizations. Genie asks for clarification when a question is ambiguous and keeps conversational context so users can refine questions naturally. See AI/BI Genie.
• Consistent, certified definitions. Certified Metric Views let you define KPIs once at the data layer and enforce consistent semantics across dashboards, Genie Agents, and applications. When Genie answers from a Metric View it uses the approved definition rather than a locally recreated formula, so a metric means the same thing everywhere. See What is AI/BI?.
• Transparent and trustworthy. Genie shows the SQL behind every answer so results can be verified, and space authors can curate trusted assets and example questions to guide users toward governed data products. See How leading companies are delivering trusted, AI-powered self-service analytics.
• Auditable access. Genie interactions are captured as conversation history and in Unity Catalog system tables for audit events, data-access lineage, and executed SQL, and dashboards integrate with audit logs to track access and modifications — creating a complete record of who accessed what data and when.
• A simplified door for business users. Databricks One gives business users a read-only interface to reach shared dashboards and Genie Agents through a single entry point, without exposing technical complexity, while the same Unity Catalog controls stay in force.

Getting started

• Explore the Databricks AI/BI product overview to see how Genie and Dashboards work together.
• Define access controls once in Unity Catalog with row filters, column masks, and grants; they automatically apply to every Genie query and AI/BI dashboard. See What is AI/BI?.
• Model your key KPIs as certified Metric Views so answers resolve from governed definitions, and curate Genie Agents with example questions and business vocabulary.
• Give business users access through Databricks One, then read AI/BI Genie is now generally available.

FAQs

Do business users need to know SQL to use self-service analytics?

No. AI/BI Genie lets users ask questions in plain English and returns narrative answers, tables, and visualizations, while data teams curate the underlying Genie Agent with governed tables and business vocabulary so answers stay accurate.

How does self-service stay governed?

Unity Catalog enforces row-level security, column masks, and table grants at the SQL engine, so the same policies apply whether a user asks Genie, opens a dashboard, or runs a notebook, and every query is logged for audit and lineage.

How do we keep business definitions consistent?

Certified Metric Views define each KPI once at the data layer, so dashboards, Genie Agents, and applications all resolve the same trusted calculation instead of locally recreated formulas.

How can business users access analytics without technical complexity?

Databricks One provides a simplified, read-only interface where business users reach shared dashboards and Genie Agents through a single door, with Unity Catalog governance applied throughout.