Which control should internal audit examine to validate sustainability data?

Which control should internal audit examine to validate sustainability data?

• A. Cash management
• B. Recruitment controls
• C. Data governance framework and data lineage controls
• D. Inventory valuation

Answer: (C)

Ensuring the reliability of sustainability data hinges on how data is governed and how its provenance is tracked. A robust data governance framework defines who owns data, what the data means, how it’s collected, and the quality standards it must meet. It establishes policies, data definitions, formats, and timeliness, plus controls to monitor data quality across dimensions such as accuracy, completeness, and consistency. Data lineage controls map the journey of data from source systems through every transformation to the final report, providing visibility into where data originated, how calculations were made, and who changed it. This traceability is essential for verification, reproducibility, and detecting errors or inappropriate edits. Internal audit should focus on both governance and lineage artifacts—data owners and responsibilities, data dictionaries, metadata, data quality dashboards, reconciliation procedures between source data and reported figures, access and change controls over data processing, and documentation of data flows from source systems (like ERP, energy meters, supplier data) to the reporting platform. Other controls such as cash management, recruitment processes, or even inventory valuation do not address the end-to-end assurance of data quality and traceability for sustainability metrics, even though related inputs might interact with those areas. The governance and lineage mechanisms provide the foundational assurance needed to validate sustainability data.

Ensuring the reliability of sustainability data hinges on how data is governed and how its provenance is tracked. A robust data governance framework defines who owns data, what the data means, how it’s collected, and the quality standards it must meet. It establishes policies, data definitions, formats, and timeliness, plus controls to monitor data quality across dimensions such as accuracy, completeness, and consistency. Data lineage controls map the journey of data from source systems through every transformation to the final report, providing visibility into where data originated, how calculations were made, and who changed it. This traceability is essential for verification, reproducibility, and detecting errors or inappropriate edits.

Internal audit should focus on both governance and lineage artifacts—data owners and responsibilities, data dictionaries, metadata, data quality dashboards, reconciliation procedures between source data and reported figures, access and change controls over data processing, and documentation of data flows from source systems (like ERP, energy meters, supplier data) to the reporting platform.

Other controls such as cash management, recruitment processes, or even inventory valuation do not address the end-to-end assurance of data quality and traceability for sustainability metrics, even though related inputs might interact with those areas. The governance and lineage mechanisms provide the foundational assurance needed to validate sustainability data.