Environmental, social, and governance (ESG) factors have become increasingly important in the banking sector. Regulators are implementing more requirements, investors are demanding concrete returns, and both corporate and retail customers are seeking diverse offerings. As a result, ESG is no longer just a risk and compliance matter; it now influences nearly every aspect of banking operations.

While financial data, customer data, and other, more traditional types of information are usually managed by specific departments, ESG data is scattered across the bank, stored separately and classified inconsistently without a clear owner. These inefficiencies lead to increased costs and risks and to missed opportunities.

It’s time for banks to professionalize how they manage ESG data. The good news is that doing so doesn’t need to involve change-the-bank level investment. By adapting their approach and creating an integrated data platform, banks can turn ESG data from a headache to an opportunity. This article explains how.

Banks Have an ESG Data Problem

Bank clients we’ve spoken to share a similar story: ESG data is more important than ever but is harder than ever to manage. One reason is the sheer volume of information involved. Banks need metrics on every dimension of ESG, from energy consumption and emissions to workplace health and safety to diversity, equity, and inclusion. That data must cover banks’ own operations and its value chain, both upstream across supplier and partner activities and downstream to the broader client portfolio.

This is more data from more sources than banks have historically needed to collect, and it comes in more unstructured forms than many banks are used to, including PDF files, imagery, and web scraping. While many banks have analytics engines that can process structured and unstructured data, few have invested in mechanisms that can do so specifically for ESG data. As a result, while more users across banks need ESG data, they can’t get it in a timely way. Limited data availability, siloed data management approaches, and an inability to process complex and unstructured data are key challenges. (See Exhibit 1.) Metrics and calculations also vary widely, complicating modeling efforts and making it hard for business teams to provide clear and consistent reporting. With pressure on banks to step up leadership on ESG issues—and a burgeoning list of regulatory pronouncements—most know they need a different approach.

Build the platform with the future in mind. Banks need an ESG data platform that can grow with them. A data architecture that is holistic, integrated, and modular can give banks that flexibility. Generally speaking, this architecture has four main layers. (See Exhibit 3.) The data sourcing layer helps banks ingest, store, process, and reconcile metrics and other data from internal and external sources, housing it centrally and refreshing it continually. The data life cycle management layer then organizes information within the platform around specific business topics and marries it with appropriate controls. That domain-based structure democratizes access to data within a consistent governance framework. Metrics and KPIs are captured and codified in a similarly centralized manner. The top layer, use cases, incorporates the functionality that day-to-day users need, such as calculation and distribution processes and self-service business intelligence (BI) capabilities.

Building a platform in this way helps banks achieve significant performance benefits—in particular, cutting use case development time and allowing data democratization. Moreover, by creating a “single source of truth” for ESG data, quality is better assured. Business users can access the information they need when they need it and in formats they can readily apply, saving time, reducing error, and accelerating results. And because the entire platform is designed with flexibility in mind, it can scale with the bank’s capabilities and needs. For example, banks could migrate over time from metrics that are calculated on a monthly basis, leveraging batch processes to source data, to metrics that are fully embedded in day-to-day activities and computed in real time to enable such activities as ESG product pricing.

Enhance the data governance framework to manage new types of data. Most banks have some form of data governance system in place. But most frameworks will need to be updated to account for the newer data types that ESG platforms use, such as unstructured data and information from external providers. The most effective frameworks define data management processes at the strategic, tactical, and operational levels, supported by tools that help with version control and automate key quality indicators. Getting this foundation in place can help teams innovate faster, without having to jump through unplanned process hoops or, worse, having to roll back work due to data quality oversights. In Europe, the new Corporate Sustainability Reporting Directive will require banks to give their assurance that reported information is correct, making data quality even more important. 

Start Now and Grow Incrementally

Banks can no longer afford to push ESG data issues to the side of the desk. The needs for this information are simply too great, and the risks associated with inadequate reporting and analytics are growing. But implementing an ESG strategy doesn’t have to be an overwhelming task. We’ve found that focusing on four “must-do” requirements and “can do now” capabilities can allow banks to build an ESG data platform that is successful from the start. Here are the steps we recommend. (See Exhibit 4.)