Which approach to obtaining and retaining high quality data is likely the most expensive?

The approach of repairing data after it has been identified as poor quality is typically the most expensive method for managing data quality. This is largely due to the resources needed to correct errors, clean data, and potentially reprocess affected data entries. When data is erroneous, it can lead to significant costs in terms of time, labor, and potentially lost revenue, as organizations may need to revisit multiple processes to rectify the issues stemming from incorrect data.

In addition, repairing data often involves employing specialized tools and personnel that can increase costs. The cumulative effect of these factors makes the repair approach less efficient than other methods such as prevention or detection. Prevention, for example, focuses on establishing processes and controls to ensure high-quality data from the outset, which can save money and resources in the long term. Detection involves identifying data quality issues as they arise, which can also be less costly than repairing issues after they have been integrated into systems. Therefore, focusing on preventive measures and detection strategies is generally more cost-effective compared to the reactive nature of repair.