Impact Analysis

Impact analysis in NopeSight helps you understand the consequences of changes, failures, or disruptions to your services. By leveraging service mapping and AI-powered predictions, you can make informed decisions and minimize business disruption.

Impact Analysis Framework

Change Impact Analysis

Pre-Change Assessment

class ChangeImpactAnalyzer:
    def __init__(self):
        self.service_map = ServiceMapRepository()
        self.impact_predictor = ImpactPredictor()
        self.risk_calculator = RiskCalculator()
        
    def analyze_change_impact(self, change_request):
        """Comprehensive change impact analysis"""
        
        impact_analysis = {
            'change_id': change_request.id,
            'change_type': change_request.type,
            'target_components': change_request.components,
            'scheduled_time': change_request.scheduled_time,
            'analysis_timestamp': datetime.now(),
            'impacts': {},
            'risks': {},
            'recommendations': []
        }
        
        # Identify affected services
        affected_services = self.identify_affected_services(
            change_request.components
        )
        
        for service in affected_services:
            service_impact = self.analyze_service_impact(
                service,
                change_request
            )
            impact_analysis['impacts'][service.id] = service_impact
            
            # Calculate business impact
            business_impact = self.calculate_business_impact(
                service,
                service_impact
            )
            impact_analysis['impacts'][service.id]['business'] = business_impact
        
        # Analyze cascade effects
        cascade_analysis = self.analyze_cascade_effects(
            affected_services,
            change_request
        )
        impact_analysis['cascade_effects'] = cascade_analysis
        
        # Risk assessment
        risk_assessment = self.assess_change_risk(
            impact_analysis,
            change_request
        )
        impact_analysis['risks'] = risk_assessment
        
        # Generate recommendations
        recommendations = self.generate_recommendations(
            impact_analysis,
            risk_assessment
        )
        impact_analysis['recommendations'] = recommendations
        
        return impact_analysis
    
    def analyze_service_impact(self, service, change):
        """Analyze impact on specific service"""
        
        impact = {
            'service': service.name,
            'criticality': service.criticality,
            'direct_impact': False,
            'indirect_impact': False,
            'downtime_required': False,
            'performance_impact': None,
            'functionality_impact': []
        }
        
        # Check direct impact
        for component in change.components:
            if component in service.components:
                impact['direct_impact'] = True
                
                # Analyze component role
                component_role = service.get_component_role(component)
                
                if component_role.is_critical:
                    impact['downtime_required'] = True
                    impact['downtime_duration'] = self.estimate_downtime(
                        component,
                        change.type
                    )
                
                # Performance impact
                if change.type in ['upgrade', 'configuration']:
                    perf_impact = self.predict_performance_impact(
                        component,
                        change
                    )
                    impact['performance_impact'] = perf_impact
        
        # Check indirect impact through dependencies
        dependencies = self.get_service_dependencies(service)
        
        for dep in dependencies:
            if any(comp in change.components for comp in dep.components):
                impact['indirect_impact'] = True
                impact['dependency_impacts'].append({
                    'dependency': dep.name,
                    'impact_type': self.classify_dependency_impact(dep, change),
                    'mitigation_available': dep.has_failover
                })
        
        return impact

Change Risk Matrix

Risk Matrix:
  Dimensions:
    Probability:
      - Very Low (< 10%)
      - Low (10-30%)
      - Medium (30-60%)
      - High (60-90%)
      - Very High (> 90%)
      
    Impact:
      - Minimal (< 10 users)
      - Minor (10-100 users)
      - Moderate (100-1000 users)
      - Major (1000-10000 users)
      - Severe (> 10000 users)
      
  Risk Levels:
    Critical: Probability >= High AND Impact >= Major
    High: Probability >= Medium AND Impact >= Moderate
    Medium: Probability >= Low AND Impact >= Minor
    Low: All other combinations
    
  Actions:
    Critical:
      - Executive approval required
      - Rollback plan mandatory
      - War room standby
      
    High:
      - Management approval required
      - Detailed testing required
      - Communication plan needed

Change Simulation

class ChangeSimulator:
    def simulate_change(self, change_request, environment='staging'):
        """Simulate change impact before implementation"""
        
        simulation = {
            'environment': environment,
            'start_time': datetime.now(),
            'scenarios': []
        }
        
