Skill: datadrivenconstruction/data-evolution-analysis (version 2.1.0)

--- 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 ```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] 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] } ``` ## Common Use Cases ### Assess Current Digital Maturity ```python 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]}") ``` ### Track Evolution Over Time ```python 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'])}") ``` ### Generate Executive Report ```python report = analyzer.generate_report(assessment) print(report) # Save to file with open("maturity_report.md", "w") as f: f.write(report) ``` ## Quick Reference | 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 | ## Resources - **Book**: "Data-Driven Construction" by Artem Boiko, Chapter 1.1 - **Website**: https://datadrivenconstruction.io ## Next Steps - Use [data-silo-detection](../../Chapter-1.2/data-silo-detection/SKILL.md) to identify integration gaps - Use [erp-integration-analysis](../../Chapter-1.2/erp-integration-analysis/SKILL.md) for system integration - Use [digital-maturity-assessment](../../Chapter-5.1/digital-maturity-assessment/SKILL.md) for detailed assessments


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