Quality Assurance of Analytics at Scale: Emerging Methods for Continuous Validation in Real-Time Data Pipelines

The quality of analytical outputs is now a key, but under-researched issue, in the face of the growing dependence on large-scale analytics to support operational, strategic, and automated decision-making in organizations. Although much focus has been on data quality management, analytics quality of scale goes beyond data correctness to include model consistency, metrics, interpretability, and trustworthiness of decisions. This review is based on the synthesis of the existing literature in the field of business intelligence, big data analytics, and AI-driven decision systems to analyze the ways in which quality risks arise and spread throughout the analytics lifecycle. The paper critically examines quality dimensions, assurance methods, and governance systems needed to maintain analytical integrity in distributed, real-time, and automated systems. The main issues, such as the opaqueness of abstractions, drift in concepts, and the gap in accountability in an organization, are identified. The review finally precedes by stating the research gaps and suggesting future prospects for the onward path of persistent, automated, and governance-congruent quality assurance frameworks of analytics at scale.