When it comes to interesting data use cases, few would disagree that the notion of Environmental, Social and Governance (ESG) data has its special challenges. While the idea of ESG oriented investing has been around for decades, society has been paying more attention to it as of late. The attention, however, has not all been positive with some sectors using expressions such as “woke capitalism” and “greenwashing”. Despite the negative reactions of some, the idea that the “bottom line” might not be the only indicator of long-term stability and actions toward social responsibility could be indicative of long-term performance.
At Verstand AI, we like challenging data use cases, so we decided to look at the various ESG data issues to see if we could develop a data management approach that made sense to both investor and corporation alike. Part 1 of our ESG series will give some context behind the problem. In subsequent parts, we explain how to better identify and manage ESG data—for either company evaluation or corporate responsibility purposes. Our goal is to understand the issue, the data and see if a “decision science” approach can help bring clarity.
The Data Dilemma Background
Currently, in the United States, there are no mandatory federal regulations requiring companies to publish ESG data, nor is there a widely followed set of best practices on the data that is reported. This makes finding consistently published and reliable sources difficult. For those of us in the data management business, this is a “data provenance” problem. In addition, the lack of data transparency creates the perfect marketing environment for tactics such as “greenwashing”—a false impression that a company or its products are environmentally conscious or friendly. This lack of set definitions for metric evaluation and inclusion also allows companies who opt to report ESG information to portray themselves solely in a positive light.
While most companies are not out to manipulate data, the lack of consistent and standardized definitions makes it difficult to compare companies when making investment decisions and it is here where the release of ESG standards and frameworks can help businesses navigate this increasingly important area.
While lacking in the United States, the Europeans have been making moves to standardize. The European Union (EU)’s passing of the Sustainable Finance Disclosure Regulation (SFDR) in March 2021 is a step toward data harmony and has already made a significant impact on entities’ ability to label themselves ESG friendly. Interestingly, this regulation impacts US companies and asset managers if they are financial market participants with EU shareholders or they are marketing themselves in the EU.
While these guidelines and regulations point towards a more structured future, there is still the problem of how to handle the ESG environment as it now stands. As mentioned above, objective analysis of this data has proven difficult. In addition to the lack of validated and accurate data, collecting it is a time-consuming process, as there is no single source where these details can be located. Data is found in a wide variety of places: annual reports, company websites, sustainability reports, and firm press releases just to name a few. If that were not enough of a challenge, Verstand analysts have noted that most ESG data is soft, meaning it is hard to measure and as such, assigning a monetary or quantitative value is difficult.
Despite the problem of varied and scattered sources of data, there are companies that assign ESG scores to businesses using proprietary formulas—a veritable “black box” approach where even if one assumes all data collected is accurate, the validity of these scores still comes into question. The scores vary greatly between each provider indicating subjectivity and biases—a problem when using the information to screen companies for important investment decisions. The problem of subjectivity worsens as many metrics are interlinked and could fall into more than one of the ESG categories. This leaves the responsibility of deciding how those metrics will be classified up to each scoring company.
I will end the brief treatment of context at this point. The reader should know that the ESG reporting environment is very volatile, and that both individuals and corporations alike will have to take care of how they both analyze and report the data.
Follow along through the Verstand AI ESG Insights blog, as the team reports on its efforts to makes sense of the data, how Verstand helps companies organize their ESG related data and understand how we collaborate with data providers to develop a platform where transparency can help companies drive global environmental, social, and governance goals.
At Verstand, we strive to help clients make the most of their data. If you’d like to learn more about how we can enable your ESG analyst workforce, please contact us at firstname.lastname@example.org.