Innovation Analytics Architecture: Technical Implementation of Measurement Systems

from typing import Dict, List, Any, Optional
from dataclasses import dataclass
from datetime import datetime, timedelta
import pandas as pd
import numpy as np
from pyspark.sql import SparkSession
from pyspark.sql.functions import col, when, lag, lead, window

@dataclass
class InnovationDataSchema:
    """Schema definition for innovation measurement data lake."""

    project_data: Dict[str, Any]
    market_data: Dict[str, Any]
    performance_data: Dict[str, Any]
    external_indicators: Dict[str, Any]

class InnovationDataLake:
    """Enterprise data lake for innovation measurement and analytics."""

    def __init__(self, spark_session: SparkSession, config: Dict[str, Any]):
        self.spark = spark_session
        self.config = config
        self.data_quality_engine = DataQualityEngine(config)
        self.schema_registry = SchemaRegistry(config)

    def ingest_innovation_data(self, data_source: str, data_batch: Dict[str, Any]):
        """Ingest innovation data with quality validation and schema enforcement."""

        # Validate data quality
        quality_report = self.data_quality_engine.validate_data_batch(
            data_source, data_batch
        )

        if not quality_report["passes_quality_checks"]:
            self._handle_data_quality_issues(data_source, quality_report)
            return

        # Apply schema transformations
        standardized_data = self.schema_registry.apply_schema_transformations(
            data_source, data_batch
        )

        # Enrich with metadata
        enriched_data = self._enrich_with_metadata(
            data_source, standardized_data
        )

        # Partition and store in data lake
        self._store_partitioned_data(data_source, enriched_data)

        # Update data catalog
        self._update_data_catalog(data_source, enriched_data)

    def create_innovation_analytics_view(self, view_definition: Dict[str, Any]):
        """Create unified analytics view combining multiple data sources."""

        view_sql = self._generate_view_sql(view_definition)

        innovation_view = self.spark.sql(view_sql)

        # Apply business logic transformations
        transformed_view = self._apply_business_transformations(
            innovation_view, view_definition
        )

        # Calculate derived metrics
        metrics_view = self._calculate_derived_metrics(transformed_view)

        return metrics_view

    def _apply_business_transformations(self, data_frame, view_definition):
        """Apply innovation-specific business logic transformations."""

        # Calculate innovation lifecycle stages
        stage_logic = view_definition.get("stage_calculations", {})

        for stage, conditions in stage_logic.items():
            data_frame = data_frame.withColumn(
                f"is_{stage}",
                when(eval(conditions["condition"]), True).otherwise(False)
            )

        # Calculate time-based metrics
        data_frame = data_frame.withColumn(
            "time_since_start",
            (col("current_date") - col("project_start_date")).cast("integer")
        )

        # Calculate moving averages for performance metrics
        window_spec = Window.partitionBy("project_id").orderBy("date")

        data_frame = data_frame.withColumn(
            "performance_trend",
            avg(col("performance_score")).over(
                window_spec.rangeBetween(-30, 0)  # 30-day moving average
            )
        )

        return data_frame

import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestRegressor, GradientBoostingClassifier
from sklearn.metrics import mean_squared_error, classification_report
from sklearn.preprocessing import StandardScaler
import warnings
warnings.filterwarnings('ignore')

class InnovationPerformanceAnalytics:
    """Advanced analytics engine for innovation performance measurement."""

    def __init__(self, data_lake, config):
        self.data_lake = data_lake
        self.config = config
        self.models = {}
        self.scalers = {}
        self._initialize_models()

    def analyze_innovation_portfolio_performance(self, portfolio_data):
        """Comprehensive analysis of innovation portfolio performance."""

        # Portfolio composition analysis
        composition_analysis = self._analyze_portfolio_composition(portfolio_data)

        # Performance trend analysis
        trend_analysis = self._analyze_performance_trends(portfolio_data)

        # Risk-return optimization analysis
        optimization_analysis = self._analyze_portfolio_optimization(portfolio_data)

        # Competitive positioning analysis
        competitive_analysis = self._analyze_competitive_positioning(portfolio_data)

        return {
            "portfolio_composition": composition_analysis,
            "performance_trends": trend_analysis,
            "optimization_opportunities": optimization_analysis,
            "competitive_positioning": competitive_analysis,
            "overall_portfolio_score": self._calculate_portfolio_score([
                composition_analysis, trend_analysis,
                optimization_analysis, competitive_analysis
            ])
        }

    def predict_innovation_success_probability(self, innovation_features):
        """Predict probability of innovation success using ensemble models."""

