How can I make business intelligence easier for nontechnical users?

Summary

• The biggest lever is letting people ask questions in plain language instead of building queries: a natural-language interface over governed data lets a nontechnical user get a chart or number without writing SQL.
• Trust matters as much as ease. The tool should show the SQL and steps it generated so a business user (and their analyst) can verify the answer, and it should respect the permissions IT already set.
• Low-code, natural-language dashboard authoring lets analysts build interactive dashboards fast, and lets non-experts read, filter, and share them without semantic-model setup or data extracts.
• On Databricks, AI/BI Genie (conversational, generally available) and AI/BI Dashboards run on Unity Catalog, so self-service answers stay governed and consistent with the rest of the platform.

How can I make business intelligence easier for nontechnical users?

Meet users where they are: let them ask questions in plain language and get a trustworthy answer, instead of asking them to learn SQL or a dashboard builder. Two things make BI genuinely easy for non-experts. First, a conversational interface that turns a plain-English question into a query, a chart, and a short summary. Second, dashboards that are simple to read, filter, and share. Both only pay off if answers stay governed and explainable, so a business user can trust the number and see how it was produced.

What makes BI hard for nontechnical users?

The barrier usually isn't curiosity, it's the interface. Traditional BI expects a business user to know table names, join logic, and a query language, or to file a request and wait for an analyst. Even prebuilt dashboards stall the moment someone has a question the dashboard wasn't designed to answer. Making BI easy means removing the translation step between a business question and the data, without giving up accuracy or governance.

Key capabilities to evaluate

Capability	Why it matters
Natural-language Q&A	Ask in plain English and get an answer, with no SQL or schema knowledge required.
Explainable answers	The tool shows the generated SQL and steps, so users can verify a result instead of trusting a black box.
Conversational follow-up	Refine and drill in through dialogue, the way people actually explore a question.
Shared business definitions	Metrics mean the same thing everywhere, so a term like "revenue" resolves to one agreed number.
Low-code dashboard authoring	Build and edit dashboards with drag-and-drop and natural language, not just code.
Governed by default	Answers respect existing permissions and lineage, with no separate copy or extract to secure.
Read-friendly consumption	Filtering, cross-filtering, and scheduled snapshots make dashboards usable by non-experts.

How Databricks approaches this

AI/BI is a business intelligence product built to open analytics to everyone, not just analysts. It's a compound AI system that draws on the context of your data estate (Unity Catalog metadata, pipelines, and past queries) to interpret questions and return relevant answers.
AI/BI Genie, which is generally available, lets business users ask questions in plain English, such as "where are we over budget this quarter," and returns charts, numbers, and a short summary. It shows the SQL and reasoning steps it generated so the answer is verifiable, supports follow-up questions, asks for clarification when a question is ambiguous, and improves from human feedback. Because Genie is grounded in your governed tables, answers reflect trusted data rather than a guess.
AI/BI Dashboards give analysts a low-code, drag-and-drop experience with natural-language authoring, so building an interactive dashboard is fast. Consumers filter and cross-filter, and can receive scheduled PDF snapshots by email. Dashboards don't require semantic models, data extracts, or extra management services, which keeps setup light.
Shared semantics tie it together: define metrics and business terms once in the governance layer, and dashboards, SQL, and Genie all use the same definitions, so users don't have to guess which number to trust. Everything runs on Unity Catalog, so Genie answers and dashboards honor the same permissions and lineage as the rest of the platform. Because AI/BI reads your existing data in place instead of extracting it to a separate BI engine, data stays fresh and governance stays simple. See the AI/BI product page, the AI/BI Genie GA announcement, and the AI/BI concepts docs.

Use cases

Business teams: A sales or finance manager asks Genie for pipeline by stage or spend against budget and gets an answer without waiting on an analyst.
Executives: A simple consumer view surfaces governed dashboards and lets leaders ask plain-language follow-ups on what they see.
Analysts: Build dashboards faster with natural-language authoring, then hand business users a Genie Agent scoped to trusted tables so self-service stays governed.

FAQs

Do business users need to know SQL?

No. They ask questions in plain language, and AI/BI Genie generates the SQL for them and shows it, so they can read or verify the query behind an answer.

How can we trust an AI-generated answer?

Genie displays the query and the steps it used, and grounds answers in governed tables. An analyst can inspect the SQL, so answers are auditable rather than opaque.

Is AI/BI Genie generally available?

Yes. AI/BI Genie is generally available, and AI/BI Dashboards are part of the same AI/BI product on the Databricks platform.

Does self-service BI bypass our data permissions?

No. AI/BI runs on Unity Catalog and honors the same permissions and lineage, so users only see data they're already allowed to access.

Do we need to build a semantic model or extract data first?

No. AI/BI Dashboards don't require semantic models or data extracts, and defining shared metrics once keeps answers consistent across dashboards, SQL, and Genie.