Business Intelligence System - Documentation

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Comprehensive Business Data Processing and Analysis Platform

Table of Contents

• Overview
• Features
• API Endpoints
• Installation
• Usage
• Development
• Contributing
• License

Overview

This API provides advanced business analytics capabilities for company data, offering 10 core analytical functions to transform raw business data into actionable insights. The system is built with Python and Flask, designed for reliability, scalability, and performance.

Features

1. Prepare Monthly Data Description: Automates the consolidation and standardization of raw business data into structured monthly datasets.

   Capabilities:

   • Data cleaning and normalization
   • Month-over-month reconciliation
   • Cross-data source validation
   • Output in analysis-ready format

2. Profitability Analysis Description: Deep dive into revenue streams and cost centers to identify profitability drivers.

   Metrics:

   • Gross/Net profit margins by product, region, channel
   • Contribution margin analysis
   • Break-even point calculation
   • Customer/product profitability ranking

3. Time Series Trend Description: Advanced time-based pattern recognition and visualization.

   Features:

   • Seasonality detection
   • Moving averages (7-day, 30-day, quarterly)
   • Year-over-year comparison
   • Trend decomposition (trend, seasonal, residual)

4. Top Performers Description: Identifies and ranks high-performance business elements.

   Dimensions:

   • Top-selling products/services
   • Highest-growth regions
   • Most efficient departments
   • Best-performing marketing campaigns

5. Anomaly Detection Description: Automated identification of statistical outliers and unusual patterns.

   Methods:

   • Z-score analysis
   • Moving range deviation
   • Machine learning-based anomaly scoring
   • Threshold-based alerts

6. Financial Forecasting Description: Predictive modeling for future business performance.

   Models:

   • ARIMA for short-term forecasting
   • Exponential smoothing
   • Regression-based projections
   • Scenario modeling (optimistic/pessimistic)

7. Cost Allocation Analysis Description: Detailed breakdown and attribution of organizational costs.

   Approaches:

   • Activity-based costing
   • Departmental cost distribution
   • Product/service cost absorption
   • Overhead allocation visualization

8. Auto-Tagging Description: Intelligent categorization of transactions and records.

   Features:

   • NLP-based description analysis
   • Rule-based classification
   • Machine learning categorization
   • Custom taxonomy support

9. Cash Flow Cycle Description: Working capital and liquidity analysis.

   Metrics:

   • Days Sales Outstanding (DSO)
   • Days Payable Outstanding (DPO)
   • Days Inventory Outstanding (DIO)
   • Cash conversion cycle visualization

10. Interactive Reports Description: Dynamic, parameter-driven business intelligence.

    Components:

    • Drill-down capabilities
    • Filtering by multiple dimensions
    • Export to PDF/Excel
    • Scheduled report generation

API Endpoints

Endpoint	Method	Description	Parameters
/api/v1/prepare-monthly	POST	Process raw data into monthly format	raw_data, fiscal_settings
/api/v1/profitability	POST	Run profitability analysis	period, segmentation
/api/v1/trend-analysis	POST	Generate time series insights	metrics, time_range
/api/v1/top-performers	GET	Retrieve top performers	limit, timeframe
/api/v1/anomaly-detection	POST	Identify data anomalies	sensitivity, methods
/api/v1/forecast	POST	Generate financial forecasts	horizon, confidence_level
/api/v1/cost-allocation	POST	Allocate costs across dimensions	allocation_method
/api/v1/auto-tagging	POST	Automatically categorize records	taxonomy_version
/api/v1/cash-flow-cycle	GET	Calculate cash flow metrics	detailed
/api/v1/reports	POST	Generate interactive reports	report_type, parameters

installation

Prerequisites

• Python 3.8+
• PostgreSQL 12+ (or preferred database)
• Redis (for caching)

Setup

    # Clone the repository
    git clone https://github.com/JavadTorabiKh/BusinessIntelligenceSystem.git
    cd business-analytics-api

    # Create virtual environment
    python -m venv venv
    source venv/bin/activate  # On Windows use `venv\Scripts\activate`

    # Install dependencies
    pip install -r requirements.txt

    # Configure environment
    cp .env.example .env
    # Edit .env with your configuration

    # Initialize database
    flask db upgrade

    # Run the application
    flask run

Usage

Authentication

    curl -X POST https://api.yourdomain.com/auth \
    -H "Content-Type: application/json" \
    -d '{"username":"your_username", "password":"your_password"}'

Example API Call

    curl -X POST https://api.yourdomain.com/api/v1/profitability \
    -H "Authorization: Bearer YOUR_TOKEN" \
    -H "Content-Type: application/json" \
    -d '{"period": {"start": "2023-01-01", "end": "2023-03-31"}, "segmentation": ["product", "region"]}'

Development

Project Structure

    BusinessIntelligenceSystem/
    ├── app/                  # Application code
    │   ├── core/            # Core functionality
    │   ├── services/        # Business logic
    │   ├── models/          # Data models
    │   ├── routes/          # API endpoints
    │   └── utils/           # Helper functions
    ├── tests/               # Test cases
    │ 
    ├── config/                  # Application code
    │   ├── core/            # Core functionality
    │   ├── services/        # Business logic
    │   ├── models/          # Data models
    │   ├── routes/          # API endpoints
    │   └── utils/           # Helper functions
    ├── tests/               # Test cases
    ├── data/               
    ├── migrations/          # Database migrations
    └── config.py            # Configuration

Testing

    pytest tests/ --cov=app --cov-report=html

Contributing

• Fork the repository
• Create your feature branch (git checkout -b feature/AmazingFeature)
• Commit your changes (git commit -m 'Add some AmazingFeature')
• Push to the branch (git push origin feature/AmazingFeature)
• Open a Pull Request

License

This project is licensed under the MIT License

Maintained by javad torabi - javadtorabi462@gmail.com

Business Analytics API © 2023