BMAD-METHOD/bmad-system/strategic-intelligence/strategic-intelligence-dashboard.md

# Strategic Intelligence Dashboard

## Executive-Level Strategic Intelligence and Decision Support for Enhanced BMAD System

The Strategic Intelligence Dashboard provides sophisticated executive-level insights, predictive analytics, and strategic decision support that enable organizations to make data-driven strategic decisions with real-time intelligence, trend analysis, and scenario modeling across all business and technology domains.

### Strategic Intelligence Architecture

#### Comprehensive Strategic Intelligence Platform
```yaml
strategic_intelligence_dashboard:
  intelligence_domains:
    business_intelligence:
      - strategic_kpi_monitoring: "Monitor strategic key performance indicators"
      - business_performance_analytics: "Analyze business performance across dimensions"
      - market_intelligence_integration: "Integrate market intelligence and trends"
      - competitive_analysis_dashboard: "Competitive landscape analysis and positioning"
      - customer_intelligence_insights: "Customer behavior and satisfaction analytics"
      
    technology_intelligence:
      - technology_portfolio_analytics: "Analyze technology portfolio and investments"
      - innovation_pipeline_tracking: "Track innovation pipeline and R&D initiatives"
      - technical_debt_strategic_view: "Strategic view of technical debt across organization"
      - architecture_evolution_tracking: "Track architectural evolution and modernization"
      - technology_risk_assessment: "Assess technology risks and mitigation strategies"
      
    operational_intelligence:
      - operational_efficiency_metrics: "Monitor operational efficiency and optimization"
      - resource_utilization_analytics: "Analyze resource utilization across organization"
      - process_performance_monitoring: "Monitor process performance and bottlenecks"
      - quality_metrics_dashboard: "Quality metrics and improvement tracking"
      - capacity_planning_intelligence: "Capacity planning and forecasting analytics"
      
    financial_intelligence:
      - financial_performance_dashboard: "Financial performance and profitability analysis"
      - cost_optimization_insights: "Cost optimization opportunities and savings"
      - investment_roi_analytics: "Return on investment analysis for initiatives"
      - budget_variance_monitoring: "Budget variance analysis and forecasting"
      - financial_risk_assessment: "Financial risk assessment and mitigation"
      
    strategic_planning_intelligence:
      - strategic_goal_tracking: "Track strategic goals and objectives"
      - initiative_portfolio_management: "Manage and track strategic initiatives"
      - scenario_planning_and_modeling: "Scenario planning and what-if analysis"
      - strategic_risk_monitoring: "Monitor strategic risks and opportunities"
      - stakeholder_value_analysis: "Analyze stakeholder value creation and impact"
      
  analytics_capabilities:
    predictive_analytics:
      - trend_forecasting: "Forecast business and technology trends"
      - performance_prediction: "Predict future performance based on current data"
      - risk_prediction_modeling: "Predict risks and their potential impact"
      - opportunity_identification: "Identify emerging opportunities and trends"
      - scenario_outcome_prediction: "Predict outcomes of different strategic scenarios"
      
    prescriptive_analytics:
      - optimization_recommendations: "Recommend optimization strategies and actions"
      - resource_allocation_optimization: "Optimize resource allocation across initiatives"
      - investment_prioritization: "Prioritize investments based on strategic value"
      - decision_support_recommendations: "Provide recommendations for strategic decisions"
      - action_plan_optimization: "Optimize action plans for strategic initiatives"
      
    diagnostic_analytics:
      - root_cause_analysis: "Perform root cause analysis for performance issues"
      - variance_analysis: "Analyze variances from planned performance"
      - correlation_analysis: "Identify correlations between different metrics"
      - impact_analysis: "Analyze impact of decisions and changes"
      - performance_attribution: "Attribute performance to specific factors"
      
    descriptive_analytics:
      - performance_benchmarking: "Benchmark performance against industry standards"
      - trend_analysis: "Analyze historical trends and patterns"
      - comparative_analysis: "Compare performance across different dimensions"
      - portfolio_analysis: "Analyze portfolio performance and composition"
      - stakeholder_analysis: "Analyze stakeholder engagement and satisfaction"
      
