The Role of Data Analytics in Business Strategy for 2025: Transforming Decision-Making Through Intelligent Insights and Predictive Intelligence

import pandas as pd
import numpy as np
from datetime import datetime, timedelta
from typing import Dict, List, Optional, Any
from dataclasses import dataclass, field
from sklearn.ensemble import RandomForestRegressor, GradientBoostingClassifier
from sklearn.metrics import mean_absolute_error, accuracy_score
from sklearn.model_selection import train_test_split
import json
import uuid
from enum import Enum

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

class BusinessFunction(Enum):
    SALES = "sales"
    MARKETING = "marketing"
    OPERATIONS = "operations"
    FINANCE = "finance"
    HR = "human_resources"
    CUSTOMER_SERVICE = "customer_service"

@dataclass
class KPI:
    """Key Performance Indicator with targets and actuals"""
    kpi_id: str
    name: str
    business_function: BusinessFunction
    target_value: float
    actual_value: float
    unit: str
    measurement_date: datetime
    trend: str = "stable"  # improving, declining, stable
    variance_percentage: float = 0.0
    
@dataclass
class BusinessInsight:
    """Strategic business insight derived from analytics"""
    insight_id: str
    analytics_type: AnalyticsType
    business_function: BusinessFunction
    title: str
    description: str
    confidence_score: float
    impact_level: str  # high, medium, low
    recommended_actions: List[str] = field(default_factory=list)
    generated_date: datetime = field(default_factory=datetime.now)
    
@dataclass
class StrategicInitiative:
    """Strategic business initiative driven by analytics"""
    initiative_id: str
    name: str
    objective: str
    supporting_insights: List[str] = field(default_factory=list)
    success_metrics: List[str] = field(default_factory=list)
    estimated_roi: float = 0.0
    timeline_months: int = 12
    resource_requirements: Dict[str, Any] = field(default_factory=dict)
    status: str = "planned"  # planned, active, completed
    
class BusinessAnalyticsStrategy:
    """Comprehensive business analytics strategy framework"""
    
    def __init__(self, organization_name: str):
        self.organization_name = organization_name
        self.kpis: Dict[str, KPI] = {}
        self.insights: List[BusinessInsight] = []
        self.initiatives: Dict[str, StrategicInitiative] = {}
        
        # Analytics models for different business functions
        self.predictive_models = {
            'sales_forecasting': RandomForestRegressor(n_estimators=100, random_state=42),
            'customer_churn': GradientBoostingClassifier(n_estimators=100, random_state=42),
            'demand_planning': RandomForestRegressor(n_estimators=100, random_state=42),
            'price_optimization': RandomForestRegressor(n_estimators=100, random_state=42)
        }
        
        # Business data storage
        self.historical_data = {
            'sales': pd.DataFrame(),
            'customers': pd.DataFrame(),
            'operations': pd.DataFrame(),
            'finance': pd.DataFrame(),
            'marketing': pd.DataFrame()
        }
        
        # Analytics configuration
        self.analytics_config = {
            'real_time_monitoring': True,
            'predictive_modeling': True,
            'automated_insights': True,
            'alert_thresholds': {
                'kpi_variance': 0.1,  # 10% variance triggers alert
                'trend_significance': 0.05  # 5% significance level
            }
        }
        
        # Strategic focus areas
        self.strategic_priorities = {
            'customer_experience': 0.25,
            'operational_efficiency': 0.25,
            'market_expansion': 0.20,
            'innovation': 0.15,
            'cost_optimization': 0.15
        }
        
    def register_kpi(self, kpi: KPI) -> bool:
        """Register new KPI for monitoring and analysis"""
        # Calculate variance
        if kpi.target_value != 0:
            kpi.variance_percentage = ((kpi.actual_value - kpi.target_value) / kpi.target_value) * 100
        else:
            kpi.variance_percentage = 0
            
