Solving ESG data integration challenges at a critical moment

This article was written by Saba Mirsattari, Data Management Specialist at Bloomberg.

ESG data is not a new consideration, but it has never been more relevant than it is today. These issues are pervasive – nowhere in the world is immune from social inequality, the impact of the pandemic, or climate change.

Financial institutions find themselves at a critical moment in time when ESG has become a top priority for business and government leaders. At Bloomberg’s flagship Data Management event in Zurich, we brought top experts together to explore how ESG data is becoming more mainstream — looking at the evolution of ESG data and technology needs for 2022 and beyond. Panelists, including Christophe Tummers, Head of ESG data at UBS, discussed how data governance is key to trust and understand your ESG data and allow senior management to stand behind it.

Companies that outperform competitors are likely to be those that commit to ESG– with innovation and a data-driven approach.

ESG data quality & context

We have seen a dramatic increase in interest for sustainable investments. The value of global asset managers applying ESG data to drive investment decisions has more than tripled over eight years, to almost $38 trillion in 2020, but how good is the data supporting it?

“Assets in portfolios still lack ESG information. Equities and bonds are well covered, but other asset classes are lagging behind. Firms end up sourcing data from multiple vendors to build their own view,” shared Tobias Hurst, Sustainable Investing Specialist, LGT Private Banking. Despite the push for more disclosures, ESG data quality and coverage remain an issue. As a result, the assessment of a company’s ESG performance requires a lot of different metrics to get the full picture.

Consolidating this data can be resource intensive and very complex. “Firms need to source data from multiple ESG vendors so they can have access to what’s most appropriate and useful for them. Linking ESG Data to your internal data is tricky as you need to source ESG data from multiple vendors and then map to your internal systems,” explained Steinar Vinne, Business Engineer, ZKB.

Integrating ESG with internal ecosystems

ESG data does not exist in isolation. “ESG data needs to be integrated with entity, instruments and country data. It is very difficult for large banks to centralize the ESG data function entirely. A better approach is to define a subset of attributes that are key and non-negotiable in terms of their consistent usage across the bank,” said Christophe Tummers, Head of ESG Data, UBS.

Companies link data to create greater value and correlate information from different kinds of systems. For example, connecting ESG and supplier data or ESG and client data. Companies often struggle to connect disparate systems and data in an easily scalable way. Despite the challenges, the key importance of integration was shared by all our speakers. “Our approach is to integrate all our ESG data in the centralized portfolio management system, not to have the ESG know-how in a green ivory tower,” said Rico Keller, Responsible Investment Manager at Swiss Life.

Tobias Hurst also addressed questions about ESG integration. “Rather than building a new platform for ESG data only, it is a better decision to include the data in existing platforms wherever possible to make the information more available to our relationship managers and across the company,” he shared.

ESG data management

Data management plays a crucial role in ESG. “It ensures the right data point is used for the right purpose and delivered to clients or the regulator. There is still a lot of room for improvement to have more flexibility to test which data point is better to assess carbon emission or gender diversity. A sort of data hub is very important to be able to pick and choose the right data,” said Keller.

The model governance behind ESG is equally important. “Do we understand and trust the underlying models? Do we have the right model governance to confidently defend the models in front of customers and regulators?” said Tummers.

Bloomberg’s data management solution offers a data model that unifies different data sets such as Entity and Instrument data and ESG in a centralized location, this is the first step in getting a consistent view of this data across an organization. However, it is only possible to achieve this consistent view if the client can easily access the data and embed it into their applications and workflows. In order to help the client achieve this, Bloomberg’s Data Management Solution offers a number of options when it comes to the collection of the modelled and linked data.

These options include:

1. Bloomberg Per Security over SFTP, a widely understood format within organizations facilitating the rapid on-boarding of the service.
2. A restful API.
3. Most recently producing the modelled data in table ready format for consumption within a customer’s cloud environment.

New technology

With newly available modern technologies, firms are taking a fresh look at how they handle data. “Data is still delivered in a file and batch way today. We want to speak to data vendors about collection from the cloud based on simple permission system,” said Tummers.

Scalable, cloud-based infrastructure is been increasingly adopted across the industry as a way for organizations to leverage the benefits of big data tools for research and analysis.

Companies are building data lakes in the cloud to provide better access to data via business intelligence tools and APIs, often replacing existing in-house infrastructure entirely. Bloomberg is starting this journey with the Cloud data warehouse company Snowflake and will provide the solution across a number of cloud-based data warehouses over time.