name: "data-evolution-analysis" description: "Analyze data evolution patterns in construction organizations. Assess digital maturity and data strategy for construction companies" homepage: "https://datadrivenconstruction.io" metadata: {"openclaw": {"emoji": "📚", "os": ["win32"], "homepage": "https://datadrivenconstruction.io", "requires": {"bins": ["python3"]}}}

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

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]
    recommendations: List[str]
    roadmap: Dict[str, List[str]]


class DataEvolutionAnalyzer:
    """
    Analyze data evolution and digital maturity in construction organizations.
    Based on DDC methodology Chapter 1.1.
    """

    def __init__(self):
        self.assessment_criteria = self._load_criteria()
        self.evolution_stages = self._define_evolution_stages()

    def _load_criteria(self) -> Dict[DataCategory, Dict]:
        """Load assessment criteria for each category"""
        return {
            DataCategory.DESIGN: {
                "tools": ["CAD", "BIM", "Collaboration Platform"],
                "metrics": ["model_usage", "clash_detection", "design_reviews"],
                "weight": 0.20
            },
            DataCategory.COST: {
                "tools": ["Spreadsheets", "Estimating Software", "ERP"],
                "metrics": ["automation_level", "historical_data", "benchmarking"],
                "weight": 0.15
            },
            DataCategory.SCHEDULE: {
                "tools": ["Gantt Charts", "CPM Software", "4D BIM"],
                "metrics": ["resource_loading", "progress_tracking", "forecasting"],
                "weight": 0.15
            },
            DataCategory.QUALITY: {
                "tools": ["Checklists", "QC Software", "Defect Tracking"],
                "metrics": ["inspection_digitization", "defect_analytics", "compliance"],
                "weight": 0.12
            },
            DataCategory.SAFETY: {
                "tools": ["Incident Reports", "Safety Software", "IoT Sensors"],
                "metrics": ["incident_tracking", "predictive_safety", "training"],
                "weight": 0.12
            },
            DataCategory.PROCUREMENT: {
                "tools": ["RFQ Manual", "e-Procurement", "Supply Chain"],
                "metrics": ["vendor_management", "material_tracking", "integration"],
                "weight": 0.10
            },
            DataCategory.DOCUMENT: {
                "tools": ["File Shares", "DMS", "CDE"],
                "metrics": ["version_control", "access_control", "searchability"],
                "weight": 0.08
            },
            DataCategory.COMMUNICATION: {
                "tools": ["Email", "Collaboration", "Unified Platform"],
                "metrics": ["response_time", "transparency", "audit_trail"],
                "weight": 0.08
            }
        }

    def _define_evolution_stages(self) -> Dict[MaturityLevel, Dict]:
        """Define characteristics of each evolution stage"""
        return {
            MaturityLevel.LEVEL_0_PAPER: {
                "name": "Paper-Based",
                "description": "Manual, paper-based processes",
                "characteristics": [
                    "Physical document storage",
                    "Manual data entry",
                    "Limited data sharing",
                    "No real-time visibility"
                ],
                "typical_tools": ["Paper forms", "Physical filing"]
            },
            MaturityLevel.LEVEL_1_BASIC: {
                "name": "Basic Digital",
                "description": "Basic digitization with standalone tools",
                "characteristics": [
                    "Spreadsheets for calculations",
                    "Email for communication",
                    "File shares for storage",
                    "Manual data transfer between systems"
                ],
                "typical_tools": ["Excel", "Word", "Email", "File shares"]
            },
            MaturityLevel.LEVEL_2_STRUCTURED: {
                "name": "Structured Data",
                "description": "Structured databases and specialized software",
                "characteristics": [
                    "Department-specific software",
                    "Structured databases",
                    "Basic reporting",
                    "Some standardization"
                ],
                "typical_tools": ["CAD", "Estimating software", "Project software"]
            },
            MaturityLevel.LEVEL_3_INTEGRATED: {
                "name": "Integrated Systems",
                "description": "Connected systems with data flow",
                "characteristics": [
                    "ERP integration",
                    "BIM adoption",
                    "Automated workflows",
                    "Cross-department data sharing"
                ],
                "typical_tools": ["BIM", "ERP", "CDE", "BI dashboards"]
            },
            MaturityLevel.LEVEL_4_AUTOMATED: {
                "name": "Automated & Analytics",
                "description": "Automation and advanced analytics",
                "characteristics": [
                    "Automated data collection",
                    "Machine learning models",
                    "Predictive analytics",
                    "Real-time dashboards"
                ],
                "typical_tools": ["ML platforms", "IoT", "Advanced analytics"]
            },
            MaturityLevel.LEVEL_5_PREDICTIVE: {
                "name": "Predictive & Autonomous",
                "description": "AI-driven, predictive operations",
                "characteristics": [
                    "Digital twins",
                    "Autonomous decision support",
                    "Continuous optimization",
                    "Predictive maintenance"
                ],
                "typical_tools": ["Digital twins", "AI/ML", "Autonomous systems"]
            }
        }

