What data integrity control mechanism is used to locate invalid, obsolete, redundant, or outdated data from a database or data warehouse?

The correct choice is scrubbing, which refers to the process of cleaning data by identifying and correcting or removing invalid, obsolete, redundant, or outdated information from a database or data warehouse. This mechanism is critical for maintaining data integrity, as it ensures that the data is reliable, accurate, and up to date, thereby enhancing the overall quality and usability of the data for analysis and decision-making.

Scrubbing involves various techniques such as validation, standardization, and deduplication, which help to refine the dataset. By implementing scrubbing, organizations can mitigate risks associated with poor data quality, leading to more reliable insights and operational efficiencies.

In contrast, the other mechanisms mentioned serve different purposes. Tokenization is primarily focused on replacing sensitive data with non-sensitive equivalents to enhance security, while anonymization aims to protect individual privacy by removing identifiable information from datasets. Integrity management, although concerned with the overall integrity of data, does not specifically target the removal of invalid or outdated data. Instead, it typically encompasses broader practices to ensure data remains accurate and trustworthy over time.