Implementation Guidance

Universal Product Passport Standards (UPPS)

Version 1.0

Effective: May 2026

Data Quality & Assurance Guide

Comprehensive guidance on establishing data quality systems and assurance processes for UPPS implementation.

This non-normative guide provides practical recommendations for ensuring product passport data meets quality standards and can withstand verification. It aligns with ESRS, GRI, and IFRS requirements for data quality and assurance.

1. Purpose and Scope

This guidance document assists organizations in implementing robust data quality management systems and assurance processes for UPPS disclosures. It addresses the unique challenges of product-level data collection across multi-tier supply chains.

Why Product-Level Data Quality Matters

Unlike corporate-level ESG reporting, product passports require data aggregation across multiple supply chain tiers, often involving hundreds of suppliers and complex material compositions. This creates unique data quality challenges:

  • •Multi-tier traceability: Data must flow from raw material extraction through manufacturing to end-of-life
  • •Supplier data reliability: Reliance on third-party supplier data with varying quality standards
  • •Material complexity: Complex material compositions requiring precise attribution
  • •Dynamic updates: Real-time or periodic updates vs. annual reporting cycles

Alignment with Major Frameworks

This guidance aligns with data quality and assurance requirements from:

ESRS

5 qualitative characteristics: Relevance, Faithful Representation, Comparability, Understandability, Verifiability

GRI

Reporting principles for quality and consistency of sustainability reporting

IFRS S1

High-quality, verifiable, and accurate sustainability information requirements

2. Data Quality Dimensions

UPPS data quality is built on six core dimensions that ensure disclosed information is decision-useful and reliable. These dimensions align with ESRS qualitative characteristics and financial reporting principles.

Accuracy

Data correctly represents the real-world attributes and impacts of the product.

Implementation Practices:

  • Validate data against multiple sources where possible
  • Use standardized calculation methodologies (e.g., GHG Protocol, ISO 14067)
  • Implement data validation rules and automated checks
  • Cross-reference with industry benchmarks or similar products
  • Document assumptions and estimation methodologies

Completeness

All required disclosure elements are present without material omissions.

Implementation Practices:

  • Conduct gap analysis against UPPS disclosure requirements
  • Identify and document data unavailability with justification
  • Use reasonable estimates when primary data is unavailable
  • Clearly distinguish between reported and estimated data
  • Implement systematic data collection processes

Consistency

Data is consistent over time and across comparable products and reporting periods.

Implementation Practices:

  • Maintain consistent methodologies and calculation approaches
  • Document and communicate any changes in methodology
  • Use consistent units of measurement and reporting boundaries
  • Provide restated comparatives when methodologies change
  • Implement data lineage tracking for audit trails

Timeliness

Data is sufficiently current to support decision-making and reflects recent product changes.

Implementation Practices:

  • Establish data refresh cycles based on product lifecycle
  • Disclose data collection period and age of data
  • Implement real-time updates for dynamic product attributes
  • Communicate data currency to stakeholders
  • Prioritize high-impact disclosures for frequent updates

Verifiability

Data can be corroborated through evidence and traced to original sources.

Implementation Practices:

  • Maintain complete audit trails for all data points
  • Store supporting documents (certificates, test reports, invoices)
  • Implement two-way traceability for all disclosed values
  • Use secure, tamper-evident data storage systems
  • Provide evidence to assurance providers on request

Relevance

Data is material to stakeholder decision-making and reflects significant impacts.

Implementation Practices:

  • Conduct materiality assessments for disclosure prioritization
  • Focus data collection efforts on high-impact attributes
  • Engage stakeholders to identify information needs
  • Apply double materiality (impact and financial materiality)
  • Regularly review and update materiality assessments

3. Data Quality Management System

Organizations should establish a formal Data Quality Management System (DQMS) to systematically ensure product passport data meets quality standards. The DQMS should integrate with existing ESG data systems and financial reporting controls.

