In today’s dynamic asset management landscape, integrating environment, social, and governance (ESG) factors into downstream business processes has become imperative for executives navigating complex investment management and regulatory compliance environments.
Evaluating the current tools utilized for ESG data aggregation and reporting by business workflows is crucial in modernizing the technology and architecture being used. This will aid in sprucing up the potential for capabilities such as Generative AI.
ESG reporting requirements involve disclosing a company's performance on ESG issues. Key ESG reporting requirements include determining the reporting framework in line with global regulations such as Corporate Sustainability Reporting Directive, EU Taxonomy, and Sustainable Finance Disclosure Regulation (SFDR).
Mandated ESG reporting entails identifying and disclosing crucial metrics and goals across ESG domains, including greenhouse gas emissions, diversity, board composition, and human capital data. This disclosure is pivotal for investors, regulators, and stakeholders assessing sustainability performance.
ESG data acquisition involves tapping into internal systems (operations, supply chain, finance), external databases (rating agencies), real-time feed (market trends, industry performance), sustainability surveys, and third-party providers.
Updating an organization’s platform will ensure that the business has the necessary architecture to enable effective ingestion, storage, and accessibility of data for downstream ESG reporting. To implement an effective platform, platform requirements must be rationalized against business strategy, cost barriers, and technical resource availability.
Businesses at different stages require different levels of modernization. This could range from rehosting the entire organization from legacy on-prem systems to the cloud to refactoring, to simply optimizing certain applications for specific operations. Data must be optimized by movement into modern databases, with an effective data model to ensure it is accessible, consolidated, and available in real-time.
Current-state assessments, gap analysis, and technology assessments are all key to defining the best future-state strategic roadmap for the organization’s platform modernization. Using a test environment for running ESG data through the entire lifecycle from ingestion to downstream analytics helps effectively deploy a platform in a production environment. To meet the required ESG obligations of investors and regulators effectively and efficiently, financial institutions must ensure they have the appropriate platforms and technology as the foundation for all ESG reporting needs.
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