  visualization_capabilities:
    executive_dashboards:
      - ceo_strategic_dashboard: "CEO-level strategic overview dashboard"
      - cto_technology_dashboard: "CTO-level technology and innovation dashboard"
      - cfo_financial_dashboard: "CFO-level financial performance dashboard"
      - coo_operational_dashboard: "COO-level operational efficiency dashboard"
      - board_governance_dashboard: "Board-level governance and risk dashboard"
      
    interactive_visualizations:
      - dynamic_charts_and_graphs: "Interactive charts, graphs, and visualizations"
      - drill_down_capabilities: "Drill-down from high-level to detailed views"
      - cross_dimensional_analysis: "Cross-dimensional analysis and filtering"
      - real_time_data_visualization: "Real-time data visualization and updates"
      - mobile_responsive_interfaces: "Mobile-responsive dashboard interfaces"
      
    strategic_mapping:
      - strategy_map_visualization: "Visualize strategy maps and objectives"
      - value_chain_analysis_maps: "Value chain analysis and visualization"
      - stakeholder_mapping: "Stakeholder relationship and influence mapping"
      - risk_heat_maps: "Risk heat maps and assessment visualization"
      - opportunity_landscape_maps: "Opportunity landscape and market maps"
      
    reporting_and_communication:
      - executive_summary_reports: "Executive summary reports and briefings"
      - board_presentation_materials: "Board presentation materials and slides"
      - stakeholder_communication_packages: "Stakeholder communication packages"
      - regulatory_reporting_dashboards: "Regulatory reporting and compliance dashboards"
      - investor_relations_materials: "Investor relations materials and presentations"
      
  decision_support_capabilities:
    scenario_modeling:
      - what_if_analysis: "What-if analysis and scenario modeling"
      - sensitivity_analysis: "Sensitivity analysis for key variables"
      - monte_carlo_simulations: "Monte Carlo simulations for uncertainty modeling"
      - decision_tree_analysis: "Decision tree analysis for complex decisions"
      - optimization_modeling: "Optimization modeling for resource allocation"
      
    strategic_planning_support:
      - goal_setting_and_tracking: "Goal setting, tracking, and achievement monitoring"
      - initiative_prioritization: "Initiative prioritization and portfolio management"
      - resource_planning_optimization: "Resource planning and allocation optimization"
      - timeline_and_milestone_planning: "Timeline and milestone planning and tracking"
      - strategic_roadmap_development: "Strategic roadmap development and visualization"
      
    risk_and_opportunity_management:
      - risk_assessment_and_monitoring: "Risk assessment, monitoring, and mitigation"
      - opportunity_identification_and_evaluation: "Opportunity identification and evaluation"
      - threat_analysis_and_response: "Threat analysis and response planning"
      - competitive_intelligence_and_response: "Competitive intelligence and response strategies"
      - market_dynamics_analysis: "Market dynamics analysis and strategic positioning"
```

#### Strategic Intelligence Dashboard Implementation
```python
import asyncio
import pandas as pd
import numpy as np
import plotly.graph_objects as go
import plotly.express as px
from plotly.subplots import make_subplots
import dash
from dash import dcc, html, Input, Output, State
import dash_bootstrap_components as dbc
from typing import Dict, List, Any, Optional, Tuple, Union
from dataclasses import dataclass, field
from enum import Enum
from datetime import datetime, timedelta
import json
import uuid
from collections import defaultdict, deque
import logging
from abc import ABC, abstractmethod
import sqlite3
from sqlalchemy import create_engine
import warnings
warnings.filterwarnings('ignore')

class DashboardLevel(Enum):
    EXECUTIVE = "executive"
    SENIOR_MANAGEMENT = "senior_management"
    OPERATIONAL = "operational"
    DEPARTMENTAL = "departmental"
    PROJECT = "project"

class IntelligenceType(Enum):
    BUSINESS = "business"
    TECHNOLOGY = "technology"
    OPERATIONAL = "operational"
    FINANCIAL = "financial"
    STRATEGIC = "strategic"
    COMPETITIVE = "competitive"

class AnalyticsType(Enum):
    DESCRIPTIVE = "descriptive"
    DIAGNOSTIC = "diagnostic"
    PREDICTIVE = "predictive"
    PRESCRIPTIVE = "prescriptive"

class MetricCategory(Enum):
    KPI = "kpi"
    OKR = "okr"
    PERFORMANCE = "performance"
    RISK = "risk"
    QUALITY = "quality"
    FINANCIAL = "financial"

@dataclass
class StrategicMetric:
    """
    Represents a strategic metric for dashboard visualization
