ESG Data Integration Challenges
As investors think about integrating Environmental, Social and Governance (ESG) factors into their investment processes and strategies, the most important consideration is how quickly they can gain meaningful insights from ESG data to identify opportunities and mitigate risks.
This requires investors having access to ESG data that can seamlessly integrate into current workflows and existing foundational data like security master, pricing, reference, corporate actions and more. Since ESG data can come in a multitude of forms and formats, it can take significant time to decipher the data and figure out what is relevant for analysis and meld this data with existing frameworks for holistic analysis. A flexible, but strong data foundation is paramount.
Industry bodies are aware of this challenge and have released ESG reporting standards to streamline and codify ESG data reporting. For example, in November 2021, the IFRS Foundation launched the International Sustainability Standards Board (ISSB) to bring consistency and comparability to ESG reporting standards. To further harmonize the sustainability reporting system, the IFRS will work with the Global Reporting Initiative (GRI) to coordinate their work programs and standard setting activities. Meanwhile, the European Commission has adopted a proposal to amend and replace the current Non-Financial Reporting directive (NFRD) with Inline XBRL in reporting structured ESG data to enable machine readability.
In addition to these efforts for standardizing ESG data, identifiers that don’t change over time and enable interoperability are key foundational tools for seamlessly connecting ESG data with existing data infrastructures so firms can glean valuable insights faster.
ESG Integration Step One: Entity Identification
To analyze a company's performance across ESG factors, investors first need to unambiguously identify the entity in question to understand things like hidden climate-related risks or Scope 3 greenhouse gas emissions. When legal entities, such as investment funds, government entities, corporate events, are defined and tracked using different identifiers, it can be exceedingly challenging for investors or regulators to analyze the ESG performance for a given entity over time since all those different trackers need to be reconciled and mapped back to the underlying entity.
To solve for this, over 200 regulations require or recommend using a Legal Entity Identifier (LEI); and as of July 2022, more than 2 million LEIs have been issued globally. The reason this tool comes so highly recommended is that it clearly and uniquely identifies a given legal entity for the entirety of its lifespan. This, in turn, enables users to link ESG data to corresponding issuers and instruments with greater confidence, across jurisdictions and corporate structures. This is especially critical given that evolving rules and regulations are requiring asset managers to publish entity-level reports on how climate-related matters are accounted for when managing or administering investments on behalf of clients.
For example, in Europe, the Sustainable Finance Disclosure Regulation (SFDR) requires all European asset managers to provide standardized disclosure on how ESG factors are integrated at both the entity and product level. Meanwhile, regulators in the UK, the EU, Brazil, Hong Kong, Japan, New Zealand, Singapore, and Switzerland have begun using the Task Force on Climate-related Financial Disclosures (TCFD) recommendations as a basis for mandatory disclosures. This requires firms to release an annual TCFD entity report on how climate-related risks and opportunities are taken into account. A globally accepted entity identifier, like LEI, enables investors and regulators to identify legal entities in a consistent manner and ensure they can access comparable and reliable ESG data over time.
ESG Integration Step Two: Getting FIGI with It
Precise financial instrument identification is crucial in many areas, however, managing the steadily growing volume of diverse sustainability data adds further urgency to financial firms’ need to achieve high data quality in their operations.
Since firms pull from multiple ESG data sources and then need to analyze specific instruments, which may be extracted from different databases, maintaining a security master can quickly become costly—especially if firms are using identifiers with licensing costs. Compounding this challenge, the same instrument may be issued under different identifiers in different jurisdictions with different classifications and may change over time—making both aggregation and historical tracking difficult.
To simplify this data reconciliation work, a unique identification system like the FIGI (Financial Instrument Global Identifier) can help as it enables interoperability with existing legacy identifiers, while providing an embedded data model for instrument relationships across the different contexts where legacy identifiers may exist. FIGI is a unique, publicly available identifier assigned to financial instruments across all asset classes. FIGI was originally developed by Bloomberg to help solve licensing challenges and shortcomings in data governance, but its metadata approach allows for extensibility of the core model, which helps the broader industry to solve for a variety of different problems, such as those highlighted by the needs of the ESG market.
Key for LEI and FIGI is that they are both open data standards that users can share freely, enabling communication and transparency without artificial barriers from licensing or intellectual property restrictions.
The Future of Identifying ESG Assets
Without data interoperability, unnecessary time, money and resources are wasted on trying to rationalize ESG data prior to it being actionable and usable. Friction exists because of different models being created on top of legacy embedded identification standards that are necessarily split by jurisdictions, asset types, and functional needs throughout the trading and settlement process. Helping remove this friction is the challenge given to Chief Data Officers and their data organizations to solve. Utilizing newer standards based on modern methodologies and open data policies allow data professionals to move away from mapping exercises of the past to data modeling processes of today, and they provide a foundation for better rationalization of the growing mountain of ESG data.
About the Author
Richard Robinson is Chief Strategist, Open Data and Standards at Bloomberg and works with regulators, legislators, and industry leaders around the world addressing data standards issues to create more efficient and transparent markets.
Richard has over 30 years of experience in the financial services industry and has held leadership positions at major global custodian banks, brokerages, and industry utilities, leading transformative projects in data, operations workflow, and messaging.
Richard is also the author of the book, “Understanding Financial Services Through Linguistics” which explores how to improve the creation and application of data, standards, and regulation in financial services through an applied linguistics lens.
He holds an MBA in Organizational Behavior and Information Technology from NYU’s Stern School of Business and a B.S. in Industrial Management from Carnegie Mellon University.