        # Create simulation environment
        sim_env = self.create_simulation_environment(
            change_request.target_service,
            environment
        )
        
        # Run baseline tests
        baseline = self.run_baseline_tests(sim_env)
        simulation['baseline'] = baseline
        
        # Apply simulated change
        self.apply_simulated_change(sim_env, change_request)
        
        # Test scenarios
        scenarios = [
            'normal_load',
            'peak_load',
            'failure_condition',
            'dependency_failure'
        ]
        
        for scenario in scenarios:
            result = self.run_scenario(sim_env, scenario)
            
            simulation['scenarios'].append({
                'scenario': scenario,
                'result': result,
                'performance_delta': self.calculate_delta(baseline, result),
                'issues_found': result.issues,
                'recommendation': self.analyze_scenario_result(result)
            })
        
        # Generate simulation report
        simulation['summary'] = self.generate_simulation_summary(simulation)
        simulation['confidence_score'] = self.calculate_confidence(simulation)
        
        return simulation

Failure Impact Analysis

Failure Propagation

Real-time Failure Analysis

class FailureImpactAnalyzer {
    constructor() {
        this.serviceGraph = new ServiceDependencyGraph();
        this.impactCalculator = new ImpactCalculator();
        this.aiPredictor = new AIImpactPredictor();
    }
    
    async analyzeFailure(failedComponent) {
        const analysis = {
            component: failedComponent,
            timestamp: new Date(),
            immediateImpact: await this.getImmediateImpact(failedComponent),
            propagation: await this.tracePropagation(failedComponent),
            businessImpact: {},
            estimatedRecovery: {},
            recommendations: []
        };
        
        // Trace impact propagation
        const impactGraph = await this.buildImpactGraph(failedComponent);
        
        // Calculate service impacts
        for (const service of impactGraph.affectedServices) {
            const serviceImpact = await this.calculateServiceImpact(
                service,
                failedComponent
            );
            
            analysis.serviceImpacts[service.id] = {
                service: service.name,
                degradation: serviceImpact.degradationLevel,
                affectedCapabilities: serviceImpact.capabilities,
                userImpact: serviceImpact.affectedUsers,
                alternativePaths: await this.findAlternativePaths(service, failedComponent)
            };
        }
        
        // Calculate business impact
        analysis.businessImpact = await this.calculateBusinessImpact(
            analysis.serviceImpacts
        );
        
        // AI predictions
        const predictions = await this.aiPredictor.predict({
            failure: failedComponent,
            currentState: analysis
        });
        
        analysis.predictions = {
            cascadeRisk: predictions.cascadeRisk,
            estimatedDuration: predictions.duration,
            additionalFailures: predictions.likelyFailures
        };
        
        // Generate recovery recommendations
        analysis.recommendations = this.generateRecoveryPlan(analysis);
        
        return analysis;
    }
    
    async tracePropagation(failedComponent) {
        const propagation = {
            waves: [],
            timeline: []
        };
        
        let currentWave = [failedComponent];
        let waveNumber = 0;
        
        while (currentWave.length > 0 && waveNumber < 10) {
            const nextWave = [];
            const waveImpacts = [];
            
            for (const component of currentWave) {
                const dependents = await this.getDependents(component);
                
                for (const dependent of dependents) {
                    const impact = await this.calculatePropagationImpact(
                        component,
                        dependent
                    );
                    
                    if (impact.severity > 0.3) {  // Significant impact
                        nextWave.push(dependent);
                        waveImpacts.push({
                            component: dependent,
                            impact: impact,
                            estimatedTime: waveNumber * 5 + impact.propagationDelay
                        });
                    }
                }
            }
            
            if (waveImpacts.length > 0) {
                propagation.waves.push({
                    wave: waveNumber + 1,
                    impacts: waveImpacts
                });
            }
            
            currentWave = nextWave;
            waveNumber++;
        }
        
        return propagation;
    }
}

Failure Scenarios

Common Failure Scenarios:
  Database Failure:
    Impact:
      - Data unavailability
      - Transaction failures
      - Report generation stops
      - API errors
    Mitigation:
      - Read replica promotion
      - Cache serving
      - Graceful degradation
      - Queue buffering
      
