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Data quality assessment has become a prominent component in the successfulexecution of complex data-driven artificial intelligence (AI) software systems.In practice, real-world applications generate huge volumes of data at speeds.These data streams require analysis and preprocessing before being permanentlystored or used in a learning task. Therefore, significant attention has beenpaid to the systematic management and construction of high-quality datasets.Nevertheless, managing voluminous and high-velocity data streams is usuallyperformed manually (i.e. offline), making it an impractical strategy inproduction environments. To address this challenge, DataOps has emerged toachieve life-cycle automation of data processes using DevOps principles.However, determining the data quality based on a fitness scale constitutes acomplex task within the framework of DataOps. This paper presents a novel DataQuality Scoring Operations (DQSOps) framework that yields a quality score forproduction data in DataOps workflows. The framework incorporates two scoringapproaches, an ML prediction-based approach that predicts the data qualityscore and a standard-based approach that periodically produces the ground-truthscores based on assessing several data quality dimensions. We deploy the DQSOpsframework in a real-world industrial use case. The results show that DQSOpsachieves significant computational speedup rates compared to the conventionalapproach of data quality scoring while maintaining high prediction performance.

DQSOps: Data Quality Scoring Operations Framework for Data-Driven Applications