Traditional data analysis is often bound by rigid tables and processes, but an excellent analyst's thinking should be fluid and open. Imagine, if there were an infinitely expansible canvas, how could your insights be re-expressed?
AskTable AI Canvas was created for exactly this.
As an immersive AI data analysis and creation platform, we have perfectly integrated the free unlimited canvas, intelligent multi-data source connection and depositable template system. Here, you can break the constraints of systems and create freely like an artist, making data analysis不再是日复一日的"重复劳动"，而是"知识积累"；不再是孤芳自赏的"个人技艺"，而是可被传承的"团队资产".
The Trouble of Data Silos
Modern enterprise data is scattered across various databases like MySQL, PostgreSQL, ClickHouse, as well as file data like Excel and CSV. The traditional approach is to build a data warehouse, collecting data uniformly through complex ETL processes. But this brings new problems:
- •High maintenance costs: Data source changes require modifying ETL processes
- •Insufficient flexibility: Temporary analysis needs are difficult to respond to quickly
- •Over-engineered solution: Lightweight cross-data-source analysis doesn't need a data warehouse, yet lacks alternative solutions
The Loop of Repetitive Labor
Every analysis project starts from scratch:
- •Same query logic needs to be written repeatedly
- •Similar charts need to be reconfigured
- •Same analysis approaches cannot be reused
- •Team members work independently, lacking collaboration
The Gap of Technical Barriers
Traditional BI tools are powerful, but the learning curve is steep:
- •SQL writing requires professional skills
- •Chart configuration is complex and tedious
- •Data processing requires programming foundation
- •Business personnel struggle to complete analysis independently
Why Choose Canvas?
Canvas represents freedom and creation. We observed that data analysts' thinking processes are inherently non-linear—they need to view multiple data views simultaneously, establish connections between different charts, and quickly try various visualization methods.
Traditional fixed layouts limit this creativity. AI Canvas adopts unlimited canvas design, allowing analysts to:
- •Free layout: Place components as they wish, building their own analysis narrative
- •Spatial organization: Use two-dimensional space to express the hierarchical relationships of analytical logic
- •Visual thinking: Organize data insights like a mind map
This design philosophy stems from our belief: good analysis is not just data presentation, but the expression of thinking.
Why Connect Data Sources Directly?
We challenged the traditional notion of "must build a data warehouse." Through real-time multi-source connection technology, AI Canvas allows you to:
- •Skip ETL: Pull the latest data directly from source systems
- •Hybrid queries: Use multiple data sources simultaneously on one canvas
- •Flexible exploration: No need to wait for data engineers to configure new data pipelines
The thinking behind this is: the essence of data analysis is exploration, and exploration requires immediate feedback. When you can verify hypotheses immediately and iterate on analysis quickly, innovation happens naturally.
Why Emphasize Templates?
Templates are not restrictions, but the crystallization of knowledge. Behind every in-depth data analysis lies business understanding, analysis methods and technical implementation. In traditional approaches, these precious knowledge dissipate as projects end.
AI Canvas's template system achieves:
- •Idea solidification: Precipitate excellent analysis methodologies into reusable templates
- •Quick start: New projects modify based on templates rather than starting from scratch
- •Team collaboration: Best practices spread naturally within teams
- •Continuous evolution: Templates continuously iterate and optimize with business development
This reflects our core philosophy: data analysis capability should be an organizable asset that can be accumulated and passed down.
AI's Role Positioning
We don't believe AI will replace data analysts; AI's value lies in eliminating repetitive work, allowing humans to focus on creative thinking.
AI Canvas's AI capability design follows the "human-machine collaboration" principle:
- •AI is responsible for execution: Generate SQL, JavaScript, Python code
- •Humans are responsible for decision-making: Raise requirements, verify results, optimize directions
- •Bidirectional feedback: AI learns from human corrections, humans gain inspiration from AI suggestions
Canvas Creation: A Free Expression Analysis Space
What It Is
AI Canvas provides an infinitely expansible two-dimensional canvas where you can freely add, move and organize various analysis components: data tables, charts, text descriptions, images, etc.
Why It Matters
Data analysis is not linear report generation, but an exploratory thinking process. Canvas gives you:
- •Spatial freedom: Not restricted by templates, moving with creativity
- •Relational expression: Express logical relationships through positional relationships
- •Iteration-friendly: Easily adjust layouts, quickly try different presentation methods
Typical Use Scenarios
- •Exploratory analysis: Expand multiple analysis directions simultaneously on canvas, compare different hypotheses
- •Analysis presentations: Export canvas as PPT, report insights to management
- •Collaboration dashboards: Team members jointly build analysis dashboards on canvas
Multi-Source Connection: Breaking Data Silos
Technical Innovation
AI Canvas supports direct connection to various data sources without data migration:
- •Relational databases: MySQL, PostgreSQL, SQL Server, Oracle, etc.
- •Big data platforms: ClickHouse, StarRocks, Doris, etc.
- •Local files: Excel, CSV, etc.
More importantly, you can mix different data sources on the same canvas, and AI will intelligently handle data fusion.
