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"
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_integrations / max_integrations)
def _calculate_automation_score(
self,
systems: List[Dict],
survey: Dict
) -> float:
"""Calculate automation score"""
scores = []
for system in systems:
system_score = 0.0
if system.get("has_api"):
system_score += 0.3
if system.get("automated_imports"):
system_score += 0.3
if system.get("automated_exports"):
system_score += 0.2
if system.get("workflow_automation"):
system_score += 0.2
scores.append(system_score)
return sum(scores) / len(scores) if scores else 0.0
def _calculate_category_scores(
self,
data_flows: List[DataFlowAssessment],
survey: Dict
) -> Dict[DataCategory, float]:
"""Calculate maturity score for each category"""
scores = {}
for flow in data_flows:
# Combine different aspects
tool_score = survey.get(f"{flow.category.value}_tool_maturity", 0.5)
process_score = survey.get(f"{flow.category.value}_process_maturity", 0.5)
category_score = (
tool_score * 0.3 +
process_score * 0.2 +
flow.integration_level * 0.2 +
flow.automation_level * 0.2 +
flow.data_quality_score * 0.1
)
scores[flow.category] = category_score
return scores
def _score_to_level(self, score: float) -> MaturityLevel:
"""Convert numeric score to maturity level"""
if score < 0.1:
return MaturityLevel.LEVEL_0_PAPER
elif score < 0.25:
return MaturityLevel.LEVEL_1_BASIC
elif score < 0.45:
return MaturityLevel.LEVEL_2_STRUCTURED
elif score < 0.65:
return MaturityLevel.LEVEL_3_INTEGRATED
elif score < 0.85:
return MaturityLevel.LEVEL_4_AUTOMATED
else:
return MaturityLevel.LEVEL_5_PREDICTIVE
def _identify_gaps(
self,
scores: Dict[DataCategory, float]
) -> tuple[List[str], List[str]]:
"""Identify strengths and weaknesses"""
avg_score = sum(scores.values()) / len(scores)
strengths = [
f"{cat.value}: {score:.0%}"
for cat, score in scores.items()
if score > avg_score + 0.1
]
weaknesses = [
f"{cat.value}: {score:.0%}"
for cat, score in scores.items()
if score < avg_score - 0.1
]
return strengths, weaknesses
def _identify_flow_issues(
self,
systems: List[Dict],
integration: float,
automation: float
) -> List[str]:
"""Identify issues in data flow"""
issues = []
if integration < 0.3:
issues.append("Low system integration - data silos likely")
if automation < 0.3:
issues.append("Manual data transfer required")
if len(systems) > 3:
issues.append("Multiple overlapping systems")
return issues
def _generate_recommendations(
self,
level: MaturityLevel,
weaknesses: List[str],
flows: List[DataFlowAssessment]
) -> List[str]:
"""Generate improvement recommendations"""
recommendations = []
# Level-specific recommendations
level_recs = {
MaturityLevel.LEVEL_0_PAPER: [
"Implement basic digital tools (spreadsheets, file sharing)",
"Digitize critical paper-based processes",
"Train staff on basic digital skills"
],
MaturityLevel.LEVEL_1_BASIC: [
"Adopt specialized construction software",
"Implement structured data storage",
"Standardize data formats and naming conventions"
],
MaturityLevel.LEVEL_2_STRUCTURED: [
"Integrate key systems (ERP, PM, BIM)",
"Implement Common Data Environment (CDE)",
"Develop automated workflows"
],
MaturityLevel.LEVEL_3_INTEGRATED: [
"Implement advanced analytics and dashboards",
"Explore IoT for automated data collection",
"Develop machine learning models for prediction"
],
MaturityLevel.LEVEL_4_AUTOMATED: [
"Implement digital twin technology",
"Deploy AI-driven decision support",
"Enable predictive maintenance and operations"
],
MaturityLevel.LEVEL_5_PREDICTIVE: [
"Continuous optimization of AI models",
"Expand autonomous decision-making",
"Industry leadership and knowledge sharing"
]
}
recommendations.extend(level_recs.get(level, []))
# Address specific weaknesses
for flow in flows:
if flow.integration_level < 0.3:
recommendations.append(
f"Improve {flow.category.value} system integrations"
)
if flow.data_quality_score < 0.5:
recommendations.append(
f"Implement data quality controls for {flow.category.value}"
)
return recommendations[:10] # Top 10 recommendations
def _create_roadmap(
self,
current_level: MaturityLevel,
recommendations: List[str]
) -> Dict[str, List[str]]:
"""Create phased improvement roadmap"""
return {
"Phase 1 (0-6 months)": recommendations[:3],
"Phase 2 (6-12 months)": recommendations[3:6],
"Phase 3 (12-24 months)": recommendations[6:],
"Target Level": [
f"Move from {current_level.name} to "
f"{MaturityLevel(min(current_level.value + 1, 5)).name}"
]
}
def compare_assessments(
self,
assessments: List[MaturityAssessment]
) -> Dict:
"""Compare multiple assessments over time or across organizations"""
comparison = {
"assessments": len(assessments),
"levels": [a.overall_level.name for a in assessments],
"trends": {},
"best_practices": []
}
# Track category trends
for category in DataCategory:
