ESG data analytics to help support ESG reporting

Analyzing ESG data

With the data collected, the next step is organizing, normalizing, cleaning, and analyzing the information. Data analytics techniques identify and correct the accumulated data’s errors, inconsistencies, and missing values. ESG analytics will also require normalization since it will likely aggregate data from different sources. Data cleansing and normalization ensure the accuracy and reliability of the ESG metrics reported. 

ESG analytics also calculates and tracks key ESG metrics, such as carbon emissions, water usage, waste generation, employee diversity, and human rights records. Robust metrics reporting enables companies to monitor their progress toward ESG goals and identify areas for improvement. As part of the analysis, ESG analytics helps companies compare their ESG performance against industry peers, regulatory standards, and best practices. For example, if a target goal is to increase the percentage of employees with veteran status, analyzing employee records in a large retail corporation can easily include over a million records in a population with seasonal hiring and high turnover. ESG analytics can provide real-time insight into multiyear trends on this metric. Comparative benchmarking provides valuable context for evaluating the company’s ESG standing and identifying potential risks or opportunities. From a risk management perspective, ESG data analytics models different scenarios to assess potential ESG risks so companies can anticipate and mitigate ESG-related challenges before these materialize.

Confirming completeness and accuracy of ESG data reporting

Since ESG reporting standards are still evolving, internal audit teams must pay close attention to their organization’s reporting and disclosure processes. The most common practice among internal auditors is ensuring the organization has provided complete and accurate data in their disclosures. ESG data analytics uncovers patterns and trends in ESG data, providing internal auditors with the insights they need to conclude on the effectiveness of internal controls related to the organizations’ process for gathering and analyzing the ESG data. Internal auditors are also keenly aware of fraud risk in ESG reporting (often related to greenwashing) and the susceptibility of managers to provide misleading information that leads to a false impression of progress toward some ESG goals. By using ESG analytics, the team is providing data-driven insights that can be confirmed to source systems for comparison to reported information. A high level of traceability will prove extremely valuable as auditors confirm compliance with various ESG reporting standards and validate the accuracy of the reported information.