casemark/managing-esg-data-quality

Managing ESG Data Quality

When To Use

• Evaluating the reliability of ESG data from company disclosures, third-party providers (MSCI, Sustainalytics, Bloomberg, ISS), or internal collection systems
• Comparing ESG scores or metrics across multiple data providers to identify divergence and its root causes
• Assessing disclosure gaps ahead of regulatory filings (CSRD, SEC climate rules, ISSB/IFRS S1-S2) or investor reporting
• Auditing estimation methodologies used to fill missing Scope 1/2/3 emissions, water usage, or social metrics
• Building or improving an ESG data governance framework for portfolio-level or enterprise-level reporting

Inputs To Gather

• Data sources under review: Identify each provider, self-reported dataset, or survey instrument; note vintage year and update frequency
• Metric scope: List specific KPIs (e.g., GHG Scope 1-3 in tCO2e, water withdrawal in m3, board diversity %, LTIR)
• Reporting framework(s): Which standards apply — GRI, SASB, ISSB, TCFD, EU Taxonomy, CSRD/ESRS, CDP questionnaire [VERIFY applicable framework versions]
• Coverage universe: Number of entities, asset classes, or facilities; geographic distribution
• Known gaps or disputes: Any metrics previously flagged by auditors, regulators, or data consumers

Workflow

1. Map the data landscape

   • Catalog every ESG metric required by the target framework(s)
   • For each metric, record: source, collection method (reported / estimated / modeled), refresh cadence, and coverage rate (% of universe with actual data)
   • Flag metrics sourced from estimates vs. verified disclosures

2. Score data quality per metric

   • Apply a consistent rubric across five dimensions:
     • Completeness: % of entities with non-null values
     • Accuracy: Cross-check against primary filings (annual reports, CDP responses, EPA GHGRP) where available
     • Timeliness: Lag between reporting period end and data availability
     • Consistency: Year-over-year variance analysis; flag anomalies exceeding sector norms
     • Comparability: Methodological alignment across providers (e.g., whether Scope 3 categories included differ)
   • Assign a rating (High / Adequate / Low / Unavailable) to each metric-dimension pair

3. Analyze provider divergence

   • Where multiple providers cover the same metric, compute divergence (absolute difference, rank correlation)
   • Identify root causes: differing sector classifications (GICS vs. BICS vs. proprietary), estimation model assumptions, inclusion/exclusion of subsidiaries, treatment of avoided emissions
   • Document which provider methodology best aligns with the selected reporting framework

4. Assess estimation methodology

   • For any estimated metric, document the model type (sector-average, revenue-intensity, physical-activity-based, econometric)
   • Evaluate estimation uncertainty: sample size, proxy quality, geographic representativeness
   • Note whether the estimation approach is accepted under the target standard [VERIFY — e.g., GHG Protocol permits spend-based Scope 3 estimation but CSRD/ESRS may require activity-based data for certain categories]

5. Identify disclosure gaps and remediation actions

   • List metrics with Low or Unavailable quality ratings
   • For each gap, recommend a remediation path: direct engagement with portfolio companies, alternative data sources (satellite, IoT, supply-chain platforms), or improved estimation with documented uncertainty bands
   • Prioritize gaps by materiality (financial impact, regulatory requirement, stakeholder sensitivity)

6. Compile the data quality assessment report

   • Structure output per the format below
   • Include a summary heatmap or matrix showing quality ratings across metrics and dimensions

Output

Structure the deliverable as follows:

• Executive summary: Key findings, overall data quality posture, top 3-5 action items
• Data quality matrix: Metric x Dimension grid with ratings (High / Adequate / Low / Unavailable)
• Provider comparison table: Side-by-side methodology notes, coverage rates, and divergence metrics for each provider evaluated
• Estimation methodology inventory: For each estimated metric — model type, key assumptions, uncertainty range, framework acceptance status
• Gap register: Metric, current quality rating, materiality tier, recommended remediation, estimated timeline, responsible party
• Appendix: Raw data samples, methodology references, glossary of terms

Quality Checks

• Every metric rated Low or Unavailable has a corresponding entry in the gap register with a remediation action
• Provider divergence analysis covers at least the top 10 most material metrics, not just those with obvious discrepancies
• Estimation methodologies are evaluated against the specific framework version in scope — do not assume GHG Protocol defaults apply to all frameworks [VERIFY]
• Timeliness assessment accounts for regulatory filing deadlines (e.g., CSRD phased timelines, SEC compliance dates) [VERIFY current effective dates]
• No estimated value is presented without an explicit confidence qualifier or uncertainty range
• Cross-check that Scope 3 category boundaries are consistent across all sources compared; mismatched category inclusion is the most common source of false divergence
• Flag any metric where the provider's coverage rate falls below 60% of the target universe — partial coverage can distort portfolio-level aggregations