        # Determine trend (simplified - would use historical data)
        if kpi.variance_percentage > 5:
            kpi.trend = "improving"
        elif kpi.variance_percentage < -5:
            kpi.trend = "declining"
        else:
            kpi.trend = "stable"
            
        self.kpis[kpi.kpi_id] = kpi
        
        # Check for alerts
        if abs(kpi.variance_percentage) > self.analytics_config['alert_thresholds']['kpi_variance'] * 100:
            self._generate_kpi_alert(kpi)
            
        print(f"Registered KPI: {kpi.name} ({kpi.business_function.value})")
        return True
        
    def generate_predictive_insights(self, business_function: BusinessFunction, 
                                   time_horizon_months: int = 6) -> List[BusinessInsight]:
        """Generate predictive insights for specific business function"""
        insights = []
        
        if business_function == BusinessFunction.SALES:
            sales_insight = self._generate_sales_forecast_insight(time_horizon_months)
            if sales_insight:
                insights.append(sales_insight)
                
        elif business_function == BusinessFunction.MARKETING:
            marketing_insights = self._generate_marketing_insights(time_horizon_months)
            insights.extend(marketing_insights)
            
        elif business_function == BusinessFunction.OPERATIONS:
            operations_insight = self._generate_operations_optimization_insight()
            if operations_insight:
                insights.append(operations_insight)
                
        elif business_function == BusinessFunction.FINANCE:
            finance_insight = self._generate_financial_forecast_insight(time_horizon_months)
            if finance_insight:
                insights.append(finance_insight)
                
        # Store insights
        self.insights.extend(insights)
        
        return insights
        
    def perform_competitive_analysis(self, competitors: List[str], 
                                   market_data: Dict[str, Any]) -> BusinessInsight:
        """Analyze competitive positioning and market opportunities"""
        
        # Competitive analysis logic (simplified)
        market_share = market_data.get('our_market_share', 0.15)
        growth_rate = market_data.get('market_growth_rate', 0.08)
        competitor_performance = market_data.get('competitor_avg_growth', 0.06)
        
        # Generate competitive insights
        competitive_advantages = []
        recommended_actions = []
        
        if growth_rate > competitor_performance:
            competitive_advantages.append("Above-average market growth rate")
            recommended_actions.append("Increase marketing investment to capture market share")
            
        if market_share < 0.20:
            recommended_actions.append("Focus on market share expansion through targeted campaigns")
            
        confidence_score = min(0.8, market_share * 2 + (growth_rate - competitor_performance))
        
        insight = BusinessInsight(
            insight_id=f"COMP_{uuid.uuid4()}",
            analytics_type=AnalyticsType.DIAGNOSTIC,
            business_function=BusinessFunction.MARKETING,
            title="Competitive Market Position Analysis",
            description=f"Market analysis reveals {len(competitive_advantages)} key advantages and {len(recommended_actions)} strategic opportunities",
            confidence_score=confidence_score,
            impact_level="high" if len(recommended_actions) > 2 else "medium",
            recommended_actions=recommended_actions
        )
        
        self.insights.append(insight)
        return insight
        
    def optimize_resource_allocation(self, budget_constraints: Dict[str, float]) -> Dict[str, Any]:
        """Optimize resource allocation based on analytics insights"""
        
        total_budget = sum(budget_constraints.values())
        
        # Analyze ROI potential by business function
        roi_analysis = {}
        for function in BusinessFunction:
            function_kpis = [kpi for kpi in self.kpis.values() if kpi.business_function == function]
            
            if function_kpis:
                avg_performance = np.mean([kpi.variance_percentage for kpi in function_kpis])
                roi_potential = max(0.1, min(2.0, 1.0 + (avg_performance / 100)))
            else:
                roi_potential = 1.0
                
            roi_analysis[function.value] = roi_potential
            
        # Apply strategic priorities
        optimized_allocation = {}
        for function, priority in self.strategic_priorities.items():
            if function in roi_analysis:
                base_allocation = total_budget * priority
                roi_multiplier = roi_analysis.get(function, 1.0)
                optimized_allocation[function] = base_allocation * roi_multiplier
                
        # Normalize to budget constraints
        total_optimized = sum(optimized_allocation.values())
        if total_optimized > 0:
            normalization_factor = total_budget / total_optimized
            optimized_allocation = {k: v * normalization_factor for k, v in optimized_allocation.items()}
            
        optimization_result = {
            'original_allocation': budget_constraints,
            'optimized_allocation': optimized_allocation,
            'roi_analysis': roi_analysis,
            'expected_improvement': self._calculate_expected_improvement(optimized_allocation, roi_analysis),
            'reallocation_recommendations': self._generate_reallocation_recommendations(budget_constraints, optimized_allocation)
        }
        
        return optimization_result
        
    def create_strategic_initiative(self, name: str, objective: str, 
                                  supporting_insight_ids: List[str]) -> StrategicInitiative:
        """Create strategic initiative based on analytics insights"""
        
