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This paper presents a novel approach of leveraging Inter-Annotator Agreement(IAA), traditionally used for assessing labeling consistency, to optimize DataManagement Operations (DMOps). We advocate for the use of IAA in predicting thelabeling quality of individual annotators, leading to cost and time efficiencyin data production. Additionally, our work highlights the potential of IAA inforecasting document difficulty, thereby boosting the data constructionprocess's overall efficiency. This research underscores IAA's broaderapplication potential in data-driven research optimization and holdssignificant implications for large-scale data projects prioritizing efficiency,cost reduction, and high-quality data.

Transcending Traditional Boundaries: Leveraging Inter-Annotator Agreement (IAA) for Enhancing Data Management Operations (DMOps)