    """
    metric_id: str
    name: str
    category: MetricCategory
    intelligence_type: IntelligenceType
    description: str
    current_value: float
    target_value: float
    trend_direction: str  # up, down, stable
    unit: str
    frequency: str  # daily, weekly, monthly, quarterly
    data_source: str
    calculation_method: str
    thresholds: Dict[str, float] = field(default_factory=dict)
    historical_data: List[Dict[str, Any]] = field(default_factory=list)
    benchmarks: Dict[str, float] = field(default_factory=dict)

@dataclass
class DashboardWidget:
    """
    Represents a dashboard widget with visualization configuration
    """
    widget_id: str
    title: str
    widget_type: str  # chart, table, metric, map, etc.
    metrics: List[str]
    visualization_config: Dict[str, Any]
    position: Dict[str, int]  # row, column, width, height
    access_level: DashboardLevel
    refresh_frequency: int  # minutes
    interactive: bool = True
    drill_down_enabled: bool = False

@dataclass
class StrategicInsight:
    """
    Represents a strategic insight generated from analytics
    """
    insight_id: str
    title: str
    description: str
    intelligence_type: IntelligenceType
    analytics_type: AnalyticsType
    confidence_level: float
    impact_level: str  # high, medium, low
    time_horizon: str  # short, medium, long
    recommendations: List[str] = field(default_factory=list)
    supporting_data: Dict[str, Any] = field(default_factory=dict)
    created_timestamp: datetime = field(default_factory=datetime.utcnow)

class StrategicIntelligenceDashboard:
    """
    Comprehensive strategic intelligence dashboard with executive-level insights
    """
    
    def __init__(self, claude_code_interface, config=None):
        self.claude_code = claude_code_interface
        self.config = config or {
            'dashboard_refresh_interval': 300,  # 5 minutes
            'data_retention_days': 365,
            'real_time_updates': True,
            'predictive_analytics_enabled': True,
            'mobile_responsive': True,
            'security_enabled': True,
            'export_formats': ['pdf', 'excel', 'powerpoint'],
            'ai_insights_enabled': True
        }
        
        # Core dashboard components
        self.data_manager = StrategicDataManager(self.claude_code, self.config)
        self.analytics_engine = StrategicAnalyticsEngine(self.config)
        self.visualization_engine = VisualizationEngine(self.config)
        self.insight_generator = InsightGenerator(self.config)
        
        # Specialized intelligence modules
        self.business_intelligence = BusinessIntelligenceModule(self.config)
        self.technology_intelligence = TechnologyIntelligenceModule(self.config)
        self.financial_intelligence = FinancialIntelligenceModule(self.config)
        self.operational_intelligence = OperationalIntelligenceModule(self.config)
        self.competitive_intelligence = CompetitiveIntelligenceModule(self.config)
        
        # Dashboard services
        self.dashboard_builder = DashboardBuilder(self.config)
        self.alert_service = StrategicAlertService(self.config)
        self.export_service = DashboardExportService(self.config)
        self.collaboration_service = CollaborationService(self.config)
        
        # State management
        self.metric_repository = MetricRepository()
        self.dashboard_configurations = {}
        self.active_sessions = {}
        self.insight_history = []
        
        # AI and ML components
        self.ml_engine = MLIntelligenceEngine(self.config)
        self.nlp_processor = NLPProcessor(self.config)
        self.recommendation_engine = RecommendationEngine(self.config)
        
    async def create_executive_dashboard(self, executive_role, requirements):
        """
        Create personalized executive dashboard based on role and requirements
        """
        dashboard_creation_session = {
            'session_id': generate_uuid(),
            'start_time': datetime.utcnow(),
            'executive_role': executive_role,
            'requirements': requirements,
            'dashboard_config': {},
            'widgets': [],
            'data_sources': {},
            'analytics_config': {}
        }
        
        try:
            # Analyze executive requirements
            requirements_analysis = await self.analyze_executive_requirements(
                executive_role,
                requirements
            )
            dashboard_creation_session['requirements_analysis'] = requirements_analysis
            
            # Design dashboard layout
            dashboard_layout = await self.design_dashboard_layout(
                executive_role,
