Spotlight Series: Katie Lam
ESG has become a top priority in asset management, with the industry no longer considering why it should integrate ESG into investment processes but how it should do it. However, investment managers focussed on ESG adoption and implementation are running into a significant roadblock in their journey – data. The sobering reality is that ESG cannot become an integral part of asset management processes until this problem is solved.
The challenge of ESG data encompasses both the lack of reliable and consistent ESG data as well as the wider issue of poor data management for ESG. Currently, ESG data is not readily consumable by users, including asset managers, investment advisors and quant teams, and cannot easily be integrated into investment management workflows.
Investment professionals are looking to screen investments by ESG and impact investment factors – across client preference capture, and portfolio modelling and reporting. This is fraught with difficulties as data assessment and screening for ESG requires advanced data transformation solutions to the challenges posed by ESG data – namely silos, volume, value, fidelity and timeliness. It can be difficult to trust data quality and composition or to render data palatable and easy to use for end users. Vastly heterogeneous ESG data is sourced from multiple providers with different and evolving scoring systems. This requires a data management solution that accommodates and alters configuration of incoming ESG data sources and transforms them to customer preferences.
Data management for ESG should be built on a strong data foundation and a scalable data and technology architecture. Advanced software and data engineering capabilities are required across the full data lifecycle covering data capture, curation, consumption and governance. Spanning architecture, user-interface development, server-side development, and quality assurance, these capabilities help create a common language across the data. They can solve the problem of poor fidelity and composition of data, ensure the data is rendered truly amenable to users and build effective data screening and management functionalities.
The ideal approach mobiles an Agile methodology, and a willingness to share knowledge with development partners, and is grounded in asset management industry domain experience. By adopting this approach technology teams, working with investment managers and quant teams, can help build ESG functionality into the process of constructing, analysing, monitoring and reporting on investment portfolios. This makes it possible to perform ESG data load and transformation, ESG client preference capture and portfolio ESG modelling and reporting. It also enables the asset management industry to develop new and different ESG products and propositions and stay ahead of evolving market demand.
We have acquired strong expertise in this newly emerging, fast-moving area, which builds on our two decades’ of experience solving complex data and software challenges for both traditional and alternative investment managers. For example, we recently worked with partners to help build an ESG functionality for wealth managers that enables them to screen investments by product avoidance preferences and asset exposures, ESG factor preferences, including negative and positive screening and asset exposures, impact target preferences and asset exposures, and CO2 emission preferences and asset exposures. These functional elements allow for customisable and flexible deployment of ESG criteria and automatic checking of portfolios against ESG preferences.
Advanced software and data engineering is a crucial enabler of ESG implementation in asset management – allowing ESG factors to be seamlessly integrated into the investment process – and a leading differentiator of commitment and quality in ESG and sustainable investing.
For more information on how technology and data expertise can accelerate ESG implementation please contact here.
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