The 8 Biggest ESG Data Pain Points and How We Solve Them

As the ESG data market continues to grow and evolve, financial institutions face a range of challenges in acquiring and utilizing this data effectively. Based on this EY article, we list the top 8 concerns of ESG data purchasers, listed in order of relevance, and highlight how our solution addresses them.

Data Quality and Cost Challenges

1. Quality of Data: Contradictions

Concern: Inconsistent and conflicting ESG data ratings from different vendors lead to confusion and unreliable decision-making.

Solution: At Cleartraced, we only collect publicly available data, ensuring authenticity and reliability. Unlike other providers, we do not estimate where data is lacking and sell guestimates but transparently indicate data gaps. Our AI-driven software streamlines the sourcing, extraction, and enhancement of ESG data from corporate reports worldwide, reducing contradictions and improving data reliability by focusing solely on verifiable information.

2. Escalating Cost

Concern: High costs are associated with acquiring multiple data sources and additional in-house expenses for analysis.

Solution: Unlike other data providers that fully or partly rely on manual collection methods, our AI software automates the entire data localisation and collection process, eliminating the need for human intervention. This automation significantly cuts costs and ensures scalability as the volume of ESG data explodes. Our model, free from manual processes, offers competitive pricing and high efficiency even as market demands expand.

3. Conflicting Vendor Ratings and Scores

Concern: Different methodologies and focuses lead to varied ratings for the same companies, complicating investment decisions.

Solution: At Cleartraced, we prioritize action and results over ambitions. We do not include ESG ratings, reporting initiatives, targets, or carbon offsets in our database. Boston Consulting Group reports that only 14% of companies align their carbon reduction efforts with their ambitions. Despite many companies setting science-based targets, the actual progress in emissions reduction is very limited. Our focus is on providing accurate, actionable data rather than ratings that fail to reflect actual environmental impact.

4. Lack of Comparable Data Across Regions

Concern: Variability in data availability and quality across different geographic regions limits comprehensive analysis.

Solution: Our model is language-independent. We also classify all companies into GICS (Global Industry Classification Standard) and regions, ensuring global representation. We promote global ESG data standards and invest in regional data collection partnerships to fill gaps and improve coverage in underrepresented areas.

Coverage and Transparency Challenges

5. Lack of Coverage Across E, S, and G

Concern: Incomplete data on environmental, social, and governance aspects hinders holistic ESG analysis.

Solution: Initially focused on GHG data, we realized from customer feedback that a broader solution was needed. Today, we extract any and all ESG data, e.g. Principal Adverse Impact (PAI) values for all companies. We constantly expand our data collection efforts to cover more ESG aspects, including biodiversity and social impact metrics.

6. Lack of Consistent Data Across Sectors

Concern: Disparities in data availability and quality across various industry sectors skew analysis and reporting.

Solution: If there is publicly available data, we will find it. Additionally, we maintain statistics on all companies we extract data from to ensure good industry and sector coverage.

7. Lack of Data Within Sectors

Concern: Insufficient data on specific sectors, particularly SMEs and private markets, creates information gaps.

Solution: Our AI software is designed to localize and extract ESG data from various sources, including SMEs and private companies. Using advanced data collection methods such as OCR (Optical Character Recognition) and AI-driven structuring, we fill data gaps within sectors and provide a complete picture.

8. Lack of Transparency

Concern: Limited visibility into the methodologies and sources used by data providers undermines trust and reliability.

Solution: One of our main findings is that ESG data is restricted and kept behind paywalls and terminals, hindering impactful actions. We aim to be transparent and spread data widely, with a publicly available methodology. Every datapoint we deliver links back to its exact source, giving our clients full auditability.

By addressing these top concerns with targeted solutions, Cleartraced enhances the reliability, consistency, and usefulness of ESG data, ultimately supporting more informed and impactful decision-making in the financial services sector. As the market continues to mature, our collaborative approach and continuous innovation will be key to overcoming these challenges and driving sustainable growth. For a broader view of how the ESG data market is evolving, see our analysis of the rapidly increasing ESG data market. Banks navigating the new EBA ESG risk management guidelines face many of these pain points at once, particularly the data gap for SMEs and private counterparties.

Frequently Asked Questions

Why does ESG data quality vary so much between providers?

ESG data providers use different methodologies, data sources, weighting frameworks, and materiality definitions. Some rely on company self-reported data, others on estimates and proxies, and others on AI-driven extraction from public sources. This leads to situations where the same company can receive vastly different ESG ratings depending on the vendor, a problem well-documented by academic research and regulators alike.

How does Cleartraced handle companies that don’t publish ESG data?

Unlike traditional providers who fill gaps with estimates or industry averages, Cleartraced transparently reports what data exists and what doesn’t. Our AI pipeline searches across regulatory filings, company websites, sustainability reports, environmental permits, and public procurement records to maximize coverage, but we never fabricate data. If a datapoint isn’t publicly available, we flag the gap rather than filling it with a proxy.

What regulations are driving the demand for better ESG data?

The key EU regulations include CSRD (corporate sustainability reporting), SFDR (sustainable finance disclosures for fund managers), the EBA ESG risk guidelines (for banks), and the EU Taxonomy. Globally, the ISSB sustainability standards and SEC climate disclosure rules are also increasing data requirements. Together, these regulations mean that financial institutions need granular, traceable ESG data on more companies than ever before.