Scalable vs. Credible ESG Data: Do You Have to Choose?

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Environmental, social, and governance (ESG) issues have grown into a major class of risk in recent years, while ESG data has become an important resource for guiding decision-making about business relationships. Investment decisions are one established use case; others include supplier and vendor selection and compliance due diligence.

With reputational damage becoming a top concern for finance and risk leaders, it makes sense that ESG risks would now be a focal point in investment, supplier, and third-party portfolios. As the demand for ESG risk data increases, and as deeper integration of supply chain and private-market ESG analysis becomes more established, ESG data providers are broadening their data sets to make them as comprehensive and scalable as possible. 

But as the ESG data vendor landscape continues to develop, the validity of the data used for ESG-based assessments has come under scrutiny. Understandably, investors and internal users want assurance that their decision-making processes — conducted in a macroeconomic environment of persistent volatility — are founded on trustworthy intelligence.

Three Main Segments of ESG Data Providers

ESG data providers can be sorted into three categories according to the operating models they use to assemble their data sets:.

  • Analyst-first with scores — These firms employ highly trained professionals with the skills to perform both fundamental and quantitative ESG analysis on assets and entities. Firms may use technical mechanisms to source and clean data, but the ESG analysis is based on fundamental information.

  • Analytically and quantitatively driven — These firms leverage primarily quantitative frameworks (including finely tuned yet generalized models) to produce more objective ESG analyses that can be reproduced more often.

  • “Human-in-the-loop” — These firms integrate the best of human and AI models by training machine learning models with human supervision, employing analysts to augment the lower-level work done by AI.1

This evolving market is already seeing convergence between the analyst and automation-first approaches. Analyst firms are building out their internal tools, and automation-driven vendors are expanding their analyst operations as they increase their revenue and tackle new data sets. This reflects a recognition that the choice between ESG data scalability and credibility is increasingly unfeasible for end users.

Manual ESG measurement and analysis of entities’ ESG profiles takes a great deal of time; the credibility of the resulting data is high, but the process of obtaining it is a barrier to scalability. The more scalable methods of data collection, including polling companies, yields faster results but data credibility is compromised as a result.

Choosing an ESG Data Vendor

Companies that seek to balance the ESG data scalability and credibility factors will need to carefully consider and research the vendors in this market. The current vendor landscape contains many established firms, but their data collection and management capabilities can vary significantly. One suggestion is to look for a vendor whose core position is based on an extensive collection of global company information, with highly trusted and diversified solution sets providing third-party and trade credit data to clients. Vendors of this type can expand from this core position and extend their data sets to cover ESG requirements.

For example: Utility bills from companies offer insight on their electricity usage. By applying known factors on how much of that electricity came from carbon-emitting sources, the data provider could credibly assess the companies’ respective carbon footprints. A data provider already including such corporate utility bills as part of its data-sourcing strategy for trade credit assessments would be able to triangulate these verified records. By combining credible ESG data sources and applying consistent scoring based on leading sustainability frameworks such as the Global Reporting Initiative (GRI) and UN Sustainable Development Goals (SDGs), the vendor has the capability to scale its ESG coverage on all companies in its database.

A recent report from Chartis Research on ESG data aggregators and scorers can help you learn more about the vendor landscape for ESG data, with guidance on choosing a data provider that can proactively harness ESG insights to mitigate risk and generate competitive benefits. In this report, Chartis provided its assessment of the market potential and completeness of Dun & Bradstreet’s ESG data services.


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1Jay Wolstenholme et al., "ESG Aggregators and Scorers, 2022: Market and Vendor Landscape," Chartis Research,
4 May 2022.