What’s driving investment and wealth managers’ ESG strategies?
Data connects E, S and G
TO THE POINT
Takeaways to consider:
- Set clear processes and controls across the ESG data lifecycle, from collecting and ingesting, to managing and modeling, mapping to the appropriate frameworks, and publishing to end-users or your clients.
- Collect and confirm ESG data from material public companies before use; address data gaps in private markets by engaging directly with investee companies or using smarter technologies (e.g., artificial intelligence).
- Seek diverse, alternative data sources to improve the baseline understanding of the risks faced by both individual assets and the whole portfolio.
- Establish a cohesive data-gathering plan, collate separate requirements, and identify overlaps to minimize and streamline information requests.
- Nominate clear data owners and assign data stewardship to provide ongoing oversight.
- Establish ESG data frameworks to provide standardized results across diverse asset classes at both the asset and portfolio levels.
- Create clear connection points and leverage synergies between data and corporate strategies.
Data is central to investment and wealth managers’ (“managers”) environmental, social and governance (ESG) strategy. However, collecting and managing the right data can be a “wicked problem”: broad, complex, and with constantly evolving business and regulatory requirements. The effort to address one challenge may uncover or even create another.
But what does this mean for you? The implications vary across the three ESG pillars.
Environmental challenges are market wide, affecting assets both publicly and privately owned. They have created a systemic need for data.
By contrast, the consideration of an investment’s social impact, the need for managers to address it and the data required remain immature at the business and regulatory levels.
Finally, governance is overseen at the corporate level. Managers are now shifting their focus to linking corporate and enterprise data governance, and how this can help establish a more streamlined and robust framework for managers to execute their investment strategies.
This article addresses the E, S and G data challenges by exploring the data lifecycle journey and outlines a series of takeaways for your ESG strategies.
E: private market data is tough to find, even when you know what you’re looking for
While public market investors were the first to focus on ESG concerns, private markets have now caught up.
Standards are finally emerging, albeit slowly. But while requirements are evolving, it is difficult to access ESG data. Limited information on private companies’ climate positions is publicly available or easily accessed. Moreover, the data provided by companies themselves is often immature or incomplete. While investors can look to third-party data providers, this process is often costly with patchy results.
Accordingly, analysts and managers are scrambling to find new, alternative data sources(i). These should be agile, accurate, reliable, comparable, diverse, granular, and forward-looking. Examples of alternative data sources to ingest or import are:
Real-time traceability data(ii) that includes information on portfolio impact (e.g., product passports, carbon footprints and social criteria). By providing transparency, managers build trust and confidence in sustainable products and services.
Environmental geospatial data (i.e., time-based data related to a specific location) that is integrated into the data framework to gain insights on initial and ongoing environmental impacts.
Forward-looking data (i.e., data points that seem disconnected or irrelevant but form a useful input for analysts when connected, or smart web scraping of information that is hard or costly to find through traditional sources) to build algorithms and artificial-intelligence-driven profiles for private companies.
S: go the extra (social) mile
Managers mainly focus on the E and G in ESG since both benefit from relatively accessible and measurable data. By contrast, quantifying the social impact of investments is a significant and complex challenge, with the lack of standardized social metrics and well-defined regulatory requirements leading to confusing S reporting.
Nonetheless, the S needs to be considered as much as the E and the G. Academic research(iii) has shown a positive correlation between firms’ social and financial performance. Managers should examine how companies measure social objectives and monitor them.
The EU Social Taxonomy has suggested three social objectives:
1. Decent work for employees along the value chain;
2. Adequate living standards and wellbeing for consumers; and
3. Inclusive and sustainable communities.
While the EU has not yet specified regulatory requirements for data, managers will be held accountable for the impact of their investments along these social objectives.
For example, are investee companies supporting their employees’ health and wellbeing through absence and churn management processes? Managers could monitor changes in key performance indicators (KPIs)(iv), such as staff turnover, training and qualifications, absenteeism rates and reasons, and the workforce’s maturity, and document the evolution over time.
To prevent “social washing” risks(v), managers should actively engage with investee companies to collect data points that map to these social objectives. For example, oil and gas companies should mitigate the risks of job losses in oil-dependent cities like Aberdeen as they transition to renewable energy
Managers should also gather information on the community impacts of investee companies’ activities. These impacts have historically either been wholly overlooked or minimally considered in the desire to produce financial results.
G stands for governance: data as well as corporate
Managers have realized the importance of ESG data and consider it a valuable commodity, not only to cater to regulatory requirements but also for investor reporting, product development and alpha generation.
Nonetheless, when it comes to the G, the most-used metrics focus on traditional concepts, such as corporate governance, business conduct, risk management, supply chain management and materiality policies. Few metrics explicitly link the concept of governance to data governance.
The increase in data sources and volume, both internal and external, have created operational challenges for ESG data governance across all steps of the data lifecycle. But the most critical challenge is how to aggregate and transform data while maintaining consistency and quality.
Non-standardized data inhibits managers from assessing ESG-related performance and may lead to greenwashing risks. Therefore, ESG data governance and associated data quality control are key. To meet evolving regulatory and client requirements and improve current data governance processes, managers may seek to enhance their current tech infrastructure.