  Network Partition:
    Impact:
      - Service isolation
      - Split-brain risk
      - Data inconsistency
      - Communication failures
    Mitigation:
      - Quorum-based decisions
      - Circuit breakers
      - Eventual consistency
      - Manual intervention
      
  Service Outage:
    Impact:
      - Feature unavailability
      - Dependent service failures
      - User experience degradation
      - Revenue loss
    Mitigation:
      - Failover activation
      - Feature flags
      - Cached responses
      - Alternative services

Business Impact Calculation

Impact Metrics

class BusinessImpactCalculator:
    def calculate_business_impact(self, technical_impact):
        """Calculate business impact from technical failures"""
        
        business_impact = {
            'financial': self.calculate_financial_impact(technical_impact),
            'operational': self.calculate_operational_impact(technical_impact),
            'reputational': self.calculate_reputational_impact(technical_impact),
            'compliance': self.calculate_compliance_impact(technical_impact),
            'strategic': self.calculate_strategic_impact(technical_impact)
        }
        
        # Overall impact score
        business_impact['overall_score'] = self.calculate_overall_score(
            business_impact
        )
        
        # Impact classification
        business_impact['classification'] = self.classify_impact(
            business_impact['overall_score']
        )
        
        return business_impact
    
    def calculate_financial_impact(self, technical_impact):
        """Calculate financial impact"""
        
        financial_impact = {
            'revenue_loss': 0,
            'additional_costs': 0,
            'penalties': 0,
            'opportunity_cost': 0
        }
        
        # Revenue loss calculation
        for service in technical_impact.affected_services:
            if service.revenue_generating:
                downtime_minutes = technical_impact.get_downtime(service)
                revenue_per_minute = service.revenue_per_minute
                
                financial_impact['revenue_loss'] += (
                    downtime_minutes * revenue_per_minute
                )
                
                # Factor in partial availability
                if technical_impact.partial_availability:
                    degradation = technical_impact.get_degradation_factor(service)
                    financial_impact['revenue_loss'] *= degradation
        
        # SLA penalties
        for sla in technical_impact.breached_slas:
            penalty = self.calculate_sla_penalty(sla)
            financial_impact['penalties'] += penalty
        
        # Additional operational costs
        financial_impact['additional_costs'] = self.estimate_recovery_costs(
            technical_impact
        )
        
        # Opportunity cost
        financial_impact['opportunity_cost'] = self.calculate_opportunity_cost(
            technical_impact
        )
        
        financial_impact['total'] = sum(financial_impact.values())
        
        return financial_impact

Impact Visualization

class ImpactVisualizer {
    visualizeImpact(impactAnalysis) {
        // Create impact heatmap
        const heatmapData = this.prepareHeatmapData(impactAnalysis);
        
        const heatmap = {
            type: 'heatmap',
            data: heatmapData,
            colorScale: {
                0: '#00ff00',    // No impact
                25: '#90ee90',   // Low impact
                50: '#ffff00',   // Medium impact
                75: '#ff8c00',   // High impact
                100: '#ff0000'   // Critical impact
            },
            dimensions: ['Service', 'Time'],
            metrics: ['Impact Severity', 'User Count', 'Revenue Loss']
        };
        
        // Create impact timeline
        const timeline = {
            type: 'gantt',
            data: this.prepareTimelineData(impactAnalysis),
            tracks: [
                'Component Failures',
                'Service Degradation',
                'Recovery Actions',
                'Full Resolution'
            ]
        };
        
        // Create dependency impact graph
        const dependencyGraph = {
            type: 'force-directed',
            nodes: this.prepareNodes(impactAnalysis),
            edges: this.prepareEdges(impactAnalysis),
            animations: {
                failurePropagation: true,
                recoveryProgress: true
            }
        };
        
        return {
            heatmap,
            timeline,
            dependencyGraph,
            summary: this.createSummaryDashboard(impactAnalysis)
        };
    }
}

