By: Lori Silverstein, SVP Straive
As ESG data strategies mature into new models, gaps are becoming increasingly clear. While development in ESG scoring and benchmarking is ongoing, many firms have created a solid baseline and are now looking further to improve and expand their model to understand the deeper financial impact of ESG and its implications.
Some current challenges include:
- Private company ESG data
- Well defined and unbiased qualitative data extraction
- Enhanced and extended climate data
Without the use of ML, AI, and custom designed solutions, these three issues cannot be solved.
Private Company ESG Data: Private companies, especially the mid- and small-cap market, are not required to disclose ESG information. Adding to this challenge is the fact that these companies have very few, if any, public disclosures. This requires the ESG data to be pulled from news/media, third party sources like CDP, and company websites. By consistently tracking news and media sites, it is possible to capture both positive and negative ESG events that over time, will start to point to ESG trends of companies.
Well Defined and Unbiased Qualitative Data Extraction: A good section of current ESG data is qualitative — answering questions about subjects like diversity, ratings, governance, and more. While today’s provider model generates an acceptable baseline for this data, as enterprises mature, it’s important to define some data points according to their strategy and use of this ESG data. Even with well-defined data points, a mature data extraction model is needed to ensure that no analyst bias occurs. This level of customization is currently unavailable in the traditional information provider model, meaning firms will need to look toward alternate options from their own internal data teams to a more customized service delivery component.
Enhanced and Extended Climate Data: In the last few years, as firms have been able to clearly link environmental risks with financial implications, there is an increased demand for additional and deeper metrics related to climate. Today, data points like carbon footprint are well tracked. However, a larger impact can occur by tracking data like environmental risks of physical assets, data related to power generation, fossil fuels, and other third-party energy data that is indirectly connected to a company’s operations. Sources like FEMA can help map location-based environmental data. The EPA helps track data for energy companies and the International Energy Agency provides international energy and carbon data. Incorporating data from these sources and being able to accurately map data from these sources to portfolio companies, can help improve the maturity of ESG models, especially climate data.
Trends will continue to change and expand, and firms’ data models need to follow suit. Creating a flexible, customized data pipeline that meets your firm’s individual needs is paramount to ensure that these challenges are tackled.
About the author:
Lori Silverstein, SVP Straive, has over 30 years of experience in creating value driven solutions for clients around the world in Financial Services, Real Estate, Risk and Compliance, Legal, Localization and Business Information Services.
She is currently working with both data providers and individual investment firms to help improve the accuracy, coverage, and depth of their ESG data & Ratings.