        # Feature engineering
        engineered_features = self._engineer_innovation_features(innovation_features)

        # Scale features
        scaled_features = self.scalers["success_prediction"].transform(
            engineered_features
        )

        # Generate predictions from ensemble models
        predictions = {}

        for model_name, model in self.models["success_prediction"].items():
            prediction = model.predict_proba(scaled_features)
            predictions[model_name] = prediction[:, 1]  # Probability of success

        # Ensemble prediction
        ensemble_prediction = np.mean(list(predictions.values()), axis=0)

        # Feature importance analysis
        feature_importance = self._analyze_feature_importance(
            scaled_features, innovation_features.columns
        )

        return {
            "success_probability": ensemble_prediction,
            "individual_model_predictions": predictions,
            "feature_importance": feature_importance,
            "confidence_intervals": self._calculate_prediction_confidence(predictions)
        }

    def _analyze_portfolio_composition(self, portfolio_data):
        """Analyze innovation portfolio composition and balance."""

        composition_metrics = {}

        # Risk category distribution
        risk_distribution = portfolio_data.groupby('risk_category')['investment'].sum()
        composition_metrics["risk_distribution"] = risk_distribution.to_dict()

        # Time horizon distribution
        horizon_distribution = portfolio_data.groupby('time_horizon')['investment'].sum()
        composition_metrics["horizon_distribution"] = horizon_distribution.to_dict()

        # Strategic alignment distribution
        strategy_alignment = portfolio_data.groupby('strategic_objective')['investment'].sum()
        composition_metrics["strategy_alignment"] = strategy_alignment.to_dict()

        # Portfolio diversity score
        diversity_score = self._calculate_portfolio_diversity(portfolio_data)
        composition_metrics["diversity_score"] = diversity_score

        # Optimal composition benchmarking
        optimal_benchmark = self._get_optimal_portfolio_benchmark()
        composition_metrics["benchmark_deviation"] = self._calculate_benchmark_deviation(
            composition_metrics, optimal_benchmark
        )

        return composition_metrics

    def _analyze_performance_trends(self, portfolio_data):
        """Analyze innovation portfolio performance trends over time."""

        trend_metrics = {}

        # Time series preparation
        time_series_data = portfolio_data.set_index('date').sort_index()

        # Performance trend calculation
        performance_trend = time_series_data.groupby(
            pd.Grouper(freq='M')
        )['performance_score'].mean()

        trend_metrics["monthly_performance"] = performance_trend.to_dict()

        # Trend analysis
        trend_slope = np.polyfit(
            range(len(performance_trend)),
            performance_trend.values,
            1
        )[0]
        trend_metrics["trend_slope"] = trend_slope

        # Volatility analysis
        performance_volatility = performance_trend.std()
        trend_metrics["performance_volatility"] = performance_volatility

        # Cycle analysis
        cycle_analysis = self._analyze_innovation_cycles(time_series_data)
        trend_metrics["cycle_analysis"] = cycle_analysis

        # Seasonal patterns
        seasonal_patterns = self._identify_seasonal_patterns(time_series_data)
        trend_metrics["seasonal_patterns"] = seasonal_patterns

        return trend_metrics

    def generate_innovation_insights(self, analytics_results):
        """Generate actionable insights from innovation analytics."""

        insights = []

        # Portfolio optimization insights
        portfolio_insights = self._generate_portfolio_insights(
            analytics_results["portfolio_composition"],
            analytics_results["optimization_opportunities"]
        )
        insights.extend(portfolio_insights)

        # Performance improvement insights
        performance_insights = self._generate_performance_insights(
            analytics_results["performance_trends"]
        )
        insights.extend(performance_insights)

        # Strategic alignment insights
        strategic_insights = self._generate_strategic_insights(
            analytics_results["competitive_positioning"]
        )
        insights.extend(strategic_insights)

        # Risk management insights
        risk_insights = self._generate_risk_insights(analytics_results)
        insights.extend(risk_insights)

        return {
            "actionable_insights": insights,
            "priority_recommendations": self._prioritize_recommendations(insights),
            "implementation_roadmap": self._generate_implementation_roadmap(insights)
        }

from kafka import KafkaConsumer, KafkaProducer
import redis
import json
from datetime import datetime, timedelta
from typing import Dict, List, Any
import asyncio

class RealTimeInnovationMonitor:
    """Real-time monitoring system for innovation metrics and alerts."""

    def __init__(self, kafka_config, redis_config, alert_config):
        self.kafka_consumer = KafkaConsumer(**kafka_config)
        self.kafka_producer = KafkaProducer(**kafka_config)
        self.redis_client = redis.Redis(**redis_config)
        self.alert_config = alert_config
        self.monitoring_rules = self._load_monitoring_rules()

    async def monitor_innovation_metrics(self):
        """Continuous monitoring of innovation metrics with real-time alerting."""

        for message in self.kafka_consumer:
            try:
                # Parse innovation event
                event_data = json.loads(message.value.decode('utf-8'))

                # Process real-time metrics
                await self._process_real_time_metrics(event_data)

                # Check alert conditions
                alert_triggered = await self._check_alert_conditions(event_data)

                if alert_triggered:
                    await self._send_alerts(alert_triggered, event_data)