                requirements_analysis
            )
            dashboard_creation_session['dashboard_layout'] = dashboard_layout
            
            # Configure metrics and KPIs
            metrics_config = await self.configure_executive_metrics(
                executive_role,
                requirements_analysis
            )
            dashboard_creation_session['metrics_config'] = metrics_config
            
            # Setup data sources and integrations
            data_sources = await self.setup_dashboard_data_sources(
                metrics_config
            )
            dashboard_creation_session['data_sources'] = data_sources
            
            # Create dashboard widgets
            widgets = await self.create_dashboard_widgets(
                dashboard_layout,
                metrics_config,
                data_sources
            )
            dashboard_creation_session['widgets'] = widgets
            
            # Configure analytics and insights
            analytics_config = await self.configure_dashboard_analytics(
                executive_role,
                metrics_config
            )
            dashboard_creation_session['analytics_config'] = analytics_config
            
            # Setup alerts and notifications
            alert_config = await self.setup_dashboard_alerts(
                executive_role,
                metrics_config
            )
            dashboard_creation_session['alert_config'] = alert_config
            
            # Build and deploy dashboard
            dashboard_deployment = await self.dashboard_builder.build_and_deploy_dashboard(
                dashboard_creation_session
            )
            dashboard_creation_session['dashboard_deployment'] = dashboard_deployment
            
        except Exception as e:
            dashboard_creation_session['error'] = str(e)
        
        finally:
            dashboard_creation_session['end_time'] = datetime.utcnow()
            dashboard_creation_session['creation_duration'] = (
                dashboard_creation_session['end_time'] - dashboard_creation_session['start_time']
            ).total_seconds()
        
        return dashboard_creation_session
    
    async def analyze_executive_requirements(self, executive_role, requirements):
        """
        Analyze executive requirements to determine dashboard needs
        """
        requirements_analysis = {
            'primary_focus_areas': [],
            'key_metrics': [],
            'decision_support_needs': [],
            'stakeholder_requirements': {},
            'time_horizons': {},
            'visualization_preferences': {},
            'interaction_patterns': {}
        }
        
        # Define role-specific focus areas
        role_focus_mapping = {
            'CEO': [
                'strategic_performance',
                'financial_results',
                'market_position',
                'stakeholder_value',
                'risk_management'
            ],
            'CTO': [
                'technology_innovation',
                'technical_debt',
                'architecture_evolution',
                'team_performance',
                'security_posture'
            ],
            'CFO': [
                'financial_performance',
                'cost_optimization',
                'investment_roi',
                'budget_management',
                'financial_risk'
            ],
            'COO': [
                'operational_efficiency',
                'process_performance',
                'resource_utilization',
                'quality_metrics',
                'capacity_planning'
            ]
        }
        
        requirements_analysis['primary_focus_areas'] = role_focus_mapping.get(
            executive_role,
            ['business_performance', 'strategic_metrics']
        )
        
        # Determine key metrics based on role and requirements
        key_metrics = await self.determine_key_metrics(
            executive_role,
            requirements_analysis['primary_focus_areas'],
            requirements
        )
        requirements_analysis['key_metrics'] = key_metrics
        
        # Analyze decision support needs
        decision_support_needs = await self.analyze_decision_support_needs(
            executive_role,
            requirements
        )
        requirements_analysis['decision_support_needs'] = decision_support_needs
        
        return requirements_analysis
    
    async def design_dashboard_layout(self, executive_role, requirements_analysis):
        """
        Design optimal dashboard layout for executive role
        """
        dashboard_layout = {