A better ESG data governance process: action points in the ESG data lifecycle
Managers need to set goals for their ESG data lifecycle across four stages:
1. Collect and ingest data from various sources for both public and private investee companies;
2. Normalize data, and build a data model that aligns with your operational model;
3. Map the data to your appropriate frameworks and processes; and
4. Analyze, approve internally, and publish to internal and external end-users, i.e., regulators and investors/customers.
Table 1 lists the actions that managers can take at each step in the ESG data lifecycle.
Collecting and ingesting data
While every company has both positive and negative E, S and G impacts, there is a huge divergence in ESG ratings from vendors, with discrepancies in measures, scope and weights. While variations in manager’ preferences around scope and weights are acceptable, divergence in measurement is problematic(viii). Given their fiduciary duty, managers must choose their data providers carefully and be ready to explain why they have made certain choices to all stakeholders, from employees to customers and regulators.
Managers should tailor the scoring methodology to their business model. They should make a cohesive data-gathering plan, collate separate requirements, and identify regulatory overlaps to minimize and streamline information requests from investee companies. While managers can collect data directly from private companies, for public companies, they can use proxies from modeling approaches and incorporate existing information from entity structures.
For material companies, ongoing updates and confirmation should be incorporated into an incremental update to the active engagement agenda in the operating model.
Managing and modeling data
A sound data-governance process is required to effectively manage the data collected. This should include a single platform that standardizes and consolidates data from various sources, and a well-designed data model flexible enough to incorporate new data and extend the database. Any new data should feed into the same platform so all stakeholders can access the same information.
The rapidly broadening regulatory requirements for market data—from indexation, valuation and pricing data to newer data such as geospatial, sentiment or cardinal emotion—are so complex that few participants can understand them. This naturally leads to a layer of review and oversight, which is not intuitively achievable by managers today. Therefore, education on these key measures and how to use them is essential.
From a data perspective, this requires a clear nomination of data owners and stewards who can provide the ongoing steer and oversight of the data. Ownership should not lie with risk and compliance nor with sales and trading. Instead, the business should own it directly, with the technology team as the enabler and the data management team as the steward.
The enabler needs to collect and ingest data from various sources, while the owner and steward should collaborate to set rules for both data quality and the monitoring process. The steward should also assess data fields’ criticality and monitor the process and critical data fields over time to ensure all data is aggregated promptly and accurately.
This data governance structure can ensure the information is correctly held and the data “use-case” is appropriately measured. The ability to fully automate this process and provide it to the decision-makers heavily relies on controls and high data quality. Data governance should also be embedded in the operations model with assurance.
Mapping, analyzing and publishing data
A broad range of disclosure standards and frameworks has been developed, where managers can organize the data by mapping it to the appropriate framework(s). Table 1 lists some examples of widely used ESG frameworks. While no existing regulatory framework exists for the S, an alternative is to map data to companies’ social objectives.
After documenting the data, companies need to publish how their metrics have evolved over time. An analysis framework is also required to access the datasets and gain actionable insights. New tools and technologies can help in extracting and delivering insights at scale.
For example, many public companies have been proactively disclosing information through detailed sustainable reports and newswires using NLP or sensitivity analysis. Processing reports with NLP can retrieve information directly and promptly, and compare and assess vendors’ or internal ratings. Managers should centralize the most accurate view of the data once an update is collected or overridden.
While managers are free to follow their own procedures, a common framework is necessary to facilitate and align the E, S and G to support decision-making and reporting.
[i] CFA Institute, AI pioneers in investment management, September 30, 2019.
[ii] We appreciate Andrew Matthews, Deloitte and Andrew McNeill, Deloitte’s content contribution on traceability data.
[iii] Joshua D. Margolis, Hillary Anger Elfenbein and James P. Walsh, Does it Pay to Be Good...And Does it Matter? A Meta-Analysis of the Relationship between Corporate Social and Financial Performance, March 1, 2009.
[iv] The European Federation of Financial Analysts Societies Framework, KPIs for ESG, 2009, provides managers and companies with recommended KPIs within the framework of an existing performance communication, e.g., financial reporting, management discussion and analysis, corporate social responsibility reports, and the Global Reporting Initiative's G3 Guidelines.
[v] Etica Sgr, Social washing: What is it and why could COVID-19 be making it worse?, July 3, 2020: “Social washing can be defined as a practice aimed at improving a company’s reputation through social responsibility initiatives which are not effective or, in the worst cases, under the guise of social responsibility but with the goal of economic return.”
[vi] Rick Lacaille, Anna Bernasek, Karen Wong and Phil Kim, The future of ESG: Supplying the demand, State Street, June 2022.
[vii] J.P. Morgan Asset Management, ESG social factors: Accessing the “S” in ESG, April 2022.
[viii] Florian Berg, Julian Kölbel and Roberto Rigobon, Aggregate Confusion: The Divergence of ESG Ratings, Forthcoming Review of Finance, August 15, 2019.