In the ESG landscape, some of the pain points for asset managers, are a lack of comparable, high-quality, high-frequency raw data, a lack of standardized definitions of sustainable activities, conflicting ESG taxonomies, and divergence in scoring methodologies across traditional ESG data vendors. This conundrum remains despite a proliferation of ESG data vendors since current ESG scoring methodologies are limited
by voluntary and sparse non-financial input data and must be supplemented by alternative datasets such as events data, real-time data, satellite imagery data, traditional and social media data, all of which capture material issues and events not reflected in a company’s financial and regulatory filings. Extracting, evaluating, and standardizing these alternative datasets require significant manual effort, creating strong incentives for the use of machine learning (ML), artificial intelligence (AI), and natural language processing (NLP) techniques.

To evaluate all the components of the S-Pillar, the paper will review social data vendors in 3 parts: Part I: Social Capital, Part 2: Human Capital and Part 3: AI & NLP for Social Analysis. There is no one vendor that covers all categories of social data in detail. This report aims to help readers identify and understand which vendors can cover each of the social categories, what regions they cover, the granularity of the datasets and other key metrics of these data vendors’ datasets. With the introduction of new sustainable finance disclosure regulations and taxonomies from the UN and across the EU, North America, and Asia, it is more important now than ever to understand ESG data and where investors can source reliable ESG data.

Mandatory and non-mandatory ESG reporting obligations vary massively by region. Researchers have highlighted 1750 sustainability reporting provisions across 60 countries worldwide. And these reporting regulations are in constant flux. Just this year both the EU and the SEC in the US have signalled their intention to tighten non-financial disclosure requirements, though we are some way off a single harmonized global system. The shifting and inconsistent requirements have increased the importance of alternative datasets from independent third parties when assessing the ESG credentials of an investment.