Predictive Impact Analysis

AI-Powered Predictions

class PredictiveImpactAnalyzer:
    def __init__(self):
        self.ml_models = {
            'failure_predictor': self.load_failure_model(),
            'impact_estimator': self.load_impact_model(),
            'recovery_predictor': self.load_recovery_model()
        }
        self.historical_data = HistoricalIncidentData()
        
    def predict_future_impacts(self, current_state, horizon='24h'):
        """Predict potential impacts in the future"""
        
        predictions = {
            'horizon': horizon,
            'predicted_incidents': [],
            'risk_areas': [],
            'preventive_actions': []
        }
        
        # Analyze current trends
        trends = self.analyze_current_trends(current_state)
        
        # Predict component failures
        failure_predictions = self.ml_models['failure_predictor'].predict(
            features=self.extract_features(current_state, trends),
            horizon=horizon
        )
        
        for prediction in failure_predictions:
            if prediction.probability > 0.7:
                # Predict impact of this failure
                impact = self.predict_failure_impact(
                    prediction.component,
                    prediction.failure_type,
                    current_state
                )
                
                predictions['predicted_incidents'].append({
                    'component': prediction.component,
                    'probability': prediction.probability,
                    'estimated_time': prediction.estimated_time,
                    'impact': impact,
                    'confidence': prediction.confidence
                })
        
        # Identify risk areas
        risk_areas = self.identify_risk_areas(
            current_state,
            predictions['predicted_incidents']
        )
        predictions['risk_areas'] = risk_areas
        
        # Generate preventive actions
        for incident in predictions['predicted_incidents']:
            actions = self.generate_preventive_actions(incident)
            predictions['preventive_actions'].extend(actions)
        
        return predictions
    
    def predict_failure_impact(self, component, failure_type, current_state):
        """Predict impact of a specific failure"""
        
        # Find similar historical incidents
        similar_incidents = self.historical_data.find_similar(
            component=component,
            failure_type=failure_type,
            limit=20
        )
        
        # Extract patterns
        impact_patterns = self.extract_impact_patterns(similar_incidents)
        
        # Current context features
        context_features = {
            'current_load': current_state.get_load(component),
            'time_of_day': datetime.now().hour,
            'day_of_week': datetime.now().weekday(),
            'dependencies': len(self.get_dependencies(component)),
            'redundancy': self.has_redundancy(component),
            'recent_changes': self.get_recent_changes(component)
        }
        
        # Predict impact
        predicted_impact = self.ml_models['impact_estimator'].predict(
            component_features=self.get_component_features(component),
            failure_features=self.get_failure_features(failure_type),
            context_features=context_features,
            historical_patterns=impact_patterns
        )
        
        return {
            'severity': predicted_impact.severity,
            'duration': predicted_impact.duration,
            'affected_services': predicted_impact.services,
            'user_impact': predicted_impact.users,
            'financial_impact': predicted_impact.financial,
            'confidence': predicted_impact.confidence
        }

Scenario Planning

Impact Scenarios:
  Best Case:
    - Quick detection
    - Automatic failover works
    - No data loss
    - Minimal user impact
    - Fast recovery
    
  Expected Case:
    - Normal detection time
    - Some manual intervention
    - Limited functionality
    - Moderate user impact
    - Standard recovery time
    
  Worst Case:
    - Delayed detection
    - Failover fails
    - Data corruption
    - Full outage
    - Extended recovery
    
  Planning Factors:
    - Time of occurrence
    - Available staff
    - System load
    - Dependencies
    - External factors

Mitigation Strategies

Automated Mitigation

class MitigationEngine:
    def generate_mitigation_plan(self, impact_analysis):
        """Generate automated mitigation strategies"""
        
        mitigation_plan = {
            'immediate_actions': [],
            'short_term_actions': [],
            'long_term_improvements': [],
            'automation_opportunities': []
        }
        
        # Immediate actions (< 5 minutes)
        if impact_analysis.severity >= 'high':
            mitigation_plan['immediate_actions'] = [
                self.activate_failover(impact_analysis.failed_components),
                self.enable_circuit_breakers(impact_analysis.at_risk_services),
                self.scale_healthy_components(impact_analysis.bottlenecks),
                self.activate_cache_serving(impact_analysis.affected_services),
                self.notify_stakeholders(impact_analysis.business_impact)
            ]
        
