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Data Management and Flow

Introduction​

The Data Management and Flow module is the backbone of the ESG Management System, providing sophisticated data handling capabilities that ensure accuracy, consistency, and reliability across all ESG metrics. The system implements a multi-layered approach to data management, from real-time sensor data ingestion to complex metric calculations and automated report generation.

1. Data Architecture Overview​

Hierarchical Data Structure​

The system implements a hierarchical data structure that supports complex ESG reporting requirements:

2. ESG Metric Value Management​

Intelligent Metric Processing​

The ESGMetricValue class provides sophisticated data management capabilities with built-in validation, calculation, and verification features:

Data Validation and Quality Control​

def validate_metric_data(self):
    """Validate the metric data based on definition"""
    if not self.metric_definition:
        return
        
    # Get metric definition
    metric_def = frappe.get_doc("ESG Metric Definition", self.metric_definition)
    
    # Check if the value is within expected range
    if metric_def.validation_rules:
        if "min:" in metric_def.validation_rules and "max:" in metric_def.validation_rules:
            min_val = float(metric_def.validation_rules.split("min:")[1].split(",")[0].strip())
            max_val = float(metric_def.validation_rules.split("max:")[1].split(",")[0].strip())
            
            if flt(self.value) < min_val:
                frappe.msgprint(f"Warning: Value {self.value} is below expected minimum")
            
            if flt(self.value) > max_val:
                frappe.msgprint(f"Warning: Value {self.value} exceeds expected maximum")

Anomaly Detection and Historical Comparison​

# Check for significant deviation from historical data
if prev_values:
    prev_avg = sum(flt(v.value) for v in prev_values) / len(prev_values)
    
    # Check for significant deviation (more than 50% change)
    if prev_avg > 0 and (flt(self.value) / prev_avg > 1.5 or flt(self.value) / prev_avg < 0.5):
        frappe.msgprint(
            f"Warning: Current value {self.value} deviates significantly from previous average {prev_avg:.2f}",
            indicator="orange", alert=True
        )

Advanced Calculation Engine​

The system supports complex metric calculations through a flexible formula execution engine:

Formula-Based Calculations​

def execute_calculation_formula(self):
    """Execute the calculation formula from the metric definition"""
    if not self.metric_definition:
        return
    
    try:
        # Get the metric definition
        metric_def = frappe.get_doc("ESG Metric Definition", self.metric_definition)
        
        # Collect data needed for the calculation
        data = self.collect_calculation_data()
        
        # Create a safe namespace for execution
        namespace = {"frappe": frappe}
        
        # Execute the formula code
        exec(metric_def.calculation_formula, namespace)
        
        # Execute the calculation function
        result = namespace["calculate"](data)
        
        if result is not None:
            self.value = round(float(result), 3)
            frappe.msgprint(f"Value calculated successfully: {self.value}", indicator="green")
            
    except Exception as e:
        frappe.log_error(f"Error executing calculation: {str(e)}", "ESG Metric Calculation Error")

3. Real-time Data Processing Pipeline​

Multi-Source Data Integration​

The system processes data from multiple sources with intelligent routing and processing:

Data Source Classification​

  • IoT Sensors: Real-time environmental monitoring data
  • Manual Entry: User-input data with validation workflows
  • External APIs: Third-party system integrations
  • Bulk Imports: Large-scale data imports from ERP/SCADA systems

Processing Workflow​

Data Quality Management​

Quality Scoring System​

def check_data_quality(self):
    """Perform data quality checks and set quality indicator"""
    quality_score = 100  # Start with perfect score
    
    # Check timestamp - data shouldn't be from the future
    if self.timestamp and self.timestamp > now_datetime():
        quality_score -= 30
        self.status = "Suspect"
        
    # Check for missing fields
    if not self.reading_unit:
        quality_score -= 10
        
    # Check for consistent units
    expected_units = {
        "Energy": ["kWh", "MWh", "GWh", "J", "kJ", "MJ", "GJ"],
        "Water": ["m³", "L", "gal", "ML"],
        "Emissions": ["kg", "t", "kg CO2e", "t CO2e"]
    }
    
    # Set quality indicator (capped between 0-100)
    self.quality_indicator = max(0, min(100, quality_score))

4. Category-Specific Data Management​

Environmental Data Processing​

The system provides specialized data collection and processing for different environmental categories:

Energy Data​

def add_environmental_data(self, data, subcategory):
    """Add environmental-specific data to the calculation dataset"""
    if subcategory == "Energy":
        # Get energy consumption data
        energy_data = frappe.get_all(
            "Energy Consumption",
            filters=filters,
            fields=["energy_source", "quantity", "energy_value", "renewable"]
        )
        
        if energy_data:
            # Calculate total and renewable energy
            total_energy = sum(flt(e.energy_value) for e in energy_data)
            renewable_energy = sum(flt(e.energy_value) for e in energy_data if e.renewable)
            
            data["total_energy"] = total_energy
            data["renewable_energy"] = renewable_energy
            data["energy_sources"] = {e.energy_source: flt(e.energy_value) for e in energy_data}

