Which of these approaches to obtaining and retaining high quality data is likely the most expensive?

The most expensive approach to obtaining and retaining high-quality data is repairing the data after issues or errors have occurred. Repair involves identifying inaccuracies, inconsistencies, or missing information and then correcting those problems. This process can be resource-intensive, requiring considerable time, effort, and often specialized personnel to clean and validate the data properly.

Repairing data can also lead to significant indirect costs, such as delays in decision-making, decreased trust in the data, and potential negative impacts on business operations as a result of relying on flawed information. Additionally, if data errors are not addressed promptly, they may escalate issues that increase the overall cost of repair.

In contrast, prevention, while it might require an initial investment, often saves resources in the long run by addressing quality issues before they arise. Outsourcing can vary widely in cost depending on the arrangement but doesn't inherently cost more than repair. Detection focuses on identifying existing issues but doesn’t involve correcting them, thus typically being less expensive than repair.