What are the best AI assistants for analyzing data?

Summary

• The best AI assistants for analyzing data answer from governed data instead of model memory, apply each person's own access rights, and show where an answer came from.
• Consistent answers depend on shared business definitions, so metrics, joins and example queries need an owner.
• Accuracy has to be measured: a Databricks Genie Agent can hold up to 500 benchmark questions, and its Monitor tab shows the questions users actually asked and how the agent answered.
• Databricks offers Genie One for business users, backed by Genie Agents that data teams configure and by Unity Catalog governance.
• Start narrow, with one topic, one audience and five or fewer tables.

What are the best AI assistants for analyzing data?

The best AI assistants for analyzing data work from governed, current data in the platform where it lives, not from a model's training data. They apply the asking person's own permissions, use the company's own metric definitions, show where an answer came from, and let owners test accuracy. On Databricks, that combination is Genie One for business users, backed by Genie Agents that data teams configure for a topic.

What should you look for in a data analysis assistant?

Capability	Why it matters
Governed data access	An answer should never expose data the person asking can't already see.
Shared business definitions	"Customer", "active user" and "revenue" have to mean one thing across teams.
Visible sources	People trust, and can correct, answers they can trace.
Measurable accuracy	A fixed set of test questions catches regressions that spot checks miss.
Feedback loop	Owners need to see what users ask and which answers they flag.
Reach	Answers should arrive where people already work, such as email or a messaging app.
Cost controls	Admins need limits and alerts on AI usage.

How do data analysis assistants work?

A Genie Agent uses a compound AI system to interpret a business question and generate an answer. It writes SQL and runs it on a pro or serverless SQL warehouse, against data registered in Unity Catalog. Its owner supplies the context that drives accuracy: knowledge store snippets (table descriptions, join relationships and SQL expressions), example SQL queries, SQL functions and text instructions.
Genie One sits one level up. Chat first searches the available Genie Agents for a match. If none fits, it searches dashboards, queries and metric views.

How does Databricks approach AI data assistants?

Governance. Genie Agents evaluate data access with each end user's own Unity Catalog permissions, and Unity Catalog row filters and column masks apply per user, however the agent is shared. Feedback and review-request events are recorded in system.access.audit under the service name aibiGenie.
Business context. A knowledge store is scoped to its Genie Agent and doesn't change Unity Catalog metadata. Genie Ontology (Public Preview) adds a context layer shared by Genie One and Genie Code. It combines modeled context you define, through metric views, domains and Pages, with inferred context that Genie extracts from existing assets and usage.
Business users. Genie One is the simplified Databricks UI for business users, who can ask data questions in natural language, use AI/BI dashboards and run Databricks Apps. Chat in Genie One is generally available, and citation icons show the knowledge sources a response used. Agent mode in Genie Agents (generally available) breaks complex questions into sub-tasks, runs multiple SQL queries and returns a structured report.
Reach. Scheduled tasks (Public Preview) post results in a chat thread and email them. The Databricks Genie app for Slack (Public Preview) answers with the credentials of the Slack user who asks, but a public response is visible to everyone in the channel, so use private visibility for sensitive questions.
Accuracy and cost. Benchmarks are for evaluation only, and Genie doesn't use them to improve its context. Account admins can set Genie budgets that send alerts or block usage. Budgets track LLM usage, not SQL warehouse compute. For GenAI agents you build yourself, MLflow Tracing records each step's inputs, outputs, latency, token usage and cost, and MLflow scorers grade the traces.

Which use cases fit a data assistant?

• Self-service questions. A sales manager asks which accounts need attention this quarter, and the answer comes from governed tables the manager is allowed to query.
• Recurring briefings. The Genie One documentation gives this example prompt: "Send me a daily briefing of all new customer reviews for my store location."

How do you roll out a data assistant?

Databricks' Genie Agent guidance works as a template for any assistant.

1. Scope narrowly: one topic and audience, with five or fewer tables to start.
2. Define business meaning in SQL. Prefer SQL expressions and example SQL over text instructions.
3. Be the first user, and examine the SQL that Genie generates.
4. Bring in a business user to upvote or downvote responses.
5. Add benchmarks that cover your most frequent questions, and rerun them as you refine the agent.

FAQs

Can AI assistants replace data analysts?

No. Assistants answer routine questions on curated data. People still choose which tables and metrics an agent covers, check the SQL it generates, and write the examples and benchmarks that keep it accurate.

What happens when an assistant gets an answer wrong?

Each Genie response asks "Is this correct?" Users can confirm it, flag it with Fix it, or request review. A Genie Agent's behavior doesn't change from user feedback alone, so owners use the feedback to spot the questions Genie struggles with, then update the agent with example SQL, SQL expressions or instructions.

Can an assistant show someone data they aren't allowed to see?

In a Genie Agent, data access is evaluated with each end user's own Unity Catalog permissions, and row filters and column masks apply per user.