ESG, or environmental, social, and governance, has become inextricable from your business and, as a result, having a robust ESG Data Architecture is an imperative. We offer guidance on how to get started or continue to build out your ESG data solution.
2022 is shaping up to be the year when regulators will issue numerous new ESG-related rules. 2022 is also likely to see the rise of the conscious consumption trend accelerating, as customers put an ever-greater focus on a company’s sustainable and ethical practices.
In the EU, the Regulatory Technical Standards (RTS) issued under the Sustainable Finance Disclosure Regulation (SFDR) are likely to go into effect by mid 2022. In the UK, the Financial Conduct Authority (FCA) published an ESG discussion paper last November in alignment with the COP26, the UN’s climate change conference. In the US, Gary Gensler, the head of the Securities and Exchange Commission (SEC), has indicated that new rules are likely to be issued this year. And in China, who has been seen to lag efforts to promote ESG in the past, the China Securities Regulatory Commission (CSRC) has now proposed ESG-related reporting rules as well.
The conscious consumption trend and its focus on sustainability continues to grow. Surveys continue to show sustainability as a prominent issue for more than 75% of buyers. Society is voting with their dollars, pounds, euros… The pull of market demand is working together with the push of regulations to direct companies toward a future attuned to sustainability and moral stewardship. ESG is becoming a proxy for good management. As a result, a robust ESG Data Architecture must exist if management is to succeed.
GETTING STARTED ON AN ESG DATA ARCHITECTURE
Endava has worked with our clients to design and implement ESG Data Architectures that enable rapid growth, accommodate changing requirements, facilitate flexible reporting, and ensure data security. Taking a strategic approach to enhancing or building their data architecture is a challenge for most organisations. Our experience has shown that there are several key steps necessary to succeed.
Ensure both top-down and organisation-wide support
If ESG is, indeed, a proxy for good management, then the entire organisation must be inextricably committed to being a sustainable and ethical institution. Management must understand and lead the organisation to recognise that ESG enables top-line growth, lower costs, elevated morale, and optimised returns. Employees must feel empowered to execute the direction set by management. There must be a mutual understanding of what is possible and necessary.
And because good management also requires access to good data, the entire organisation must have access to and take ownership of the ESG data. Determining data ownership can be difficult. For example, should it lie with the risk function, a function which often has ultimate responsibility for compliance? Does it belong with the sales or the trading groups because they will be using it for commercial purposes? The responsibility for gathering and maintaining this data has similarly challenged both corporations and financial institutions. The tension of having to get your own house in order before pronouncing it to the rest of the world…
Evaluate and select multiple data providers
Building an effective ESG Data Architecture starts with having the right data sources. Reviewing multiple data providers and selecting several is a good first step. Consideration must be given to current, known requirements but also to the ability of the data providers to meet future needs as requirements change and evolve. Other aspects of the data sources must also be considered, like access to data on both public and private businesses. In the realm of ESG data providers, the number and frequency of acquisitions of data services makes selecting the right set of providers a challenge.
Understand the key components
Every data architecture has several critical components. The enterprise must understand their detailed requirements in the context of each component. What are the minimum requirements in terms of data quality? How will the transparency of the data be facilitated? What are the requirements for integrating the data into the enterprise? Finally, what is the model for ensuring the proper governance of the data? For ESG data, these questions are especially difficult to answer because so many distinct parts of the organisation have a need for that data.
Design a data architecture focused on interoperability and integration
What tools and systems will be used to ensure the data is available across the entire organisation? The data governance model must be enabled by the right set of tooling. The size of the data sets and the complexity of the data relationships requires technology solutions to ensure enterprise-wide access to the data. Numerous software packages and cloud-based solutions are available that address the barriers to achieving interoperability and integration.
Ensure the use of effective data query tools
Pick flexible data query tools. Well-sourced data, managed through effective governance and stored in efficient repositories, is of less value if it cannot be retrieved quickly and analysed effectively. Attention should also be paid to the roles of the data users. Access by external parties like customers and vendors is as important as the internal user community. Data tooling is another area where numerous software solutions and cloud services exist to help resolve the challenges.
Design and build in secure access form the start
Data security must be designed and built into the data architecture from the very beginning. Multi-layered protection of the data that allows for various data access roles or personas cannot be easily added later or “applied on top”. Data security must be built into every component of the data architecture.
Clients and customers are voting with their money as they turn to companies that conduct their business with a focus on sustainability and ethical practices. Regulators are steadily establishing the rules for how sustainability and ethical behaviour metrics are to be reported. This combination of “push and pull” forces means that companies must have an ESG Data Architecture capable of acquiring, managing, and reporting large, diverse data sets.
Our experience in designing and implementing data solutions shows that there are certain key actions to be taken. These include ensuring a strong commitment to operating according to ESG-oriented principles, evaluating and selecting fitting data providers, understanding the key components of a robust data architecture, bearing in mind interoperability and integration, having flexible data query tools, and, finally, designing data security into the solution from the beginning and on multiple levels.