        # Gather supporting insights
        supporting_insights = [insight for insight in self.insights if insight.insight_id in supporting_insight_ids]
        
        # Estimate ROI based on insight impact levels
        high_impact_count = len([i for i in supporting_insights if i.impact_level == "high"])
        medium_impact_count = len([i for i in supporting_insights if i.impact_level == "medium"])
        
        estimated_roi = (high_impact_count * 2.5) + (medium_impact_count * 1.5)
        
        # Generate success metrics
        success_metrics = []
        for insight in supporting_insights:
            if insight.business_function == BusinessFunction.SALES:
                success_metrics.append("Revenue growth rate")
                success_metrics.append("Sales conversion rate")
            elif insight.business_function == BusinessFunction.MARKETING:
                success_metrics.append("Customer acquisition cost")
                success_metrics.append("Marketing ROI")
            elif insight.business_function == BusinessFunction.OPERATIONS:
                success_metrics.append("Operational efficiency ratio")
                success_metrics.append("Cost per unit")
                
        # Remove duplicates
        success_metrics = list(set(success_metrics))
        
        initiative = StrategicInitiative(
            initiative_id=f"INIT_{uuid.uuid4()}",
            name=name,
            objective=objective,
            supporting_insights=supporting_insight_ids,
            success_metrics=success_metrics,
            estimated_roi=estimated_roi,
            timeline_months=max(6, min(18, len(supporting_insights) * 3)),
            resource_requirements={
                'budget': estimated_roi * 50000,  # Simplified calculation
                'team_size': max(2, len(supporting_insights)),
                'technology_requirements': ['analytics_platform', 'reporting_tools']
            }
        )
        
        self.initiatives[initiative.initiative_id] = initiative
        return initiative
        
    def generate_strategic_dashboard(self) -> Dict[str, Any]:
        """Generate comprehensive strategic dashboard"""
        
        # KPI summary
        kpi_summary = {
            'total_kpis': len(self.kpis),
            'kpis_on_target': len([kpi for kpi in self.kpis.values() if abs(kpi.variance_percentage) <= 5]),
            'kpis_above_target': len([kpi for kpi in self.kpis.values() if kpi.variance_percentage > 5]),
            'kpis_below_target': len([kpi for kpi in self.kpis.values() if kpi.variance_percentage < -5]),
            'average_performance': np.mean([kpi.variance_percentage for kpi in self.kpis.values()]) if self.kpis else 0
        }
        
        # Insights summary
        insights_by_type = {}
        insights_by_impact = {'high': 0, 'medium': 0, 'low': 0}
        
        for insight in self.insights:
            insights_by_type[insight.analytics_type.value] = insights_by_type.get(insight.analytics_type.value, 0) + 1
            insights_by_impact[insight.impact_level] += 1
            
        # Strategic initiatives summary
        initiatives_summary = {
            'total_initiatives': len(self.initiatives),
            'planned_initiatives': len([i for i in self.initiatives.values() if i.status == 'planned']),
            'active_initiatives': len([i for i in self.initiatives.values() if i.status == 'active']),
            'completed_initiatives': len([i for i in self.initiatives.values() if i.status == 'completed']),
            'total_estimated_roi': sum(i.estimated_roi for i in self.initiatives.values())
        }
        
        dashboard = {
            'organization': self.organization_name,
            'generated_date': datetime.now().isoformat(),
            'kpi_performance': kpi_summary,
            'insights_analysis': {
                'total_insights': len(self.insights),
                'insights_by_type': insights_by_type,
                'insights_by_impact': insights_by_impact,
                'average_confidence': np.mean([i.confidence_score for i in self.insights]) if self.insights else 0
            },
            'strategic_initiatives': initiatives_summary,
            'strategic_priorities': self.strategic_priorities,
            'analytics_maturity': self._assess_analytics_maturity(),
            'key_recommendations': self._generate_key_recommendations()
        }
        
        return dashboard
        
    def simulate_scenario_analysis(self, scenarios: List[Dict[str, Any]]) -> Dict[str, Any]:
        """Perform scenario analysis for strategic planning"""
        
        scenario_results = {}
        
        for i, scenario in enumerate(scenarios):
            scenario_name = scenario.get('name', f'Scenario_{i+1}')
            
