data-evolution-analysis

# Data Evolution Analysis

## Overview

Based on DDC methodology (Chapter 1.1), this skill analyzes data evolution patterns in construction organizations, assessing digital maturity levels from paper-based workflows to fully data-driven operations.

**Book Reference:** "Эволюция использования данных в строительной отрасли" / "Evolution of Data Usage in Construction"

## Quick Start

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

class MaturityLevel(Enum):
    """Digital maturity levels based on DDC methodology"""
    LEVEL_0_PAPER = 0      # Paper-based, no digital tools
    LEVEL_1_BASIC = 1      # Basic digital (spreadsheets, email)
    LEVEL_2_STRUCTURED = 2  # Structured databases, some integration
    LEVEL_3_INTEGRATED = 3  # ERP/BIM integration, workflows
    LEVEL_4_AUTOMATED = 4   # Automated processes, ML/AI
    LEVEL_5_PREDICTIVE = 5  # Predictive analytics, digital twins

class DataCategory(Enum):
    """Categories of construction data"""
    DESIGN = "design"
    COST = "cost"
    SCHEDULE = "schedule"
    QUALITY = "quality"
    SAFETY = "safety"
    PROCUREMENT = "procurement"
    DOCUMENT = "document"
    COMMUNICATION = "communication"

@dataclass
class DataFlowAssessment:
    """Assessment of data flow in an organization"""
    category: DataCategory
    source_systems: List[str]
    storage_format: str
    integration_level: float  # 0-1
    automation_level: float   # 0-1
    data_quality_score: float # 0-1
    issues: List[str] = field(default_factory=list)

@dataclass
class MaturityAssessment:
    """Complete digital maturity assessment"""
    organization_name: str
    assessment_date: datetime
    overall_level: MaturityLevel
    category_scores: Dict[DataCategory, float]
    data_flows: List[DataFlowAssessment]
    strengths: List[str]
    weaknesses: List[str]
    recommendati...