        # Short-term actions (< 1 hour)
        mitigation_plan['short_term_actions'] = [
            self.provision_additional_resources(impact_analysis),
            self.implement_traffic_shaping(impact_analysis),
            self.enable_degraded_mode(impact_analysis.affected_services),
            self.prepare_communication(impact_analysis.user_impact)
        ]
        
        # Long-term improvements
        mitigation_plan['long_term_improvements'] = 
            self.analyze_improvement_opportunities(impact_analysis)
        
        # Automation opportunities
        mitigation_plan['automation_opportunities'] = 
            self.identify_automation_opportunities(impact_analysis)
        
        return mitigation_plan
    
    def activate_failover(self, failed_components):
        """Activate failover for failed components"""
        
        failover_actions = []
        
        for component in failed_components:
            if component.has_failover:
                action = {
                    'type': 'failover',
                    'component': component.id,
                    'target': component.failover_target,
                    'steps': [
                        f"Verify {component.failover_target} health",
                        f"Update DNS/load balancer to {component.failover_target}",
                        f"Redirect traffic from {component.id}",
                        "Verify service restoration",
                        "Monitor failover stability"
                    ],
                    'automation_available': component.automated_failover,
                    'estimated_time': '2-5 minutes'
                }
                failover_actions.append(action)
        
        return failover_actions

Communication Planning

Communication Strategy:
  Stakeholder Matrix:
    Executive Team:
      - Trigger: Revenue impact > $100k or Users > 10k
      - Format: Executive summary + impact metrics
      - Frequency: Initial + hourly updates
      
    Technical Teams:
      - Trigger: Any service degradation
      - Format: Technical details + action items
      - Frequency: Real-time updates
      
    Customer Support:
      - Trigger: User-facing impact
      - Format: User impact + workarounds
      - Frequency: Initial + status changes
      
    Customers:
      - Trigger: Service degradation > 10%
      - Format: Status page + email
      - Frequency: Initial + resolution
      
  Message Templates:
    Initial Notification:
      - Issue summary
      - Services affected
      - User impact
      - Current actions
      - Next update time
      
    Update Notification:
      - Progress made
      - Current status
      - Revised timeline
      - Workarounds available
      
    Resolution Notification:
      - Issue resolved
      - Root cause summary
      - Prevention measures
      - Follow-up actions

Impact Reporting

Impact Dashboard

class ImpactDashboard {
    constructor() {
        this.widgets = {
            currentImpact: new ImpactSummaryWidget(),
            impactTimeline: new TimelineWidget(),
            serviceHealth: new ServiceHealthWidget(),
            businessMetrics: new BusinessMetricsWidget(),
            predictions: new PredictionsWidget()
        };
    }
    
    renderDashboard(impactAnalysis) {
        const dashboard = {
            layout: 'responsive-grid',
            refreshInterval: 30, // seconds
            widgets: []
        };
        
        // Current impact summary
        dashboard.widgets.push({
            id: 'impact-summary',
            type: 'card',
            size: 'large',
            data: {
                title: 'Current Impact',
                severity: impactAnalysis.severity,
                affectedServices: impactAnalysis.services.length,
                userImpact: impactAnalysis.totalUsers,
                estimatedLoss: impactAnalysis.financialImpact,
                trend: impactAnalysis.trend
            }
        });
        
        // Service health matrix
        dashboard.widgets.push({
            id: 'service-health',
            type: 'heatmap',
            size: 'wide',
            data: this.prepareServiceHealthData(impactAnalysis)
        });
        
        // Impact timeline
        dashboard.widgets.push({
            id: 'impact-timeline',
            type: 'timeline',
            size: 'full-width',
            data: this.prepareTimelineData(impactAnalysis)
        });
        
