Understanding ESG Data Quality

What You Need to Know

In recent years, there has been an increasing global demand for environmental, social, and governance (ESG) data from stakeholders, investors, and regulators. Data is the foundation of ESG reporting. Accurate and relevant data is essential for making informed decisions about an organization’s sustainability performance. This data is necessary to make sound judgments about an organization’s ESG performance.

Carbon emissions, labour practices and management compensations are all examples of ESG data. Each of these metrics necessitates a unique set of data, ranging from self-reported information to third-party assessments.

No universally acknowledged definition of “ESG” or consistent approach to ESG reporting exists. In the lack of a global consensus on harmonized disclosures, a wide range of standards and frameworks have emerged, which financial institutions may be required to work with depending on their jurisdiction. Examples include the European Sustainability Reporting Standards (ESRS) and the International Sustainability Standards Board (ISSB).

Simply put, organizations can no longer rely on publicizing specific aspects of their sustainable compliance. Today, a growing amount of socially and ecologically responsible investment activity includes evaluating performance metrics, not just financial returns. This type of assessment meets the need for evidence of sustainable processes, methods, and management.

Stimulating that demand is climate change pushing organizations to be more interested in the impact on communities. These factors are collectively guiding organizations towards ethical reporting standards.

Metrics of Quality Data: The Key to Successful ESG Reporting.

Quality data metrics are critical for building confidence in capital markets. The measures of quality include accuracy, completeness, consistency, relevance, availability, and transparency. Organizations can develop trust with stakeholders and demonstrate their commitment to sustainability by ensuring the data fulfils these quality requirements.

• Accuracy: Data must be accurate and reliable, which means that the data must be error-free and consistent with other sources of information. For example, if a company bases an emission calculation on 100 kWh of energy usage, but it was 200 kWh, then the data needs to be more accurate.

• Completeness: ESG data must be comprehensive. Limited data needs to capture the entire picture. With regular updates across all metrics, ESG data can comprehensively reflect a company’s sustainability journey. For example, if a corporation reports water usage from only one of three sites, then the data is incomplete.

• Consistency: Data must be consistent across several sources and periods. To directly correlate the before and after data, the data must be collected and reported consistently, independent of the origin or time frame. For example, the data is contradictory if a corporation calculates its greenhouse gas emissions for each subsidiary using different methodologies.

• Relevance: ESG data should directly relate to a company’s sustainability impacts, risks, and strategic priorities. Reporting should focus on the metrics most material to the business based on its operational profile. This targeted approach connects ESG disclosures to the company’s core goals and objectives. Relevant sustainability data provides tailored insights into how each organization manages its most significant ESG risks and opportunities. For example, if a company reports on its water usage in a country without a water shortage, the data is irrelevant to its ESG performance.

• Availability: ESG data should be accessible when stakeholders need it. Disclosures should be easy to understand and use. Presenting sustainability information clearly and timely allows audiences to comprehend and apply the data in their decisions readily. For example, if a corporation reports on its greenhouse gas emissions once a year, but stakeholders require quarterly data. In that case, the data is outdated by the end of the first quarter.

• Transparency: Data must be auditable, which implies that the data must be verifiable by an impartial third party. Organizations should retain extensive data collection and reporting records to ensure auditability. For example, if an organization reports on its greenhouse gas emissions but fails to link it to any supporting documentation, the data is not auditable.

How Quality Data Can Help Your Business Thrive in the ESG Era?

High-quality data inputs are critical to successful ESG investing. Quality data can improve decision-making by accurately portraying an organization’s performance in ESG issues. By consistently monitoring data, organizations can identify inefficiencies in facility-level energy use and areas for improvement in sustainability indicators, which can contribute to cost savings. Furthermore, high-quality ESG data can enhance transparency and accountability, improving an organization’s reputation and brand image. With the plethora of incomplete or erroneous data and greenwashing in the reporting area, organizations that take the time to provide complete metrics will be perceived as high-value investments.

The ESG Data Quality Challenge: A Growing Concern for Businesses and Investors.

The absence of standardization in the data scenery and the absence of consistency in the definitions of ESG bring several challenges and negative consequences.

• With voluntary self-reporting, organizations use different models and standards for ESG disclosures. This fragmented approach prevents investment companies from applying comparisons between companies as reporting methodologies vary widely.

• Limitations increase as ESG data moves through the value chain from companies to providers. Reliance on inconsistent self-reported data leads to amplified inaccuracies as each actor interprets and processes the data differently.

• With no standardized reporting, ESG evaluations from different organizations often conflict due to unverified data collection processes. Financial institutions still need more means to confirm the accuracy of gathered third-party data as there is no single source of data.

• The availability and quality of ESG data depend heavily on company size, location, and industry. Organizations often provide outdated disclosures that fail to reflect current sustainability initiatives.

• In addition, organizations often lack the skills and resources to effectively collect, analyze, and report ESG data, which increases the risks of misinformed decisions based on flawed information.

• Poor quality data accumulates costs from manual processing, missed opportunities, and perceived investment risks. Attracting investors becomes more challenging as liabilities appear inflated.

The Future of ESG Data Quality.

Stakeholders now prioritize the understanding of an organization’s sustainability impacts. ESG reporting has become mission-critical as investors and consumers sharpen their focus on social and environmental performance. Only accurate, comprehensive disclosures can meet rising corporate transparency and accountability expectations.

However, there are challenges in collecting high-quality ESG data, such as the lack of standardization in data and reporting frameworks and consistency in ESG definitions. Organizations must navigate these challenges to mitigate the risks of inaccurate ESG data.

ESG data is quantitative and auditable; raw data sources and acquisition techniques for environmental and climate-related data are critical.

Understanding a company’s genuine performance on ESG concerns through quality data inputs is essential for investors looking to manage risk, uncover long-term performance drivers, or invest according to their preferences. Furthermore, an organization must understand what data is necessary because irrelevant or questionable ESG data can lead to poor investment decisions and harm an organization’s reputation. Businesses can enhance their environmental performance and attract investment by investing in high-quality ESG data.

Adopting a consistent framework, investing in data collecting and management, collaborating with third-party data providers, and carrying out ESG due diligence can help organizations enhance the quality of their ESG data. By implementing these measures, organizations can ensure that they provide high-quality ESG data to investors and other stakeholders.

The future of data quality in ESG reporting is promising. Organizations are investing in new technologies and solutions to improve the quality of their ESG data as demand for ESG data grows. These investments are assisting organizations in collecting and managing ESG data more effectively and communicating their ESG performance more clearly and transparently.