            'layout_type': 'executive_summary',
            'sections': [],
            'navigation': {},
            'responsive_breakpoints': {},
            'accessibility_features': {}
        }
        
        # Design sections based on role
        if executive_role == 'CEO':
            sections = [
                {
                    'section_id': 'strategic_overview',
                    'title': 'Strategic Overview',
                    'position': {'row': 1, 'column': 1, 'width': 12, 'height': 4},
                    'widget_types': ['kpi_summary', 'strategic_goals_progress']
                },
                {
                    'section_id': 'financial_performance',
                    'title': 'Financial Performance',
                    'position': {'row': 2, 'column': 1, 'width': 6, 'height': 6},
                    'widget_types': ['revenue_trends', 'profitability_analysis']
                },
                {
                    'section_id': 'market_intelligence',
                    'title': 'Market Intelligence',
                    'position': {'row': 2, 'column': 7, 'width': 6, 'height': 6},
                    'widget_types': ['market_share', 'competitive_position']
                },
                {
                    'section_id': 'risk_and_opportunities',
                    'title': 'Risk & Opportunities',
                    'position': {'row': 3, 'column': 1, 'width': 12, 'height': 4},
                    'widget_types': ['risk_heat_map', 'opportunity_pipeline']
                }
            ]
        elif executive_role == 'CTO':
            sections = [
                {
                    'section_id': 'technology_health',
                    'title': 'Technology Health',
                    'position': {'row': 1, 'column': 1, 'width': 12, 'height': 4},
                    'widget_types': ['system_performance', 'technical_debt_overview']
                },
                {
                    'section_id': 'innovation_pipeline',
                    'title': 'Innovation Pipeline',
                    'position': {'row': 2, 'column': 1, 'width': 6, 'height': 6},
                    'widget_types': ['rd_initiatives', 'technology_adoption']
                },
                {
                    'section_id': 'team_performance',
                    'title': 'Team Performance',
                    'position': {'row': 2, 'column': 7, 'width': 6, 'height': 6},
                    'widget_types': ['team_velocity', 'skill_development']
                },
                {
                    'section_id': 'security_posture',
                    'title': 'Security Posture',
                    'position': {'row': 3, 'column': 1, 'width': 12, 'height': 4},
                    'widget_types': ['security_metrics', 'vulnerability_trends']
                }
            ]
        else:
            # Generic executive layout
            sections = [
                {
                    'section_id': 'executive_summary',
                    'title': 'Executive Summary',
                    'position': {'row': 1, 'column': 1, 'width': 12, 'height': 4},
                    'widget_types': ['key_metrics', 'performance_trends']
                },
                {
                    'section_id': 'performance_details',
                    'title': 'Performance Details',
                    'position': {'row': 2, 'column': 1, 'width': 12, 'height': 8},
                    'widget_types': ['detailed_analytics', 'comparative_analysis']
                }
            ]
        
        dashboard_layout['sections'] = sections
        
        return dashboard_layout
    
    async def generate_strategic_insights(self, timeframe="current_quarter"):
        """
        Generate comprehensive strategic insights from available data
        """
        insight_generation_session = {
            'session_id': generate_uuid(),
            'start_time': datetime.utcnow(),
            'timeframe': timeframe,
            'insights_generated': [],
            'analytics_performed': {},
            'recommendations': []
        }
        
        try:
            # Collect data for analysis
            strategic_data = await self.data_manager.collect_strategic_data(timeframe)
            insight_generation_session['data_collected'] = len(strategic_data)
            
            # Perform multi-dimensional analytics
            analytics_results = await self.analytics_engine.perform_comprehensive_analytics(
                strategic_data
            )
            insight_generation_session['analytics_performed'] = analytics_results
            
            # Generate business insights
            business_insights = await self.business_intelligence.generate_insights(
