Celent’s upcoming report, “Smoke and Mirrors? Embracing Technology to Solve for ESG Data Challenges” is the third report in a series of three on the topic of ESG adoption in the wealth management industry.
The series is titled, “ESG Initiatives in Motion: A Catalyst for Change” and includes the reports:
1. Traversing the ESG Plain with Eyes Towards the Horizon: Data as a Differentiator
3. Smoke and Mirrors? Embracing Technology to Solve for ESG Data Challenges
Celent’s upcoming report will highlight the key themes in the current ESG environment and how the incorporation of emerging technologies into ESG data models can help mitigate the challenges around data quality.
- The widespread adoption of ESG investing continues to take place on a global level.
- However, the industry faces significant challenges across multiple fronts, including an ever-changing and complex ESG data regulatory environment, as well as achieving data objectivity.
- Firms struggle to manage ESG data gaps and requirements, particularly as many data collection processes are still manual.
- The need to modernize ESG data management practices towards more automated and integrated solutions is critical.
An ESG data model is a structured framework for organizing and managing ESG data. ESG data models typically include a set of standard definitions, categories, and metrics for ESG data.
- This allows organization to collect, analyze, and report on ESG data in a consistent and comparable way.
- Comprehensive data models are essential for organizations to effectively collect, manage, and deliver reliable ESG data. But, financial institutions face challenges, such as data gaps, aggregation, and lack of automation, when gathering and reporting on ESG data
- Technology-driven solutions, particularly those that incorporate emerging and cognitive technologies, can help improve data objectivity and clarity.
The report will also address questions, such as:
- What should a wealth manager consider when looking to integrate ESG into their portfolio (e.g., resourcing, cost, regulation)?
- What are the best practices for improving the efficiency and accuracy of ESG data consolidation?
- How can we streamline and automate the ESG data consolidation process to reduce manual intervention and improve efficiency?
Data is the differentiator for investment managers with regard to their ESG capabilities and proposition. To achieve future growth, wealth and asset managers must overcome data challenges by utilizing new technology solutions that have emerged in the data value chain.
Celent has also recently published supplemental research on ESG:
