Posted on : October 8th 2021
Posted by : Sudhakaran Jampala
Reliable environmental and social governance (ESG) scores require a lot of data that need to be parsed, analyzed, and translated into benchmark scores. However, analyzing, even collecting, ESG data is a complex process. Stakeholders primarily collect ESG data from various public sources, such as the World Bank, the United Nations, the International Monetary Fund, other civic agencies like nongovernmental organizations, company websites, news clippings, and social media.
Generally, only a limited number of ESG indicators are developed from privately sourced data points.  For example, rating providers use proprietary methodologies that use company-reported ESG data, along with data from websites, other public sources (like news articles), and, in some cases, primary research contacts within companies. 
Typically, even large firms do not report ESG parameters in the same lucid way as they do financial metrics. Thus, every opportunity to gather ESG data points must be grabbed, so those points can be put through further vetting and analysis. Every sliver of information from any reliable public source is precious because information taken from company sources can be compared against any evidence available from non-company public sources, such as news articles. Doing so also helps with triage and triangulation processes.
Investors can leverage sources such as reports by the U.S. Occupational Safety and Health Administration (OSHA) to gather more evidence about actual company practices.  For instance, OSHA reports can provide evidence about companies’ workforce pay practices. This type of evidence can add value and deepen insights from data points culled from other public sources, such as World Bank reports or articles published by the United Nations.
Because ESG criteria are generally not integrated under a structured data-oriented manner, capturing whatever data are available should be systematic.  Where ESG analytical frameworks lack transparency, ESG integration can be facilitated by systematic data capture, which provides the additional benefit of repeatedly obtaining data in the future.
Investors find that most ESG data providers procure data from public sources. The key challenge for the investment community is to systematically process these data, structure them, and keep track of updates. Investors who do it well gain competitive advantage. Data providers’ opaque methodologies can hide how exactly data points are treated or what assumptions are involved. Thus, having only top-line scores available, without underlying data or transparent calculations, for companies is a vital issue to address.
In such a scenario, a competitive advantage is possible by blending data available from company websites, which contain a myriad of information, with rarely tapped data sources such as OSHA. In essence, adding these data to existing ESG ratings can improve the underlying models and their mapping to financial materiality.
The main ESG challenge of today is the lack of universally accepted principles regarding how companies disclose ESG metrics. Thus, tracking and assessing how companies are performing on their ESG goals are difficult. Systematically capturing data from various sources and blending them with financially material factors can help better join the dots.
Large, still untapped public data sources can improve ESG research and overall data acquisition strategies. Firms that tap into these sources will find tremendous value in them, especially in uncovering financial or material implications more effectively.
Because ESG reporting, unlike financial reporting, is not regulated, companies must optimize their data supply chain across public and private sources to monitor any change. Moreover, third-party ESG score and ratings providers largely provide top-line scores, often leaving customers to accept at face value any methodology deployed.
As a pushback, many companies are capturing data themselves or through vendors, thereby blending the treated data sets into their ESG analytics. Thus, procuring and monitoring ESG data will depend more on automation, particularly artificial intelligence and machine learning, for scale and accuracy.
. Environmental Finance, “ESG Data Guide 2021,” n.d.,
. Raj Gnanaraja and Gary Shorter, “Introduction to Financial Services: Environmental, Social, and Governance (ESG) Issues,” IF11716, Congressional Research Service, January 4, 2021,
. Sebastien Boitreaud, Ekaterina M. Gratcheva, Byran Gurhy, Cindy Paladines, and Andrius Skarnulis, “Riding the Wave: Navigating the ESG Landscape for Sovereign Debt Managers,” Equitable Growth, Finance & Institutions Insight, World Bank Group, 2020,
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