Towards Unsupervised Data Quality Validation on Dynamic Data


Validating the quality of data is crucial for establishing the trustworthiness of data pipelines. State-of-the-art solutions for data validation and error detection require explicit domain expertise (e.g., in the form of rules or patterns) or manually labeled examples. In real-world applications, domain knowledge is often incomplete, and data changes over time, which limits the applicability of existing solutions. We propose an unsupervised approach for detecting data quality degradation early and automatically. We will present the approach, its key assumptions, and preliminary results on public data to demonstrate how data quality can be monitored without manually curated rules and constraints.

Workshop on Explainability for Trustworthy ML Pipelines at EDBT