How to Build an Efficient Enterprise ESG Data Strategy in Banking with Cloud and Machine Learning
ESG investing is now integral to financial institutions’ activities, motivated by client demands for sustainable investment options, regulatory obligations and a growing cultural shift towards ensuring that capital is allocated in order to do better for the planet and the people on it.
Data is the magic dust that is enabling the translation of that intent into action. However, while the amount of ESG data available to institutions is huge and growing at a rapid pace, not all users have the ability to wrangle it to their needs.
The reasons for this are many but largely reside in the fact that ESG data isn’t as easy to integrate into company systems as financial data. ESG data exists in many formats and often needs the application of sophisticated processes to make it useable. This places a greater emphasis on the need for efficient and effective data management.
This paper discusses the importance of good management of ESG data for financial institutions by:
- Offering guidance on how to implement enterprise-wide ESG investment and risk-management strategies;
- Discussing the technologies needed to build out ESG data pipelines;
- Examining the data and technological challenges to good management;
- Illustrating a persuasive set of use-cases across the enterprise, and;
- Explaining how harnessing the capabilities of Snowflake and Microsoft Azure Machine Learning can help in adopting a cloud-first data mesh strategy with an ESG data domain.