For financial services organisations to satisfy investors and regulators, it is essential to have a truly integrated approach to company reporting. In this guest post, Helena Schwenk, VP and chief data & analytics office at Exasol discusses how to appease the community; easy data access and management is essential – in this financial institution’s guide to ESG reporting.
ESG (Environment, Social, and Governance) reporting helps companies improve transparency, hold themselves accountable, and achieve their goals of being more sustainable and socially conscious. With the rise in concern over climate change and the risks it poses to society and businesses, investors, and regulators – not to mention a public that is increasingly environmentally aware – are all asking for financial services companies to submit regular ESG reports.
Some of this rising pressure is now mandatory. Financial services organisations are already required to disclose ESG measures under the Sustainable Finance Disclosure Regulation (SFDR) in the EU, including sustainability risks arising from investments, and sustainability information on all products offered.
This state of play is set to become more complicated in the coming years, with new regulations on the horizon. The EU has introduced plans for its Corporate Sustainability Reporting Directive (CSRD), a new standard where up to 50,000 large public organisations will be asked to produce ESG reporting alongside all companies publicly listed on EU regulated markets from 2024.
ESG-focused investing is also rising in popularity. According to Bloomberg, ESG funds were set to surpass $42 trillion in assets in 2022. Additionally, 85% of investment managers are increasingly considering ESG criteria when making investment decisions, according to a survey by the CFA Institute of over 4,000 investors and 2,800 finance practitioners.
Customers too are concerned about sustainability, and businesses that neglect to consider sustainability may be going against their own best interests. In fact, 76% of consumers report that they’d discontinue relationships with companies that treat the environment poorly.
With all these demands, 92% of companies listed on S&P were filing ESG reports by the end of 2020 – but doubts remain about their effectiveness. 53% of respondents in a BlackRock client survey said that the availability and quality of ESG data and analytics was a barrier to ESG reporting, muddying the waters when it came to further investments in sustainability.
There’s an interesting problem here. With demand for ESG reporting only set to increase, companies need to ensure that they can access, evaluate, and compile the necessary data to make sure that reports are an accurate and meaningful reflection of their environmental impact. That, in turn, requires the tools and the analytics capabilities to deliver the level of detailed insight that will be required in future.
Challenges with legacy tech
Given the need to look forward, it’s no surprise that the most common obstacle here is the limitations of legacy tech. A study of 1,300 organisations across 13 different countries by Workiva and Coleman Parks found that more than half (55%) don’t think they have the sufficient tools for effective ESG reporting. 30% specifically stated that their legacy tech was incompatible with newer technologies.
ESG and sustainability are data-intensive, requiring sophisticated data analytics to gather data from departments across the business, including operations, supply chain, and the downstream value chain. This process is reliant upon modern technologies combining to form a cohesive data analytics stack. In too many cases, legacy technologies isolate individual departments, causing silos of data.
While there is growing momentum of decentralised data architectures and tooling seeking to address this, it can still be difficult to find and transfer the right data, from so many different platforms and in so many formats, over to a centralised database.
These technologies spawn their own manual problems, too. Collecting and analysing ESG data is a complicated task, and many organisations still collate spreadsheets manually from a variety of different sources. There can often be confusion regarding the parameters of the data set to be analysed, the exact formatting of the resulting report, and how the disclosure process is managed – with the need to satisfy a diverse set of stakeholders with this data adds yet another layer of complexity to the fold too.
Getting smarter with your ESG data
These problems are all different dimensions of a fragmented approach to data analytics. Banks and other financial institutions need an integrated real-time reporting approach that will ensure investors meet regulations and make the most of efforts to improve sustainability. Developing this process will allow them to report accurately, meaningfully measuring their performance against sustainability benchmarks and clearly communicating their ESG achievements to external stakeholders.
The first step in this process is examining the company’s data architecture. Every company needs to develop a structure for the flow of their data that prioritises transparency and efficiency, drawing data from every department in the business. This ensures that data is accessible and can be combined and integrated quickly and accurately.
The next step is checking that data quality is up to par – a perfect data architecture is no use if the data being fed into it is meaningless. Data governance is needed to improve the quality of the data and to provide the relevant controls to improve transparency and build trust.
To effectively report ESG data, the data management layer in the database also needs to be easy to query and analyse with appropriate data tooling for the task at hand. Data managers and technicians will be able to interrogate data sets with ease in this scenario and clean up any data that needs some care and attention. That same toolset will also help to create up-to-date summaries and data visualisations which can be shown to stakeholders and improve the overall reporting experience.
Finally, integrating internal data with external data from trustworthy third-party providers can be a key step for ESG data management and reporting because it provides necessary context to benchmark against. Data from ESG ratings firms can be used to create a sustainability score or report that can easily describe the progress made so far against the wider industry or organisational goals.
Opportunity in ESG reporting
Understanding, querying, analysing, and acting upon ESG data is a way for financial services organisations to improve their business, manage risk, and stay aligned with increased interest from consumers and investors in ESG initiatives. For example, gaining insight into the investment landscape can help funds add sustainability-focused businesses to their portfolios or help central banks or regulators ensure financial systems are able to withstand the negative impacts of climate change.
It’s time to invest in a data analytics platform that has the power to break through data barriers – accelerating time-to-insight and supercharging evolving ESG data journeys.
About Author
Helena Schwenk serves as a key member of Exasol’s Chief Data & Analytics Office, where she collaborates closely with data leaders, industry analysts, and media influencers to navigate and evaluate the challenges, opportunities, and best practices shaping adoption of data and analytics technology.
She has worked on various research studies covering cloud adoption, the data literacy gap and the critical role of Chief Data Officers in helping organisations maximize the value of data and analytics investments. Her expertise and deep understanding of the rapidly-evolving data landscape gives her a unique perspective on the market. In turn, enabling her to provide valuable and trusted insights that help individuals and organisations stay ahead of the curve.
Before joining Exasol, Helena spent 18 years as an industry analyst with a focus on big data, business intelligence, and analytics. In addition, she spent six years as a practitioner, where she developed, led, and managed BI and data warehousing teams.
