  Data quality is paramount in today's data-driven world, especially in the era
of generative AI. Dirty data with errors and inconsistencies usually leads to
flawed insights, unreliable decision-making, and biased or low-quality outputs
from generative models. The study of repairing erroneous data has gained
significant importance. Existing data repair algorithms differ in information
utilization, problem settings, and are tested in limited scenarios. In this
paper, we initially compare and summarize these algorithms using a new guided
information-based taxonomy. We then systematically conduct a comprehensive
evaluation of 12 mainstream data repair algorithms under the settings of
various data error rates, error types, and downstream analysis tasks, assessing
their error reduction performance with a novel metric. Also, we develop an
effective and unified repair optimization strategy that substantially benefits
the state of the arts, as empirically confirmed. We demonstrate that, the pure
clean data may not necessarily yield the best performance in data analysis
tasks and data is always worth repairing regardless of error rate. Based on the
found observations and insights, we provide some practical guidelines for 5
scenarios and 2 main data analysis tasks. We anticipate this paper enabling
researchers and users to well understand and deploy data repair algorithms in
practice. Finally, we outline research challenges and promising future
directions in the data repair field.