Core Components of DQMS

1. Data Governance Framework

Establish clear roles, responsibilities, and accountability for data quality:

  • Data owners responsible for specific data domains (environmental, social, supply chain)
  • Data stewards managing day-to-day data quality processes
  • Data custodians maintaining technical infrastructure and security
  • Executive sponsorship with authority to enforce data quality standards

2. Data Collection Standards

Standardize data collection processes across the organization:

  • Standardized data collection templates and questionnaires for suppliers
  • Clear definitions and data dictionaries for all disclosure fields
  • Predefined units of measurement and reporting formats
  • Supplier data quality requirements and onboarding criteria
  • Regular supplier training and capacity building

3. Data Validation and Controls

Implement automated and manual validation controls:

  • Automated validation rules (range checks, format validation, logic checks)
  • Exception reporting for data quality issues
  • Manual review of high-impact or complex data points
  • Statistical sampling and verification of supplier data
  • Cross-validation against internal operational data

4. Data Documentation and Lineage

Maintain comprehensive documentation for audit trails:

  • Data lineage tracking from source to disclosure
  • Methodology documentation for all calculations and estimations
  • Assumption logs with rationale and sensitivity analysis
  • Change logs documenting data updates and corrections
  • Retention policies for supporting documentation

5. Continuous Improvement

Establish processes for ongoing data quality enhancement:

  • Regular data quality assessments and scorecards
  • Root cause analysis for data quality issues
  • Corrective action plans with timelines and ownership
  • Feedback loops from assurance providers and stakeholders
  • Investment in data collection infrastructure and supplier capabilities

4. Handling Data Gaps and Estimations

In product-level disclosure, complete primary data is often unavailable, particularly for upstream supply chain stages. Organizations should establish transparent approaches for handling data gaps while maintaining credibility.

Data Hierarchy Approach

Tier 1: Primary Data (Preferred)

Direct measurement from suppliers, facility-specific data, actual test results

Tier 2: Secondary Data (Acceptable)

Industry averages, sector-specific databases, peer group data

Tier 3: Estimates (Last Resort)

Engineering estimates, proxy data, extrapolation from similar processes

Transparency Requirements

When using secondary data or estimates, organizations must:

  • Clearly disclose which data points are estimated vs. measured
  • Document data sources and methodologies used for estimations
  • Provide uncertainty ranges or confidence intervals where feasible
  • Explain why primary data is unavailable and plans to obtain it
  • Avoid mixing data tiers without clear disclosure

Recommended Data Sources

Environmental Data

  • • Ecoinvent database
  • • GaBi databases
  • • IDemat (materials database)
  • • DEFRA emission factors
  • • IPCC emission factors

Social Data

  • • ILO labor statistics
  • • World Bank indicators
  • • Social Hotspots Database
  • • Supplier-specific audits
  • • Industry sector studies

5. Assurance and Verification

Assurance enhances the credibility of UPPS disclosures by providing independent verification of data quality and compliance. This section outlines assurance requirements aligned with ESRS CSRD mandates and international best practices.

Assurance Levels

Limited Assurance

Provides reasonable assurance that disclosures are free from material misstatement:

  • Inquiry of personnel and review of documentation
  • Analytical procedures and ratio analysis
  • Testing of key controls and data points
  • Focus on high-risk and material disclosures
  • Less extensive testing than reasonable assurance

Required under ESRS CSRD for first reporting year

Reasonable Assurance

Provides high assurance that disclosures are free from material misstatement:

  • Extensive testing of data and controls
  • Re-performance of calculations
  • Confirmation with third parties (suppliers, customers)
  • Physical inspection and observation where applicable
  • Comprehensive review of all material disclosures

ESRS may escalate to reasonable assurance in future years

When Assurance is Required

UPPS Assurance Triggers:

  • •Tier 1 Organizations: Annual limited assurance of material disclosures
  • •High-Impact Products: Batteries, textiles, electronics subject to sector regulations
  • •Regulatory Requirements: When mandated by EU Battery Regulation, CSRD, or other regulations
  • •Stakeholder Requests: When requested by major customers, investors, or regulators

Assurance Provider Selection Criteria

Assurance providers should meet the following criteria:

  • Independence from the reporting organization (no conflicts of interest)
  • Professional qualifications (ISAE 3000 or equivalent certification)
  • Experience in sustainability assurance and product-level disclosures
  • Understanding of relevant UPPS standards and regulatory requirements
  • Established quality management systems for assurance engagements

Assurance Process

1

Engagement Planning

Define scope, materiality thresholds, and risk assessment

2

Risk Assessment

Identify high-risk disclosures and data quality issues

3

Testing Procedures

Perform testing, re-performance, and confirmation procedures

4

Conclusion and Reporting

Issue assurance opinion with findings and recommendations

6. Sector-Specific Considerations

Different product categories present unique data quality and assurance challenges. This section provides sector-specific guidance for UPPS implementation.