        // Business metrics
        dashboard.widgets.push({
            id: 'business-metrics',
            type: 'metrics-grid',
            size: 'medium',
            data: {
                metrics: [
                    {
                        label: 'Revenue Impact',
                        value: impactAnalysis.revenueImpact,
                        trend: impactAnalysis.revenueTrend,
                        format: 'currency'
                    },
                    {
                        label: 'SLA Status',
                        value: impactAnalysis.slaCompliance,
                        threshold: 99.9,
                        format: 'percentage'
                    },
                    {
                        label: 'Recovery Time',
                        value: impactAnalysis.estimatedRecovery,
                        target: impactAnalysis.rto,
                        format: 'duration'
                    }
                ]
            }
        });
        
        return dashboard;
    }
}

Post-Incident Analysis

class PostIncidentAnalyzer:
    def analyze_incident_impact(self, incident_id):
        """Comprehensive post-incident impact analysis"""
        
        incident = self.get_incident(incident_id)
        
        analysis = {
            'incident_id': incident_id,
            'duration': incident.resolved_at - incident.created_at,
            'timeline': self.build_incident_timeline(incident),
            'technical_impact': self.analyze_technical_impact(incident),
            'business_impact': self.analyze_business_impact(incident),
            'response_effectiveness': self.analyze_response(incident),
            'lessons_learned': self.extract_lessons(incident),
            'improvement_actions': self.generate_improvements(incident)
        }
        
        # Technical impact
        analysis['technical_impact'] = {
            'root_cause': incident.root_cause,
            'affected_components': incident.affected_components,
            'service_degradation': self.calculate_degradation(incident),
            'data_integrity': self.verify_data_integrity(incident),
            'performance_impact': self.analyze_performance_impact(incident)
        }
        
        # Business impact
        analysis['business_impact'] = {
            'users_affected': incident.affected_users,
            'transactions_failed': incident.failed_transactions,
            'revenue_loss': self.calculate_revenue_loss(incident),
            'sla_breaches': incident.sla_breaches,
            'reputation_impact': self.assess_reputation_impact(incident)
        }
        
        # Generate report
        analysis['report'] = self.generate_impact_report(analysis)
        
        return analysis

Best Practices

1. Proactive Analysis

• ✅ Regular impact assessments
• ✅ Scenario planning
• ✅ Dependency reviews
• ✅ Risk monitoring

2. Accuracy

• ✅ Validate impact models
• ✅ Historical data analysis
• ✅ Regular model updates
• ✅ Feedback incorporation

3. Speed

• ✅ Real-time analysis
• ✅ Automated assessments
• ✅ Pre-calculated scenarios
• ✅ Quick decision support

4. Communication

• ✅ Clear impact metrics
• ✅ Stakeholder-specific views
• ✅ Regular updates
• ✅ Actionable insights

Integration Examples

Change Management Integration

class ChangeManagementIntegration:
    def integrate_impact_analysis(self, change_request):
        """Integrate impact analysis with change management"""
        
        # Run impact analysis
        impact = self.impact_analyzer.analyze_change_impact(change_request)
        
        # Update change request
        change_request.impact_analysis = impact
        change_request.risk_score = impact.risk_score
        
        # Set approval requirements based on impact
        if impact.risk_score > 0.8:
            change_request.add_approval_requirement('executive')
            change_request.add_approval_requirement('business_owner')
        elif impact.risk_score > 0.5:
            change_request.add_approval_requirement('manager')
            change_request.add_approval_requirement('technical_lead')
        
        # Attach mitigation plan
        change_request.mitigation_plan = impact.mitigation_plan
        
        # Schedule based on impact
        if impact.has_user_impact:
            change_request.schedule = self.find_low_impact_window(impact)
        
        # Create rollback plan
        change_request.rollback_plan = self.generate_rollback_plan(impact)
        
        return change_request

Monitoring Integration

Monitoring Integration:
  Real-time Alerts:
    - Component failure detected
    - Impact analysis triggered
    - Affected services identified
    - Business impact calculated
    - Notifications sent
    
  Predictive Alerts:
    - Risk threshold exceeded
    - Impact prediction generated
    - Preventive actions suggested
    - Approval requested
    - Actions implemented
    
  Dashboard Updates:
    - Live impact metrics
    - Service health scores
    - Business KPIs
    - Recovery progress
    - Prediction accuracy

Next Steps

• 📖 Service Health - Monitoring service health
• 📖 Visualization - Visualizing service maps and impacts
• 📖 Best Practices - Service mapping best practices