Improving Data Quality in Big Data Systems: Best Practices and Automation Strategies

Abstract

Data quality is a foundational requirement for trustworthy analytics, reliable machine learning, and compliant data operations in large-scale (volume, variety, velocity) environments. Unlike traditional BI stacks where errors can often be traced and fixed manually big data systems amplify quality defects through distributed processing, schema drift, and multi-team transformation layers. This article synthesizes best practices for improving data quality across ingestion, processing, storage, and consumption, with an emphasis on automation strategies: shift-left validation, continuous monitoring, policy-as-code governance, and observable quality SLOs. It further connects operational observability (logs/metrics/traces) to measurable data quality outcomes and proposes a pragmatic automation blueprint using modern orchestration and monitoring patterns. The discussion is grounded in established big-data quality research and industry guidance and aligned with modern governance automation architectures and data pipeline observability techniques [1]–[4].