Battery Products (UPPS 601)

Key Data Quality Challenges:

  • Complex material composition (lithium, cobalt, nickel, manganese)
  • Multi-tier supply chain with critical raw material traceability
  • Carbon footprint calculation across global supply chains
  • Recycled content verification and certification

Assurance Focus:

  • EU Battery Regulation requires third-party verification
  • Focus on carbon footprint, recycled content, and due diligence
  • Verification of battery passport QR code functionality

Textile Products (UPPS 602)

Key Data Quality Challenges:

  • Fiber composition verification across multiple material blends
  • Chemical usage and hazardous substance tracking
  • Water consumption and microplastic shedding data
  • Social compliance across geographically dispersed suppliers

Assurance Focus:

  • Fiber composition testing and verification
  • Chemical inventory and REACH compliance
  • Social audit verification and supply chain due diligence

Electronics Products (UPPS 603)

Key Data Quality Challenges:

  • Complex bill of materials with hundreds of components
  • Critical raw materials (tantalum, tin, tungsten, gold) traceability
  • Repairability and spare parts availability verification
  • Software support period and update commitment tracking

Assurance Focus:

  • Conflict minerals due diligence verification
  • Repairability index calculation verification
  • Hazardous substance compliance (RoHS, REACH)

7. Integration with Corporate ESG Assurance

UPPS data quality and assurance should integrate with existing corporate ESG reporting systems to avoid duplication and ensure consistency across organizational disclosures.

Alignment with ESRS CSRD

UPPS data quality requirements align with ESRS qualitative characteristics:

ESRS Relevance

→ UPPS Materiality Assessment

ESRS Faithful Representation

→ UPPS Accuracy & Completeness

ESRS Comparability

→ UPPS Consistency

ESRS Verifiability

→ UPPS Verifiability & Assurance

Avoiding Duplication

Organizations should leverage existing ESG data systems:

  • Use existing data governance frameworks for UPPS data
  • Extend current assurance engagements to include product-level disclosures
  • Map UPPS data fields to ESRS and GRI disclosure requirements
  • Reuse supplier data collection processes and questionnaires
  • Integrate UPPS data quality metrics into corporate ESG scorecards

Coordinated Assurance Approach

Consider combining assurance engagements:

  • Single assurance provider for both corporate ESG and UPPS disclosures
  • Integrated assurance opinion covering multiple frameworks
  • Shared work programs and testing procedures
  • Coordinated timing of assurance cycles

8. Technology and Tools

Technology can significantly improve data quality management and assurance processes. This section recommends tools and systems that support UPPS implementation.

Data Management Systems

ESG Software Platforms

Specialized platforms for sustainability data collection, validation, and reporting

PLM Systems

Product Lifecycle Management systems for material and product data

LCA Software

Life Cycle Assessment tools for environmental impact calculations

Blockchain/DLT

Distributed ledger for tamper-evident supply chain traceability

Recommended System Capabilities

  • Automated data validation and quality checks
  • Complete audit trails and data lineage tracking
  • Secure data storage with access controls
  • Supplier collaboration portals for data submission
  • Integration with ERP and other enterprise systems
  • API access for automated data exchange
  • Assurance provider collaboration features

9. Best Practices and Common Pitfalls

This section summarizes best practices for data quality and assurance, and highlights common pitfalls to avoid.

Best Practices

✓Involve assurance providers early: Engage auditors during system design to avoid rework
✓Start with high-impact disclosures: Prioritize data quality efforts on material topics
✓Document everything: Comprehensive documentation is critical for verifiability
✓Invest in supplier capabilities: Build supplier data quality capacity through training and support
✓Use standardized methodologies: Adopt recognized standards (GHG Protocol, ISO) for calculations
✓Implement continuous monitoring: Don't wait for annual audits to identify quality issues

Common Pitfalls

✗Over-reliance on estimates: Using secondary data when primary data is obtainable
✗Inconsistent boundaries: Changing reporting boundaries without disclosure
✗Poor documentation: Inadequate audit trails and methodology documentation
✗Last-minute data collection: Rushing data collection before assurance deadlines
✗Siloed systems: Data quality processes disconnected from corporate ESG systems
✗Ignoring stakeholder feedback: Not addressing data quality concerns from users and auditors
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