Advancements in Quality Assurance and Testing in Data Analytics

Abstract

As data analytics continues to play an increasingly critical role across industries, ensuring the quality and reliability of analytical processes and outcomes has become paramount. This paper provides a comprehensive review of recent advancements in quality assurance (QA) and testing methodologies for data analytics. Key areas of progress are examined, including automated testing frameworks, data quality management, model validation techniques, and emerging approaches like AI-assisted QA. The paper also explores challenges in analytics QA and proposes a forward-looking framework for holistic quality management in data science workflows. Case studies from finance, healthcare, and e-commerce illustrate the real-world impact of these advancements. The findings highlight the growing sophistication of analytics QA practices and underscore the need for continued innovation to address evolving complexities in big data environments.