Processing Layer - Analytics & Machine Learning

Processing Layer: Core Idea & Novel Contributions

What is the Processing Layer?

The Processing Layer transforms standardized data into insights through analytics, machine learning, and complex computations. It’s where raw data becomes actionable intelligence.

Core Responsibilities

1. ESGETC Scoring & Aggregation

Converts individual data points into consolidated dimension scores:

Input: 50 individual metrics
  - Employees (10)
  - Diversity ratio (0.42)
  - Revenue growth (8%)
  - ... (47 more metrics)

  ↓ Standardization & Weighting

Process:
- Normalize each metric (0-100 scale)
- Apply dimension weight (40% for Economic if CEO prioritized)
- Calculate dimension average
- Quality-weight the result

Output: ECONOMIC dimension score = 72

2. Triple Materiality Assessment (3D Scoring)

Evaluates impact through three lenses:

Financial Materiality: Business impact

How does this issue affect revenue, costs, profitability?
What’s the financial risk if we don’t address it?
How much would improvement be worth?

Impact Materiality: Stakeholder effect

Who is affected and how severely?
Does this connect to UN SDG targets?
Alignment with stakeholder priorities?

Systemic Materiality: System-wide influence

Does this affect broader systems and others downstream?
Are there feedback loops or cascading effects?
What’s the leverage for transformation?

Result: 3D position in 8-octant decision space

3. Stakeholder Salience Analysis

Maps stakeholder influence and importance:

                    Legitimacy (0-100)
                           |
              High Urgency  |  Low Urgency
      Definitive  |__________________|
      (Power)     |    Definitive    |
                  |     Urgency      |
   Low Priority   |_________|________|_______
                  |                 |
      Dependent   |__________________|
      (Marginal)  |    Dependent    |
                           |
                    Low Legitimacy

Each stakeholder placed based on:

Legitimacy: Legal/moral/culturally acceptable stake
Urgency: Time sensitivity of concern
Power: Ability to compel attention

Generates stakeholder engagement strategy automatically.

4. Benchmark Comparison

Positions organization against peers:

Your Organization (score: 65) vs. Benchmarks:

Economic Dimension:
- Global Average: 58
- Industry Median: 62
- Top Quartile: 78

Your position: Above average, room for improvement

Recommendation: Focus on top-quartile companies to understand gaps

Enables:

Identify strengths and weaknesses relative to peers
Set realistic targets
Motivate improvement through comparison

5. Machine Learning Pipelines

Multiple ML models working in parallel:

Predictive Models

What will ESGETC scores be in 12 months?
Which organizations most likely to succeed/fail?
Where are anomalies/risks?

Classification Models

Categorize entities automatically (business, NGO, university)
Assign sector classifications
Detect document types and extract metadata

Recommendation Models

Which SDG targets most relevant for this org?
What actions will have highest impact?
Which partners should this org connect with?

Clustering Models

Find organizations similar to this one
Identify market segments
Discover unexpected groupings

Novel Contributions

1. Adaptive Algorithms & Contextualization

Unlike static models, we adapt weighting based on context:

Organization Type: Manufacturing vs. NGO use different weightings

Manufacturing: Economic and Environmental most material
NGO: Social and Connectedness more important

Geographic Context: Regional priorities matter

Sub-Saharan Africa: Social dimension especially critical (poverty, health)
Developed economy: Connectedness and Governance higher weight

Sector-Specific: Industry standards inform modeling

SASB framework provides sector-specific indicators
Different sectors face different material issues
Weighting automatically adjusts per sector

Phase of Business: Stage-of-development matters

Startup: Innovation and scaling take priority
Mature: Efficiency and governance more important
Declining: Resilience and stakeholder management critical

Result: Organization sees benchmarks and recommendations tailored to their context, not generic one-size-fits-all.

