data-silo-detection

# Data Silo Detection

## Overview

Based on DDC methodology (Chapter 1.2), this skill detects and maps data silos in construction organizations, identifying disconnected data sources, duplicate data, and integration opportunities.

**Book Reference:** "Технологии и системы управления в современном строительстве" / "Technologies and Management Systems in Modern Construction"

## Quick Start

```python
from dataclasses import dataclass, field
from enum import Enum
from typing import List, Dict, Optional, Set, Tuple
from datetime import datetime
import json
from collections import defaultdict

class DataDomain(Enum):
    """Construction data domains"""
    DESIGN = "design"
    COST = "cost"
    SCHEDULE = "schedule"
    QUALITY = "quality"
    SAFETY = "safety"
    PROCUREMENT = "procurement"
    SITE = "site"
    DOCUMENT = "document"
    FINANCIAL = "financial"
    HR = "hr"

class SiloSeverity(Enum):
    """Severity level of data silo"""
    CRITICAL = "critical"      # Major business impact
    HIGH = "high"              # Significant inefficiency
    MEDIUM = "medium"          # Noticeable issues
    LOW = "low"                # Minor inconvenience

class DataSourceType(Enum):
    """Types of data sources"""
    DATABASE = "database"
    SPREADSHEET = "spreadsheet"
    FILE_SHARE = "file_share"
    CLOUD_APP = "cloud_app"
    DESKTOP_APP = "desktop_app"
    PAPER = "paper"
    EMAIL = "email"
    PERSONAL = "personal"

@dataclass
class DataSource:
    """Represents a data source in the organization"""
    id: str
    name: str
    type: DataSourceType
    domain: DataDomain
    owner: str
    department: str
    users: List[str]
    data_entities: List[str]
    connections: List[str] = field(default_factory=list)
    update_frequency: str = "unknown"
    access_level: str = "department"  # personal, department, organization
    has_api: bool = False
    last_modified: Optional[datetime] = None

@d...