Data Analysis

Intelligent data processing and analysis using SkinFlow's multi-agent system.

Overview

This example demonstrates how to build intelligent data analysis workflows that can process various data types, generate insights, and create reports.

Capabilities

• Data Ingestion: Handle multiple data formats (CSV, JSON, Excel, databases)
• Data Cleaning: Automated data preprocessing and quality checks
• Statistical Analysis: Generate descriptive and inferential statistics
• Pattern Recognition: Identify trends, anomalies, and correlations
• Report Generation: Create comprehensive analysis reports and visualizations
• Predictive Modeling: Build and evaluate predictive models

Use Cases

• Business Intelligence: Sales analysis, customer segmentation, market trends
• Financial Analysis: Portfolio analysis, risk assessment, fraud detection
• Scientific Research: Experimental data analysis, hypothesis testing
• Operational Analytics: Process optimization, performance monitoring
• Customer Analytics: Behavior analysis, churn prediction, satisfaction metrics

Setup

cd examples/data-analysis
npm install
cp env.example .env

Basic Usage

Simple Data Analysis

import { createMultiAgentFramework } from 'skingflow'

const framework = await createMultiAgentFramework({
  llm: {
    provider: 'openai',
    apiKey: process.env.OPENAI_API_KEY,
    model: 'gpt-4'
  }
})

// Analyze sales data
const result = await framework.processRequest(
  "Analyze this sales data and identify key trends, outliers, and insights",
  {
    userId: 'data-analyst',
    context: {
      dataType: 'sales',
      timePeriod: 'Q1-2024',
      metrics: ['revenue', 'units_sold', 'customer_acquisition']
    }
  }
)

Advanced Analysis Workflow

// Define comprehensive analysis workflow
const analysisWorkflow = {
  dataSources: ['sales.csv', 'customer_data.json', 'market_trends.xlsx'],
  objectives: [
    'identify_growth_opportunities',
    'analyze_customer_behavior',
    'detect_seasonal_patterns',
    'predict_future_trends'
  ],
  deliverables: [
    'executive_summary',
    'detailed_findings',
    'visualizations',
    'recommendations'
  ]
}

const result = await framework.processRequest(
  `Execute comprehensive data analysis: ${JSON.stringify(analysisWorkflow)}`,
  { userId: 'business-intelligence-team' }
)

Data Processing Pipelines

Data Ingestion and Cleaning

// Automated data preprocessing
const preprocessingResult = await framework.processRequest(
  "Clean and preprocess this dataset for analysis",
  {
    userId: 'data-engineer',
    context: {
      rawDataset: salesData,
      requirements: {
        handleMissingValues: true,
        normalizeFormats: true,
        removeOutliers: true,
        standardizeColumns: true
      }
    }
  }
)

Statistical Analysis

// Generate comprehensive statistics
const statsResult = await framework.processRequest(
  "Perform statistical analysis and generate descriptive statistics",
  {
    userId: 'statistician',
    context: {
      dataset: cleanedData,
      analysisTypes: [
        'descriptive_statistics',
        'correlation_analysis',
        'trend_analysis',
        'distribution_analysis'
      ]
    }
  }
)

Pattern Recognition

// Identify patterns and anomalies
const patternResult = await framework.processRequest(
  "Identify patterns, trends, and anomalies in this data",
  {
    userId: 'ml-specialist',
    context: {
      data: timeSeriesData,
      methods: [
        'time_series_analysis',
        'anomaly_detection',
        'clustering',
        'classification'
      ]
    }
  }
)

Advanced Analytics

Predictive Modeling

// Build predictive models
const predictionResult = await framework.processRequest(
  "Build and evaluate predictive models for forecasting",
  {
    userId: 'data-scientist',
    context: {
      historicalData: salesHistory,
      targetVariable: 'revenue',
      timeHorizon: '6_months',
      modelTypes: ['linear_regression', 'random_forest', 'neural_network']
    }
  }
)

What-If Analysis

// Scenario analysis
const scenarioResult = await framework.processRequest(
  "Perform what-if analysis and scenario modeling",
  {
    userId: 'business-analyst',
    context: {
      baseModel: predictiveModel,
      scenarios: [
        { name: 'optimistic', changes: { market_growth: '+20%' } },
        { name: 'pessimistic', changes: { market_growth: '-10%' } },
        { name: 'realistic', changes: { market_growth: '+5%' } }
      ]
    }
  }
)

