Skip to main content

ESG Whitepaper Series: Addressing challenges in ESG data management


The modern-day investment manager faces many challenges when integrating and enabling ESG data in the decision-making processes. In essence, these issues are not dissimilar from those integrating other types of security master data, but the key problem is that ESG data is wide and disperse – even amongst the top ESG data providers – and to gather some meaningful insight from it, it has to be combined. Paradoxically, this proves that the challenge to empirically resolve a company’s ESG rating cannot rely on just one source, but equally grows harder with the increasing number of disparate sources one uses. To overcome this one can augment a vendors’ classification and rating data with so-called new vendor’s raw ESG data. The paper introduces an approach for that.

The paper further shows that ESG data is essentially trying to capture information around market externalities and that these translate into a company’s Beta, β. It shows that in the case of negative externalities there is a Beta that captures society’s interests, βSoc, and that βSoc < β. And so, we posit the logic to adjust a company’s Beta downward with a certain percentage in case it scores highly from an ESG perspective compared to its peers.

To make adjustments like these involves organizing the market data centrally within a system that has strong analytic capabilities, can scale, and governs data lineage appropriately. But above all, it should enable business users to explore diverse data sets across all asset classes via a strong user interface and API, whilst giving access to data environment that natively runs Python and R. Spreadsheets, unfortunately, can’t help here as they don’t scale and become increasingly slow and harder to manage when working with such large datasets and structures.