scores = [a.category_scores[category] for a in assessments]
comparison["trends"][category.value] = {
"scores": scores,
"improvement": scores[-1] - scores[0] if len(scores) > 1 else 0
}
return comparison
def generate_report(
self,
assessment: MaturityAssessment
) -> str:
"""Generate executive summary report"""
stage_info = self.evolution_stages[assessment.overall_level]
report = f"""
# Digital Maturity Assessment Report
## {assessment.organization_name}
**Assessment Date:** {assessment.assessment_date.strftime('%Y-%m-%d')}
**Overall Maturity Level:** {assessment.overall_level.name} - {stage_info['name']}
### Executive Summary
{stage_info['description']}
### Category Scores
"""
for cat, score in assessment.category_scores.items():
bar = "█" * int(score * 10) + "░" * (10 - int(score * 10))
report += f"- {cat.value.title()}: {bar} {score:.0%}\n"
report += "\n### Strengths\n"
for strength in assessment.strengths:
report += f"- {strength}\n"
report += "\n### Areas for Improvement\n"
for weakness in assessment.weaknesses:
report += f"- {weakness}\n"
report += "\n### Recommendations\n"
for i, rec in enumerate(assessment.recommendations, 1):
report += f"{i}. {rec}\n"
report += "\n### Roadmap\n"
for phase, items in assessment.roadmap.items():
report += f"\n**{phase}**\n"
for item in items:
report += f"- {item}\n"
return report
class DataEvolutionTracker:
"""Track data evolution over time"""
def __init__(self, organization_name: str):
self.organization = organization_name
self.history: List[MaturityAssessment] = []
self.milestones: List[Dict] = []
def add_assessment(self, assessment: MaturityAssessment):
"""Add new assessment to history"""
self.history.append(assessment)
self._check_milestones(assessment)
def _check_milestones(self, assessment: MaturityAssessment):
"""Check if any milestones were reached"""
if len(self.history) > 1:
prev = self.history[-2]
# Level improvement
if assessment.overall_level.value > prev.overall_level.value:
self.milestones.append({
"date": assessment.assessment_date,
"type": "level_up",
"description": f"Advanced from {prev.overall_level.name} "
f"to {assessment.overall_level.name}"
})
# Category improvements
for cat in DataCategory:
if assessment.category_scores[cat] - prev.category_scores[cat] > 0.2:
self.milestones.append({
"date": assessment.assessment_date,
"type": "category_improvement",
"description": f"Significant improvement in {cat.value}"
})
def get_evolution_summary(self) -> Dict:
"""Get summary of evolution over time"""
if not self.history:
return {"error": "No assessments recorded"}
return {
"organization": self.organization,
"first_assessment": self.history[0].assessment_date,
"latest_assessment": self.history[-1].assessment_date,
"starting_level": self.history[0].overall_level.name,
"current_level": self.history[-1].overall_level.name,
"total_assessments": len(self.history),
"milestones": self.milestones,
"level_progression": [a.overall_level.value for a in self.history]
}
analyzer = DataEvolutionAnalyzer()
# Define systems in use
systems = [
{"name": "AutoCAD", "category": "design", "has_api": False},
{"name": "Revit", "category": "design", "has_api": True, "integrations": ["Navisworks"]},
{"name": "Excel", "category": "cost", "has_api": False},
{"name": "MS Project", "category": "schedule", "has_api": False},
{"name": "Email", "category": "communication", "has_api": False}
]
# Survey responses (from questionnaire)
survey = {
"design_tool_maturity": 0.6,
"design_process_maturity": 0.5,
"design_data_quality": 0.7,
"cost_tool_maturity": 0.3,
"cost_process_maturity": 0.4,
"cost_data_quality": 0.5,
"schedule_tool_maturity": 0.4,
"schedule_process_maturity": 0.3,
"schedule_data_quality": 0.4
}
assessment = analyzer.assess_organization(
organization_name="Construction Co",
survey_responses=survey,
system_inventory=systems
)
print(f"Maturity Level: {assessment.overall_level.name}")
print(f"Recommendations: {assessment.recommendations[:3]}")
tracker = DataEvolutionTracker("Construction Co")
# Add quarterly assessments
tracker.add_assessment(q1_assessment)
tracker.add_assessment(q2_assessment)
tracker.add_assessment(q3_assessment)
summary = tracker.get_evolution_summary()
print(f"Progress: {summary['starting_level']} → {summary['current_level']}")
print(f"Milestones: {len(summary['milestones'])}")
report = analyzer.generate_report(assessment)
print(report)
# Save to file
with open("maturity_report.md", "w") as f:
f.write(report)
| Component | Purpose |
|-----------|---------|
| DataEvolutionAnalyzer | Main assessment engine |
| MaturityLevel | 6 levels from paper to predictive |
| DataCategory | 8 categories (design, cost, schedule, etc.) |
| DataFlowAssessment | Analyze data flows per category |
| MaturityAssessment | Complete assessment results |
| DataEvolutionTracker | Track progress over time |
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