A global manufacturing company is implementing a new ERP system across multiple regions. The project manager has identified a risk that data migration from legacy systems may cause data corruption, leading to production delays. The risk owner proposes conducting a full data reconciliation after migration. However, the IT director argues that this would be too time-consuming and suggests only sampling data for verification. The risk manager must decide on the risk response. The project timeline is tight, and the company has a low tolerance for data integrity issues. Which of the following is the BEST course of action?
Full reconciliation directly addresses the risk and aligns with low tolerance for data integrity issues.
Why this answer
Full data reconciliation is the correct risk response because the company has a low tolerance for data integrity issues and the risk of data corruption could cause production delays. While time-consuming, this approach directly mitigates the identified risk by ensuring all migrated data is verified, aligning with the risk appetite. Sampling would leave a margin of error unacceptable for a low-tolerance environment, and the other options either fail to address the risk or are impractical.
Exam trap
The trap here is that candidates may choose data sampling (Option A) as a compromise to save time, overlooking that the company's low tolerance for data integrity issues demands full verification, not a statistical shortcut.
How to eliminate wrong answers
Option A is wrong because accepting the risk with data sampling ignores the company's low tolerance for data integrity issues and could leave undetected corruption that causes production delays. Option B is wrong because avoiding the risk by postponing the ERP implementation is an extreme overreaction that does not address the immediate need for migration and would cause significant business disruption. Option D is wrong because transferring the risk via insurance does not prevent data corruption or production delays; it only provides financial compensation after the fact, which does not meet the requirement for data integrity.