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November 7, 2021

In the Absence of a Shared Standard, How Can You Maximize ESG Data?

For institutional investors hoping to act on ESG goals, data is the name of the game. But investors will have to ensure they have access to the needed data, as well as the ability to draw insights from it.

By Paul Fahey

Across the industry, we’ve seen an increase in the number of asset managers who have committed to utilizing ESG data. In fact, according to “Art of Alpha: It’s All About Investment Data Science”, a survey of asset managers by Northern Trust and WBR during Q2 2021, well over half (59%) currently factor ESG data into their investment process.

As more managers and investors target sustainability goals, the demand for ESG data has grown. Transparency and accountability are the foundation of ESG investing, and managers feel the pressure – whether based on regional regulations or investor demands – to demonstrate that their products are, in fact, green, and not greenwashed.

While today there are multiple vendors that offer access to ESG data, there is still a lack of shared standards around what datasets are tracked, how they are tracked, and how to draw out and act on insights from that data.

Without a shared industry standard of ESG analysis and reporting, acting on these pressures has been challenging for institutional investors, and many questions have arisen as a result. How can they make their small sustainability investing teams work smarter? Can they access the needed data to drive portfolio-level ESG decisions? What signals can they derive from the data that will help guide these decisions?

Four applications for the broad, non-standardized world of ESG data analytics

Armed with the right data, responsible investing teams have to know how to apply it to produce actionable insights. Institutional investors are making plans for where their ESG data and insights will be coming from in the future – according to a Northern Trust webinar poll held in October 2021, 49% are hoping to partner with a main service provider. A quarter (26%) hope to manage ESG data and insights internally with direct data relationships

No matter where firms concentrate their ESG data relationships, a properly data-supported investment strategy will allow institutional investors to leverage the following applications to meet their ESG goals.

  1. Screening – Screening securities for ESG performance is likely the most common and straightforward way of using ESG data to date, involving analysis of certain ESG factors of a company before deciding if it’s a fit for a portfolio. However, managers should be sure they have the capacity to go deep on their screening, including comparing multiple securities to understand their impacts, especially across the different segments of environmental, social and governance, and across industries. For example, comparing the ESG score of a credit card company with an electric car company may feel like trying to compare apples and oranges at the surface. Clearly the environmental factors of the car company weigh heavier on its score than that of a credit card company who makes social elements of ESG a priority for their structure and operations. The right data solution should allow you to analyze and weigh these different factors on a level playing field to understand which one truly is the better fit for your ESG goals.
  1. Understanding risk and performance drivers – As asset owners begin defining their own ESG goals and benchmarking against them, they will need to grasp how their asset managers’ decisions are aligning with those ESG goals. Asset managers have been and will continue to feel this pressure from investors that ask for customized ESG reporting on very specific responsible investing angles and considerations. For example, one investor may put special emphasis on gender equality risk while another cares primarily about carbon emission risk. Being able to quickly pull custom data views on different holdings will be crucial to gaining investors’ trust and cutting through their concerns of greenwashing.
  1. Portfolio optimization and simulation – When managers and self-managed asset owners bring new ESG focuses to their strategies and as they hope to see ESG-friendly returns over time, understanding how changes would affect their portfolios before the trade is crucial. Firms should seek data tools and feeds which allow them to simulate changes to their portfolio and understand how they affect different ESG scores surrounding their portfolios.
  1. Tracking change within specific holdings – For managers or self-managed owners who wind up taking an activist stake in an issuer and hope to see it improve not only in value but on an ESG front, the ability to access relevant ESG metrics for the organization as well as track change over time will allow them to hold management accountable to the ESG-focused promises they make to their stakeholders.
  1. Reporting and benchmarking – A post-trade view on ESG investment decisions assists investors in taking a big-picture, hindsight look at how they’re adhering to their ESG goals and how they progress over time. With the right reporting approach, they can view rates of return or contribution by E, S and G ratings or view aggregate scoring, as well as track relative performance of each ESG pillar over time to understand the effectiveness of high vs low scoring allocations. Having this kind of big picture view allows them to adjust their investment decisions to bring them closer to achieving those goals.

To keep up with the many pressures that are emerging as ESG becomes a bigger consideration, relying on data to make decisions and to communicate those decisions to stakeholders will be key.

Aside from manager/investor pressure, as ESG remains a focus, regulatory frameworks are likely to emerge, and managers and investors will be ahead of the curve if they focus on how to put ESG data to work now.

Getting ahead of the curve and making ESG a priority in one’s portfolio and operations can’t be achieved without the right tools and partners. With this in mind, institutional investors committed to an ESG-centric future within the industry should find their key partners now.

Paul Fahey portrait

Paul Fahey

Head of Investment Data Science, Asset Servicing, Americas
As Head of Investment Data Science, Paul works with asset managers and allocators to better understand the investment decision process and to improve future decision making. Partnering with expert fintech firms to leverage the power of data, behavioral analytics, investment analytics, and Northern Trust’s peer universe data to drive meaningful improvement to their investment strategy.
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