Data management is increasingly becoming a central issue in financial institutions' sustainable investment strategies. To encourage the sharing of experiences on this important topic within the community, WeeFin hosted a breakfast event in London on 27 November for around 30 sustainable finance professionals. On the agenda: coffee, croissants... and a panel of experts to take stock of the state of ESG data management as 2025 draws to a close.
Joining Marion Aubert, co-founder of WeeFin, were Frances MacGregor Grier (Head of Sustainable Investment Models, Schroders), Peter Cox (ESG Product Lead, Northern Trust) and Matthew Dodsley (Sustainable Investing & Data Consultant, Phoenix).
The speakers highlighted the challenge facing financial institutions today: managing ever-increasing volumes of sustainability data with human and financial resources that are not growing at the same rate. This challenge is all the more difficult to address as it is compounded by another requirement: data quality. Volume alone does not guarantee that the data will cover all the uses of financial institutions, nor that it will be usable without lengthy processes of aggregation, quality control, matching, etc. This is an issue that asset managers must take into account, as Frances MacGregor Grier points out: “There’s no perfect dataset, and there probably won’t be for years. What matters is being consistent in the choices you make and honest about the limitations.‘ On the asset owner side, the message is similar. ’We're working with imperfect information. The key is to be transparent about uncertainty and make decisions that remain robust even when the data evolves," explains Matthew Dodsley.
As a direct consequence of these challenges, asset managers and asset owners, encouraged by their clients, want greater control over their data. This desire has not escaped the attention of the WeeFin teams: ‘We see a clear trend: financial players want more control over their data. They want transparency, comparability and flexibility,’ emphasises Marion Aubert. To achieve this, closer collaborations with data providers are emerging, but financial institutions are also increasingly turning to technology, which allows for the automation of a large part of data management.
Faced with evolving regulations and increasingly demanding customer needs, financial institutions must also anticipate their own transformation. ‘It's not just about meeting today's requirements. We need approaches that are resilient to change, whether it's new methodologies, new vendors or new regulations,’ emphasises Matthew Dodsley. That is why several speakers reiterated the importance of relying on resilient and adaptable systems. Institutions need systems that grow with them. Today's requirements are only the baseline: assurance, transparency and interoperability will define the next stage.
This observation has long been shared by the WeeFin teams and has dictated the design of our technology platform: modular and adaptable, particularly for the rapid integration of new regulations. ‘Our clients need flexibility. Data models evolve, vendors change, and regulation moves fast. So the architecture has to adapt without rebuilding everything every year,’ Marion Aubert pointed out.
We received a large number of questions, including the two below.
Q: Where do you see biggest gap-in selecting ‘right’ dataset/ in integrating them consistently across platforms/ integrating outputs for investment teams?
A: Choosing the right dataset is essential to ensure sufficient data coverage. But it is then essential to integrate this data with the right processes and tools to ensure that it is usable. The challenge is not just to choose a single suitable dataset, but rather to understand how to use several to cover all needs, while ensuring that the data is consistent and structured.
Q: AI is only as good as the data you provide. Does this impact the product and its capabilities?
A: At WeeFin, as part of our AI Lab programme involving around ten clients and partners, we are currently developing an AI assistant for pre-investment analysis. It is a powerful tool that draws on the user's ‘golden source’ of data to make relevant recommendations and enable them to save up to 90% of their analysis time. Our vision is therefore clearly to leverage the quality of data that our platform is able to offer users in order to make the most of the potential of AI.
See more details on the programme and join it here.
