VERTICAL FLOW
Data Flows
Evidence moving up through the system – MRV, traceability, and disclosure.
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.
Three Pillars of Data Flows
Our Data Flows architecture spans evidence generation, enabling technologies, and the economics of who controls and benefits from sustainability data.
Pillar 1: Evidence Types
The four categories of data that flow through sustainability systems
MRV Systems
Evidence for ecosystem outcomes
The foundation of credible carbon credits, biodiversity claims, and nature-positive targets. Satellite imagery, field sampling, third-party verification.
Traceability
Evidence for sourcing claims
Tracking products from origin to shelf. Essential for EUDR compliance, deforestation-free commitments, and chain of custody certification.
Disclosure Data
Evidence for regulatory compliance
Structured data for CSRD, ISSB, CDP, and TNFD. Increasingly subject to assurance requirements and digital tagging mandates.
Impact Evidence
Evidence for outcome claims
Demonstrating interventions actually work. Additionality, permanence, leakage assessment. The bridge between activities and outcomes.
Pillar 2: Technology Lenses
The infrastructure and intelligence enabling data flows at scale
AI in Data Flows
Intelligence layer for sustainability data
Machine learning for satellite analysis, NLP for disclosure extraction, predictive models for deforestation risk. Where AI transforms raw data into actionable insight.
Digital Infrastructure
The pipes and platforms that move data
APIs, interoperability standards, digital product passports, blockchain for provenance. The foundational layer that makes data flows possible.
Pillar 3: Governance & Economics
Who owns, controls, and benefits from sustainability data
Data Value & Ownership
The economics of sustainability data
Who captures value from ESG data? The tension between data originators and aggregators, smallholder data rights, indigenous data sovereignty, and emerging models for equitable benefit sharing.
Coming soon: Data Governance Frameworks – standards, regulations, and institutional arrangements for sustainability data
The Data Maturity Journey
Organisations don't just need data – they need to mature through stages of data capability. Most are stuck in the early stages. The frontier is connecting data to business value.
Based on patterns observed across thousands of companies navigating sustainability data requirements.
Discovery
"What should we track?"
Identifying material metrics, aligning with frameworks (CSRD, ISSB, CDP, TNFD)
Many companies here
Collection
"How do we get this data?"
Building systems, supply chain requests, internal processes, filling gaps
Most companies here
Validation
"Can we trust this data?"
Quality assurance, verification, audit readiness, methodology consistency
Growing focus
Materiality
"What does it mean for the business?"
Connecting to financial outcomes, risk quantification, value creation, strategic decisions
The frontier
The Benchmarking Gap
At every stage, organisations need to answer: "How do we compare?" Without peer comparison, you don't know if you're doing well or poorly. Yet benchmarking is often missing – especially for private companies and SMEs who lack access to comparable peer data.
Leading data platforms now offer 20,000+ benchmarks across industries and company sizes. Benchmarking transforms data from "numbers we report" into "intelligence we act on."
See how data maturity maps to the corporate sustainability journey →
Corporate Action & Data CapabilityThe 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.