Addressing challenges in ESG data management

Introduction. 

This white paper discusses the challenges financial institutions face when integrating Environmental, Social, and Governance (ESG) data into investment decision-making processes. The paper highlights that while ESG data is increasingly important for investment managers, it presents numerous complexities due to inconsistent standards, diverse data sources, and the need to link this data to various financial instruments.

The key message is that organisations need robust data management systems to effectively utilise ESG data, as traditional spreadsheet-based approaches cannot scale to handle the complexity and volume of ESG information. The paper proposes solutions for harmonising disparate ESG data sources, linking them to financial instruments, and applying analytical frameworks to incorporate ESG considerations into investment decision-making.

ESG Investing: The current market. 

Traditionally, investors have focused on short-term horizons—just a few years out. But as Environmental, Social, and Governance (ESG) issues take on greater importance, investment horizons are shifting to account for long-term, even generational, impacts. As a result, modern asset managers can no longer ignore ESG data or the broader social and environmental consequences of their investments. These expanding perspectives, driven by a growing number of data sources, are adding new dynamics that are already influencing global market prices.

The ESG data market is growing rapidly (projected to reach $1 billion in 2021), with annual growth rates of 20% for data and 35% for indices. With these staggering figures, it seems only just to conclude that financial institutions have recognised the value of ESG data. 

What is ESG data? 

ESG data refers to:

Information collected to evaluate how well a company or country is performing in three key areas:

1. Environmental – How responsibly it manages natural resources and environmental impact.
   
   
2. Social – How it treats people, including employees, customers, suppliers, and communities.
   
   
3. Governance – How it's led and managed, including transparency, executive pay, shareholder rights, and internal controls.

In short, ESG data is used to assess the ethical and sustainability performance of an investment, helping socially conscious investors decide where to put their money.

ESG Data Management Challenges Summarised.

ESG data offers a source of new and potentially valuable information which could contribute to a better understanding of long-term investment risks or opportunities. Yet, at the same time, structurally integrating this new data into the decision-making process is challenging. To sum up, we identify the following:

1. Too Many Data Sources – The rapid growth of ESG investing has led to a flood of data vendors, each offering different and inconsistent data.
   
   
2. Inconsistent Standards – Conflicting classifications, scores, and weightings across providers lead to contradictory ESG ratings, forcing managers to build their own consolidated view.
   
   
3. Fragmented Official Frameworks – Multiple competing ESG standards from NGOs and governments (e.g., SASB, SICS) deepen the inconsistency across the market.
   
   
4. Poor Asset Mapping – It’s hard to connect ESG ratings to specific financial instruments, especially in fixed income markets.
   
   
5. Raw Data Overload – Raw ESG data isn’t immediately usable—it must be cleaned, standardised, and structured before it can inform decisions.
   
   
6. Integration Complexity – Merging different ESG datasets (vendor + internal) requires a robust system to align scores, formats, and methodologies.
   
   
7. Unclear Financial Impact – ESG data points to external risks, but quantifying their direct effect on investment performance is still a challenge.
   
   
8. Manual Tools Limit Scale – Spreadsheets and other ad-hoc tools aren’t scalable, and they pose risks for quality, version control, and operational efficiency.

The Investors View

Institutional investors increasingly recognise that ESG factors can materially impact a company’s financial performance and valuation. To harness ESG effectively, portfolio managers need a structured system for collecting, managing, and applying ESG data—one that can handle its complexity and support smarter, more responsible investment decisions.

To make ESG data useful, investors must first explore what's available across a wide range of sources—company reports, vendor data, and internal research. Since ESG data varies widely in quality and structure, many asset managers prefer to combine multiple sources and focus on the underlying data rather than vendor scores. This approach requires a robust data management system to consolidate, standardise, and distribute ESG insights across the organisation, enabling consistent, informed decision-making.

Explore Gresham’s Prime EDM solution for accurate and effortless ESG data management. Our solution aggregates ESG indicators from various sources, including industry data points, corporate disclosures, and third-party assessments.

ESG is wide and disperse and needs to be combined. 

Modern-day investors increasingly require a longer-term investment horizon than the traditional 3-5 years in which most companies give their forecasts. They research how lasting Environmental, Social and Governance (ESG) related matters can impact their investments today. The challenge is however that today there is a lot of dissimilar ESG data available in the market to analyse companies. Investment Managers looking to incorporate this data into their workflows are thus faced with very similar issues and challenges they would have faced with traditional reference and pricing data.

Once a market data management system as a centralised structure has been put in place, there are a plethora of benefits that will exist for an organisation, from data lineage, cost management and allocation, audit trail.