            # Simulate impact on key metrics
            revenue_impact = scenario.get('revenue_change', 0)
            cost_impact = scenario.get('cost_change', 0)
            market_impact = scenario.get('market_change', 0)
            
            # Calculate scenario outcomes
            profit_impact = revenue_impact - cost_impact
            market_share_impact = market_impact * 0.1  # Simplified calculation
            
            # Risk assessment
            probability = scenario.get('probability', 0.5)
            risk_level = 'high' if probability < 0.3 else 'medium' if probability < 0.7 else 'low'
            
            scenario_results[scenario_name] = {
                'revenue_impact': revenue_impact,
                'profit_impact': profit_impact,
                'market_share_impact': market_share_impact,
                'probability': probability,
                'risk_level': risk_level,
                'recommended_actions': self._generate_scenario_actions(scenario),
                'contingency_plans': self._generate_contingency_plans(risk_level)
            }
            
        return {
            'scenario_analysis': scenario_results,
            'most_likely_scenario': max(scenario_results.items(), key=lambda x: x['probability'])[0],
            'best_case_scenario': max(scenario_results.items(), key=lambda x: x['profit_impact'])[0],
            'worst_case_scenario': min(scenario_results.items(), key=lambda x: x['profit_impact'])[0],
            'risk_mitigation_strategies': self._generate_risk_mitigation_strategies(scenario_results)
        }
        
    # Helper methods for analytics and insights generation
    def _generate_kpi_alert(self, kpi: KPI):
        """Generate alert for KPI variance"""
        alert = {
            'kpi_id': kpi.kpi_id,
            'kpi_name': kpi.name,
            'variance': kpi.variance_percentage,
            'alert_level': 'critical' if abs(kpi.variance_percentage) > 20 else 'warning',
            'timestamp': datetime.now().isoformat()
        }
        print(f"ALERT: {kpi.name} variance: {kpi.variance_percentage:.1f}%")
        
    def _generate_sales_forecast_insight(self, time_horizon_months: int) -> Optional[BusinessInsight]:
        """Generate sales forecasting insight"""
        # Simplified sales forecasting
        current_trend = np.random.uniform(-0.1, 0.2)  # -10% to +20%
        forecast_confidence = np.random.uniform(0.7, 0.95)
        
        if abs(current_trend) > 0.05:  # Significant trend
            trend_direction = "growth" if current_trend > 0 else "decline"
            
            return BusinessInsight(
                insight_id=f"SALES_{uuid.uuid4()}",
                analytics_type=AnalyticsType.PREDICTIVE,
                business_function=BusinessFunction.SALES,
                title=f"Sales {trend_direction.title()} Forecast",
                description=f"Predictive models indicate {abs(current_trend)*100:.1f}% sales {trend_direction} over next {time_horizon_months} months",
                confidence_score=forecast_confidence,
                impact_level="high" if abs(current_trend) > 0.1 else "medium",
                recommended_actions=self._get_sales_recommendations(current_trend)
            )
        return None
        
    def _generate_marketing_insights(self, time_horizon_months: int) -> List[BusinessInsight]:
        """Generate marketing insights"""
        insights = []
        
        # Customer acquisition insight
        cac_trend = np.random.uniform(-0.15, 0.1)  # CAC change
        if abs(cac_trend) > 0.05:
            insights.append(BusinessInsight(
                insight_id=f"MKT_CAC_{uuid.uuid4()}",
                analytics_type=AnalyticsType.DIAGNOSTIC,
                business_function=BusinessFunction.MARKETING,
                title="Customer Acquisition Cost Analysis",
                description=f"Customer acquisition costs trending {abs(cac_trend)*100:.1f}% {'down' if cac_trend < 0 else 'up'}",
                confidence_score=0.85,
                impact_level="medium",
                recommended_actions=["Optimize marketing channels", "Review campaign effectiveness"]
            ))
            
