Schema Validation and Visualization Tool for Enhancing Data Usability in Machine Learning Pipelines

Abstract

Ensuring high data quality is a fundamental requirement for developing reliable data analytics and machine learning systems, where schema integrity plays a critical role. In practice, errors or inconsistencies in data schemas can propagate through processing pipelines, ultimately leading to unreliable analyses and degraded model performance. Therefore, effective mechanisms for schema validation and interpretation are essential. This paper presents the Schema Validator and Visualizer, a unified framework/tool that automates schema validation while providing intuitive visual representations of schema structures, simultaneously. Specifically, the proposed framework detects anomalies, enforces structural and semantic consistency, and generates graphical views that enhance interpretability for both technical and non-technical users. Furthermore, the proposed tool supports multiple data formats, including JSON, CSV, and relational databases, thereby ensuring adaptability across diverse data science workflows. Additionally, by integrating automated validation with interactive visualization, the proposed framework streamlines data preparation processes, strengthens data governance practices, and reduces manual effort in schema management. Consequently, it facilitates the development of more reliable and reproducible analytics and machine learning pipelines. Moreover, future extensions of the proposed framework will focus on incorporating machine learning based anomaly detection, real-time schema monitoring, and tighter integration with large-scale data ecosystems. Overall, the proposed tool advances data quality management and supports more effective data-driven decision making.