Sustainably Solving ESG Data Value Chain Challenges for Financial Institutions

Let’s face it, the need for reliable, comprehensive, and real-time environmental, social, and governance (ESG) data is high. From banks to asset managers to insurance companies, there is an increasingly organized effort to evaluate and include a company’s non-financial performance in its daily decision-making process. Sustainability is at the forefront of decision-making and touches all departments, from risk management and compliance to the supply chain and the C-suite. With increasing awareness and changing criteria and standards, companies must have access to relevant data sets.

Yet one of the biggest challenges in ESG reporting according to investors is the lack of transparent and consistent data. It is important to understand and distinguish the challenges across the ESG data and insights value chain and follow a piecemeal approach to address each. In this article, we will explore the three levels of the ESG data value chain and address these challenges and how to meet them.



Disclosing and reporting intern numbers may be common for larger public companies that are front runners, but disclosure isn’t the norm for all organizations. Additionally, there isn’t a universal system to validate the authenticity of these numbers, and greenwashing is a common challenge. A classic example is capturing data for scope 3 emissions, where accuracy depends on multiple parties in the value chain. Another example is the European Banking Authority’s proposal mandating banks to report the Green Asset ratio (GAR). This in turn requires banks to maintain ESG data for clients at a product/financial instrument level, emphasizing the need for trusted data.

Regulatory and Standardization

While a focus area for the European Union (EU) has been the ‘S’ pillar of ESG, for the U.S., the focus area has been the ‘G.’ Additionally, guides are being made available to navigate the stricter frameworks for reporting data. This makes reporting inefficient, complicating the downstream harmonization and data analysis. Although initiatives like Corporate Sustainability Reporting Directive (CSRD) and Sustainable Finance Disclosure Regulation (SFDR) are helpful, implementation will take some time to come into effect.

Data Harmonization and Analysis

Multiple third parties report ESG data for corporates at an indicator or sub-indicator level. In my observation, every financial institution averages data from at least two different third parties to design solutions for multiple use cases, such as calculating ESG scores, capturing carbon data coverage, and regulatory reporting solutions. In addition to comprehensive coverage and stemming from points one and two listed above, the real challenge is varied ‘taxonomy.’

Every provider and company follows a different framework, sometimes having contrary definitions. For example,  do nuclear energy or carbon capture constitute ‘green’ investments?

Another challenge with using third-party data is the ‘black box’ for certain key identifiers like ESG scores. The end users — in this case, financial institutions — have no access to the methodology, type of data, or integrity of data being used to come up with these scores. To show progress, there is little choice but to use these scores as-is.

While financial institutions are partnering with policymakers to expedite disclosure and regulatory and standardization aspects, they have limited control over actual outcomes and time to market.

However, in the meantime, they would benefit from experimenting and setting up a robust approach for solving the ‘Data Harmonization’ challenge. In my experience working with various clients across different data and analytics maturity stages, I have had the chance to develop future-proof solutions that include defined ways of working. This experience birthed an efficient framework to get ahead of this challenge:

1. Operating Model

Given the wide range of potential use cases, it’s important to identify, and prioritize the use case roadmap by aligning the – enterprise strategy -> ESG use cases->analytics strategy including KPIs->data strategy.

Given the industry enthusiasm and wide range of potential applications, creating a centralized ESG team responsible for providing data as a service to different functional teams within the institution works well. However, this requires strategy changes as the federated models are popular with financial organizations, and the sponsors need to design new profitability criteria (dependent on use cases) for this team. The central team must include a combination of research and technical experts such that the entire data value chain can be mapped, including KPI (key performance indicators) identification per use case, source data along with third-party data provider, data model, and respective architecture for ingestion, processing, and consumption can be defined. Lastly, the adoption layer, including change management, needs to be designed to integrate outcomes with the business processes.

2. Using AI for Data Harmonization

Along with AI, automation, and data engineering solutions provide the much-needed scale to manage the volume of varied third-party data sources meaningfully. Within my team, we have designed and implemented repeatable data processing solutions for our clients in the ESG space at various points in the value chain, including data completeness and quality checks, creating golden records to create company master, implementing automated guardrails or screening criteria to select only the required data for every use case.

3. Augmenting with Alternate Data

The infancy stage of this industry allows stakeholders to devise new-age solutions. In certain scenarios, augmenting the third-party data with data sources like weather, satellite, social media, or Internet of Things (IoT) data can prove to be useful. However, this requires partnerships with niche data providers who have made the required technology investments to capture and process huge volumes of data. It is recommended that financial institutions start experimenting (if not already), with use case pilots such that appropriate measures for contextualizing and scale can be initiated.


ESG data will continue to pose a challenge as regulatory requirements and investor demands evolve. If increased stakeholder awareness and several initiatives to include ESG data in mainstream decision-making are a measure, then it’s clear that the industry needs to respond to the ‘call for action’  now more than ever. If used wisely, data, analytics, and technology can solve fundamental challenges and expedite at least half of implementation challenges while the other half are being worked upon by regulatory authorities and decision-makers across financial institutions.

Pranav Chaturvedi
Associate Vice President, Analytics & AI Solutions Posts

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