Driven both by increasing investor demand and regulatory compliance, asset managers are now acutely aware of how environmental, social, and governance (ESG) measures are a critical factor in investment strategy and allocations. Once a niche interest, global ESG assets are forecast to exceed $41 trillion by the end of 2022. However, while awareness is high, asset managers keen to deliver a robust ESG investment strategy face a number of obstacles, including a lack of global ESG reporting standards, common benchmarks, and data-gathering protocols. Chief Data Officers (CDOs) across financial services are at the center of an industry-wide transformation centered around ESG data and analytics as firms seek to realize ESG-focused strategies.
The current state of ESG in asset management
Despite recognizing the importance of ESG data in investment decisions, few firms feel they are equipped to handle it. The EY Global Institutional Investor Survey, 2021, showed that while 90% of respondents attach greater importance to ESG performance than they did before the pandemic, over half (56%) of investors rate their current ESG data management approach at low or medium maturity. Beyond performance, the offerings available by these institutions are no longer meeting client expectations, with 77% of those surveyed indicating that investors perceive their investment options to be limited, given so few investments meet their environmental criteria. Audit data is also a barrier: half (50%) of respondents to the survey reported that a lack of forward-looking disclosures places a significant limit on ESG evaluation.
The Problems with ESG Data
Multiple data providers - each operating with its own data sources, frameworks and scoring methodologies - underpin investment decision-making. Without a set of global industry standards to facilitate company performance benchmarking, there is lack of consistency, timeliness, and transparency across the ESG data landscape.
These data inconsistencies can impair both the quality of investment decisions and the level of
1. Lack of robust ESG data for private companies: While public companies are subject to some regulated reporting frameworks, depending on geography, such guardrails do not exist for private companies. Asset managers typically rely on anywhere between two and five different ESG data providers to ensure completeness of quality and coverage according to an EY analysis from 62 of the world’s largest asset managers.
2. Resolution of data hierarchy: While most ESG data is at an entity or company level, investment decisions and trades are made at the security level. This poses a major challenge in accurately matching entities to securities through commonly used market identifiers, relying on a need for manual intervention.
3. Reliability of data inputs: Successful portfolios are built upon reliable data inputs, and at present, ESG data is not considered to be a dependable source. In fact, in EY’s Institutional Investor survey, 46% of asset managers indicated that the lack of real-time data served as a major limitation of their usage of ESG data.
4. Structure of data: Despite the presence of quantitative disclosure metrics and ESG scores, the majority of the ESG data universe is unstructured. This includes policy disclosures from portfolio companies, investment analysts’ notes, engagement details from portfolio managers, and media articles around portfolio companies. Processing and integration of these unstructured data elements are key to building a comprehensive data universe.
5. Frequency of data: There are currently no requirements around the frequency at which portfolio companies release ESG. This means that there is no guarantee that available metrics reflect current conditions, making judgments based on these metrics subject to error.
6. Common data framework: Due to the sheer number of data providers in the ESG data space, each operating with a unique method of calculating ESG scores, it is difficult to consider ESG data at an aggregate level, as frequently these frameworks are not easily aligned with one another, even within a given industry.
7. Calibrated scoring: In absence of a standardized approach and the exclusion of economic, regional, or financial considerations create a major blind spot for institutional investors. Given this, ESG data is most effective when used alongside other criteria and frameworks to evaluate investments and should not be applied directly as an investment strategy, but rather can serve as a complement to one.