Visualization and Reporting

Automated Chart Generation

// Generate data visualizations
const vizResult = await framework.processRequest(
  "Create appropriate visualizations for this analysis",
  {
    userId: 'data-visualizer',
    context: {
      analysisResults: comprehensiveAnalysis,
      visualizationTypes: [
        'time_series_charts',
        'correlation_matrices',
        'distribution_plots',
        'comparative_analyses'
      ],
      outputFormat: 'interactive_dashboard'
    }
  }
)

Report Generation

// Generate comprehensive reports
const reportResult = await framework.processRequest(
  "Create a comprehensive analysis report with executive summary",
  {
    userId: 'business-intelligence',
    context: {
      findings: analysisResults,
      audience: 'executives',
      reportStructure: [
        'executive_summary',
        'methodology',
        'key_findings',
        'recommendations',
        'appendix'
      ]
    }
  }
)

Real-time Analysis

Streaming Data Processing

// Real-time data analysis
async function processStreamingData(dataStream) {
  for await (const dataPoint of dataStream) {
    const analysis = await framework.processRequest(
      "Analyze this streaming data point and detect anomalies",
      {
        userId: 'real-time-analyst',
        context: {
          currentData: dataPoint,
          historicalContext: recentData,
          thresholds: anomalyThresholds
        }
      }
    )

    // Handle real-time insights
    if (analysis.anomalyDetected) {
      triggerAlert(analysis)
    }
  }
}

Integration Examples

Database Integration

// Connect to databases for analysis
const dbAnalysisResult = await framework.processRequest(
  "Analyze data from the database and generate insights",
  {
    userId: 'database-analyst',
    context: {
      connectionString: process.env.DB_CONNECTION,
      query: "SELECT * FROM sales WHERE date >= '2024-01-01'",
      analysisType: 'sales_performance'
    }
  }
)

API Integration

// Integrate with external data sources
const apiAnalysisResult = await framework.processRequest(
  "Fetch and analyze data from external APIs",
  {
    userId: 'api-analyst',
    context: {
      apiEndpoints: [
        'https://api.example.com/market-data',
        'https://api.example.com/customer-data'
      ],
      analysisGoals: [
        'market_trends',
        'customer_segments',
        'competitive_analysis'
      ]
    }
  }
)

Best Practices

1. Data Quality First: Always validate and clean data before analysis
2. Define Clear Objectives: Know what insights you're seeking
3. Use Appropriate Methods: Match analysis techniques to data types
4. Validate Results: Cross-check findings with multiple approaches
5. Document Process: Maintain analysis methodology and assumptions

Performance Optimization

Processing Optimization

• Use streaming for large datasets
• Implement parallel processing where possible
• Cache intermediate results
• Optimize query performance

Resource Management

• Monitor memory usage for large datasets
• Implement timeout mechanisms
• Use efficient data structures
• Scale horizontally for distributed processing

Security Considerations

• Data Privacy: Ensure compliance with data protection regulations
• Access Control: Implement proper authentication and authorization
• Data Encryption: Protect sensitive data in transit and at rest
• Audit Logging: Maintain analysis activity logs

Troubleshooting

Common Issues

1. Data Quality Problems: Implement robust validation and cleaning
2. Performance Issues: Optimize algorithms and use caching
3. Memory Constraints: Use streaming and chunked processing
4. Model Accuracy: Validate with test datasets and adjust parameters

Debugging Tools

// Analysis debugging
const debugResult = await framework.processRequest(
  "Debug this analysis and identify issues",
  {
    userId: 'debug-specialist',
    context: {
      analysisProcess: currentAnalysis,
      inputData: rawData,
      expectedOutput: targetResults,
      actualOutput: currentResults
    }
  }
)

Related Examples

• Intelligent Assistant - General AI assistant for analysis tasks
• Content Creation - Analysis report generation
• Quick Start - Basic framework setup

Next Steps

• Explore custom tools for domain-specific analysis
• Implement real-time monitoring dashboards
• Set up automated reporting and alerting systems
• Integrate with business intelligence platforms