How Google is transforming BigQuery from cold storage to an interactive partner
Google decided it was time to change the paradigm. With the introduction of Conversational Analytics in BigQuery (based on Gemini models), the rules of the game are being rewritten. Now, the barrier to entry into analytics is reduced to the ability to formulate thoughts in your native language. Let’s be honest, we’ve seen dozens of Text-to-SQL solutions in recent years, but in practice, most of them have proven unviable.
In this article, we will explore why Google’s AI agents are important and serious analytical assistants. Let’s see how it works with metadata, generates code, and, most importantly, provides enterprise-level security.
What is Conversational Analytics really?
To put it simply, it is an interface built into BigQuery (part of Data Canvas) that translates human language into SQL queries. But it would be a mistake to call it just a “translator.” It is more of an intelligent AI agent that understands context.
Previously, the process looked like this: you open the console, remember the table structure, write SELECT, then struggle with JOIN for a long time because the keys in different tables are named differently.
Now you write: “How has the average check changed for user cohorts over the last three months?” The system accepts this prompt, analyzes the tables available to you, and outputs ready-to-run SQL code. Moreover, it immediately offers to visualize the answer, suggesting a graph or a summary table.
This changes the very approach to working with data. The tool does not completely replace SQL knowledge (you still perform complex optimizations yourself), but it eliminates routine tasks. We are moving from imperative programming (“do step A, then step B”) to declarative intent queries (“I want to get result X, find a way to implement it”).
Solution architecture: metadata and semantic grounding
One of the fears of any data analyst when they hear the word “AI” is hallucinations. The model may invent a non-existent table or connect data by a field that is not intended for this purpose. Google solves this problem with a mechanism called semantic grounding.
The process of processing your “human” question relies on several key factors:
- Data schema context
AI scans INFORMATION_SCHEMA, understanding column types and table names.
- Semantic mapping
The system links business terms (e.g., “profit”) to technical names in the database (net_profit) using Descriptions.
- Hierarchy of relationships
The model takes into account the logic of Primary and Foreign keys in order to correctly combine data via JOIN without the risk of duplication.
- Syntax validation
The generated code undergoes internal verification for compliance with the GoogleSQL dialect before it appears on your screen.
It turns out that the quality of Conversational Analytics directly depends on the quality of your documentation within BigQuery. The better the fields and relationships are described, the more accurately the AI works. This transforms the analyst’s work from writing code to designing the environment: you create clean showcases with clear descriptions, and the AI takes care of the routine of extracting insights.
Transparency: a “white box” instead of a black box
One of the main problems with many AI tools is the lack of transparency. You ask a question, get a number, and have no idea how it was obtained. In corporate analytics, this is unacceptable.
Google BigQuery takes the path of complete transparency. The generated SQL code is always available for viewing and editing. You don’t have to blindly trust the machine. You can see: OK, here the system used a filter by order creation date, not payment date. If the logic doesn’t suit you, you can correct the code manually or refine the prompt. For example, add: “Only count paid orders.”
This approach makes the tool useful even for senior analysts. It’s easier to ask AI to sketch out the “skeleton” of a complex query with joins and window functions, and then tweak the details. The tool completes the logic of the query itself, not just corrects the syntax.
From SQL to business insights and forecasting
Conversational Analytics is not limited to data extraction. Modern business requires not just dry tables, but answers to the questions “What does this mean?” and “What will happen next?” In this context, the AI agent takes on the role of an advanced analytical assistant, providing the following capabilities:
- Automatic interpretation of results
After executing a query, the AI agent generates a short summary in plain language. It independently highlights significant changes, trends, or anomalies that might otherwise go unnoticed in an array of numbers.
Search for correlations
The system is capable of comparing data from different planes. For example, it can notice that a drop in traffic correlates with holidays or technical work on the site, and directly point this out in the response.
Integrated forecasting
Thanks to the connection with BigQuery ML, you can request forecasts without knowing complex syntax. The phrase “Forecast sales for next month” triggers an automatic cycle: selecting the appropriate model (e.g., ARIMA), training on historical data, and outputting the result with confidence intervals.
- Instant visualization
Instead of exporting data to third-party BI tools for one-time hypothesis testing, you get a ready-made graph or chart directly in the BigQuery interface in seconds.
This transforms BigQuery from a passive storage facility into an active consultant. Analysts can test hypotheses faster. Instead of spending hours building a dashboard in Looker or Tableau for a one-time check, you can get a visualization and forecast right in the BigQuery interface in a couple of minutes. This dramatically speeds up the hypothesis testing cycle (Time-to-Insight).
Security and Governance: The AI agent will not see anything unnecessary
When it comes to the use of artificial intelligence in the corporate sector, the first question is always about security. Will our financial reports leak into the public domain? Will a junior manager see the CEO’s salary just by phrasing the question correctly to the bot?
Google adheres to strict corporate security standards here. Conversational Analytics works exclusively within the framework of existing IAM (Identity and Access Management) policies. The AI agent acts on behalf of the user making the request. If a specific employee does not have bigquery.tables.getData rights for the salary table, then no AI magic will happen — the query will return an access error, just as if the employee had written SQL manually. The model does not have “superuser” access.
Moreover, Google guarantees that your data and prompts are not used to train Gemini global models. Your business context remains isolated within your Google Cloud project. This eliminates the main risks of data leakage and allows the tool to be used even in regulated industries such as fintech or healthcare, provided that the perimeter is configured correctly.
Practical application: Scenarios for different roles
The implementation of this tool impacts the entire team, but the scenario is set individually for each role. Understanding these scenarios will help you integrate the technology into your company’s processes correctly.
The implementation of Conversational Analytics changes the usual routine of the entire team. Instead of waiting in line for an analyst, employees receive a tool for independent work, which is expressed in specific scenarios:
- For marketers
Quickly check campaign effectiveness and segment audiences without involving technical specialists.
- For product managers
Instantly search for anomalies in user behavior after a product update or the launch of promotions.
- For data analysts
Express research of new datasets and generation of “drafts” of complex queries for further refinement.
- For managers
Receiving operational reports in a “question-answer” format for making decisions based on current figures here and now.
As a result, business users get fast analytics and data interpretation, while technical specialists are freed from a stream of trivial tasks (“unload the top 10 customers for me”) and can focus on data architecture, pipeline quality, and complex mathematics that AI cannot yet handle.
Conclusion
Conversational Analytics in BigQuery is a sign of market maturity. We are moving away from an era when access to data was the privilege of the technical elite. Now data is becoming a truly democratic asset for companies.
However, it is important to understand that this is not a magic button. For an AI agent to provide quality answers, your team must invest in the quality of the data and metadata itself. Dirty data, inaccurate column names, and a lack of descriptions will cause even the smartest AI agent to produce garbage. But if you are ready to clean up your data warehouse, Conversational Analytics will become the lever that significantly improves decision-making efficiency in your company.
As an Official Google Marketing Platform Sales Partner, we have the deep expertise needed to build complex ecosystems and integrate cutting-edge solutions into your media split. Our specialists will help you at every stage: from strategic auditing of your current BigQuery settings to scaling campaigns based on data-driven approaches.
We provide a full cycle of support:
- Consulting and audit
We will assess the readiness of your infrastructure for the implementation of AI tools.
- Custom settings
We will help you structure your data in BigQuery so that Conversational Analytics works with maximum accuracy.
- Support and training
We will teach your team how to effectively use Gemini and Google Cloud for daily tasks.
Want to implement Conversational Analytics and evaluate the effect on your business? Contact us for professional advice: hello-gmp@admixeradvertising.com.
Together, we will make your data accessible and your analytics truly interactive.
