Data Ingestion Validation Through Stable Conditional Metrics with Ranking and Filtering

Abstract

We introduce an advanced method for validating data quality, which is crucial for ensuring reliable analytics insights. Traditional data quality validation relies on data unit tests, which use global metrics to determine if data quality falls within expected ranges. Unfortunately, these existing approaches suffer from two limitations. Firstly, they offer only coarse-grained assessments, missing fine-grained errors. Secondly, they fail to pinpoint the specific data causing test failures. To address these issues, we propose a novel approach using conditional metrics, enabling more detailed analysis than global metrics. Our method involves two stages: unit test discovery and monitoring/error identification. In the discovery phase, we derive conditional metric-based unit tests from historical data, focusing on stability to select appropriate metrics. The monitoring phase involves using these tests for new data batches, with conditional metrics helping us identify potential errors. We validate the effectiveness of this approach using two datasets and seven synthetic error scenarios, showing significant improvements over global metrics and promising results in fine-grained error detection for data ingestion validation.