Data Collection and Management

The Importance of ESG Data

ESG reporting quality depends on data quality. Without reliable data, reports lack credibility and usefulness. Effective data management is the operational backbone of corporate sustainability programs.

ESG Data Challenges

Data Availability

Internal data gaps: Many ESG metrics weren't traditionally tracked

Supply chain opacity: Limited visibility into supplier practices

Historical baselines: Lack of historical data for trend analysis

Data Quality

Inconsistent definitions: Different parts of organization interpret metrics differently

Manual processes: Spreadsheet-based collection prone to errors

Verification gaps: Limited validation of reported data

Data Complexity

Multiple sources: ESG data comes from many systems and functions

Various formats: Qualitative and quantitative, absolute and intensity-based

Frequent changes: Reporting requirements evolve, requiring new data

Organizational Challenges

Cross-functional: ESG data comes from operations, HR, procurement, finance

Geographic distribution: Global operations with varying data capabilities

Resource constraints: Limited staff dedicated to data management

Building an ESG Data Management System

Assessment and Planning

Gap analysis: Identify what data you have vs. what you need

Source mapping: Where does each data element come from?

Process mapping: How does data currently flow?

Technology assessment: What systems exist vs. what's needed?

Data Governance

Ownership: Assign responsibility for each data element

Definitions: Establish clear definitions and calculation methods

Validation: Define quality checks and approval processes

Documentation: Document methodologies and procedures

Data Architecture

Central repository: Single source of truth for ESG data

Integration: Connect to source systems where possible

Historical storage: Maintain historical data for trend analysis

Reporting layer: Enable analysis and report generation

Data Collection by Type

Environmental Data

Energy:

• Sources: Utility bills, meter readings, energy management systems
• Challenges: Multiple sites, leased facilities, disaggregating by type
• Solutions: Utility data aggregation services, smart metering

Emissions:

• Sources: Activity data (fuel, electricity) plus emission factors
• Challenges: Scope 3 complexity, emission factor selection
• Solutions: GHG Protocol guidance, calculation tools, supplier engagement

Water:

• Sources: Utility bills, meter readings
• Challenges: Multiple sources, water stress context
• Solutions: Water accounting systems, risk assessment tools

Waste:

• Sources: Waste hauler reports, internal tracking
• Challenges: Classification consistency, recycling verification
• Solutions: Waste tracking systems, hauler data integration

Social Data

Workforce:

• Sources: HR information systems
• Challenges: Global definitions, contractor inclusion, privacy
• Solutions: Standardized HR reporting, privacy-compliant processes

Health and safety:

• Sources: Incident management systems, safety records
• Challenges: Consistent classification, contractor coverage
• Solutions: Safety management systems, standardized definitions

Supply chain:

• Sources: Supplier surveys, audits, certifications
• Challenges: Response rates, data verification
• Solutions: Supplier platforms, audit programs, certification tracking

Governance Data

Board and executive:

• Sources: Corporate secretary records
• Challenges: Keeping current, consistent definitions
• Solutions: Board management software, regular updates

Ethics and compliance:

• Sources: Hotline systems, training records
• Challenges: Confidentiality, completeness
• Solutions: Ethics management platforms, training systems

Technology Solutions

ESG Data Management Platforms

Dedicated platforms for ESG data:

Capabilities:

• Data collection from multiple sources
• Calculation engines
• Audit trails
• Report generation
• Framework mapping

Examples: Workiva, Sphera, Enablon, Watershed, Persefoni (carbon)

Enterprise System Integration

Connecting ESG to existing systems:

ERP integration: Financial data, procurement data

HR system integration: Workforce data

Operations integration: Energy, water, production data

Emerging Technologies

IoT: Real-time environmental monitoring

AI/ML: Data quality checks, estimation where primary data unavailable

Blockchain: Supply chain traceability, carbon credit verification

Data Quality Management

Quality Dimensions

Accuracy: Data reflects reality

Completeness: All relevant data is captured

Consistency: Data is defined and calculated the same way

Timeliness: Data is available when needed

Relevance: Data addresses reporting needs

Quality Assurance Processes

Validation rules: Automated checks for reasonableness

Cross-checks: Comparison across data sources

Trend analysis: Identification of unusual variations

Documentation: Clear audit trail

Quality Improvement

Root cause analysis: Understanding why errors occur

Process improvement: Addressing systemic issues

Training: Building capability across data owners

Technology: Automating where possible to reduce manual error

Estimation and Assumptions

When Estimation is Necessary

Primary data isn't always available:

• Supply chain emissions
• Historical baselines
• Leased facilities
• Acquired operations

Estimation Approaches

Proxy data: Using similar data as a substitute

Industry averages: Using sector benchmarks

Modeling: Calculating based on activity data

Extrapolation: Extending known data to estimate unknown

Managing Estimation

Transparency: Disclose what is estimated vs. measured

Conservatism: When uncertain, err toward overstating impacts

Improvement plan: Work to replace estimates with primary data

Documentation: Record assumptions and methodologies

Internal Controls for ESG Data

Control Environment

Tone at top: Leadership commitment to data quality

Roles and responsibilities: Clear accountability

Policies and procedures: Documented processes

Control Activities

Segregation of duties: Separation of data entry and review

Authorization: Approval requirements for data

Reconciliation: Matching data across sources

Documentation: Evidence of controls performed

Monitoring

Ongoing monitoring: Continuous quality checks

Periodic assessment: Regular control effectiveness reviews

Issue resolution: Process for addressing identified problems

Preparing for Assurance

Assurance Readiness

External assurance is increasingly expected or required:

Documentation: Ensure methodologies are documented

Evidence: Maintain supporting documentation

Controls: Implement and document internal controls

Consistency: Apply methodologies consistently

Working with Assurers

Early engagement: Involve assurers in planning

Clear scope: Define what will be assured

Access: Provide access to data, systems, and personnel

Response: Address findings promptly

Data Security and Privacy

Data Security

Access controls: Limit who can view and modify data

System security: Protect platforms from unauthorized access

Backup: Ensure data is backed up and recoverable

Privacy Considerations

Personal data: Handle employee data according to privacy laws

Consent: Obtain appropriate consent where required

Geographic variations: Comply with local privacy requirements

Key Takeaways

• Data quality is foundational to ESG reporting credibility
• Key challenges include availability, quality, complexity, and organizational coordination
• Effective data management requires governance, architecture, and processes
• Different data types (environmental, social, governance) have different sources and challenges
• Technology platforms can help but require proper implementation
• Quality assurance processes should validate accuracy, completeness, and consistency
• Estimation is necessary but should be transparent and improved over time
• Internal controls build confidence and prepare for external assurance
• Data security and privacy must be maintained

Next Module

Module 9 covers ESG strategy implementation—how to translate commitments and reports into actual organizational change.