ESG data can offer important insights into material risks and opportunities and will therefore continue to evolve as an integral component of the investment decision framework. With demand larger than ever, investment managers are competing at an escalating rate to both innovate with new products and to integrate ESG screening into existing funds.
At the same time, institutional managers face a number of challenges with ESG content integration.
- First and foremost, it’s important to understand exactly what the ESG data is intended to do: profile risk, identify alpha or drive environmental engagement and stewardship with corporates? Each requires its own approach to data analysis and impact output.
- Another challenge is how to source and efficiently integrate multiple sources of ESG data into the cross-asset investment decision and implementation processes
- Given the lack of standardized metrics and ratings, methodology divergence can result in a single security being rated very differently according to each provider
While this is an emerging domain, the basic concept of sourcing, mapping, validating and distributing high-quality data to different business functions remains key. Managed services in this space can help firms to deliver their ESG operational model foundation and vision at the rate of change that the industry is demanding. By having ESG data sets collated and mapped across their comprehensive investible issuer and securities universe, firms can take the broadest possible view when implementing and monitoring portfolio decisions.
The ESG challenge
As ESG factors gain popularity, a huge field of material is being harvested by a wide range of providers, many with insight into specific company types, sectors, environments and social or political trends. Each provider can also present the data in their own unique way.
The result is that firms are faced with a mountain of unstructured data. Attempting the necessary analysis is hampered by a lack of mapping or cross-referencing across the data sets. This hinders the ability to compare data sets, look for patterns or create rule-based hierarchies.
Agility: ESG data selection is client-driven and based on users’ specific demands and requirements. We’re happy to work with any provider to meet your evolving requirements.
Cross-referencing: RIMES data is cross-referenced and mapped across issuer assets with LEI and common identifiers, making it fast and easy to analyze.
Customization: Solutions can be customized to accommodate mapping, enrichment and scoring across your specified multi-asset universe of interest.
Flexibility: Data is made available via APIs such as Python, MatLab and R or through customized feed formats, so it’s easy to integrate into your processes.
Efficiency: Clients can integrate an unrivalled number of ESG providers but only need to work with one partner, allowing you to concentrate on using the data – not managing it.
Proactivity: RIMES is proactively working to partner with additional ESG providers to ensure we continue to offer the industry’s broadest ESG data service.