                strategic_data,
                analytics_results
            )
            insight_generation_session['insights_generated'].extend(business_insights)
            
            # Generate technology insights
            technology_insights = await self.technology_intelligence.generate_insights(
                strategic_data,
                analytics_results
            )
            insight_generation_session['insights_generated'].extend(technology_insights)
            
            # Generate financial insights
            financial_insights = await self.financial_intelligence.generate_insights(
                strategic_data,
                analytics_results
            )
            insight_generation_session['insights_generated'].extend(financial_insights)
            
            # Generate operational insights
            operational_insights = await self.operational_intelligence.generate_insights(
                strategic_data,
                analytics_results
            )
            insight_generation_session['insights_generated'].extend(operational_insights)
            
            # Generate competitive insights
            competitive_insights = await self.competitive_intelligence.generate_insights(
                strategic_data,
                analytics_results
            )
            insight_generation_session['insights_generated'].extend(competitive_insights)
            
            # Generate strategic recommendations
            recommendations = await self.recommendation_engine.generate_strategic_recommendations(
                insight_generation_session['insights_generated'],
                analytics_results
            )
            insight_generation_session['recommendations'] = recommendations
            
            # Prioritize insights and recommendations
            prioritized_insights = await self.prioritize_insights_and_recommendations(
                insight_generation_session['insights_generated'],
                recommendations
            )
            insight_generation_session['prioritized_insights'] = prioritized_insights
            
        except Exception as e:
            insight_generation_session['error'] = str(e)
        
        finally:
            insight_generation_session['end_time'] = datetime.utcnow()
            insight_generation_session['generation_duration'] = (
                insight_generation_session['end_time'] - insight_generation_session['start_time']
            ).total_seconds()
            
            # Store insights in history
            self.insight_history.extend(insight_generation_session.get('insights_generated', []))
        
        return insight_generation_session
    
    async def create_scenario_analysis(self, scenario_parameters):
        """
        Create comprehensive scenario analysis with predictive modeling
        """
        scenario_analysis = {
            'analysis_id': generate_uuid(),
            'timestamp': datetime.utcnow(),
            'scenario_parameters': scenario_parameters,
            'scenarios': [],
            'impact_analysis': {},
            'recommendations': {},
            'risk_assessment': {}
        }
        
        # Define scenarios to analyze
        scenarios = await self.define_analysis_scenarios(scenario_parameters)
        
        for scenario in scenarios:
            scenario_result = await self.analyze_single_scenario(scenario)
            scenario_analysis['scenarios'].append(scenario_result)
        
        # Perform comparative impact analysis
        impact_analysis = await self.perform_scenario_impact_analysis(
            scenario_analysis['scenarios']
        )
        scenario_analysis['impact_analysis'] = impact_analysis
        
        # Generate scenario-based recommendations
        recommendations = await self.generate_scenario_recommendations(
            scenario_analysis
        )
        scenario_analysis['recommendations'] = recommendations
        
        # Assess risks across scenarios
        risk_assessment = await self.assess_scenario_risks(
            scenario_analysis['scenarios']
        )
        scenario_analysis['risk_assessment'] = risk_assessment
        
        return scenario_analysis
    
    async def define_analysis_scenarios(self, scenario_parameters):
        """
        Define scenarios for analysis based on parameters
        """
        scenarios = []
        
        # Base scenario (current trajectory)
        base_scenario = {
            'scenario_id': 'base',
            'name': 'Current Trajectory',
            'description': 'Continuation of current trends and performance',
