Facing data challenges
Financial institutions need a critical, unbiased, and transparent view of their ESG performance.
It is important for presenting a long-term view and avoiding misconceptions of deception and greenwashing. However, this is difficult to achieve as ESG performance in the commercial sphere has never been prescriptive. It has always been an incidental outcome of efforts, voluntarily pursued and disclosed. The reported sustainability impact is highly subjective, with performance data submerged in glossy text-laden reports that are open to interpretations and have less traceability. The absence of quantifiable and standard data further poses challenges of comparability, agnostics, and aggregation difficulties in terms of:
• Tackling non-performance in ESG
• Formulating sustainable strategies and plans for the future
• Providing investors with a true picture
• Managing risks effectively
An increase in new reporting dimensions and a plethora of agencies in the fray have only complicated the domain. Rating agencies use proprietary and hardly comparable methods to formulate ESG scores, resulting in apples-to-oranges comparisons while investing in funds, assessing lending proposals, and understanding the quality and ESG risk of assets. Data is either piecemeal in its lowest granularity or wholesome (aggregate or pillar level score). Today, bankers, investment analysts, and underwriters are interested in getting a custom handle on the ESG information of clients, customers, companies, and suppliers. Their focus is to analyze the data with their private risk parameters and analytical dimensions limited to their set of client profiles rather than getting bulk third-party ESG data topped with analytics bias. The following aspects further amplify the challenges:
Disparate data sources
ESG data sources include self-published reports of companies; commercial or subscribed data from data aggregators, rating agencies, and other industry and regulatory organizations; and social media. The primary need is a defined set of criteria to understand the authenticity of the data and qualify it. This is important, as at times, data from even a circ*mspect source can bring down the credibility of data in investment decisions, asset management, and underwriting.
Poor data quality
Data is present in formats ranging from quantitative reports to qualitative commentaries, making it non-hom*ogeneous in terms of representations, units of measurement, and methodologies adopted in derivations. Data is highly relative but not always absolute. Companies often report data that is not time-synchronized to be comparable within their operations or with peers. ESG data feeds from social media further complicate the situation. Furthermore, data is not available from private companies, which form a sizeable clientele of financial institutions. This calls for the abstraction of data from macro-industry statistics, peer comparison, and the like. Without high-quality and authentic ESG data, decisions on lending, investment, underwriting, and reporting are bound to be defective, resulting in business losses, lack of opportunities, non-compliance, and a dent in brand equity.
Not agnostic
Third parties use proprietary algorithms to procure ESG data. Hence, it comes with an inherent ‘proprietary analyst bias,’ which may differ from the analytical perspective of financial institutions that use this data. As institutional strategies and perspectives change, there is a desperate need for subject matter experts to use granular raw data. The availability of an enabling platform powered with raw data provides enormous flexibility in the hands of research analysts and ESG experts. The platform would be suggestive of standard indicators in addition to building custom indicators on ESG performance.
Data is expensive
ESG data is available at a cost. In addition to a license fee, it comes with fixed conditions in terms of usage and distribution. Indiscriminate use of ESG data within financial institutions may result in substantial costs, which is why there are usage, storage, distribution, and geo-specific restrictions in place.
