By Martijn Groot, VP Marketing and Strategy, Alveo
The ESG market is on a steep upward trend, with global assets expected to surge to $53 trillion by 2022, according to analysis from Celent. ESG data has come to the forefront due to changing investment trends and upcoming new disclosure requirements.
Depending on the investment style, ESG information plays a key role in research, fund product development, external manager selection, asset selection, performance tracking, client and regulatory reporting. In short, ESG data is needed through the chain and must be made available to stakeholders across the investment process.
The ESG data landscape:
Given this context, the challenge for finance companies is how do they put ESG data to work and make it more actionable today? The first step on the road is understanding today’s complex and varied ESG landscape. It is a landscape that can be subdivided into three major sub-categories:
Corporate disclosures: These can be found in the annual report or specific sustainability disclosures. or are reported via questionnaires sent to firms by companies collecting primary data such as Morningstar and Sustainalytics
ESG ratings: These are essentially expert opinions on firms’ ESG characteristics, given by third parties. Firms involved include RepRisk, Arabesque and MSCI
Sentiment data. These are summary scores based on how a firm is portrayed in the news and other publicly available data. Companies involved include Truvaluelabs (FactSet) and Orenda.
ESG information needs to be standardised, to be able to roll up company-based information to portfolio-level information, track ESG criteria against third-party indices or for external reporting requirements. Firms will also need to develop benchmarks to show: the performance of the fund in ESG criteria terms versus overall industry and versus competing funds (with a similar risk profile) and the historical performance of the fund in ESG criteria terms.
Operationalizing ESG data
Data management practices typically start with improvisation through desk level tools including spreadsheets and local databases. This is gradually streamlined, centralised, operationalised and embedded into core processes to become business-as-usual (BAU). When it comes to ESG data management, the investment management industry is in the middle of this process.
Yet today, ESG data quality issues often still prevent effective integration into the end-to-end investment operation. Firms will need to look to solutions here that incorporate dashboards showing the sourcing, processing and completion status of data requirements, as well as insight into data quality metrics and complete lineage to show the provenance of reported data fields.
Readiness and governance
When it comes to data management and reporting, asset managers not only need to fulfil their own disclosure requirements but also have to meet the data and reporting requirements of their institutional investors. The new ESG disclosure requirements lead to higher market barriers and competition across the asset management industry. Being ahead on the ESG regime adaptation curve requires early operational readiness across the value chain by addressing the major decision points around the operating model and governance, target data and system architecture and an effective implementation.
A clear framework of data ownership is crucial to lay the ground for further detailing the operating model and data architecture. When it comes to ESG data, the data owner has several roles:
- Ensuring the data quality and integrity of the ESG data
- Specification of workflows for the export and data distributions via interfaces to front, middle and back offices
- Authorisation for the publication of data, or definition of restrictions regarding approved data recipients.