        return insights
        
    def _generate_operations_optimization_insight(self) -> Optional[BusinessInsight]:
        """Generate operations optimization insight"""
        efficiency_opportunity = np.random.uniform(0, 0.25)  # 0-25% efficiency gain
        
        if efficiency_opportunity > 0.1:  # Significant opportunity
            return BusinessInsight(
                insight_id=f"OPS_{uuid.uuid4()}",
                analytics_type=AnalyticsType.PRESCRIPTIVE,
                business_function=BusinessFunction.OPERATIONS,
                title="Operational Efficiency Optimization",
                description=f"Process optimization could yield {efficiency_opportunity*100:.1f}% efficiency improvement",
                confidence_score=0.8,
                impact_level="high" if efficiency_opportunity > 0.2 else "medium",
                recommended_actions=["Automate manual processes", "Optimize workflow design", "Implement lean practices"]
            )
        return None
        
    def _generate_financial_forecast_insight(self, time_horizon_months: int) -> Optional[BusinessInsight]:
        """Generate financial forecasting insight"""
        cash_flow_trend = np.random.uniform(-0.1, 0.15)
        
        return BusinessInsight(
            insight_id=f"FIN_{uuid.uuid4()}",
            analytics_type=AnalyticsType.PREDICTIVE,
            business_function=BusinessFunction.FINANCE,
            title="Financial Performance Forecast",
            description=f"Financial models predict {abs(cash_flow_trend)*100:.1f}% {'improvement' if cash_flow_trend > 0 else 'decline'} in cash flow",
            confidence_score=0.9,
            impact_level="high",
            recommended_actions=["Monitor cash flow closely", "Adjust financial planning", "Review investment priorities"]
        )
        
    # Additional helper methods (simplified implementations)
    def _calculate_expected_improvement(self, allocation: Dict, roi_analysis: Dict) -> float:
        return sum(allocation.get(k, 0) * roi_analysis.get(k, 1.0) for k in allocation) / sum(allocation.values()) if allocation else 1.0
        
    def _generate_reallocation_recommendations(self, original: Dict, optimized: Dict) -> List[str]:
        recommendations = []
        for area, opt_amount in optimized.items():
            orig_amount = original.get(area, 0)
            if opt_amount > orig_amount * 1.1:
                recommendations.append(f"Increase investment in {area} by {((opt_amount/orig_amount - 1)*100):.0f}%")
            elif opt_amount < orig_amount * 0.9:
                recommendations.append(f"Reduce investment in {area} by {((1 - opt_amount/orig_amount)*100):.0f}%")
        return recommendations
        
    def _assess_analytics_maturity(self) -> Dict[str, Any]:
        maturity_score = min(100, len(self.insights) * 10 + len(self.kpis) * 5)
        return {
            'overall_score': maturity_score,
            'level': 'advanced' if maturity_score > 80 else 'intermediate' if maturity_score > 40 else 'basic',
            'strengths': ['KPI monitoring', 'Insight generation'] if maturity_score > 50 else ['Basic reporting'],
            'improvement_areas': ['Predictive modeling', 'Real-time analytics'] if maturity_score < 80 else ['Advanced AI integration']
        }
        
    def _generate_key_recommendations(self) -> List[str]:
        recommendations = [
            "Enhance predictive analytics capabilities for better forecasting",
            "Implement real-time dashboards for faster decision-making",
            "Develop cross-functional analytics teams",
            "Invest in advanced visualization tools",
            "Establish data governance framework"
        ]
        return recommendations[:3]  # Return top 3
        
    def _get_sales_recommendations(self, trend: float) -> List[str]:
        if trend > 0:
            return ["Increase inventory to meet demand", "Scale marketing efforts", "Expand sales team capacity"]
        else:
            return ["Implement retention strategies", "Review pricing strategy", "Analyze competitive positioning"]
            
    def _generate_scenario_actions(self, scenario: Dict) -> List[str]:
        return ["Monitor key indicators", "Prepare contingency plans", "Adjust resource allocation"]
        
    def _generate_contingency_plans(self, risk_level: str) -> List[str]:
        if risk_level == 'high':
            return ["Emergency cost reduction plan", "Alternative revenue streams", "Risk mitigation protocols"]
        elif risk_level == 'medium':
            return ["Flexible resource allocation", "Performance monitoring", "Scenario-based planning"]
        else:
            return ["Standard monitoring", "Regular strategy review"]
            
    def _generate_risk_mitigation_strategies(self, scenarios: Dict) -> List[str]:
        return ["Diversify revenue streams", "Maintain cash reserves", "Develop agile response capabilities"]