                # Update real-time dashboards
                await self._update_real_time_dashboards(event_data)

            except Exception as e:
                await self._handle_monitoring_error(e, message)

    async def _process_real_time_metrics(self, event_data):
        """Process real-time innovation metrics from event stream."""

        event_type = event_data.get("event_type")

        if event_type == "project_milestone":
            await self._process_milestone_event(event_data)
        elif event_type == "market_feedback":
            await self._process_market_feedback_event(event_data)
        elif event_type == "resource_allocation":
            await self._process_resource_event(event_data)
        elif event_type == "competitive_intelligence":
            await self._process_competitive_event(event_data)

    async def _process_milestone_event(self, event_data):
        """Process project milestone events for real-time tracking."""

        project_id = event_data["project_id"]
        milestone_type = event_data["milestone_type"]

        # Update project progress metrics
        progress_key = f"project_progress:{project_id}"
        current_progress = self.redis_client.hgetall(progress_key)

        # Calculate updated metrics
        updated_metrics = self._calculate_milestone_metrics(
            current_progress, event_data
        )

        # Store updated metrics
        self.redis_client.hmset(progress_key, updated_metrics)

        # Calculate portfolio-level impacts
        portfolio_impact = await self._calculate_portfolio_impact(
            project_id, updated_metrics
        )

        # Publish portfolio update event
        portfolio_event = {
            "event_type": "portfolio_update",
            "project_id": project_id,
            "milestone_type": milestone_type,
            "portfolio_impact": portfolio_impact,
            "timestamp": datetime.utcnow().isoformat()
        }

        self.kafka_producer.send(
            "innovation_portfolio_updates",
            json.dumps(portfolio_event).encode('utf-8')
        )

    async def _check_alert_conditions(self, event_data):
        """Check if real-time events trigger alert conditions."""

        triggered_alerts = []

        for rule_id, rule in self.monitoring_rules.items():
            if await self._evaluate_alert_rule(rule, event_data):
                alert = {
                    "rule_id": rule_id,
                    "alert_type": rule["alert_type"],
                    "severity": rule["severity"],
                    "message": rule["message_template"].format(**event_data),
                    "triggered_at": datetime.utcnow().isoformat(),
                    "event_data": event_data
                }
                triggered_alerts.append(alert)

        return triggered_alerts if triggered_alerts else None

    async def _evaluate_alert_rule(self, rule, event_data):
        """Evaluate individual alert rule against event data."""

        conditions = rule["conditions"]

        # Time-based conditions
        if "time_window" in conditions:
            if not await self._check_time_window_condition(
                conditions["time_window"], event_data
            ):
                return False

        # Threshold conditions
        if "threshold" in conditions:
            if not await self._check_threshold_condition(
                conditions["threshold"], event_data
            ):
                return False

        # Pattern conditions
        if "pattern" in conditions:
            if not await self._check_pattern_condition(
                conditions["pattern"], event_data
            ):
                return False

        return True

class InnovationAnalyticsOptimizer:
    """Performance optimization for innovation analytics systems."""

    def __init__(self, cache_config, computation_config):
        self.cache_config = cache_config
        self.computation_config = computation_config
        self.cache_manager = CacheManager(cache_config)
        self.computation_optimizer = ComputationOptimizer(computation_config)

    async def optimize_metric_calculations(self, metric_requests):
        """Optimize metric calculations using caching and parallel processing."""

        # Identify cacheable metrics
        cacheable_metrics = self._identify_cacheable_metrics(metric_requests)

        # Check cache for existing calculations
        cached_results = await self.cache_manager.get_cached_metrics(
            cacheable_metrics
        )

        # Identify metrics requiring computation
        computation_required = self._identify_computation_required(
            metric_requests, cached_results
        )

        # Optimize computation plan
        computation_plan = self.computation_optimizer.optimize_computation_plan(
            computation_required
        )

        # Execute parallel computations
        computed_results = await self._execute_parallel_computations(
            computation_plan
        )

        # Cache new results
        await self.cache_manager.cache_computed_metrics(computed_results)

        # Combine cached and computed results
        final_results = self._combine_results(cached_results, computed_results)

        return final_results

    async def _execute_parallel_computations(self, computation_plan):
        """Execute computations in parallel based on dependency graph."""

        results = {}

        # Execute computations in dependency order
        for computation_batch in computation_plan.execution_batches:
            batch_tasks = []

            for computation in computation_batch:
                task = asyncio.create_task(
                    self._execute_single_computation(computation, results)
                )
                batch_tasks.append((computation.metric_id, task))

            # Wait for batch completion
            batch_results = await asyncio.gather(
                *[task for _, task in batch_tasks],
                return_exceptions=True
            )

            # Process batch results
            for (metric_id, _), result in zip(batch_tasks, batch_results):
                if isinstance(result, Exception):
                    # Handle computation error
                    await self._handle_computation_error(metric_id, result)
                else:
                    results[metric_id] = result

        return results