“Putting ESG data to work” – How financial firms can optimize this rich new investment resource
By Martijn Groot, VP Marketing and Strategy, Alveo
The ESG market is on a strong upward trend, with global assets expected to reach $53 trillion by 2022, according to analysis by Celent. ESG data has come to the fore due to changing investment trends and new disclosure requirements coming soon.
Depending on the investment style, ESG information plays a key role in research, fund product development, external manager selection, asset selection, performance monitoring, client and regulatory reporting. In short, ESG data is needed throughout the chain and must be made available to stakeholders throughout the investment process.
The ESG data landscape:
In this context, the challenge for financial companies is how to harness ESG data and make it more actionable today? The first step on the road is to understand today’s complex and varied ESG landscape. It is a landscape that can be subdivided into three main sub-categories:
Corporate Disclosures: These can be found in the annual report or in specific sustainability information. or are reported via questionnaires sent to companies by companies collecting primary data such as Morningstar and Sustainalytics
ESG ratings: These are essentially expert opinions on the ESG characteristics of companies, given by third parties. Companies involved include RepRisk, Arabesque and MSCI
Sentiment data. These are summary scores based on how a company is portrayed in the news and other publicly available data. Companies involved include Truvaluelabs (FactSet) and Orenda.
ESG information should be standardized, to be able to aggregate company information into portfolio-level information, track ESG criteria against third-party indices, or for external reporting requirements. Companies will also need to develop benchmarks to show: the fund’s performance in terms of ESG criteria relative to the wider industry and relative to competing funds (with a similar risk profile) and the fund’s historical performance in terms of terms of ESG criteria.
Operationalization of ESG data
Data management practices typically begin with improvisation using desktop-level tools, including spreadsheets and local databases. This is gradually being streamlined, centralized, operationalized and integrated into core processes to become business-as-usual (BAU). When it comes to managing ESG data, the investment management industry is in the middle of this process.
Yet today, ESG data quality issues often prevent effective integration into the end-to-end investment operation. Businesses will need to look to solutions that incorporate dashboards that show the provisioning, processing, and completion status of data requirements, as well as an overview of data quality metrics and lineage. complete to show where the declared data fields come from.
Preparation and governance
When it comes to data management and reporting, asset managers must not only meet their own disclosure requirements, but also the data and reporting requirements of their institutional investors. New ESG disclosure requirements lead to increased trade barriers and competition in the asset management industry. Being ahead of the ESG regime adaptation curve requires early operational readiness across the entire value chain by addressing key decision points around operating model and governance, target data and system architecture and effective implementation.
A clear data ownership framework is essential to lay the foundation for a more detailed description of the operating model and data architecture. With respect to ESG data, the data owner has several roles:
- Guarantee the quality and integrity of ESG data
- Specification of workflows for exporting and distributing data via interfaces to the front, middle and back office
- Authorization to publish data or set restrictions on authorized data recipients.