Final answer:
A data integrator prefers inconsistency in data accuracy over consistent inaccuracy.
Step-by-step explanation:
The statement is true.
A data integrator typically prefers inconsistency in data accuracy over consistent inaccuracy because inconsistent data accuracy can still be corrected through data cleansing and transformation processes. In contrast, if the data is consistently inaccurate, it becomes much more difficult to correct or align with other datasets.
For example, a data integrator might have multiple sources of customer data with different levels of accuracy. In this case, they would prioritize inconsistent data accuracy as it allows them to identify discrepancies and take steps to improve overall data quality.