ESG Data Collection and Management: Best Practices for Australian Businesses

Robust ESG data management is the backbone of credible, compliant AASB S1 and S2 reporting. For Australian organisations preparing for mandatory sustainability disclosure, data governance systems must be designed to ensure accuracy, completeness, and auditability of sustainability metrics. This guide explains how to establish effective ESG data collection and management processes.

For broader ESG strategy context, see our complete ESG guide for Australian businesses.

Why ESG Data Management Matters

Regulatory Requirement

AASB S1 requires disclosure of material sustainability-related financial information. AASB S2 requires specific greenhouse gas emissions metrics (Scope 1, 2, and where material, Scope 3). Both standards require that this information be accurate, complete, and verifiable. Underlying data systems and controls are essential to meet these requirements.

Assurance Requirement

AASB S1 requires limited external assurance of sustainability disclosures. External assurance providers will evaluate the robustness of your data collection, management, and internal controls. Weak data governance creates audit risk and can result in qualification of the assurance opinion.

Credibility and Stakeholder Trust

Investors and stakeholders expect ESG data to be as rigorously managed as financial data. Weak data governance undermines credibility. Conversely, transparent documentation of data processes and limitations builds stakeholder confidence.

Establishing ESG Data Governance

Define Data Governance Structure

Assign clear roles and responsibilities:

• ESG/Sustainability Manager: Overall data governance owner, coordinates across departments, ensures compliance with AASB requirements
• Data Stewards: Department heads responsible for collecting and certifying data in their areas (HR for workforce metrics, Facilities for energy/water, Operations for emissions, etc.)
• Finance Lead: Ensures data quality, reconciles ESG metrics with financial systems where applicable, oversees audit trail
• IT/Systems Lead: Manages data systems and platforms, ensures system integrity and security
• Board/Audit Committee: Provides oversight and approves data governance framework

Develop Data Governance Policies

Document policies addressing:

• Data definition: Clear, consistent definitions of all metrics (e.g., what counts as “Scope 1 emissions,” which entities are included)
• Data quality standards: Accuracy levels, completeness expectations, validation procedures
• Collection processes: Who collects what, when, how, using which systems
• Approval workflows: Sign-off authorities, review processes, escalation procedures
• Data retention: How long data and supporting documentation are retained, where stored, security measures
• Change management: How methodology changes are documented and communicated
• Audit trail: How changes to data are tracked and documented

Designing ESG Data Collection Systems

Decide on Data Collection Platform

Options include:

• Spreadsheet-based: Excel or similar. Simple, low-cost, but limited scalability, error risk, version control challenges
• ESG-specific software: Dedicated platforms (various vendors) designed for ESG data collection, calculation, and reporting. Higher cost but better controls, automation, audit trails
• Enterprise systems: Integration with existing ERP, finance, or HR systems to pull data automatically. Highest quality control but requires system configuration
• Hybrid approach: Combination of systems, e.g., ESG software pulling from finance system and manual entry for operational data

For organisations subject to mandatory AASB reporting, spreadsheet-based systems carry significant control risk. Consider investing in ESG-specific software or enhanced enterprise system integration.

Document Data Flow and Sources

For each metric, document:

• Data owner: Who is responsible for collecting this metric
• Source systems: Where does the data come from? (e.g., energy bills from utility company, employee counts from HR system)
• Collection frequency: Monthly, quarterly, annually?
• Calculation method: If data is calculated rather than directly measured, document the formula and assumptions
• Assumptions and conversions: Any standard factors or conversion assumptions (e.g., emission factors for converting energy to CO₂e)
• Quality checks: What verification procedures are applied before data is submitted?

Create Data Collection Templates

Develop templates (spreadsheets or system forms) for each department to submit data:

• Clearly label data fields with definitions and units
• Include notes on assumptions, estimates, or data quality issues
• Require sign-off by data owner confirming accuracy
• Include version control and date information
• Build in basic validation (e.g., flag if current year is significantly different from prior year)

Data Collection Best Practices

Inventory and Boundary Definition

Before collecting data, clearly define what is included:

• Entity boundary: Which legal entities, operating units, facilities are included? This should match your financial reporting consolidation boundary
• Operational boundary: Are you reporting on Scope 1 only, or Scope 1+2? Is Scope 3 material and being measured?
• Organisational changes: If you acquired or divested entities during the year, document how comparability is maintained (e.g., acquisition date, whether prior-year comparatives are adjusted)

Emissions Data Collection (Scope 1, 2, 3)

Scope 1 (Direct Emissions):

• Natural gas usage (collect from utility bills, convert using emission factors)
• Fuel consumption for owned vehicles and equipment (fuel purchase records)
• Process emissions from industrial facilities (production records, emission calculations)
• Fugitive emissions from refrigeration, natural gas leaks (equipment maintenance records, calculations)

Scope 2 (Purchased Energy):

