Platform Architecture

Platform Architecture Overview

2.1 Six-Layer Architecture

The SDGL-SaaS Platform follows a modular architecture designed for scalability, security, and extensibility:

Source Layer

Ingests data from multiple channels:

• Direct user input through the web interface
• External ESG data providers (Bloomberg, Refinitiv, etc.)
• Financial systems (accounting, budget, reporting platforms)
• Operational systems (supply chain, HR, facilities)
• Survey and stakeholder feedback systems

Integration Layer

Transforms and normalizes incoming data:

• API connectors for common business systems
• Data validation and quality assurance
• Format standardization across diverse sources
• Real-time and batch processing

Processing Layer

Applies business logic and algorithms:

• Multidimensional assessment engine
• Scoring and normalization
• Weighting across dimensions and indicators
• Contextual adaptation based on geography, sector, organizational profile

Storage Layer

Secure, scalable data persistence:

• Time-series data for trend analysis
• Audit trails for governance compliance
• Encrypted sensitive information
• Multi-tenant data isolation

Analytics Layer

Transforms raw data into insights:

• Diagnostic dashboards and reports
• Scenario modeling and predictive analytics
• Hotspot detection and priority mapping
• Trade-off and synergy analysis

Visualization Layer

User-facing interface and outputs:

• Interactive dashboards
• Customizable report generation
• Mobile-responsive design
• Export to multiple formats (PDF, Excel, GeoJSON)

2.2 Modular Microservices

Each functional area operates as an independent, scalable service:

• Assessment Engine: Multidimensional scoring and normalization
• Engagement Platform: Survey builder, response collection, analytics
• Planning Module: Action generators, SMART template management, progress tracking
• Integration Hub: Data connectors and API management
• Reporting Service: Report compilation, export, distribution
• Analytics Engine: Trend analysis, modeling, predictions
• Security & Governance: Access control, auditing, compliance

2.3 Data Flow & Processing Pipelines

Data moves through the platform via event-driven pipelines:

1. Ingestion → Source systems provide data via API, file upload, or manual entry
2. Validation → Quality checks and schema conformance
3. Transformation → Normalization to platform standard formats
4. Enrichment → Add context (weather, socioeconomic, geographic data)
5. Processing → Run through assessment algorithms
6. Storage → Persist processed data with full audit trail
7. Analytics → Generate insights and visualizations
8. Distribution → Push to dashboards, reports, and integrations

2.4 Security, Privacy, and Compliance Architecture

• Encryption: AES-256 at rest, TLS 1.3 in transit
• Multi-Tenancy: Complete data isolation between organizations
• Access Control: Role-based permissions, IP allowlisting, activity logging
• Compliance: GDPR, HIPAA, SOC 2 Type II certified
• Data Residency: Choice of region (US, EU, APAC)
• Audit Trails: Immutable logs of all data access and modifications

2.5 Extensibility & Plugin Ecosystem

Customize the platform through:

• Custom Indicators: Define organization-specific metrics
• Webhooks: Real-time event notifications to external systems
• API Access: Full programmatic access to data and workflows
• Report Templates: Build custom report formats
• Dashboard Widgets: Embed visualizations in external applications