ESG Data: Standard Practice

Media Coverage September 5, 2019

Full article available on Funds Europe
By Nicholas Pratt


Third-party ESG data services are plentiful but rarely aligned or transparent, often leaving fund managers frustrated. Nicholas Pratt asks if technology is the answer.

ESG investing may be one of the biggest growth areas in the funds industry, but there is a significant data problem at the heart of it.

While companies may only make limited disclosures on ESG matters, there are vast numbers of third-party providers offering ESG ratings and data services to investors and asset managers. At the end of 2016, more than 125 ESG data providers were in operation, according to the Global Initiative for Sustainability Ratings. These include both well-established, global data providers like MSCI, Bloomberg and FTSE; ESG specialists like Sustainalytics, Vigeo Eiris and TruValue Labs; and specialists within the ESG world such as Trucost, which focuses on carbon data.

However, there is very little correlation in what data they choose to collect, how they estimate any missing data, how they weight various ESG factors and, ultimately, the ratings they produce.

For fund managers and investors, it is a serious issue. As Rakhi Kumar, head of ESG investment and asset stewardship at State Street Global Advisors (SSGA), says: “Managers are allocating capital based on that data. It is driving investment decisions.”


The third parties
The third-party data providers recognise that difficulties in the market exist. “The first challenge is that there is no regulatory body dictating how companies report their ESG data,” says Hendrik Bartel, co-founder and CEO of TruValue Labs. “Instead we have lots of different third-party providers employing different frameworks. Some of them measure policies, others attribute consumption. There is very little correlation between them all.”

This is why you need technology, says Bartel. “TruValue uses AI technology to read hundreds and thousands of unstructured ESG data sources. It is an uber-analyst. It can quantify, structure and understand (cognitively and semantically) all of this data and attribute it accordingly. It would take a normal analyst six years to go through the information we get in one day just on the automotive industry.”