Business Value
The value this capability brings far exceeds the technical level:
- •Real-time: Always use the latest data, bid farewell to "data delay" troubles
- •Flexibility: Temporary analysis needs no longer require "going through procedures"
- •Cost savings: Reduce data warehouse construction and maintenance costs
- •Risk control: Data stays in the original system, meeting security compliance requirements
Template Deposition: Making Knowledge Flow
Knowledge Management Philosophy
Each template is an encapsulation of "analysis methodology":
- •Data connection configuration: Pre-defined data source connections
- •Query logic: Verified SQL query templates
- •Visualization solutions: Carefully designed charts and layouts
- •Analysis approach: Analysis logic expressed through component arrangement
Usage Process
- •Create template: Save an excellent canvas as a template
- •Share and disseminate: Share templates within the team or organization
- •Quick reuse: Create new canvas based on templates, modify parameters for use
- •Continuous iteration: Optimize templates based on feedback, forming best practices
Typical Applications
- •Business monitoring templates: Daily operational metrics dashboards
- •Topic analysis templates: User behavior analysis, marketing effectiveness analysis, etc.
- •Industry solution templates: E-commerce analysis, financial risk control and other industry-common analyses
Query Data: AI Generates SQL
How It Works
You describe your needs in natural language, AI understands and automatically generates SQL queries:
When to Use
- •Quick exploration: Uncertain about data structure, need to quickly verify ideas
- •Complex queries: Involving multi-table joins, window functions and other advanced SQL
- •Learning SQL: Learn best practices through AI-generated SQL
Human-Machine Collaboration Points
- •After AI generates SQL, you can still manually edit and optimize
- •Provide clear context information to help AI understand needs
- •Verify results, adjust prompts to regenerate if necessary
Create Charts: AI Generates JavaScript
Source of Flexibility
Different from traditional BI tools' predefined chart types, AI Canvas creates charts by generating JavaScript code. This means:
- •Infinite possibilities: Not limited by chart libraries; any visualization can be implemented
- •Deep customization: Precisely control every visual element of charts
- •Multi-source fusion: Easily combine data from different data sources
How It Works
Usage Suggestions
- •Start simple: First describe in natural language, let AI generate basic charts
- •Optimize gradually: Fine-tune based on generated code, add personalized elements
- •Save as template: Save satisfactory visualization solutions for direct reuse next time
Process Data: AI Generates Python
A New Way of Data Transformation
When you need to clean, calculate or transform data, tell AI your needs:
Technical Capabilities
- •Pandas operations: Data cleaning, aggregation, pivot tables, etc.
- •NumPy calculations: Statistical analysis, mathematical operations
- •Custom logic: Complex business rule implementations
Applicable Scenarios
- •Data preprocessing: Standardization, normalization, missing value handling
- •Feature engineering: Creating derived metrics, user segmentation
- •Complex calculations: YoY comparison, MoM comparison, moving averages, trend prediction
AI Canvas fully reuses AI Engine's database Q&A capabilities and already supports more than 20 types of data sources including the following databases (data warehouses):
Relational Databases
- •MySQL / MariaDB
- •PostgreSQL
- •SQL Server
- •Oracle
- •……
Big Data / OLAP
- •ClickHouse
- •Apache Doris
- •StarRocks
- •……
Files / Others
- •Excel (.xlsx, .xls)
- •CSV
Data Analysts
Value proposition: Shift time from SQL writing and chart configuration to insight mining and value creation
- •Quickly respond to business departments' temporary analysis needs
- •Build reusable analysis templates, improve work efficiency
- •Focus on analysis approaches, let AI handle technical details
Business Analysts
Value proposition: Can independently complete data analysis without deep technical background
- •Query data using natural language, no need to learn SQL
- •Quickly generate business reports based on ready-made templates
- •Solidify business insights into shareable canvases
Data Team Managers
Value proposition: Build the team's analysis knowledge base, precipitate organizational capabilities
- •Precipitate team's best practices into templates
- •Reduce new employee training costs
- •Improve the team's overall analysis efficiency and quality
Product Managers
Value proposition: Drive product decisions with data without relying on data team scheduling
- •Monitor product core metrics in real-time
- •Quickly verify product hypotheses
- •Create data-driven product proposals
Typical Workflow
1. Create Canvas
- •Start from a blank canvas
2. AI Query Data & AI Process Data
- •Select a database or upload an Excel file
- •Describe needs in natural language
- •AI generates SQL and executes queries
- •AI generates Python to process data
- •Preview and verify data
3. AI Create Charts
- •Tell AI the visualization effect you want
- •AI generates interactive charts
- •Adjust styles and configurations
4. Create Dashboard
- •Freely drag and drop components
- •Add text descriptions
- •Build analysis narrative
6. Share and Collaborate
- •Share dashboard links with colleagues
- •Export as PDF, PPT
1. Human Wisdom Primary, AI Secondary
AI is a powerful assistant, but the final analytical judgment comes from humans. When designing each feature, we ensure:
- •Humans maintain control over the analysis process
- •AI's output is transparent and verifiable
- •Humans can intervene, correct and optimize at any time
2. Lower Threshold, Don't Lower the Ceiling
We hope:
- •Beginners can get started quickly, completing basic analysis through natural language
- •Experts can deeply customize, writing complex SQL and visualization code
- •Intermediate users can continuously improve skills by learning from AI-generated code
3. Knowledge Should Flow and Accumulate
Analysis should not be "one-time work" but should:
- •Precipitate into reusable templates
- •Spread and evolve within teams
- •Accumulate over time to become organizational assets
4. Balance Flexibility with Standardization
We provide:
- •Canvas freedom: Don't limit your creativity
- •Template standards: Ensure consistency in team collaboration
- •AI intelligence: Find balance between freedom and efficiency
Start your AI Canvas journey! 🚀
Whether you are an experienced data expert or a business person just starting with data analysis, AI Canvas will become your capable partner.
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