In today’s dynamic asset management landscape, the integration of environment, social and governance (ESG) factors into downstream business processes has emerged as an imperative for executives navigating complex investment management and regulatory compliance environment.

Building on our previous topics - ESG data management strategy and automated ESG data ingestion - this blog tackles the technology component for facilitating automated ESG activities, enabling faster processing of structured and unstructured data for better reporting, enhanced decision-making and increased compliance.

Ensuring appropriate design and strategic implementation of modern technology aids in optimizing the utilization of ESG data across asset management workflows.

To determine if this applies to your firm, ask yourself this question - is my firm leveraging technology that aids our ability to integrate ESG data into our investment decision-making and regulatory compliance practices for sustainable growth?

Evaluating the current tools utilized for ESG data aggregation and reporting by your business workflows is crucial in modernizing your technology and architecture. This will aid in sprucing up the potential for capabilities such as Generative AI.

Now let us look at the approach to designing a modernized technology platform for comprehensive ESG reporting needs. The first step is defining the key requirements and scope of your ESG activities.

Step 1: Define the key ESG reporting requirements

• Determining ESG reporting frameworks: 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)
• Defining incremental ESG metrics and goals: 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
• Understanding data source needs: 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
• Integrating ESG data into business operations: Effective integration of clean, reliable ESG data into business processes is essential for aligning financial flows with net-zero economy objectives
• Downstream uses of ESG data: Utilizing ESG data for regulatory compliance, investor relations, stakeholder engagement and strategic decision-making is crucial to meet rising transparency and ESG commitment expectations

Step 2: Apply platform modernization best practices

Once you have your target ESG data sources and metrics identified, it is time to ensure you are applying industry best practices to your platform design to enable rapid digital product development. Updating your 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. Here are some of CRISIL’s key considerations and best practices:

• Platform migration approach: 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 pipeline modernization: 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
• Strategic approach: Platform modernization must be supported with a top-down approach to ensure innovation, consistency and uptake of the transformation
• Business case development: Rationalize the business-level benefits and how platform modernization will impact ESG reporting to ensure the value is recognized and consequently implemented
• Strategic roadmap: 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
• Identify stakeholders: The true impact of modernization will not be seen without commitment from users at all levels across the enterprise
  

Step 3: Design and implement your modernized technology platform for ESG

After embedding the above best practices into your strategy, you can follow the below step-by-step approach to maximize the benefits of modernizing your platform to support ESG goals:

• Plan
  • Current-state mapping: Draw out a detailed map of your current platform to enable a viewpoint of requirements across performance, reliability, security and functionality
  • Gap analysis: Identify inefficiencies and gaps in your current state. Include ESG data sources, processes, technologies and stakeholders
  • Future state goals: Define the goals of the platform, including expected outputs, update frequencies and ownership structure

• Design
  • Data governance: Design the platform under a clear and robust data governance operating model
  • Vendor assessments: Carry out thorough assessments of technology providers against your defined future-state goals
  • Data architecture: Plan how data will be stored, processed and accessed, including considerations for scalability
  • Data cataloguing: Consolidate transformation of data with standardized ESG data taxonomies
  • Data mapping: Map out how data will flow through the system from ingestion to reporting and output
  • Technical requirements: Draw up a list of required connectors, dictionary structure and solution components needed to build the downstream data pipeline

• Develop, implement and test
  
  • Data migration: Develop connectors, extract, transform and load processes and configure solution parameters to align on requirements needed for data migration into the new platform environment
  • Test: Using a test environment, run ESG data through the entire lifecycle from ingestion to downstream analytics
    

• Deploy
  
  • Go-live: Deploy the platform in a production environment
  • Monitor: Set up a post-deployment monitoring workstream to identify and resolve issues
  • Training: Develop training and user manuals alongside training sessions to ensure effective knowledge transfer and user efficiency of new platform environment
    

ESG data automation in practice

Now that we have walked through the steps required for this, let us take a look at a live case study of establishing an enterprise-wide ESG data taxonomy and ingestion platform.

Conclusion

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.

Modernizing your organization’s technology should be the foundation for designing and implementing comprehensive ESG aggregation and reporting. This will ensure financial institutions are properly set up to navigate the complexities of sustainability reporting, fostering informed decision-making and compliance for a more sustainable future.