This third and final publication in our 3 part series covers the key underlying elements of a successful ESG data integration, including setting principles over data governance and data structuring.
Our first article in the series covered the context (drivers and challenges) and the acquisition of non-financial data.
The second article covered ESG Data Processing (with a focus on Data Quality Management & Enrichment) & Distribution.
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.
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.
The successive steps of a data life cycle
Key elements to achieving sound and efficient ESG data management
Setting sound principles to govern data
Data collection, preparation, quality assessment and availability are all essential components to optimising how an asset manager effectively consumes ESG data. In structuring its approach to data, an asset manager needs to be mindful of a few key principles:
- Data prioritization: every single ESG data point cannot be controlled. Some are more relevant and impactful for business’ (for example, ESG data used in the pre-trade investment process)
- Data control and distribution: Data must be controlled and made available for all data consumers simultaneously. It is crucial to define a straightforward and quick process for data collection, review and distribution
- Data use case: All data does not share the same use cases. It is essential to differentiate data that should be integrated in information systems from data consumed for cases like model reviews or specific qualitative analysis
The degree of agility required in the experimentation phase (during implementation) might cause considerable tensions with the IT department and deviations from a costs / business needs / ROI perspective. Therefore, it is vital to build a robust IT architecture based on key guiding principles, which are understood and validated upfront by all stakeholders. This alignment upfront on the strategy will help the different business units involved to dedicate the right amount of time and staff resources, thus improve efficiency.
In addition to static master data, there are two types of dynamic ESG data: raw data (primary data) and computed data (secondary data).
- Primary (i.e. raw) data can be sourced directly from companies (e.g. via surveys, direct company communication, company reports, presentations and public documents) or indirectly (e.g. via media, third-party reports, and investment research) by the asset manager or their data providers
- Secondary data can be sourced from data providers who calculate their own issuer ESG scoring or from applying internal scoring methodologies run by the asset manager. These calculations/scoring methodologies rely on historical data series that are stored and enriched by proprietary calculation engines. Typical use cases of such data include assessing issuer comparability on particular ESG measures, and managing portfolio level ESG specific / sustainability risks, such as portfolio level emissions against transition pathways or low carbon benchmarks
This back-end layer of storage and/or calculation typically relies on an EDM (Enterprise Data Management), a DWH (Data Warehouse) or a dedicated database. This environment is the ESG data golden source and must overcome the numerous challenges mentioned above.
Increasing client and regulatory expectations to implement robust control environments is driving out inefficient manual processes within Asset Managers. Firms that have recognised this are implementing robust processes and tools for data collection, integration, preparation, quality management and dissemination of ESG data within their ESG data integration programmes.
Asset Managers must define clear guiding principles, appropriately scope data requirements and implement a value adding data functionality that offers traceability of analysis and aligns to ever increasing levels of transparency requirements. Fortunately, technical solutions and external players are emerging, which offer accelerators with a strong added value that can be quickly integrated into an Asset Manager’s operational structure.
Accordingly, the topics outlined in this three-part series are designed to support decision makers with the structuring and allocation of resources to their ESG data strategy / integration programmes, and we hope you have found it useful.