Analyser ses Données Business avec l'IA : De l'Excel aux Insights

class BusinessAnalyticsAI:
    def __init__(self):
        self.data_collector = DataCollectionEngine()
        self.data_processor = DataProcessingAI()
        self.pattern_detector = PatternDetectionAI()
        self.insight_generator = InsightGenerationAI()
        self.prediction_engine = BusinessPredictionAI()
        
    def analyze_business_data(self, data_sources, business_context):
        """
        Analyse complète données business avec IA pour insights actionnables
        """
        # Collection données multi-sources
        consolidated_data = self.data_collector.collect_from_sources(
            data_sources,
            source_types=['excel_files', 'crm_data', 'financial_systems', 
                         'web_analytics', 'sales_data', 'inventory_systems'],
            data_validation=True,
            real_time_sync=True
        )
        
        # Traitement et nettoyage IA
        processed_data = self.data_processor.process_and_clean(
            consolidated_data,
            processing_operations=['data_standardization', 'anomaly_detection',
                                 'missing_value_imputation', 'outlier_handling'],
            quality_threshold=0.95
        )
        
        # Détection patterns automatique
        detected_patterns = self.pattern_detector.detect_business_patterns(
            processed_data,
            business_context,
            pattern_types=['trends', 'correlations', 'anomalies', 'seasonality',
                          'customer_behaviors', 'operational_inefficiencies']
        )
        
        # Génération insights
        business_insights = self.insight_generator.generate_actionable_insights(
            detected_patterns,
            business_context,
            insight_categories=['revenue_opportunities', 'cost_reduction',
                              'risk_mitigation', 'process_optimization']
        )
        
        # Prédictions business
        business_predictions = self.prediction_engine.generate_predictions(
            processed_data,
            detected_patterns,
            prediction_horizons=[30, 90, 365],  # jours
            confidence_intervals=True
        )
        
        return {
            'data_quality_score': processed_data['quality_metrics'],
            'discovered_patterns': detected_patterns,
            'actionable_insights': business_insights,
            'business_predictions': business_predictions,
            'automated_dashboard': self.create_executive_dashboard(business_insights),
            'alert_system': self.setup_intelligent_alerts(detected_patterns)
        }

class ExcelToAITransformation:
    def __init__(self):
        self.excel_analyzer = ExcelFileAnalyzer()
        self.data_mapper = DataMappingAI()
        self.insight_translator = InsightTranslatorAI()
        self.automation_builder = AnalyticsAutomationBuilder()
        
    def transform_excel_to_ai_analytics(self, excel_files, business_requirements):
        """
        Transforme analyses Excel manuelles en analytics IA automatisés
        """
        # Analyse structure Excel existante
        excel_analysis = self.excel_analyzer.analyze_excel_structure(
            excel_files,
            analysis_aspects=['data_patterns', 'calculation_logic', 'chart_types',
                            'manual_processes', 'update_frequency']
        )
        
        # Mapping vers structure IA
        ai_data_model = self.data_mapper.map_to_ai_structure(
            excel_analysis,
            target_capabilities=['automated_updates', 'predictive_analytics',
                               'anomaly_detection', 'natural_language_insights']
        )
        
        # Traduction insights manuels
        automated_insights = self.insight_translator.translate_manual_insights(
            excel_analysis['manual_insights'],
            ai_data_model,
            automation_level='full'
        )
        
        # Construction automation
        automation_system = self.automation_builder.build_analytics_automation(
            ai_data_model,
            automated_insights,
            business_requirements,
            update_frequency='real_time'
        )
        
        return {
            'transformation_plan': excel_analysis,
            'ai_data_architecture': ai_data_model,
            'automated_analytics': automation_system,
            'migration_roadmap': self.create_migration_roadmap(excel_analysis),
            'roi_projection': self.calculate_transformation_roi(excel_analysis, automation_system)
        }

class DistributionAnalyticsAI:
    def __init__(self):
        self.sales_analyzer = SalesAnalyticsAI()
        self.inventory_optimizer = InventoryOptimizationAI()
        self.customer_intelligence = CustomerAnalyticsAI()
        self.financial_forecaster = FinancialForecastingAI()
        
    def analyze_distribution_business(self, data_sources):
        """
        Analyse complète business distribution avec IA
        """
        # Analytics ventes avancées
        sales_insights = self.sales_analyzer.analyze_sales_performance(
            data_sources['sales_data'],
            insights=['trend_analysis', 'seasonality_patterns', 'product_performance',
                     'sales_rep_performance', 'geographic_analysis', 'margin_optimization']
        )
        
        # Optimisation inventory intelligente
        inventory_insights = self.inventory_optimizer.optimize_inventory_levels(
            data_sources['inventory_data'],
            sales_insights,
            optimization_goals=['turnover_maximization', 'stockout_minimization', 
                              'carrying_cost_reduction']
        )
        
        # Intelligence client
        customer_insights = self.customer_intelligence.analyze_customer_behavior(
            data_sources['customer_data'],
            sales_insights,
            analysis_types=['segmentation', 'lifetime_value', 'churn_prediction',
                          'upsell_opportunities']
        )
        
        # Forecasting financier
        financial_predictions = self.financial_forecaster.forecast_financial_performance(
            [sales_insights, inventory_insights, customer_insights],
            forecast_horizons=[30, 90, 365],
            scenarios=['conservative', 'likely', 'optimistic']
        )
        
        return {
            'sales_intelligence': sales_insights,
            'inventory_optimization': inventory_insights,
            'customer_intelligence': customer_insights,
            'financial_forecasting': financial_predictions,
            'executive_summary': self.generate_executive_insights([
                sales_insights, inventory_insights, customer_insights, financial_predictions
            ])
        }

class BusinessPredictionAI:
    def __init__(self):
        self.trend_analyzer = TrendAnalysisAI()
        self.seasonality_detector = SeasonalityDetectionAI()
        self.external_factor_integrator = ExternalFactorAnalyzer()
        self.ensemble_predictor = EnsemblePredictionModel()
        
    def generate_business_predictions(self, historical_data, business_context):
        """
        Génère prédictions business multi-horizons avec IA
        """
        # Analyse des tendances
        trend_analysis = self.trend_analyzer.analyze_trends(
            historical_data,
            trend_types=['linear', 'exponential', 'polynomial', 'cyclical'],
            trend_confidence_threshold=0.75
        )
        
        # Détection saisonnalité
        seasonality_patterns = self.seasonality_detector.detect_seasonal_patterns(
            historical_data,
            seasonality_types=['weekly', 'monthly', 'quarterly', 'yearly'],
            pattern_strength_threshold=0.6
        )
        
        # Intégration facteurs externes
        external_influences = self.external_factor_integrator.integrate_external_factors(
            business_context,
            external_factors=['economic_indicators', 'industry_trends', 'competitive_landscape',
                            'weather_patterns', 'social_media_sentiment']
        )
        
        # Prédictions ensemble
        prediction_ensemble = self.ensemble_predictor.generate_ensemble_predictions(
            trend_analysis,
            seasonality_patterns,
            external_influences,
            prediction_models=['arima', 'prophet', 'neural_network', 'random_forest'],
            ensemble_method='weighted_average'
        )
        
        return {
            'short_term_predictions': prediction_ensemble['30_day_forecast'],
            'medium_term_predictions': prediction_ensemble['90_day_forecast'],
            'long_term_predictions': prediction_ensemble['365_day_forecast'],
            'prediction_confidence': prediction_ensemble['confidence_intervals'],
            'key_factors': prediction_ensemble['feature_importance'],
            'scenario_analysis': self.generate_scenario_predictions(prediction_ensemble)
        }