Published by Uma Rajagopal
Posted on September 16, 2022
5 min readLast updated: February 4, 2026
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By Martijn Groot, VP Marketing and Strategy, Alveo
Today’s financial services firms increasingly recognise the key role ESG metrics play in decision-making across the investment management process. This is causing many to ramp up their ESG data management processes. In recent Alveo research polling the views of 300 asset owners and asset managers in the UK, US, and Asia-Pacific, 95% of respondents said they are looking to improve their ESG data management.
Part of this is driven by regulation. The push towards the disclosure of ESG information under the Sustainable Finance Disclosure Regulation, which impacts any firm selling or distributing investment products into the EU means asset managers are required to report on the ESG metrics of their portfolios and provide proper documentation as to the data sources or models behind the reported information.
However, just 21% of the Alveo survey sample cited ‘regulatory reporting’ as a key driver of their use of ESG data. This indicates that beyond regulatory compliance, enhancing ESG data management is something firms see as a must do to boost the value of their business.
ESG drivers impact most business processes. In corporate banking, for instance, ESG data is increasingly crucial to support customer onboarding and, in particular, Know Your Customer processes. Banks and other sell-side financial services firms will also frequently screen suppliers, as part of a process called Know Your Third Party. They will also want to climate stress test the products they hold in their trading book for their own investment against adverse climate scenarios.
Coupled with all this, both sell-side and buy-side firms will need to integrate ESG data with data from the more traditional pricing and reference providers to give a composite view, incorporating the prices of instruments; terms and conditions and also the ESG characteristics.
Scoping the challenge
ESG data needs to be anchored across the organisation, integrating with all the different data sets to provide a composite picture, becoming a key source of intelligence, both for the front office and for workflows in risk, finance and operations.
For many firms, doing this well is difficult. Sourcing accurate ESG data and properly interpreting it is challenging, as information must be gathered from multiple datasets including third-party estimates, ratings, news and corporate disclosures.
Added to this, there are often disparities in the methodologies third-parties fuse to estimate or score firms on ESG criteria – which complicates analysis. The biggest challenge in many firms, however, is how to consistently embed ESG data all required business processes that straddle departmental boundaries to put users on a common footing. This requires quickly onboarding new data sources, integrating, harmonising and vetting data, filling in the gaps where needed and providing it to users and business applications.
Achieving all this is complex. The data management function and operating model is often siloed and not well suited to quickly onboard new information and anchor this across a firm’s operations. ESG data frequently still needs to be integrated into wider reporting, especially in finance and risk, which are typically the functions where all information flows necessarily come together. Firms are therefore focused on improving their ESG data management and prepared to invest to make that happen.
Whenever new data categories or risk metrics are introduced, data management practices typically start with improvisation through desk-level tools including spreadsheets, local databases and other workarounds. This is gradually streamlined, centralised, operationalised and ultimately embedded into core processes to become business-as-usual.
Generally speaking, firms need to cross-reference to a comprehensive data model that covers regulatory ESG information and underlying data sets. In addition, they must achieve transparency and clearly log which sources and what types of data are used, the business rules used and any manual remediation.
Finding a solution
A comprehensive approach to ESG data management is needed to provide consistent data to service multiple use cases. That means making use of, data management solutions and Data-as-a-Service offerings, which are now available to help firms acquire the ESG information they need, the capabilities to quality-check, supplement and enrich it with their own proprietary data or methods, and the integration functionality to place users and applications on a common footing.
Achieving this demands that any challenges presented by the quality of data are dealt with from the outset. What organisations need is a process that seamlessly acquires, integrates and verifies ESG information.
Any data management function should also facilitate the easy discoverability and explainability of information and effective integration into business user workflows. Specific capabilities should include cross-referencing taxonomies and condensing information, for example to report on indicators that serve as performance KPIs, or that meet reporting mandates.
Data derivation capabilities and business rules can spot gaps, highlight outliers, whether they are related to historical patterns, or within a peer group, industry or portfolio; and provide estimates where needed. Additionally, historical data to run scenarios can help with adequate risk and performance assessment of ESG factors.
The regulatory speed in stimulating a sustainable economy not only confronts companies with a very tight implementation schedule, but also with major challenges regarding the sourcing, processing and quality assurance of large sets of frequently unstructured data. Mastering this data challenge is a prerequisite for successfully competing for new market offerings and sustainable products.
Early operational readiness is key to staying ahead of the curve in adapting to the new ESG regime. The major decision points that need to be addressed right now are first, determining the target operating model and governance, second, designing the target data and system architecture and third, moving forward with a well-proven approach for a customised implementation.
Once a data management system has been implemented within an effective operating model, there are many benefits: from efficient data onboarding and provisioning business users to securing data lineage and data cost and usage management. This increases the return on any existing and future ESG data investments. Ultimately, firm-wide availability will benefit the whole organisation and ensure firms are optimising their data.
ESG stands for Environmental, Social, and Governance. It refers to the three central factors used to measure the sustainability and societal impact of an investment.
Regulatory compliance refers to the process of ensuring that an organization adheres to relevant laws, regulations, and guidelines set by governing bodies.
Investment management is the professional management of various securities and assets to meet specified investment goals for the benefit of investors.
Sustainable finance refers to financial activities that consider environmental, social, and governance (ESG) factors in investment decisions and lending.
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