    def assess_organization(
        self,
        organization_name: str,
        survey_responses: Dict[str, any],
        system_inventory: List[Dict],
        process_documentation: Optional[Dict] = None
    ) -> MaturityAssessment:
        """
        Perform comprehensive digital maturity assessment.

        Args:
            organization_name: Name of the organization
            survey_responses: Responses from maturity survey
            system_inventory: List of systems/tools in use
            process_documentation: Optional process documentation

        Returns:
            Complete maturity assessment
        """
        # Analyze data flows
        data_flows = self._analyze_data_flows(system_inventory, survey_responses)

        # Calculate category scores
        category_scores = self._calculate_category_scores(
            data_flows, survey_responses
        )

        # Determine overall maturity level
        overall_score = sum(
            score * self.assessment_criteria[cat]["weight"]
            for cat, score in category_scores.items()
        )
        overall_level = self._score_to_level(overall_score)

        # Identify strengths and weaknesses
        strengths, weaknesses = self._identify_gaps(category_scores)

        # Generate recommendations
        recommendations = self._generate_recommendations(
            overall_level, weaknesses, data_flows
        )

        # Create roadmap
        roadmap = self._create_roadmap(overall_level, recommendations)

        return MaturityAssessment(
            organization_name=organization_name,
            assessment_date=datetime.now(),
            overall_level=overall_level,
            category_scores=category_scores,
            data_flows=data_flows,
            strengths=strengths,
            weaknesses=weaknesses,
            recommendations=recommendations,
            roadmap=roadmap
        )

    def _analyze_data_flows(
        self,
        system_inventory: List[Dict],
        survey_responses: Dict
    ) -> List[DataFlowAssessment]:
        """Analyze data flows between systems"""
        flows = []

        for category in DataCategory:
            # Find systems for this category
            category_systems = [
                s for s in system_inventory
                if s.get("category") == category.value
            ]

            if not category_systems:
                flows.append(DataFlowAssessment(
                    category=category,
                    source_systems=[],
                    storage_format="none",
                    integration_level=0.0,
                    automation_level=0.0,
                    data_quality_score=0.0,
                    issues=["No systems identified for this category"]
                ))
                continue

            # Analyze integration and automation
            integration = self._calculate_integration_score(category_systems)
            automation = self._calculate_automation_score(
                category_systems, survey_responses
            )
            quality = survey_responses.get(
                f"{category.value}_data_quality", 0.5
            )

            # Identify issues
            issues = self._identify_flow_issues(
                category_systems, integration, automation
            )

            flows.append(DataFlowAssessment(
                category=category,
                source_systems=[s["name"] for s in category_systems],
                storage_format=category_systems[0].get("format", "unknown"),
                integration_level=integration,
                automation_level=automation,
                data_quality_score=quality,
                issues=issues
            ))

        return flows

    def _calculate_integration_score(
        self, systems: List[Dict]
    ) -> float:
        """Calculate integration score for systems"""
        if not systems:
            return 0.0

        total_integrations = sum(
            len(s.get("integrations", [])) for s in systems
        )
        max_integrations = len(systems) * 3  # Assume max 3 integrations per system

        return min(1.0, total