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Leveraging ESG Data to Grow Your Business

 
 

Insights Through Data | Lloyd Chapin |
02 November 2021

ESG, or environmental, social, and governance, investing is about building a more ethical investment portfolio. A portfolio that aligns with an investor’s goals regarding climate change, the protection of individual rights, and the promotion of equality. ESG investing is a force, a “megatrend”, expanding in a wave across the globe that impacts governments, businesses, financial institutions, and individuals, all of them attempting to understand how and to what extent change is necessary to protect and promote both citizens and the planet.

ESG investing presents specific challenges to financial institutions. How can they create investments that align to ESG goals? Who sets these goals? How is success measured? How can they ensure transparency about their investment decisions? And, ultimately, are the ESG investments effective in promoting change while providing investors with the expected return? A key to solving these challenges is data: collecting it, storing it, ensuring its traceability, and making it easily accessible. A robust, flexible ESG Data Architecture is a fundamental part of the solution.

And the ESG megatrend continues to expand. According to Blackrock, ESG investments are growing so fast that it is likely to become a $1 trillion investment category by 2030. As a result of this rapid growth, regulators are becoming focused on establishing standards and definitions for ESG investing. And increasingly, investors are requiring better accountability for the ethical and environmental impact of their investments. As a result – whether you are an investor seeking to better understand the impact of your investments, a fund manager developing ESG products, or a company seeking to improve your ESG score – having a comprehensive ESG Data Architecture that can evolve with the rapidly changing ESG sector is critical.

Key takeaways for your ESG Data Architecture

Deploying a robust and flexible ESG Data Architecture will enable you to do more than just meet the minimum client reporting needs and regulatory requirements. It is an opportunity to differentiate yourself from your competition by providing additional value to your clients. Differentiating your offerings and products, providing clear measures of their characteristics, and bringing them quickly to market will empower your business to grow as the ESG trend accelerates.

At Endava, we have been successfully delivering and enabling complex data engineering solutions for investment and wealth managers, consumer banks, sales and trading organisations, and investment banks – including ESG data solutions. Through these experiences we have gained many insights. In the following, I will share some of our key observations.

Requirements are rapidly evolving

Levering your ESG data is a multi-year journey where data and reporting requirements are ever-changing. The challenge is designing and building a solution today that will be effective and efficient for many years to come. Institutions need to focus on deploying a Data Architecture using modern data technologies that support strong data reporting and visualisation, high performance, and low maintenance costs.

A data taxonomy is key

Identifying the reporting elements, documenting their relationships, and confirming their source or means of derivation provides the foundation for the reporting framework. Without a common set of naming conventions and hierarchy classifications as well as data traceability, institutions will be challenged to provide the transparency necessary to satisfy both investors and regulators. In addition, the organisation will struggle internally with a common understanding of the scope, meaning, and purpose of the data. Agreeing on an ESG data taxonomy is a critical first step.

Client self-service is a must-have

As a concept, ESG reporting is not new. However, the sources of the data, its uses and applications, and the regulators’ requirements are continuously evolving. Meeting the specific ESG data needs of clients in general reports will be extremely challenging. The answer is to provide clients with their own access to a reporting capability, allowing them to view custom reporting data.

Mastery of data granularity

When defining an ESG taxonomy, understanding the data elements and their granularity requirements is important. For example, ESG rating data is widely available, but what is behind that rating? How was it derived? Is it an accurate rating? Institutions need access to the data components and values that make up the ratings, such as detailed water usage metrics, minority hiring policies, countries of operation, etc. Institutions must have the ability to work with the data at the base level, the granular level, and that ability requires implementing a robust data architecture.

Understanding the materiality

Obtaining data is certainly a challenge, but determining the usefulness of that data and its materiality to clients is an equally difficult problem to solve. As noted, ESG data and ratings abound, but what is applicable and useful of the data, even the most granular data? What is the context of the data point? Knowing a carbon dioxide emission number is data; knowing a carbon dioxide emissions metric in the context of other emissions and consumption measures is actionable information. An institution’s data architecture must support sourcing the necessary data points, storing them in context, and reporting them in relation to other data.

Do not forget data cleansing and enrichment processes

Given the diverse range of data sources and the wide variances in data reliability and materiality, automatic and manual data enrichment and data cleansing processes must be put in place. A diverse set of ESG data sources is already available, and new services continue to be developed as needs are better understood. However, it is inevitable that institutions will need to define and implement their own manual and automated processes for deriving additional data value. These data manipulation processes and tools must be developed in a manner complementary to the larger ESG reporting solution, using tools that ensure low maintenance costs.

Design your ESG reporting framework to be flexible and robust

Building a solution to meet the short- and immediate-term requirements is no guarantee that future client needs and regulatory mandates can be met. As a megatrend, ESG investing will present unexpected and surprising challenges. Regulators will develop reporting that is unanticipated. Clients will expect data that is not readily available. Therefore, institutions must invest in adaptive and flexible data engineering capabilities.

Conclusion

Like any market force, demographic trend, or disruption in the market, ESG investing will offer well prepared participants the chance to differentiate themselves from their competitors. Some firms will be able to understand customer needs more quickly than others. And some of those firms will be able to take advantage of their better understanding to develop new product offerings and services. An even smaller set of firms will also develop the ability to comply efficiently with the rapidly developing regulatory requirements. Underlying these abilities, there must be a flexible and robust ESG Data Architecture capable of accepting, storing, deriving, and reporting ESG data.

Lloyd Chapin

Senior Delivery Manager, Banking & Capital Markets

For more than three decades, Lloyd has been helping organisations from various industries improve their performance through the application of technology-enabled busines capabilities. For example, he has helped investment banks achieve greater straight-through-processing while meeting new regulatory reporting requirements. Lloyd also has supported trading organisations in better understanding and monitoring their operational risk, and he has assisted energy companies with their efforts to digitalise their customer interactions. When not in the office, Lloyd enjoys staying active, whether it’s fishing, racquet sports, or going for a run.

 

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