VERTICAL FLOW
Data Flows
Evidence moving up through the system – MRV, traceability, and disclosure.
IN THIS SECTION
In 30 Seconds
Data flows are the evidence infrastructure of sustainability. They don't sit at one layer – they move vertically through all five, connecting ground-level measurement to boardroom disclosure.
From landscapes (L2): MRV data, satellite monitoring, ground-truth verification
Through value chains (L3): Traceability, chain of custody, impact measurement
To disclosure (L4-L5): Reporting data, audit trails, assurance evidence
The principle: Every claim at L4-L5 depends on data from L1-L3. No evidence, no credibility.
Where This Fits
Data flows are a vertical element in our 5-layer sustainability model – they move through all layers, not within them:
Data flows upward – from measurement at ground level to disclosure at corporate level. The quality of data at each layer determines the credibility of claims above it.
Types of Data Flow
MRV (Measurement, Reporting, Verification)
Evidence for ecosystem outcomes
The foundation of credible carbon credits, biodiversity claims, and nature-positive targets. Without robust MRV, claims are unverifiable.
- • Measurement: Satellite imagery, field sampling, sensor networks
- • Reporting: Standardised formats, registry submissions, disclosure data
- • Verification: Third-party audits, independent validation
Supply Chain Traceability
Evidence for sourcing claims
Tracking products from origin to shelf. Essential for EUDR compliance, deforestation-free commitments, and chain of custody certification.
- • Farm-level: GPS coordinates, plot registration, producer IDs
- • Processing: Mass balance, segregation, identity preserved
- • Retail: QR codes, product passports, consumer transparency
Disclosure Data
Evidence for regulatory compliance
Structured data for CSRD, ISSB, CDP, and other frameworks. Increasingly subject to assurance requirements.
- • Quantitative: Emissions, water use, waste, biodiversity metrics
- • Qualitative: Policies, governance, risk management descriptions
- • Normalised: Intensity metrics (per revenue, per product, per employee)
Impact Evidence
Evidence for outcome claims
Demonstrating that interventions actually work. Additionality, permanence, leakage assessment for credits. Real-world outcomes for targets.
- • Baselines: What would have happened without intervention?
- • Monitoring: Ongoing measurement of outcomes
- • Attribution: Linking activities to outcomes
The Data Quality Challenge
Data quality degrades as it moves up the system. Ground-level complexity gets simplified for corporate reporting. The challenge is maintaining integrity while enabling decision-making.
Common Data Problems
- • Gaps: Missing data filled with estimates
- • Proxies: Indirect measures when direct unavailable
- • Aggregation: Detail lost in rollup
- • Timeliness: Stale data informing current decisions
- • Comparability: Different methodologies, different results
Quality Requirements
- • Completeness: Full coverage of scope
- • Accuracy: Measurement precision
- • Consistency: Same methodology over time
- • Transparency: Methods disclosed
- • Verifiability: Third-party audit possible
The tension: Perfect data doesn't exist. The question is whether data quality is good enough for the decisions being made – and whether limitations are transparently disclosed.
Who Operates in Data Flows
MRV Providers
Generating evidence
Satellite companies, verification bodies, field monitors
How do we scale measurement while maintaining quality?
Data Platforms
Aggregating and processing
ESG data providers, carbon registries, traceability platforms
How do we make data comparable and actionable?
Assurance Providers
Validating claims
Big 4 auditors, specialist verifiers, rating agencies
What gives stakeholders confidence in data?
The Pandion View
Data is the connective tissue of sustainability. Without it, every claim is an assertion. With it, commitments become credible and progress becomes measurable.
The organisations that win will be those that build robust data infrastructure – not as a compliance burden, but as a strategic asset. Good data enables better decisions, faster iteration, and more credible stakeholder communication.
As a hybrid professional, we help clients design data systems that work across layers – connecting ground-level MRV to boardroom disclosure. We understand both the technical requirements and the strategic context, ensuring data serves decision-making rather than just compliance.