BigQuery’s Conversational Analytics Goes GA: Talk to Your Data

Businesses run on fast decisions, but the teams who hold the answers are often buried under a backlog of routine requests. Today, Google is bringing Conversational Analytics in BigQuery to general availability, so both business and technical teams can query data, run multi-step analyses, and generate visual reports using natural language, right where the data lives.

Built on Google’s latest Gemini models and BigQuery’s secure, governed foundation, this release delivers an agent that behaves like an analyst who knows your business, thinks before it answers, and stands behind its work. It’s like having a capybara-sized brain in your data swamp, ready to dive into any pool of information.

Conversational Analytics for Enterprise Data

BigQuery’s conversational capabilities are built-in and available instantly, with no setup required. For deeper, more consistent insights, data professionals can author specialized agents grounded in the exact sources that matter—from projects, datasets, and tables to views, graphs, and user-defined functions.

Conversational Analytics reaches beyond native BigQuery tables to Lakehouse-managed Apache Iceberg tables and cross-cloud Lakehouse sources like Databricks Unity, AWS Glue, SAP, and Salesforce. This means you can break down data silos and analyze data across clouds from a single conversation.

As a data practitioner, you work with Conversational Analytics right inside BigQuery Studio and Data Canvas, and publish the agents you build to Gemini Enterprise, Data Studio, or your own application through the Conversational Analytics API.

Engineered Trust and Explainability

Accuracy in Conversational Analytics is by design, not aspirational. Every agent is grounded in your business context, not a model’s assumptions. That context comes from the Knowledge Catalog (glossaries, profile scans, and context bundles), BigQuery Graph for multi-hop queries, and your own verified queries and custom agent instructions.

With the new Open Knowledge Format, the wiki your team already maintains can feed straight into Knowledge Catalog. At query time, Conversational Analytics leverages existing embeddings of your column values to match your question to the right data, so asking about “Texas” finds rows stored as “TX.”

Every answer is inspectable, providing:

• Visible thinking steps: Review the agent’s step-by-step reasoning and the exact SQL it generates before it returns an answer.
• Context citations: See the precise sources behind every response, including tables, schema definitions, verified queries, and glossary terms.
• Proactive disambiguation: When a prompt is vague, the agent asks targeted clarifying questions instead of guessing.
• Long-term memory: The agent remembers what your terms and questions mean, so you don’t have to disambiguate the same thing twice.

Security and Governance by Design

One common barrier to scaling AI is governance. Conversational Analytics inherits BigQuery’s governance model, so users only query data they are authorized to see and every query is logged for auditing. It supports Access Transparency, Customer-Managed Encryption Keys, Private IP, and VPC Service Controls, and now guarantees data residency for data at rest and ML processing within EU and US multi-region endpoints.

For your most engaged users, you can configure cost controls so no user or project exceeds its allotment, cap an agent’s maximum query size in bytes, and track usage through BigQuery labels on jobs.

The Power of BigQuery AI, in Plain Language

The agent doesn’t just retrieve rows—it calls BigQuery’s AI functions for you, turning advanced analysis into a question you can ask in plain language.

• Find the “why,” not just the “what”: Ask what drove a change and the agent runs root-cause analysis with AI.KEY_DRIVERS, surfacing the exact segments behind the move.
• See what’s coming: Move past historical reporting by triggering AI.FORECAST and AI.DETECT_ANOMALIES right in the chat to project trends and flag outliers.
• Query your entire data estate: With object tables, the agent reasons over relational data and unstructured files together—PDFs, images, logs, and video—so a single conversation spans your whole estate.

From Answering Questions to Running the Investigation

Conversational Analytics agents are moving from human-scale reactive analysis to agent-scale proactive action. Deep-dive mode lets you ask “Why a metric moved?” and the agent builds its own analytical plan, mapping critical questions and working through a full multi-step investigation with no manual SQL. The result is a comprehensive report you can download and share.

Agentic workflows allow you to deploy autonomous agents that monitor your data, reason over events, run multi-step workflows on a schedule, and deliver insights straight to your chat. Set up a Monday-morning business report or daily anomaly detection across key metrics, each with a custom directive so they investigate only what you care about.

Start Talking to Your Data Today

General availability of Conversational Analytics in BigQuery marks an official exit from the static dashboard era. By embedding Gemini’s deep cognitive reasoning directly into the data warehouse, Google is enabling a self-managing environment that transforms raw data into active, corporate knowledge. This is a key component of the Agentic Data Cloud, providing a true system of action that moves past retrospective reporting, incorporates security and governance by design, and is engineered for enterprise trust.

If you are ready to get started, learn more from the documentation, reach out to your Google Cloud account representative, or get started in BigQuery Studio today to build and deploy your first agent.

For more on cloud data platforms, check out our reviews of Google Cloud, Supabase, and Neon Database.

Original announcement published on Google Cloud.