Designing an Effective ISSB-Compliant ESG Data Architecture

Introduction

As organisations move toward ISSB S1 and S2 adoption, a familiar implementation pattern is emerging. Teams are assembled, standards are reviewed, disclosure requirements are extracted and mapped against existing ESG metrics. Even data owners are nominated and mechanisms are built to track project progress through spreadsheets, shared drives, and slide decks.

On paper, this looks like an implementation, however, in reality it is an elaborate requirement gathering and organising phase with limited attention to what matters most – developing a true ESG data architecture that is both robust and sustainable.

ISSB standards do not merely expand the volume of sustainability disclosures. Instead, they change the expectations placed on the data itself—how it is generated, the underlying data model to catalogue, move and analyse the ESG data to drive financial decision-making, management insights and governance considerations. These expectations expose structural weaknesses that traditional ESG reporting approaches have so far been able to work around with.

The central challenge of an ISSB-compliant framework is not articulation of requirements or data disclosure – it is architecture. This is exactly where ESG specialist architects are required, as public information (including AI models) are tuned to advise on how organisations implement ISSB, and what data requirements are needed for ISSB S1/ S2; however, they fall short of actually developing the required technical architecture.

Why ISSB Forces an Architectural Mindset

ISSB introduces a different operating assumption from earlier sustainability frameworks. It assumes that sustainability-related information is part of the organisation’s core decision infrastructure, not a parallel reporting overhead. This shift manifests in several ways that matter architecturally.

• First, ISSB establishes a global baseline. Organisations are no longer free to interpret sustainability metrics independently across regions or business units without consequence. Inconsistencies that previously went unnoticed now surface when disclosures are compared across markets and over time.
• Second, ISSB adopts a top-down structural logic. Governance, strategy, risk management, and metrics are not independent sections of a report. They are expected to connect. That connection cannot be demonstrated convincingly unless the underlying data model and resulting data flows are designed to support it.
• Third, ISSB explicitly links sustainability information to financial outcomes—cash flows, asset values, and capital allocation. Once that link exists, sustainability data inherits the same scrutiny as financial data. Informal processes that were acceptable in voluntary reporting environments are no longer defensible.
• Finally, ISSB assumes that sustainability information is repeatable, traceable, and reviewable. These qualities are properties of systems, not documents. As such, treating ISSB as a checklist delays the inevitable. Treating it as a data architecture problem addresses the root issue.

 A.     The Canonical ESG Data Model: The Non-Negotiable Foundation of an ISSB-Compliant firm

Any organisation serious about ISSB readiness must establish a canonical ESG data model. This is not a reporting template. It is a structural definition which addresses the “single source of truth” on what ESG data is inside the organisation.

A canonical model ensures that sustainability information is:

• Uniformly defined and understood: For instance, the source and nature of the data, its attributes and variability.
• Context driven: Appropriate contextual properties are assigned. For instance, entity information, time period etc. are widely known.
• Traceable and evidential: Data source, processing/ logic and approval can be evidenced end to end.

As such, the canonical ESG data model is an organisation’s native ESG data blueprint which is managed centrally and deployed across departments.

The key pillars to achieve the canonical ESG model are as follows.

Metrics: Eliminating Parallel Truths

Metrics are the most visible part of ESG data—and often misunderstood. Under ISSB, metrics are not simply disclosure items. They are inputs into risk assessment, scenario analysis, and financial planning. That means they must behave like financial measures.

Each metric must have:

• A stable definition that does not change year to year without formal approval
• Clearly defined organisational and operational boundaries
• A documented calculation or estimation method
• Ownership and accountability

Common failure mode:
An organisation allows different teams to calculate the “same” metric for different purposes. Sustainability teams calculate emissions for reporting. Finance teams adjust numbers for modelling. Risk teams apply different assumptions for scenario analysis. None of these differences are visible at the disclosure stage driving inherent risk.

ISSB collapses these parallel processes into a single accountability surface. A canonical model eliminates parallel truths by design, by uniquely defining and centrally controlling the 

metric.

Dimensions: Where Most ISSB Implementations Break Down

Metrics alone do not satisfy ISSB expectations. Data must be contextualised. Dimensions provide that context. They allow the same metric to be analysed across:

• Legal entity and subsidiary structures
• Geographies
• Assets and projects
• Value-chain stages
• Time horizons and scenarios

This is not a reporting preference. It is what allows sustainability data to be interrogated in the same way financial data is interrogated.

Failure example:
A company reports climate-related risks at group level. When asked how those risks affect specific assets, regions, or time horizons, the organisation cannot respond without rebuilding the analysis manually. The data exists, but the structure does not.

ISSB exposes this weakness because it expects sustainability risks to be evaluated alongside enterprise risk and financial planning processes. That requires dimensions to be embedded at the data level, not added retrospectively.

Evidence: Designing for Assurance, Not Just Disclosure

ISSB does not explicitly mandate assurance methods. It does something more consequential—it assumes assurance readiness. Evidence is what allows sustainability information to be trusted, challenged, and defended. In an ISSB context, evidence must be treated as first-class data, not supporting documentation.

