ESG in the asset management industry: The CDO’s role

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.

The CDO’s role 

A CDO’s overall role is to ensure that their investment firm and asset managers are equipped with consumable and actionable data, including ESG information, in order to make their investment decisions. In the absence of common metrics, it falls to CDOs to deliver a customized ESG platform that has durable data capabilities that target scalability, simplicity, and efficiency. For many CDOs, this means partnering with an ESG expert provider, someone who can navigate the landscape while delivering a tried and tested architecture.

Successful platforms require a methodology to address and overcome known concerns with ESG data, and the functionality to capture the desired benefits. In addition, seamless integration with the existing applications’ landscape and data infrastructure is crucial for minimizing operational disruption.

It’s important to note that ESG data providers range from niche to full-spectrum data packs. However, data around ethically viable and sustainable operations is currently provided in various formats and requires being harmonized by end-users. This requirement can be met through a data product design that is mindful of the data management capabilities needed.

To correct for known data concerns and to capture the benefits of ESG data integration, an ESG data platform should have the following key elements:

1. Trusted ESG data central: To rapidly onboard and standardize ESG data across multiple internal, public, and third-party sources, an ESG solution should have interfaces capable of synchronizing disparate data and hosting it within a common data model. Additionally, it must have an entity-resolution mechanism by which ESG data is integrated and screened to ensure the highest-quality and fit-for-use labeling. It is necessary to both monitor and govern the sourcing, quality, curation, and consumption of the data.

2. Common data taxonomy and model: Incoming data must weave into existing operational data, identifying potential overlaps and gaps where applicable. The development of a common data model by which to infer ESG performance at an aggregate or portfolio level is necessary for optimal ESG integration.

3. Artificial Intelligence (AI)-driven portfolio analytics: The ability to leverage AI in the analysis of ESG data is a crucial differentiator for firms that successfully harness ESG data. An AI-driven data platform to provision proprietary ESG scoring, real-time portfolio simulations, scenario analysis, sentiment scoring, and fund modeling can enable investment professionals to seamlessly employ the benefits of using ESG data in their investment decisions, while avoiding potential damage to their portfolios.

4. Actionable reporting and insights: To deliver a variety of use cases across ESG client reporting, ESG investment compliances, stewardship, regulatory reporting, and ESG disclosures, an ideal solution should have a simple user interface upon which to build reports and apps. It should also contain the ability to select the necessary regulatory framework (e.g., SASB, etc.) for a given reporting or analytics purpose. The ability to benchmark and report company metrics within a given industry can also add a competitive advantage in the investment decision-making process.

5. Engagement data workflow: Firms should be able to capture client interactions and ESG engagement in order to better understand strengths and pain points, elevating their overall performance. Successful integration of ESG into current processes would thus require a workflow component that enables and improves ESG stewardship in order to generate a larger impact for clients and portfolio companies.

ESG data requirements will vary from firm to firm: a credit alternatives firm will have very different needs to a large-scale institutional asset manager, for example. Ideally, CDOs need to adopt a data solution that can be customized for each need across their organization.

Conclusion 

Integrating ESG into investment decision-making is a rapidly increasing need, fueled by investor demands and increasing regulation. Data and its stewardship lie at the heart of this transformation, putting CDOs front and center.  With numerous data providers operating with different frameworks and scoring methodologies, ESG data is plagued by a lack of consistency, timeliness, and transparency, forcing asset managers to base their investment decisions on data that may be conflicting or incomplete. As such, CDOs are under pressure to address and overcome their firm’s ESG challenges through development of a targeted data platform. Such a platform must simultaneously act as a trusted data center, while offering a common data taxonomy and model, AI-driven portfolio analytics, a simple user interface to deliver actionable reporting and insights, and an engagement data workflow mechanism to ensure a client-centric feedback loop. Building such a proprietary ESG data platform reduces dependency on third-party data providers, leading to a reduction in risk and an overall improvement in investment decision-making.

The potential for ESG in asset management

According to OceanTomo, more than 80% of a company’s market value is now comprised of intangible assets, including intellectual property, market share, brand awareness, goodwill, and company perception, particularly around its impact on society and the environment.

This presents an important opportunity to enhance current asset management operations with ESG data, which can be encapsulated in the following primary areas: 

ESG Funds and Portfolio Construction: Integrating ESG metrics into new and existing funds can help firms generate new revenue streams, address the market demand for impact-oriented portfolios, and benefit society at large.

ESG Investment Research and Benchmarking: The guiding principle of “build for difference, buy for parity” has led many buy-side firms to pursue investment-oriented ESG research capabilities focused on ingesting data of all types

ESG Risk Management and Portfolio Compliance: By reviewing sustainability policies, performing ESG risk evaluations, evaluating industry restrictions, and screening, asset managers can more effectively manage risk within themselves and for their stakeholders.

ESG Client Reporting: In automating reporting templates, providing reporting flexibility and assuring the reliability, utility, and traceability of data, firms can enhance their client experience.

ESG Stewardship, Engagement, and Influence: Asset managers can influence both public and non-public assets through an increased emphasis on ESG metrics, and by requiring portfolio companies to focus on their material ESG factors 

Regulatory Reporting: Strong trust and traceability in data is required for buy-side firms to address the rapidly evolving regulatory landscape, unlocking the full potential of ESG performance. 

Top of the House Reporting and Portfolio Analytics: Integrated ESG data utility supports customized internal ESG reporting, including compliance to firm policies, social commitments, and sustainability objectives.

These outlined values have already begun to be realized by numerous leaders in the asset management space and across the broader realm of financial services. ESG investing will soon no longer be considered a separate investment method, but rather a critical part of standard investment practices.