Utilizing data analytics to help support ESG reporting
ESGComplianceTháng Mười Hai 12, 2023

Utilizing data analytics to help support ESG reporting

Effective ESG reporting hinges on an organization's ability to collect, analyze, and report complete and accurate data timely. To face this requirement, organizations must find ways to consume potentially vast amounts of data from various, often isolated systems and synthesize that data into a reportable metric. ESG data analytics provides deeper insights into the information your organization has collected and enables faster, more accurate ESG reporting. This article will explore how ESG data analytics facilitates collecting and analyzing information and how internal audit teams can use ESG data analytics to test the reported data's accuracy and completeness.

  1. Collecting ESG data
  2. Analyzing ESG data
  3. Confirming completeness and accuracy of ESG data reporting
  4. Supporting reliable ESG reporting

Collecting ESG data

Since ESG covers a variety of domains, identifying and accessing the underlying data will be a challenge for many organizations. As ESG reporting, disclosure standards, and regulations move toward requiring reasonable assurance, the need for complete, accurate, and reliable supporting data is becoming clearer. Data analytics tools help organizations gather and integrate data from disparate sources, such as internal systems, external suppliers, and various databases where sustainability-related data may be stored. For example, collecting ESG data to support greenhouse gas emission reduction claims requires aggregating information from multiple offices, factories, stores, vendor locations, or even employees’ homes, including actual measurements and estimates of CO2 emissions in metric tons. Collecting ESG data in a centralized data repository can streamline the reporting process and ensure all relevant data is considered. .

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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.

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Supporting reliable ESG reporting

ESG data analytics provides numerous insights to organizations that need actionable information to report on their progress toward improving their ESG performance and making informed decisions. By using ESG data analytics, companies automate the collection and organization of information, analyze the data for trends and patterns, and generate ESG reports and disclosures while ensuring consistency, accuracy, and adherence to reporting standards. ESG data analytics is your most valuable resource for saving time and resources while improving the quality of ESG disclosures.

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Ken Petersen
Associate Director, Product Management
Ken has over 25 years of experience in developing and implementing systems and working with data in a variety of capacities while working for both Fortune 500 and entrepreneurial software development companies. Since 2002 Ken’s focus has been on the Governance, Risk, and Compliance space helping numerous customers across multiple industries implement software solutions to satisfy various compliance needs including audit and SOX.
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