Insights

Help your company overcome the ESG data challenge: A guide to sustainable reporting

Fousmi Abdul Gaffoor, Global Lead, UST ESG and Sustainability Services

Environmental, Social, and Governance (ESG) reporting was voluntary in the past, but due to increasing recognition of the risks associated with ESG factors, regulators are now making it mandatory. As ESG commitments shift to a compliance and risk-management priority, CFOs need auditable and verifiable ESG reporting. This necessitates collaboration between CFOs and sustainability leadership to apply proper governance controls to ESG data.

Poor-quality, incomplete, outdated, or untraceable data can result in inaccurate ESG reports, for which your organization may be held accountable. Manual data collection exacerbates this issue, introducing errors and inconsistencies. Creating ESG disclosures is only 20% of the task, with the remaining 80% dedicated to data management and overcoming technical challenges in integrating ESG data.

Strategic data management is crucial for the successful implementation of objectives. However, managing data is inherently complex, and adding ESG goals intensifies the challenge. A robust data strategy instills discipline, enabling meticulous monitoring of ESG metrics through automated tracking of key performance indicators (KPIs).

Such a strategy empowers organizations to provide clear evidence of and transparency in the data they collect, ensuring veracity and mitigating the risk of greenwashing (making misleading or unsubstantiated claims about the environmental benefits of a product, service, technology, or company practices to appear more environmentally friendly than they actually are), which could harm your organization's reputation and brand value.

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Challenges of implementing an ESG data strategy

Implementing an ESG data strategy poses significant challenges within organizations. Siloed operations often lead to disjointed processes and fragmented data frameworks, hindering synergies across ESG applications. Despite inevitable redundancy, there is considerable overlap in data requirements.

For example, utility data might reside within the workplace management team, Scope 3 emissions data might be with the travel team, and employee diversity information is most likely managed by a company’s human resources (HR) function. Because all of this data is most likely residing in multiple systems, integrating it is a challenge.

Ensuring the accuracy, completeness, and reliability of ESG data from these varied sources amplifies the challenge. Clear and comprehensive ESG reporting hinges on the veracity of this data, while safeguarding sensitive information against breaches and misuse remains a critical concern. In addition, inconsistent ESG standards and evolving regulations complicate implementation across sectors and geographies.

Investing in scalable data systems is resource-intensive but at the same time crucial and educating stakeholders on robust data strategy and governance further heightens the complexity. To address this, organizations should promote cross-functional collaboration, breaking down barriers imposed by functional silos and ensuring diverse teams collaborate towards shared ESG objectives.

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Identifying data sources for your KPIs and disclosures

The framework and materiality matrix you choose for your report will guide your data collection, but practical challenges arise due to varying reporting boundaries. Reporting boundaries define where an impact occurs—either from your organization's activities or relationships. Understanding these boundaries is crucial. For instance, greenhouse gas emissions can be Scope 1 direct emissions data or Scope 2 indirect emissions data, depending on whether the energy is produced internally or purchased.

A substantial portion of the ESG data you need may already exist within your organization. However, because it is not gathered using a formal process, you must identify and interview function leaders with the necessary information to streamline your ESG reporting. Work with them on the KPIs associated with their department or function and establish a process for collecting those data points.

Additionally, not all the data collected is exactly what you will be reporting on. The data might transform based on the KPIs and may require external data sources. Scope 3 emissions, the giant in the room, need data from the entire supply chain, which is again dependent on external data sources.

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Choosing the right technological approach

Setting up a robust internal data strategy is crucial in the initial phase of ESG data governance. It requires a unified and centralized approach involving multiple stakeholders. However, centralization should not dictate the data management style. Today, organizations are witnessing exponential growth in data volume, necessitating organized and logical data management for targeted usage and efficient governance.

The variety of ESG data, from structured databases to unstructured sources, adds complexity, as does the velocity at which ESG data is generated and processed, requiring real-time or near-real-time handling.

There is a notable shift from centralized IT-centric architectures to federated models such as data mesh to address this challenge. In a Data Mesh framework, data ownership is decentralized and managed within specific business domains (e.g., HR, workplace, or purchasing), promoting targeted usage and efficient governance tailored to business needs. This approach aligns perfectly with the volume aspect of Big Data, allowing each domain to manage and scale its data, preventing a centralized system from being overwhelmed.

A federated model enables quicker responses within domains. At the same time, a centralized data fabric ensures rapid integration and accessibility, enhancing the responsiveness of ESG initiatives, including the variety and velocity aspects of data.

Organizations may consider hybrid models where Data Mesh principles are implemented within business domains, while a centralized Data Fabric layer ensures overarching governance, integration, and accessibility. Centralized metadata management is critical to standardizing and making accessible data definitions, lineage, quality metrics, and access policies across domains. Standards for data quality and compliance with regulatory requirements are established centrally but applied within each domain to maintain integrity and consistency while accommodating specific needs.

The ultimate goal of managing ESG data is to derive value by obtaining actionable insights and driving positive outcomes for the organization. Having a sole source of truth for ESG reporting and analytics provides a solid foundation for sustainable initiatives, enhancing performance with reliable data and metrics. The right combination of automation, artificial intelligence (AI), and governance can lower the costs associated with ESG reporting and monitoring. These advantages increase the likelihood that sustainability data is effectively integrated into decision-making processes.

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Leverage ESG intelligence

AI, including generative AI, transforms ESG reporting through automated data collection, analysis, and accurate, timely report generation. It improves ESG strategy and audits and addresses stakeholder concerns by monitoring public sentiment. Ethical AI practices are crucial for managing data bias, privacy, and security.

Business logic in data modeling and machine learning supports sustainability, enhances compliance reporting, and integrates ESG factors into product development for long-term environmental conservation. Generative AI models scenarios to guide sustainable practices, optimize resource management, and mitigate ecological risks, forecasting environmental outcomes and enabling proactive strategies.

An enterprise-level data strategy enhances accessibility, security, and governance, maximizing AI's potential while minimizing redundant processing for sustainable operations. As AI models and data volumes grow, this strategy scales infrastructure efficiently for seamless performance, capacity, and automation. A robust ESG system based on modern data governance is vital for achieving sustainability goals.

To learn more about the transformative power of UST’s generative AI solutions or for a free two-hour demonstration of our Gen AI Sandbox and how it can help your company enhance its ESG data using the capabilities of AI, visit ust.com.