Asset managers are looking to incorporate high-quality ESG data into their investment processes as they respond to growing scrutiny of, and rising market demand for, ESG products.  Banks are also focused on promoting sustainable business practices in the businesses they fund and in their own organizations.  Both buy-side and sell-side actors play a crucial role in promoting the transition to an economy shaped around ESG principles.

However, there are formidable challenges to ESG implementation.  Data and analytics is the greatest obstacle of all, according to a recent BlackRock survey of 425 investors representing $25tn in assets under management.

Financial institutions are keenly aware that the lack of high-quality data management can impede the adoption of ESG, including implementation at scale, mass customization, monitoring against client requirements, and responding to client specific needs.

So if efforts by the finance sector to deliver on ESG relies on data quality, what can be done to get data management right?

First, financial businesses need to navigate the massive variety of data sources that exist in ESG, and the complex regulatory and industry initiatives underway to develop definitions around metrics.  Policy makers and regulators are progressing in their efforts to develop and harmonize ESG classifications and taxonomies.  However, grappling with definitions and reaching consensus on what can be termed ESG, ‘green’ or ‘sustainable’, is only part of the solution.

While keeping a watching brief on these developments, financial institutions need to press on with selecting ESG data sources and building analytics and models in support of their ESG adoption.  In this context, the root cause of data-related challenges is the lack of advanced data transformation solutions – for data silos, volume, value, fidelity and timeliness.

Data management for ESG is a newly emerging and complex area.  Currently, ESG data cannot easily be integrated into workflows or readily consumed by users.  Data management for ESG requires that a strong data foundation is implemented across data capture, curation, consumption and governance.  A common language needs to be created across the data.  This is crucial to solve the problem of poor data fidelity and composition, ensure the data is truly amenable to users and enable effective data screening and management functionalities.

Getting this right across architecture, user-interface development, server-side development, and quality assurance requires a multi-faceted and pragmatic approach built around an Agile methodology.  Combined with effective knowledge sharing with development partners, and a strong domain knowledge in financial services, this approach can be deployed to prepare and perform ESG data load and transformation, ESG client preference capture and portfolio ESG modelling and reporting.

By adopting this approach across the data maturity model, technology teams can work with businesses to build ESG functionality into the process of constructing, analyzing, monitoring and reporting on portfolios or transactions depending on the needs of the business.

Technology – and advanced software and data engineering in particular – is crucial in the effort to integrate and embed ESG in financial services.  Without data-related capabilities, ESG engagement cannot take off or fulfil its promise.

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