Evidence typically includes:

• Source system records
• Supplier or third-party data
• Meter readings and logs
• Approved methodologies
• Calculation models and scripts
• Review and approval records

Failure example:
An emissions figure is disclosed. The organisation can explain the methodology but cannot demonstrate when it was approved, who approved it, or what changed since last year. Under ISSB, that gap undermines confidence not only in the metric, but in the governance surrounding it.

A well-designed ESG architecture links evidence directly to each metric, creating an auditable chain from source to disclosure.

B.     Data Lineage: The Question Auditors Ask First

While architecting the ISSB-compliant ESG data model, it is imperative to capture the source, and each touchpoint at which the data changes or is processed.

Data Lineage answers four basic questions:

1. Where did the data come from?
2. How was it transformed?
3. What controls were applied?
4. Where was it disclosed?

An end-to-end lineage typically spans several layers.

• Source Systems

Data originates in financial systems, procurement platforms, HR tools, energy meters, climate models, and external providers. Each source introduces specific risks—completeness, accuracy, timeliness, and authority.

The architecture will have provisions to add unique identifiers to ensure the origin is tracked and immutable.

• Ingestion and Staging

Data must be ingested in a controlled manner. Manual uploads without validation are a common source of data integrity issues. The architectural best practice is to use appropriate validation logic to prevent manual inconsistencies. In addition, every change/ processing of data should be versioned and timestamped along with user ID details.

• Transformation

This is where most ESG risk resides. Calculations, estimations, scenario modelling, and aggregation occur here. If transformation logic is undocumented or uncontrolled, assurance becomes impossible.

As such, the architectural best practice is to abstract all the transformation logic into a single block which resides outside the data ingestion and reporting layers. This transformation block should be governed by strict rules of access control and reviewed from time to time – in line with the organisation’s evolving business rules.

• Controls and Review

Validation checks, exception handling, approvals, and documented challenges demonstrate oversight. Architectural principles should build in systematic controls such as multi-factor authentication, maker-checker workflows and alerts for exceptions.

• Disclosure

Final metrics are mapped to ISSB disclosures. At this stage, data should be stable. Late-stage adjustments are a red flag. Without systemised lineage, organisations struggle to answer even basic questions under assurance scrutiny.

Architectural principles at the disclosure stage should allow predominantly read-only access of information and no further processing.

C.     Reuse Across ISSB, CSRD, and GRI: An Architectural Imperative

ISSB was designed as a baseline, not a replacement. Most organisations will continue to report under CSRD, GRI, and other frameworks. Attempting to maintain separate datasets for each framework is inefficient and risky.

As such, a robust architecture separates ESG information into three layers.

The Data Layer

This is where ESG data lives. Metrics, dimensions, and evidence are created and governed here. The data layer is independent of which framework is used to drive the final reporting/ disclosure. It does not “know” about ISSB or CSRD—it simply holds validated information.

The Mapping Layer

This layer connects data to disclosure requirements. It defines which metrics appear where, how they are grouped, and which dimensions are required. Importantly, it does not alter the underlying data. Architectural principles at this stage allow data tagging and assignment, but not actual data manipulation.

The Disclosure Layer

This is where approved data is assembled for reporting. No recalculation occurs here. Any change at this stage indicates an upstream failure; which should be flagged through appropriate alerts.

Failure example:
An organisation adjusts numbers in the disclosure layer to reconcile differences between ISSB and CSRD reports. This masks inconsistency rather than resolving it and creates audit risk; signalling a potential error in the data layer where metric definitions have been built.

D.     Where Spreadsheets Become a Governance Risk

Spreadsheets remain deeply embedded in ESG processes because they are flexible and familiar. Under ISSB, those same qualities become risky due to:

• No enforced data model
• Weak or absent audit trails
• High dependence on individual expertise
• Poor access and change controls
• Formula errors that are hard to detect
• Limited integration with enterprise systems

While spreadsheets may still play a role at the point of data capture, good architectural principles dictate that these cannot serve as the system of record for ISSB-grade sustainability information.

E.      Systemised Controls: Closing the Governance Gap

ISSB implicitly expects sustainability data to be governed with discipline comparable to financial reporting. This requires controls to be embedded into systems, not layered on manually at reporting time.

Architectural best practices for systemised controls include:

• Mandatory fields and validation rules
• Standardised calculation logic
• Threshold-based anomaly detection
• Role-based access and segregation of duties
• Automated version control and change tracking
• Workflow-driven review and approval

Failure example:
Climate assumptions are updated by external consultants. The organisation cannot demonstrate internal challenge, approval, or documentation of those changes. When financial impacts shift, management cannot evidence oversight.

Conclusion: ISSB Raises the Bar Permanently

ISSB is not a passing reporting cycle. It reflects a permanent shift in how sustainability information is expected to function within organisations. It is important to invest early in building a robust and future-proof ESG data architecture. This will achieve:

• Data integrity and reporting synergy
• Reliable and decision-useful information
• Cross-framework consistency
• Stronger governance and audit readiness

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