Google Conversational Analytics: Define Your KPI Dictionary Before AI Queries Data

What did Google announce?

On 29 July 2026, Google Cloud said Conversational Analytics was generally available for BigQuery and Looker, while support for AlloyDB, Cloud SQL and Spanner was in preview. Data teams can also publish agents created across these supported sources into Gemini Enterprise.

Google presents the product as more than a natural-language layer over a database. The announcement emphasises governed business definitions, access controls, cost controls and observability. Those controls matter because a fluent answer is not automatically a trustworthy business answer.

Google's own API documentation keeps the boundary clear: early-stage Gemini for Google Cloud products can produce output that looks plausible but is factually incorrect, and users should validate output before relying on it.

Why can simple language create conflicting answers?

Ask whether revenue fell last week and three departments may calculate three different numbers. Finance may count paid orders, marketing may use attributed revenue, and operations may report the value before refunds. An AI agent connected to the same systems can produce a fourth answer if nobody has defined the metric.

The model is not the only source of uncertainty. Ambiguous joins, inconsistent time windows, test records, refunds, cancelled orders and different currency treatments can all change an answer. Natural language makes access easier, but it also makes inconsistent assumptions easier to overlook.

Google says Conversational Analytics can use Knowledge Catalog glossaries and descriptions, Looker's semantic layer, and verified logic through Golden Queries. The aim is to ground the agent in centrally governed definitions instead of letting it guess at SQL joins or business logic.

What should a practical KPI dictionary contain?

Before introducing a new analytics agent, create one shared definition for a KPI that already influences a weekly decision. RxAI recommends a compact six-field record:

1. Metric name: Use the name people already use in meetings and reports.
2. Business definition: State what the number represents and what it does not represent.
3. Calculation: Identify the fields, filters, time window and formula.
4. Exclusions: List refunds, cancellations, test records, internal activity or other exceptions.
5. Source and refresh: Name the source tables or systems and how often they update.
6. Owner and validation questions: Name the person who approves the definition and record questions with known answers.

For example: net revenue equals paid order value minus completed refunds; it excludes cancelled orders and internal test orders; it refreshes each morning; and the finance lead owns the definition.

This six-field format is an RxAI implementation method, not a Google requirement. The point is to make the business meaning explicit enough that a person, dashboard or AI agent can be checked against the same standard.

How should teams test an analytics agent before launch?

Start with three questions whose answers are already known. Choose examples that test different parts of the definition:

• one day or week with a verified total;
• one product, service or region with a known result; and
• one customer segment or exception that should be included or excluded.

Compare the agent's answer, filters, sources and calculation with the approved result. A correct total produced through the wrong logic is still a failed test because a small data change may expose the error later.

Record mismatches as definition, data, permission, logic or model issues. This creates a useful evaluation set for future changes. Re-run it when the source schema, semantic model, agent instructions or permissions change.

Which controls should surround conversational analytics?

Google's announcement describes row- and column-level permissions, query-size controls, usage labels, system activity logs and exported health, tool-use, latency and token-consumption metrics. Its cost guidance also documents project-level and user-level quotas plus a maximum billed-bytes setting for individual queries.

For a smaller business, translate those platform features into five operating controls:

• grant the minimum data access needed for the role;
• separate sensitive customer or employee fields from general reporting;
• cap usage and individual query cost before wider access;
• retain enough logs to reconstruct important answers; and
• require human review before a result triggers a financial, customer or operational action.

The right technical configuration depends on the data source and risk. The operating principle is stable: conversational access should inherit the same or stronger governance than the dashboard or database it replaces.

What should Australian SMB leaders do next?

Do not begin with a company-wide analytics chatbot. Pick one recurring question and one accountable metric owner. In a short working session:

1. complete the six-field KPI definition;
2. agree on three known-answer tests;
3. identify who may see the underlying data;
4. decide which decisions still need human review; and
5. define the cost and usage boundary for a pilot.

If two people still interpret the metric differently, resolve that disagreement before connecting an agent. The highest-value first step is often data definition, not software procurement.

RxAI can help turn that definition into a governed data and automation workflow through our AI consulting services, or you can discuss a practical pilot.

Sources

1. Google Cloud: Bringing Conversational Analytics to your entire data ecosystem — 29 July 2026 announcement covering availability, governed context, Golden Queries, permissions, observability and cost controls.
2. Google Cloud Documentation: Build data agents and chat with your data — supported data sources, authored context, IAM, logging and the requirement to validate output.
3. Google Cloud Documentation: Manage BigQuery costs for Conversational Analytics API agents — project, user and individual-query cost limits.