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ESG has gone mainstream but the journey to adoption is full of complex and difficult challenges. At the heart of the mission is technology and data.
Data management for ESG is a newly emerging and dynamic area. Both buy-side and sell-side financial institutions cite data as one of the most intractable problems they face in implementing ESG.
According to a recent BlackRock survey of 425 investors, representing $25tn in assets under management, data and analytics was the greatest obstacle to ESG implementation. Similarly, a survey by Procensus, which analysed data from 50-70 institutional investors, representing $10-15tn in assets under management, found that the biggest challenges in existing ESG research were poor data disclosure by companies, inconsistent scores and ratings by third party providers, and lack of quantification of ESG considerations in financial modelling.
Financial institutions recognise that the lack of high-quality data management can impede the adoption of ESG. So what can be done to get data management right?
Industry and regulatory efforts to achieve harmonisation of ESG typologies and definitions are progressing. But financial businesses cannot wait, and are pressing on with their ESG adoption. They need to select ESG data sources and then develop analytics and models in support of implementation. However, significant data-related challenges can arise from a lack of advanced data transformation solutions. Pain points typically relate to data silos, volume, value, fidelity or timeliness.
Data management for ESG requires the implementation of a strong data foundation and common language across data capture, curation, consumption and governance. Otherwise, ESG data cannot easily be integrated into workflows or readily consumed by users. This approach can help solve the problem of poor data fidelity and composition, ensure data is presented to users in a simple, user-friendly way and build effective data screening and management functionalities.
Crucial to success is an approach built on an Agile methodology, across architecture, user-interface development, server-side development, and quality assurance. Combined with a strong domain knowledge in financial services, and effective knowledge sharing with development partners, this approach can be applied to prepare and perform ESG data load and transformation, ESG client preference capture, and ESG portfolio modelling and reporting. It enables technology teams to work effectively with business stakeholders to build ESG functionality in a timely, pragmatic way.
Advanced technology is a key enabler of ESG implementation. It allows finance players to develop business propositions in response to market demand, open up new opportunities and accelerate business development. Without it, the financial sector – and the wider economy it serves – may fail to realise the potential of ESG.
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