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Data Analyzer

Analyze CSV data with natural language queries

What does this node do?

The Data Analyzer node lets you analyze tabular data using natural language. Ask questions about your CSV data and get AI-powered insights without writing SQL or code.

Common uses:

  • Summarize large datasets
  • Find patterns and trends
  • Answer specific questions about data
  • Generate insights and reports

Quick setup

Add the Data Analyzer node

Find it in AI Nodes → Data Analyzer

Provide CSV data

Connect a data source or paste CSV content

Ask your question

Write a natural language query about the data

Configuration

Required fields

csv_data string required

The CSV content to analyze.

Sources:

  • Direct paste
  • From Google Sheets: {{GoogleSheets_0.csv}}
  • From file upload
query string required

Your question about the data in natural language.

Examples:

  • “What is the total revenue by region?”
  • “Which products have sales above $1000?”
  • “What’s the average score for each category?”
  • “Find the top 10 customers by order value”

AI settings

Same as LLM node - model, temperature, etc.

Output

{
  "answer": "The total revenue by region is: North: $125,000, South: $98,000, East: $112,000, West: $89,000",
  "data": {
    "North": 125000,
    "South": 98000,
    "East": 112000,
    "West": 89000
  },
  "query": "What is the total revenue by region?",
  "rows_analyzed": 1500
}

Examples

Sales analysis

CSV Data:

date,product,region,revenue
2024-01-01,Widget A,North,1500
2024-01-01,Widget B,South,2300
...

Query: “What was the total revenue last month, broken down by product?”

Output:

Total revenue for January 2024:
- Widget A: $45,000
- Widget B: $62,000
- Widget C: $28,000

Total: $135,000

Finding patterns

Query: “Are there any unusual patterns in the data?”

Output:

Notable patterns detected:
1. Sales spike every Friday (25% above average)
2. Widget B outperforms in South region (40% higher)
3. December sales 3x higher than other months

Data validation

Query: “Are there any missing values or data quality issues?”

Output:

Data quality findings:
- 12 rows have missing 'region' values
- 3 revenue values are negative (possible errors)
- 5 duplicate entries found

Best practices

Be specific in queries

❌ "Tell me about the data"
✅ "What is the average order value by customer segment?"

Reference column names

❌ "What's the total?"
✅ "What's the total of the 'revenue' column?"

Ask follow-up questions

Chain multiple Data Analyzer nodes for deep analysis:

graph LR
    A[CSV Data] --> B[Summarize]
    B --> C[Find Anomalies]
    C --> D[Generate Report]
LLM

For text analysis
Google Sheets

Get CSV from Sheets