Overview
This Dify workflow automates the end-to-end data analysis process. Upload a CSV or connect a database, and the agent handles everything from data profiling to final report generation using LLM-powered reasoning at each step.
Workflow Steps
- Data Ingestion: Accept CSV upload or database connection. Validate schema and detect data types automatically.
- Data Cleaning: Handle missing values, remove duplicates, standardize formats, and detect outliers using statistical methods.
- Exploratory Analysis: Generate summary statistics, correlation matrices, and distribution analysis. LLM interprets initial patterns.
- Deep Analysis: Run hypothesis tests, trend analysis, segmentation, and predictive modeling as configured.
- Visualization: Generate charts and graphs (bar, line, scatter, heatmap) based on analysis results.
- Report Generation: Compile findings, charts, and recommendations into a formatted Markdown or PDF report.
Tools Required
- Dify (open-source LLM application platform)
- Python Code Executor node for data processing
- LLM node (OpenAI / Claude / local model) for reasoning and report writing
- Chart generation library (Matplotlib / Plotly via Code Executor)
Use Cases
- Business analysts automating weekly sales reports from CRM exports
- Marketing teams analyzing campaign performance data across channels
- Researchers running statistical analysis on survey results without coding
- Product teams monitoring user engagement metrics with automated insights
FAQ
What is a Dify data analysis agent?
It is a Dify workflow that automates the full data analysis pipeline — ingestion, cleaning, exploration, deep analysis, visualization, and reporting — using LLM reasoning at each step, with no code required.
Do I need to write code to use this workflow?
No. Dify's visual editor lets you connect CSV upload or database nodes, LLM nodes, and a Python Code Executor node. The Code Executor runs the actual data processing, but you configure it through the UI.
What data sources does it support?
CSV upload and database connections. You can add connectors for PostgreSQL, MySQL, or other databases supported by Dify, then validate schema and detect data types automatically.
Can I customize the analysis steps?
Yes. Each HowTo step maps to a node in the workflow — you can add or remove cleaning rules, switch LLM providers, change visualization types, and adjust the final report format (Markdown or PDF).