Data verification, the process of labeling data items as correct or incorrect, is a preprocessing step that may critically affect the quality of results in data-driven pipelines. Despite recent advances, verification can still produce erroneous labels that propagate to downstream query results in complex ways. We present a framework that complements existing verification tools by assessing the impact of potential labeling errors on query outputs and guiding additional verification steps to improve result reliability. To this end, we introduce Maximal Error Score (MES), a worst-case uncertainty metric that quantifies the reliability of query output tuples independently of the underlying data distribution. As an auxiliary indicator, we identify risky tuples - input tuples for which reducing label uncertainty may counterintuitively increase the output uncertainty. We then develop efficient algorithms for computing MES and detecting risky tuples, as well as a generic algorithm, named MESReduce, that builds on both indicators and interacts with external verifiers to select effective additional verification steps. We implement our techniques in a prototype system and evaluate them on real and synthetic datasets, demonstrating that MESReduce can substantially and effectively reduce the MES and improve the accuracy of verification results.

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