# 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.