Test Data Validation

The Test Data Validation module by Monolith is an AI-powered anomaly detection tool designed to help engineers identify and correct errors in complex test data. It rapidly scans hundreds of signals to detect over 90% of known issues, enabling teams to eliminate faulty tests, reduce waste, and accelerate validation workflows. The tool is optimized for high-volume, high-complexity engineering environments such as battery testing, automotive systems, and aerospace validation.

Features

• AI-Guided Anomaly Detection: Proprietary algorithms trained on real-world engineering data detect subtle and complex errors in seconds.
• Multivariate Error Detection: Identifies both single-channel and cross-signal anomalies across hundreds of data streams.
• Interactive Visualisation: Engineers can explore and prioritize anomalies using an intuitive interface.
• Automated Validation: Integrates with test systems to automate the detection process and reduce manual review.
• High Accuracy: Validated to detect more than 90% of known test data errors.

Capabilities

• Real-Time Data Inspection: Quickly analyze large volumes of test data to uncover hidden issues.
• Error Prioritization: Rank anomalies by severity and relevance to streamline corrective actions.
• System Integration: Seamlessly connects with existing test environments and Monolith’s Core Platform.
• Scalable Analysis: Handles gigabytes of data from complex systems like EV batteries or aerospace components.

Benefits

• Faster Validation: Reduces time spent manually reviewing test data, accelerating product development.
• Improved Data Quality: Ensures only clean, reliable data is used for modeling and decision-making.
• Cost Savings: Prevents wasted test runs and resource allocation due to undetected errors.
• Increased Confidence: Engineers can trust their data and focus on innovation rather than troubleshooting.