            'parameters': scenario_parameters.get('base_parameters', {}),
            'probability': 0.6
        }
        scenarios.append(base_scenario)
        
        # Optimistic scenario
        optimistic_scenario = {
            'scenario_id': 'optimistic',
            'name': 'Optimistic Growth',
            'description': 'Accelerated growth and positive market conditions',
            'parameters': self.adjust_parameters_optimistic(scenario_parameters),
            'probability': 0.2
        }
        scenarios.append(optimistic_scenario)
        
        # Pessimistic scenario
        pessimistic_scenario = {
            'scenario_id': 'pessimistic',
            'name': 'Economic Downturn',
            'description': 'Economic challenges and reduced market conditions',
            'parameters': self.adjust_parameters_pessimistic(scenario_parameters),
            'probability': 0.2
        }
        scenarios.append(pessimistic_scenario)
        
        return scenarios
    
    def adjust_parameters_optimistic(self, base_parameters):
        """Adjust parameters for optimistic scenario"""
        optimistic_params = base_parameters.copy()
        
        # Increase growth rates by 50%
        if 'growth_rate' in optimistic_params:
            optimistic_params['growth_rate'] *= 1.5
        
        # Improve efficiency metrics by 25%
        if 'efficiency_metrics' in optimistic_params:
            for metric in optimistic_params['efficiency_metrics']:
                optimistic_params['efficiency_metrics'][metric] *= 1.25
        
        return optimistic_params
    
    def adjust_parameters_pessimistic(self, base_parameters):
        """Adjust parameters for pessimistic scenario"""
        pessimistic_params = base_parameters.copy()
        
        # Decrease growth rates by 30%
        if 'growth_rate' in pessimistic_params:
            pessimistic_params['growth_rate'] *= 0.7
        
        # Decrease efficiency metrics by 20%
        if 'efficiency_metrics' in pessimistic_params:
            for metric in pessimistic_params['efficiency_metrics']:
                pessimistic_params['efficiency_metrics'][metric] *= 0.8
        
        return pessimistic_params

class StrategicDataManager:
    """
    Manages strategic data collection, integration, and processing
    """
    
    def __init__(self, claude_code, config):
        self.claude_code = claude_code
        self.config = config
        self.data_sources = {}
        
    async def collect_strategic_data(self, timeframe):
        """
        Collect comprehensive strategic data from all sources
        """
        strategic_data = {
            'business_metrics': {},
            'financial_data': {},
            'operational_metrics': {},
            'technology_metrics': {},
            'market_data': {},
            'competitive_data': {},
            'timestamp': datetime.utcnow()
        }
        
        # Collect business metrics
        business_metrics = await self.collect_business_metrics(timeframe)
        strategic_data['business_metrics'] = business_metrics
        
        # Collect financial data
        financial_data = await self.collect_financial_data(timeframe)
        strategic_data['financial_data'] = financial_data
        
        # Collect operational metrics
        operational_metrics = await self.collect_operational_metrics(timeframe)
        strategic_data['operational_metrics'] = operational_metrics
        
        # Collect technology metrics
        technology_metrics = await self.collect_technology_metrics(timeframe)
        strategic_data['technology_metrics'] = technology_metrics
        
        return strategic_data
    
    async def collect_business_metrics(self, timeframe):
        """
        Collect business performance metrics
        """
        # This would integrate with actual business systems
        # For now, return simulated data
        return {
            'revenue': 1000000,
            'customer_acquisition': 150,
            'customer_retention': 0.85,
            'market_share': 0.12,
            'brand_sentiment': 0.75
        }
    
    async def collect_financial_data(self, timeframe):
        """
        Collect financial performance data
        """
        # This would integrate with financial systems
        # For now, return simulated data
        return {
            'revenue': 1000000,
            'costs': 750000,
            'profit_margin': 0.25,
            'roi': 0.18,
            'cash_flow': 150000
        }

class StrategicAnalyticsEngine:
    """
    Performs advanced analytics on strategic data
    """
    
    def __init__(self, config):
        self.config = config
        
