Financial services have needed to put an increased focus on environmental, social, and corporate governance (ESG) over recent years and 2024 looks set to only add to this. As firms look to meet rising regulations, how important is data collection.
In just the first two months of the year, multiple countries around the world have already made a number of developments related to ESG practices. China’s major stock exchanges recently introduced fresh sustainability reporting guidelines for listed companies, the UK’s FCA launched a industry-led working group focused on bolstering capabilities in sustainable finance and Malaysia is set to host a public consultation regarding the integration of the sustainability disclosure standards. The biggest developments have come from the EU. Its most notable impact on ESG this year has been the implementation of CSRD and ESRS, which both went into effect on January 1st 2024.
The heightened regulatory landscape has made ESG practices mandatory, rather than optional. The only way firms can ensure compliance with ESG regulations, particularly the reporting standards imposed through CSRD, is through improved data collection.
Martin Hartley, CCO at IT and business consulting company emagine Consulting, stated that data collection is vital for companies committed to sustainability. He said, “If data is structured well and favours the problem you are trying to solve, it will be a lot easier to prove credibility. Well-structured data allows an organisation to build a solid ESG strategy as they can identify areas that require focus. The concept of garbage in, garbage out is relevant when it comes to ESG as you can’t create anything tangible with poor data.”
As mentioned, CSRD and ESRS are regulations that will require firms to have effective data management processes. In order to accurately complete their sustainability reports, firms will need to ensure compliance teams can easily locate all the relevant data.
One of the ways to upgrade data collection is through the implementation of AI. “It removes the leg work, improves the quality of data and streamlines the processes for data collection within a business,” Hartley said. “AI can spot patterns in data too, which can highlight key areas of focus and be a helpful tool in identifying pitfalls. Predictive analysis helps stakeholders see progression and regression in the data too, so it can be good for communication. “
Hartley has noted a number of common pitfalls that firms find themselves in relation to data collection. One of the biggest is inaccurate data collection or a lack of structured data. similarly, a typical problem is having data silos. Siloed data means there is a lack of collaboration between departments, which can mean a lot more manual workloads for compliance teams completing reports and the chance for important data to be missed. By having siloed data, unstructured or inaccurate data, the effectiveness of ESG processes are impacted, whether that is the teams responsible for meeting compliance, or those hoping to get insights into ESG data to find new business opportunities.
Another pitfall Hartley has seen is a lack of buy-in from senior leaders, which is impacting the adoption of technology that would improve the processes. He said, “I also see issues with businesses not having the essential buy-in from senior leaders and where there is a lack of commitment to ESG, issues begin to unfold. Everyone in an organisation needs to be on board with the proposed ESG strategy so that they understand the leg work required. This often involves teams committing to the process of collecting and organising data optimally.”
Data collection is vital for ESG processes and firms need to ensure they are getting their processes up to scratch. Hartley concluded, “Firms should be using the tools available to them to streamline data collection processes, such as utilising AI. Ensuring all stakeholders are on board with the strategy and goals will make the process much easier and this can be achieved through effective communication.”
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