2. Continuous Learning Engine

The platform learns from outcomes:

Feedback Loop

Organization sets target: “Improve social score from 55 to 70”
System recommends actions based on ML model
Organization executes for 6 months
System measures actual outcome (new score: 68)
Model updates: “These 3 types of actions worked well, this one didn’t”
Next recommendation refined by learning

Over time:

Models become more accurate for your sector/region
Recommendations improve
False positives/negatives decrease
Org sees positive trend: “System is getting smarter”

3. Anomaly Detection & Benchmark Engine

Real-time alerting for unusual patterns:

Type 1: Data Anomalies

“Revenue dropped 60% overnight” → Likely data error
“CO2 emissions up 400% this month” → Real concern or sensor malfunction?
System flags for investigation

Type 2: Performance Anomalies

“This org’s social score stable while all peers improving” → Why?
“Economic dimension crashed unexpectedly” → Early warning
“Score improving faster than industry norm” → Potential best practice

Type 3: Predictive Anomalies

“Based on trends, this org will fail to meet target” → Intervention opportunity
“This org at high risk based on peer failures” → Preventive action
“Network showing early signs of disruption” → Systemic risk alert

4. Big Data Pipeline & Supply Chain Analysis

Handles enterprise-scale data:

Scale

1B+ data points processed
100K+ organizations analyzed
1000K+ supply chain relationships mapped
Real-time processing of IoT streams

Supply Chain Traceability

Map supplier networks 3+ tiers deep
Identify critical nodes (single-source-of-supply risks)
Assess end-to-end sustainability
Find leakage points and inefficiencies

Network Analysis

Identify clusters and gaps in supply chain
Find substitution opportunities (alternative suppliers)
Assess concentration risk
Model resilience to disruptions

Technical Architecture

Lambda Architecture (Batch + Real-Time)

                    Live Data Stream
                           |
        ┌──────────────────┴──────────────────┐
        ↓                                      ↓
    Speed Layer          Batch Layer        Serving Layer
    (Real-time)          (Accuracy)         (Queries)
        |                     |                  |
    Spark Stream         Spark Batch         Database
    Process in <5s       Process nightly     Optimized
        |                     |                  |
        └──────────────────┬──────────────────┘
                           ↓
                    View/Query Results

Processing Jobs

Job	Frequency	Runtime	Purpose
ESGETC Scoring	Real-time	<100ms	Calculate dimension scores
3D Materiality	Real-time	<200ms	Triple lens assessment
Benchmarking	Nightly	30min	Compare to peers
ML Predictions	Weekly	2hours	Forecast & recommendations
Anomaly Detection	Continuous	<5s	Alert on issues
Supply Chain	On-demand	5-60min	Map relationships
Delphi Processing	Per-round	1hour	Consensus calculation

Distributed Computing

For large-scale processing:

Master Node
  ├─ Task Scheduler
  └─ Job Coordinator

Worker Nodes (10-100)
  ├─ Spark Executor
  ├─ Data Cache (Redis)
  └─ ML Model Serving

Distributed Storage
  ├─ Raw Data Lake (Parquet)
  ├─ Processed Results (PostgreSQL)
  └─ Cache Layer (Redis Cluster)

Maps processing jobs to available resources, scales automatically.

Machine Learning Models

Supervised Learning

Classification

Entity type (business, NGO, university, government)
Sector assignment (NAICS codes)
Risk category (low/medium/high)

Regression

Predict next-period ESGETC scores
Estimate organization size from indicators
Forecast financial impact

Unsupervised Learning

Clustering

Find similar organizations
Identify market segments
Discover new partnership opportunities

Anomaly Detection

Isolation forests for data anomalies
Statistical process control for performance anomalies
Autoencoders for pattern detection

NLP Models

Entity Extraction

Identify organization names, locations, sectors from text
Extract SDG mentions and sentiment
Classify sustainability claims

Classification

Categorize documents (annual reports, policies)
Assess sustainability commitment level
Flag greenwashing red flags

Performance Optimization

Caching Strategy

Query → Check Cache (1ms)
        ├─ Hit: Return cached result
        └─ Miss: Compute (100-1000ms) → Cache (1hour TTL)

Incremental Processing

Instead of reprocessing all 1B data points:

Only process new/changed data
Update aggregates incrementally
Maintain composite score materializations

Result: Score updates in <1 second instead of hours.

Vectorization

Use matrix operations instead of loops:

# Slow: Python loops
result = []
for metric in metrics:
    result.append(metric * weight)

# Fast: NumPy vectorization (100x faster)
result = numpy_array * weights

Best Practices

1. Data Quality First

Even the best algorithm fails on poor input data.

2. Interpretable Models

Avoid black-box models when possible. Be able to explain recommendations.

3. Regular Retraining

Models degrade over time. Retrain monthly/quarterly.

4. Monitor for Drift

Check if model predictions match reality. If not, retrain.

5. A/B Test Changes

Before deploying new model/weighting, test on subset.

6. Document Assumptions

Every model uses assumptions. Document them clearly.