• Electricity consumption (utility bills, sub-metering where available)
• Steam, heat, cooling purchases (utility bills or supplier invoices)
• Location-based method: Convert electricity to emissions using regional grid emission factors (available from NGER, electricity provider, or international sources)
• Market-based method: Use actual renewable energy certificates or contract terms to determine emission factor

Scope 3 (Value Chain Emissions):

• Supplier engagement: Request emissions data from major suppliers (increasingly common as more suppliers measure emissions)
• Secondary data: Use industry average emission factors (e.g., tCO₂e per tonne of product) where supplier data unavailable
• Spend-based calculation: Apply emission intensity factors to spending with suppliers (less accurate but broad coverage)
• Activity data: Transport distances, business travel kilometres, employee commuting patterns

Workforce and Social Data

Typically sourced from HR systems:

• Workforce metrics: Total employees (full-time, part-time, contractors), headcount by gender, age, location
• Turnover and hiring: Employee turnover rate, new hires, separations, diverse hire rates
• Remuneration: Gender pay gap, remuneration by seniority/role
• Development: Training hours, professional development spend
• Safety: Lost time injury frequency rate (LTIFR), total recordable incident rate (TRIR), fatalities

Ensure data quality by:

• Regular reconciliation with payroll/HR system records
• Consistent definitions of employee types (e.g., what qualifies as “full-time”)
• Guidance on reporting part-time employees as FTE equivalents

Supply Chain and Governance Data

• Supply chain: Number of suppliers assessed for ESG compliance, supplier diversity metrics, sourcing from certified sustainable sources
• Governance: Board composition (skills, diversity), audit committee expertise, executive remuneration linked to ESG targets
• Compliance: Ethics incidents, breaches, regulatory fines, data privacy incidents

Quality Assurance and Validation

Implement Data Validation Checks

• Completeness check: All required data fields are submitted
• Range check: Values fall within expected ranges (e.g., not negative, not implausibly large)
• Consistency check: Values are consistent with prior periods, or changes are explained
• Calculation check: Formulas are accurate (e.g., emissions calculations use correct conversion factors)
• Reconciliation: Data reconciles with other sources (e.g., energy consumption reconciles with utility invoices)

Establish Sign-Off and Approval Processes

Document approval workflow:

• Data owner (department head) certifies data is complete and accurate
• Sustainability/ESG manager reviews for quality and consistency
• Finance reviews for materiality and reconciliation
• CFO or equivalent executive reviews consolidated data
• Board (or audit committee) approves final data before disclosure

Document Assumptions and Limitations

For any estimates, assumptions, or data quality issues, document:

• Why the assumption was needed (e.g., “supplier did not provide 2024 data, so 2023 data used as proxy”)
• Impact of the assumption (e.g., “affects Scope 3 emissions by approximately 5%”)
• Plan to improve in future (e.g., “will implement direct measurement in 2025”)

Managing Data Changes and Restatements

Methodology Changes

If you change how you measure a metric (e.g., start including more facilities, change emission factors, shift from estimates to actual data):

• Document the change, reason, and implementation date
• If material, restate prior-year comparatives for consistency
• Disclose the change and impact in your report

Data Corrections

If you discover errors in previously reported data:

• Assess materiality of the error
• If material, restate prior-year data and disclose correction
• If immaterial, disclose the correction and carry forward corrected data

Frequently Asked Questions

Can we use estimates for emissions data?

Yes, where actual data is unavailable or impractical. Document the estimation methodology, justify why estimates are used, and commit to improving data in future years. AASB S1 allows qualitative disclosures where quantitative data is unavailable.

How do we handle data from suppliers we don’t control directly?

Engage suppliers to provide data directly (increasingly feasible). If not available, use secondary data (industry averages) or spend-based calculation. Document sources and limitations. Disclose that data is estimated and effort to improve supplier engagement.

What internal controls are essential for ESG data?

Critical controls include: clear data definitions, documented collection processes, sign-off workflows, reconciliation to source documents, system access controls, version control, audit trail of changes. These support both data quality and external assurance.

How detailed should data documentation be?

Document enough to enable: (1) accurate replication in future years, (2) explanation to external auditors, (3) identification and correction of errors. A data dictionary and calculation templates are essential.

Should ESG data be integrated with financial systems?

Integration is best practice where feasible (e.g., pulling energy consumption from facilities management system, employee counts from payroll). It reduces manual error and improves consistency. However, some ESG data may live in separate systems.

Moving Forward with ESG Data Management

Robust data governance and management systems are not optional for AASB S1 and S2 compliance—they’re essential. Investment in systems, processes, and training upfront creates efficiency and credibility that pays dividends over multiple reporting cycles. As reporting becomes standard, data systems become a source of competitive advantage and operational insight.

Ready to establish or enhance your ESG data management system? Book a Free ESG Strategy Session to assess your current data infrastructure and plan improvements.