# Example usage and demonstration
def run_business_analytics_strategy_demo():
    print("=== Business Analytics Strategy Framework Demo ===")
    
    # Initialize analytics strategy system
    analytics_strategy = BusinessAnalyticsStrategy("TechCorp Solutions")
    
    # Register KPIs across business functions
    kpis = [
        KPI(
            kpi_id="SALES_REV_Q3",
            name="Quarterly Revenue",
            business_function=BusinessFunction.SALES,
            target_value=5000000,
            actual_value=5250000,
            unit="USD",
            measurement_date=datetime.now()
        ),
        KPI(
            kpi_id="MKT_CAC_Q3",
            name="Customer Acquisition Cost",
            business_function=BusinessFunction.MARKETING,
            target_value=150,
            actual_value=135,
            unit="USD",
            measurement_date=datetime.now()
        ),
        KPI(
            kpi_id="OPS_EFF_Q3",
            name="Operational Efficiency",
            business_function=BusinessFunction.OPERATIONS,
            target_value=85,
            actual_value=78,
            unit="%",
            measurement_date=datetime.now()
        ),
        KPI(
            kpi_id="FIN_MARGIN_Q3",
            name="Net Profit Margin",
            business_function=BusinessFunction.FINANCE,
            target_value=18,
            actual_value=22,
            unit="%",
            measurement_date=datetime.now()
        )
    ]
    
    for kpi in kpis:
        analytics_strategy.register_kpi(kpi)
        
    print(f"\nRegistered {len(kpis)} KPIs across business functions")
    
    # Generate predictive insights
    print("\n=== Generating Predictive Insights ===")
    
    for function in [BusinessFunction.SALES, BusinessFunction.MARKETING, BusinessFunction.OPERATIONS, BusinessFunction.FINANCE]:
        insights = analytics_strategy.generate_predictive_insights(function, 6)
        
        if insights:
            for insight in insights:
                print(f"\n{function.value.title()} Insight: {insight.title}")
                print(f"  Type: {insight.analytics_type.value}")
                print(f"  Description: {insight.description}")
                print(f"  Confidence: {insight.confidence_score:.1%}")
                print(f"  Impact: {insight.impact_level}")
                print(f"  Actions: {', '.join(insight.recommended_actions[:2])}")
                
    # Perform competitive analysis
    print("\n=== Competitive Analysis ===")
    market_data = {
        'our_market_share': 0.18,
        'market_growth_rate': 0.12,
        'competitor_avg_growth': 0.08
    }
    
    competitive_insight = analytics_strategy.perform_competitive_analysis(
        ['Competitor A', 'Competitor B', 'Competitor C'],
        market_data
    )
    
    print(f"Competitive Analysis: {competitive_insight.title}")
    print(f"Impact Level: {competitive_insight.impact_level}")
    print(f"Confidence: {competitive_insight.confidence_score:.1%}")
    print(f"Key Actions: {', '.join(competitive_insight.recommended_actions)}")
    
    # Resource allocation optimization
    print("\n=== Resource Allocation Optimization ===")
    
    budget_constraints = {
        'customer_experience': 500000,
        'operational_efficiency': 300000,
        'market_expansion': 400000,
        'innovation': 200000,
        'cost_optimization': 150000
    }
    
    allocation_result = analytics_strategy.optimize_resource_allocation(budget_constraints)
    
    print("Resource Allocation Analysis:")
    print(f"Expected Improvement: {allocation_result['expected_improvement']:.1%}")
    
    print("\nReallocation Recommendations:")
    for rec in allocation_result['reallocation_recommendations'][:3]:
        print(f"  - {rec}")
        
    print("\nOptimized Allocation:")
    for area, amount in allocation_result['optimized_allocation'].items():
        original = budget_constraints.get(area, 0)
        change = ((amount - original) / original * 100) if original > 0 else 0
        print(f"  {area}: ${amount:,.0f} ({change:+.0f}%)")
        
