This first publication in our 3 articles series about ESG data integration in Asset Management covers the context (drivers and challenges) and the Acquisition of non-financial data.
The second article will focus on ESG Data Processing (data quality management & enrichment) and Distribution.
The third article of the series will cover the key underlying elements to efficiently manage ESG data integration (strategy, governance, organization, equipment and target architecture).
ESG (Environmental, Social & Governance) is one of the current key topics in the Asset Management industry, as well as in the broader economy more generally. The heightened focus on ESG is primarily fuelled by 2 major drivers: (i) a stronger pressure from regulators and (ii) an increasing demand from the market and investors.
Asset Managers are now expected to demonstrate greater transparency on the way in which they assess ESG factors of investee companies, fund products, and their investment process. Additionally, regulators and investors who have been building their own knowledge and expertise around ESG, and firms must be able to demonstrate that a robust control environment governs their ESG methodology. As a result, firms are having to source, manage, and process a significant volume non-financial data.
This significant increase in the non-financial data volume is driving the need for greater standardisation and scalability whilst ensuring that high quality ESG data is readily available across various functions within an asset manager. Firms need to have the ability to QA and manipulate this data as required for analysis and reporting. The stakes are high for firms as they navigate through significant commercial, financial, and reputational risks to get the right ESG data strategy in place.
The technical setup for ESG data comprises collection, processing, enrichment, and dissemination, while ensuring appropriate controls are in place. All steps are critical to efficiently support an Asset Manager’s end to end ESG activities. Accordingly, implementing an ESG data management strategy requires adequate governance, processes, and technology. In this first article of our three part-series, we explore the technical set up of ESG data more broadly, starting with ESG Data Acquisition.
The successive steps of a data life cycle
ESG Data Acquisition
Collecting ESG data is not an easy task for Asset Managers as this data is typically sourced from multiple external data providers.
Dealing with the data and file formats used by the difference data providers can prove to be challenging. Most commonly, providers still send this data through flat files using data grouping by sets of indicators as opposed to by issuer. The reason for this is that providers still often maintain several internal databases due to their external growth strategies.
Asset Managers may receive dozens of flat files from the same data provider that they should then process by:
- Ensuring that the data provider’s mapping is correct and that application of parent-level data to underlying entities is correctly allocated. This requires double checking the issuers tree structure used against the Asset Manager’s own referential issuers tree structure.
- Grouping all desired indicators by issuer.
- Reviewing any special consideration for certain instrument types, such as Green bonds, to ensure they are handled accordingly.
An example of good practice includes setting up a collection protocol via API to only query desired data (unitary indicators) on an issuer’s scope as defined by the Asset Manager. This technology is still emerging and has yet to be adopted by all data providers.
Increasing the volume and/or coverage of ESG data through the addition of new ESG data providers can further exacerbate issues for Asset Managers. Each data provider has their own communication channels to make the data available, own file formats, issuers tree structures, and issuer information sheets. The same issuer-level data can be different from one data provider to another due to, for instance, a delay in how the provider refreshes its ESG data, different collection protocols from different sources (e.g. survey, form, machine learning) and the data provider’s use of proprietary estimation methodologies and/or models to extrapolate data.
As a result, Asset Managers need to find solutions to properly assess the quality of the data they receive across providers and to check for possible gaps in the provider data before they can aggregate it and process it accordingly.
Certain ESG data providers have acknowledged these data challenges and are developing aggregation solutions for different ESG data collected from several third party providers (referred to as “Aggregators” in the graphic above). Data aggregation enables firms to define prioritisation rules for specific data points, streamline the number of data flows, leverage single issuer tree structure, enable overrides management and facilitate collection and propagation of the ESG data within the Asset Manager’s IT systems.