#implementingdatacanvas

Turn data into insights faster with the updated BigQuery data canvas.

Today's data-driven world requires quick insights. Many struggle to transform raw data into meaningful intelligence. Complex SQL queries, tedious investigations, and the split between technical and non-technical users slow progress. BigQuery Data Canvas is a visual workspace that democratises data research and unleashes BigQuery data potential. At Google Cloud Next '25 this month, Google Cloud introduced Data Canvas, powered by Gemini, with AI-assisted conversation. One question covers data exploration, visualisation, and workflow analysis in this chat experience.

Data canvas is a paradigm change for data practitioners, not just a new function. By seamlessly combining visual processes with BigQuery and Gemini, Google Cloud is turning raw data into valuable insights.

Key traits: In-depth

Explore the data canvas assistant's powers.

Gemini fuels AI data agents.

Google has added Gemini, their powerful AI model, to Data Canvas to boost data exploration. It lets you ask data questions, enhance queries using natural language, and obtain informative and useful replies. The Gemini-powered data canvas queries and provides dataset information when you select “Show me the top 10 customers by revenue”. Gemini's data discovery recommendations help you find relevant datasets.

The Gemini-powered AI conversation experience captures workflow analysis from data exploration to visualisation with one prompt. Not sure where to start? Start analysing your data by following the directions. BigQuery Data Canvas uses Gemini to generate natural language enquiries about your data and SQL queries based on your favourite tables. To answer your questions, Gemini might use many data sources.

You may also direct the system to transfer domain knowledge about your data to increase answer accuracy. You may tell Gemini that your company's fiscal year is not January–December using system instructions. Some system directives, such “always present findings with charts, use green colour for positive and red colour for negative,” might influence how your replies are presented and returned.

Complex Python analysis for forecasting and anomaly identification will soon be available in chat. If your inquiry is complex, Gemini chat assistant will use a Python code interpreter to reply. Turn this option on in the settings bar.

Visual query building: Explore several perspectives in one place. Imagine a single hub where you can filter, link, aggregate, or display data from several tables, each in its own container, on the same page for data analysis. Data Canvas uses a DAG (Directed Acyclic Graph) method instead of a linear one, allowing you to explore alternative views, return to prior phases, or compare outcomes.

Data may be added easily by finding and adding tables to the canvas. Starting with natural language queries to your data, Data Canvas will automatically generate the SQL, which you can analyse and edit. Our node-based technique lets SQL professionals and rookie analysts pursue insights without having to cope with complex query syntax.

Interactive visualisations: Instant insights

Data Canvas offers interactive tables, graphs, and charts. Customising visualisations, interactively examining data, and identifying patterns and irregularities are easy. Do you want to see regional sales distribution? Data Canvas automatically creates a chart using the “Region” and “Sales” variables. Just choose the best visualisation or make your own to watch your data come to life. These visualisations may be downloaded as PNGs or imported into Looker Studio for customisation and dissemination.

Implementing data canvas in practice

New AI functionalities on BigQuery data canvas may be used in many ways. Here are some industry-specific ideas to inspire you.

Telecom diagnostics and support: Faster service restoration

Consider a telecom support team helping clients. Using a data canvas, support tickets may be queried for customer phone, postcode, service, timestamp, and nearby mobile tower. BigQuery processes support tickets hourly. Each data item is maintained in a distinct node inside a canvas, so analysts may perform this study without switching query tabs. This visual method helps them find local outages, send personnel to the right towers, and fix service interruptions faster.

E-commerce analytics: Expanding sales and customer engagement

Imagine marketing personnel analysing customer purchasing data to optimise adverts. Data canvas lets them graphically mix customer and product information, filter by prior purchases, and examine demographic sales tendencies. By quickly identifying high-value customer categories, best-selling goods, and marketing success, they can make data-driven judgements.

Supply chain optimisation: logistics simplification

Logistics management may utilise Data Canvas to track inventories, delivery routes, and bottlenecks. Visualising supply chain data may boost efficiency, cut costs, and enhance delivery dates. They may also create interactive dashboards to track performance indicators and make real-time changes.