Emissions Data Processing​

if subcategory == "Emissions":
    # Get emissions data
    ghg_data = frappe.get_all(
        "GHG Emissions",
        filters=filters,
        fields=["scope1_emissions", "scope2_emissions", "total_emissions", "emission_intensity"]
    )
    
    # Get emission factors
    emission_factors = frappe.get_all(
        "GHG Emission Factor",
        filters={"organization": self.organization},
        fields=["source_type", "emission_factor"]
    )
    
    # Create activity data with emission factors
    data["activity_data"] = [{
        "source_type": a.source_type,
        "activity": flt(a.quantity),
        "energy_value": flt(a.energy_value),
        "emission_factor": data.get("emission_factors", {}).get(a.source_type, 0)
    } for a in activity_data]

Social and Governance Data Integration​

Employee and Community Data​

def add_social_data(self, data, subcategory):
    """Add social-specific data to the calculation dataset"""
    if subcategory == "Employee Development":
        # Get training data
        training_data = frappe.get_all(
            "Employee Training",
            filters=filters,
            fields=["total_hours", "total_employees", "avg_hours_per_employee"]
        )
        
        if training_data:
            data["training_hours"] = flt(training_data[0].total_hours)
            data["total_employees"] = flt(training_data[0].total_employees)
            data["avg_training_hours"] = flt(training_data[0].avg_hours_per_employee)

5. Data Verification and Audit Trail​

Multi-Level Verification System​

The system implements a comprehensive verification workflow:

Verification Status Management​

def set_verification_status(self):
    """Update verification status based on is_verified flag"""
    if self.is_verified and self.verified_by and self.verification_date:
        if self.verification_status != "Verified":
            self.verification_status = "Verified"
    else:
        # If not explicitly verified, set to In Review if it was previously Verified
        if self.verification_status == "Verified":
            self.verification_status = "In Review"
        # If it's a new entry, default to Unverified
        elif not self.verification_status or self.is_new():
            self.verification_status = "Unverified"

Audit Trail and Change Tracking​

  • Complete Change History: Every data modification is logged with timestamp and user
  • Version Control: Multiple versions of data with rollback capabilities
  • Approval Workflows: Configurable approval processes for sensitive data
  • Digital Signatures: Support for digital signatures on verified data

6. Period-Based Data Aggregation​

Flexible Period Management​

The system supports multiple reporting periods with intelligent aggregation:

Period Types Supported​

  • Monthly: Detailed monthly tracking
  • Quarterly: Standard quarterly reporting
  • Annual: Year-end consolidation
  • Custom: Flexible custom periods

Intelligent Aggregation Logic​

def add_previous_period_data(self, data):
    """Add data from previous periods for comparison and trend analysis"""
    # Get previous period data
    prev_values = frappe.get_all(
        "ESG Metric Value",
        filters={
            "metric_definition": self.metric_definition,
            "organization": self.organization,
            "esg_period": prev_period
        },
        fields=["value"],
        limit=1
    )
    
    if prev_values:
        data["previous_period_value"] = flt(prev_values[0].value)

7. Data Integration and Synchronization​

External System Integration​

The system provides robust integration capabilities:

API-Based Integration​

  • RESTful APIs: Standard REST endpoints for data exchange
  • Webhook Support: Real-time notifications for data changes
  • Batch Processing: Efficient bulk data operations
  • Error Handling: Comprehensive error handling and retry mechanisms

Data Synchronization​

8. Performance Optimization​

Caching and Indexing Strategy​

Database Optimization​

  • Strategic Indexing: Optimized indexes for ESG-specific queries
  • Query Optimization: Efficient query patterns for large datasets
  • Connection Pooling: Optimized database connection management
  • Partitioning: Table partitioning for historical data

Caching Mechanisms​

  • Redis Caching: Fast access to frequently used data
  • Application-Level Caching: Cached calculation results
  • CDN Integration: Static asset optimization
  • Query Result Caching: Cached complex query results

9. Data Security and Compliance​

Security Measures​

Data Protection​

  • Encryption at Rest: Database-level encryption for sensitive data
  • Encryption in Transit: TLS/SSL for all data communications
  • Access Control: Role-based access to sensitive ESG data
  • Data Anonymization: Privacy protection for personal data

Compliance Features​

  • GDPR Compliance: Data protection and privacy controls
  • SOX Compliance: Financial data controls and audit trails
  • Industry Standards: Compliance with ESG reporting standards
  • Regulatory Reporting: Automated regulatory submission capabilities

Key Benefits​

Data Quality Assurance​

  • Automated Validation: Real-time data validation and quality scoring
  • Anomaly Detection: Intelligent detection of data anomalies
  • Historical Comparison: Automatic comparison with historical trends
  • Multi-source Verification: Cross-validation from multiple data sources

Operational Efficiency​

  • Automated Processing: Reduce manual data processing by 90%
  • Real-time Updates: Instant data availability across the system
  • Intelligent Aggregation: Automatic period-based data aggregation
  • Error Handling: Robust error handling and recovery mechanisms

Conclusion​

The Data Management and Flow module provides a comprehensive foundation for ESG data management, ensuring data quality, accuracy, and compliance while supporting complex analytical and reporting requirements. The system's flexible architecture allows organizations to start with basic data collection and evolve into sophisticated ESG analytics and reporting platforms.