    async def perform_comprehensive_analytics(self, strategic_data):
        """
        Perform comprehensive analytics on strategic data
        """
        analytics_results = {
            'trend_analysis': {},
            'correlation_analysis': {},
            'variance_analysis': {},
            'predictive_analysis': {},
            'comparative_analysis': {}
        }
        
        # Perform trend analysis
        trend_analysis = await self.perform_trend_analysis(strategic_data)
        analytics_results['trend_analysis'] = trend_analysis
        
        # Perform correlation analysis
        correlation_analysis = await self.perform_correlation_analysis(strategic_data)
        analytics_results['correlation_analysis'] = correlation_analysis
        
        # Perform predictive analysis
        predictive_analysis = await self.perform_predictive_analysis(strategic_data)
        analytics_results['predictive_analysis'] = predictive_analysis
        
        return analytics_results
    
    async def perform_trend_analysis(self, strategic_data):
        """
        Perform trend analysis on strategic metrics
        """
        # Simplified trend analysis
        trends = {}
        
        for category, metrics in strategic_data.items():
            if isinstance(metrics, dict):
                for metric_name, value in metrics.items():
                    if isinstance(value, (int, float)):
                        # Simulate trend calculation
                        trends[f"{category}_{metric_name}"] = {
                            'current_value': value,
                            'trend_direction': 'up' if value > 0 else 'down',
                            'trend_strength': abs(value) / 1000000 if value != 0 else 0
                        }
        
        return trends

def generate_uuid():
    """Generate a UUID string"""
    return str(uuid.uuid4())

# Additional classes would be implemented here:
# - BusinessIntelligenceModule
# - TechnologyIntelligenceModule
# - FinancialIntelligenceModule
# - OperationalIntelligenceModule
# - CompetitiveIntelligenceModule
# - DashboardBuilder
# - StrategicAlertService
# - DashboardExportService
# - CollaborationService
# - MetricRepository
# - MLIntelligenceEngine
# - NLPProcessor
# - RecommendationEngine
# - VisualizationEngine
# - InsightGenerator
```

### Strategic Intelligence Commands

```bash
# Executive dashboard creation and management
bmad intelligence dashboard --create --executive-role "CEO" --personalized
bmad intelligence dashboard --configure --metrics "strategic-kpis" --real-time
bmad intelligence dashboard --deploy --mobile-responsive --secure-access

# Strategic analytics and insights
bmad intelligence analyze --comprehensive --predictive --prescriptive
bmad intelligence insights --generate --ai-powered --confidence-scoring
bmad intelligence trends --forecast --scenario-modeling --what-if-analysis

# Business intelligence and performance
bmad intelligence business --performance-analytics --market-intelligence
bmad intelligence financial --profitability-analysis --cost-optimization
bmad intelligence operational --efficiency-metrics --capacity-planning

# Technology and innovation intelligence
bmad intelligence technology --portfolio-analytics --innovation-pipeline
bmad intelligence architecture --evolution-tracking --modernization-insights
bmad intelligence security --posture-assessment --risk-intelligence

# Competitive and market intelligence
bmad intelligence competitive --landscape-analysis --positioning-insights
bmad intelligence market --dynamics-analysis --opportunity-identification
bmad intelligence customer --behavior-analytics --satisfaction-insights

# Scenario planning and decision support
bmad intelligence scenario --planning --monte-carlo --sensitivity-analysis
bmad intelligence decision --support --optimization-recommendations
bmad intelligence risk --assessment --mitigation-strategies --monitoring

# Reporting and communication
bmad intelligence report --executive-summary --board-presentation
bmad intelligence export --pdf --excel --powerpoint --interactive
bmad intelligence collaborate --stakeholder-sharing --real-time-comments

# AI and machine learning insights
bmad intelligence ai --automated-insights --pattern-recognition
bmad intelligence ml --predictive-models --anomaly-detection
bmad intelligence nlp --text-analytics --sentiment-analysis
```

This Strategic Intelligence Dashboard provides sophisticated executive-level insights, predictive analytics, and strategic decision support that enable organizations to make data-driven strategic decisions with real-time intelligence, trend analysis, and scenario modeling across all business and technology domains.