    # Create strategic initiative
    print("\n=== Strategic Initiative Creation ===")
    
    # Get some insight IDs
    insight_ids = [insight.insight_id for insight in analytics_strategy.insights[:2]]
    
    initiative = analytics_strategy.create_strategic_initiative(
        name="Customer Experience Enhancement Program",
        objective="Improve customer satisfaction and retention through data-driven personalization",
        supporting_insight_ids=insight_ids
    )
    
    print(f"Created Initiative: {initiative.name}")
    print(f"Estimated ROI: {initiative.estimated_roi:.1f}x")
    print(f"Timeline: {initiative.timeline_months} months")
    print(f"Budget Required: ${initiative.resource_requirements['budget']:,.0f}")
    print(f"Success Metrics: {', '.join(initiative.success_metrics[:3])}")
    
    # Scenario analysis
    print("\n=== Scenario Analysis ===")
    
    scenarios = [
        {
            'name': 'Economic Downturn',
            'revenue_change': -0.15,
            'cost_change': -0.05,
            'market_change': -0.10,
            'probability': 0.3
        },
        {
            'name': 'Market Expansion Success',
            'revenue_change': 0.25,
            'cost_change': 0.10,
            'market_change': 0.15,
            'probability': 0.4
        },
        {
            'name': 'Status Quo',
            'revenue_change': 0.05,
            'cost_change': 0.03,
            'market_change': 0.02,
            'probability': 0.6
        }
    ]
    
    scenario_analysis = analytics_strategy.simulate_scenario_analysis(scenarios)
    
    print(f"Most Likely Scenario: {scenario_analysis['most_likely_scenario']}")
    print(f"Best Case Scenario: {scenario_analysis['best_case_scenario']}")
    print(f"Worst Case Scenario: {scenario_analysis['worst_case_scenario']}")
    
    for scenario_name, details in scenario_analysis['scenario_analysis'].items():
        print(f"\n{scenario_name}:")
        print(f"  Profit Impact: {details['profit_impact']:+.1%}")
        print(f"  Probability: {details['probability']:.1%}")
        print(f"  Risk Level: {details['risk_level']}")
        
    # Generate strategic dashboard
    print("\n=== Strategic Dashboard ===")
    dashboard = analytics_strategy.generate_strategic_dashboard()
    
    print(f"Organization: {dashboard['organization']}")
    print(f"\nKPI Performance:")
    print(f"  Total KPIs: {dashboard['kpi_performance']['total_kpis']}")
    print(f"  On Target: {dashboard['kpi_performance']['kpis_on_target']}")
    print(f"  Above Target: {dashboard['kpi_performance']['kpis_above_target']}")
    print(f"  Below Target: {dashboard['kpi_performance']['kpis_below_target']}")
    print(f"  Average Performance: {dashboard['kpi_performance']['average_performance']:+.1f}%")
    
    print(f"\nInsights Analysis:")
    print(f"  Total Insights: {dashboard['insights_analysis']['total_insights']}")
    print(f"  High Impact Insights: {dashboard['insights_analysis']['insights_by_impact']['high']}")
    print(f"  Average Confidence: {dashboard['insights_analysis']['average_confidence']:.1%}")
    
    print(f"\nStrategic Initiatives:")
    print(f"  Total Initiatives: {dashboard['strategic_initiatives']['total_initiatives']}")
    print(f"  Estimated Total ROI: {dashboard['strategic_initiatives']['total_estimated_roi']:.1f}x")
    
    print(f"\nAnalytics Maturity:")
    maturity = dashboard['analytics_maturity']
    print(f"  Level: {maturity['level']} ({maturity['overall_score']}/100)")
    print(f"  Strengths: {', '.join(maturity['strengths'])}")
    
    print("\nKey Recommendations:")
    for i, rec in enumerate(dashboard['key_recommendations'], 1):
        print(f"  {i}. {rec}")
        
    return analytics_strategy

# Run demonstration
if __name__ == "__main__":
    demo_analytics_strategy = run_business_analytics_strategy_demo()