ESG Data Architecture & Governance

Design the data infrastructure, governance frameworks, and control systems that make sustainability reporting auditable and risk management actionable.

  • Not aspirational data, but governed, validated, traceable data that withstands assurance scrutiny and drives decision-making. We help financial institutions and data-intensive corporates build the ESG data architecture required to meet regulatory obligations and extract strategic value from sustainability information.

Who This Is For

Chief Data Officers, Heads of Data Governance, IT Directors, Heads of ESG, and Risk Data teams at banks, insurers, asset managers, and large corporates where ESG data quality is a regulatory and operational imperative.

What We Deliver

ESG Data Infrastructure Design

End-to-end architecture for ESG data collection, validation, storage, and distribution. We design data flows from source systems (ERP, HR, environmental monitoring, supplier platforms) through transformation and quality assurance to reporting outputs — ensuring data lineage is documented and every number can be traced back to its origin. Where existing data infrastructure exists, we integrate rather than replace.

KPI Governance & Control Frameworks

Sustainability KPIs require the same governance rigour as financial KPIs. We design KPI ownership structures, data quality rules, validation protocols, exception handling procedures, and sign-off workflows. Each KPI gets a control description, a responsible owner, a defined data source, a calculation methodology, and a testing protocol — the governance spine that assurance providers expect.

Data Quality Assurance

ESG data is notoriously inconsistent — different units, estimation methodologies, reporting boundaries, and collection frequencies across business units and geographies. We implement data quality frameworks covering completeness, accuracy, consistency, timeliness, and validity. Automated validation rules, exception dashboards, and remediation workflows ensure data quality improves systematically over time.

Regulatory Reporting Automation

Manual reporting processes do not scale. We help organisations automate ESG data aggregation, calculation, and report generation for CSRD/ESRS, EBA Pillar 3 ESG, SFDR periodic reporting, and EU Taxonomy disclosures. This includes template design, data mapping, automated validation checks, and audit trail generation — reducing manual effort while improving accuracy and timeliness.

AI Governance for Sustainability Data

As organisations adopt AI and machine learning for ESG data processing — automated document extraction, satellite imagery analysis, emissions estimation models — governance becomes critical. We design AI governance frameworks covering model validation, bias assessment, explainability requirements, human oversight protocols, and documentation standards aligned with the EU AI Act's risk-based approach.

Regulatory Context

This practice is driven by:

    • CSRD / ESRS — Over 1,100 ESRS datapoints driving data architecture, collection, validation, and reporting system design. → CSRD Guide | → ESRS Guide

    • SFDR / SFDR RTS — PAI indicator data, Taxonomy alignment calculations, and periodic disclosure data pipeline requirements. → SFDR Guide

    • EU Taxonomy Article 8 Reporting — Taxonomy KPI data sourcing, calculation, and disclosure across revenue, CapEx, and OpEx. Article forthcoming.

    • EBA ITS on Pillar 3 ESG Disclosures — Prudential ESG data templates, granular counterparty-level data, and sector concentration reporting for banks.

    • ISSB / IFRS S1 & S2 — Dual-framework data architecture requirements for companies reporting under both ESRS and ISSB. → ISSB Guide

    • ESEF / iXBRL ESRS Digital Taxonomy — Mandatory machine-readable digital tagging of all ESRS datapoints for automated regulatory data extraction.

    • EU AI Act (Regulation (EU) 2024/1689) — Risk-based AI governance framework affecting machine learning and algorithmic systems used in ESG data processing, scoring, and classification.

    • ESPR / Digital Product Passport — Product-level data architecture requirements for environmental performance, material composition, and circularity metrics. → ESPR Guide

    • EU Batteries Regulation — Digital Battery Passport — Battery-specific data requirements including carbon footprint, recycled content, and supply chain traceability. → EU Batteries Guide

    • Data Act (Regulation (EU) 2023/2854) — Rules on fair access to and use of data; relevant for ESG data sharing between value chain partners.

    • GDPR (Regulation (EU) 2016/679) — Data privacy requirements constraining the collection and processing of workforce, stakeholder, and supply chain personal data for ESG purposes.

    • EBA Guidelines on ESG Risk Management — Explicit data quality expectations for ESG risk data used in credit decisions and regulatory reporting. Article forthcoming.

    • ECB Guide on Climate-Related and Environmental Risks — Supervisory expectations on climate risk data governance, data quality, and management information systems.

    • EFRAG Implementation Guidance — IG 3 (Datapoint List) — Master inventory of all ESRS mandatory and voluntary datapoints driving data architecture requirements.

    • EFRAG Interoperability Mapping (ESRS-ISSB-GRI) — Cross-framework datapoint mapping enabling efficient multi-standard data architecture.

    • ECB AnaCredit / BIRD Data Quality Requirements — Prudential data quality benchmark applicable by analogy to ESG data governance maturity.

    • GRI Standards — Data Requirements — Reporting data specifications for organisations using GRI alongside ESRS.

    • TCFD / TNFD — Data Requirements — Climate and nature-related data specifications feeding into governance, strategy, risk management, and metrics disclosures.

    • ISO 8000 (Data Quality) — International standard for data quality management and measurement.

    • ISO 27001 (Information Security) — Information security management standard relevant for ESG data system controls and access governance.

    • XBRL / Inline XBRL Technical Standards — Technical standards for structured digital reporting enabling automated data consumption by regulators and investors.

    • DAMA-DMBOK (Data Management Body of Knowledge) — Industry reference framework for data governance, data quality, metadata management, and data architecture.

    • CDP Data Platform — Environmental data collection and scoring methodology shaping ESG data standards.

    • PCAF Data Quality Scoring (1-5 scale) — Data quality tier framework for financed emissions calculations driving data improvement priorities.

    • Open ESG Data Models (GLEIF, OS-Climate) — Open-source initiatives for standardised ESG entity identification and data model interoperability.

    • Third-party ESG Data Providers (EcoVadis, Sustainalytics, MSCI, Bloomberg) — Proprietary ESG scoring methodologies affecting data validation and benchmarking approaches.

    • SASB Standards (Materiality Map) — Sector-specific material issue identification useful for prioritising data collection architecture.

Enhanced by Data & Intelligence

SD-KPI serves as a core data layer — providing sector-specific materiality benchmarks and company-level ESG performance data across 68 industries and 15,000+ companies. For institutions building ESG data infrastructure, SD-KPI offers a validated external data source that complements internal data collection.

CLIMATIG data integrates into climate risk data architectures, providing the asset-level physical risk inputs required for ISSB scenario analysis and supervisory stress testing.

Explore our data capabilities →