CLOUD-NATIVE DATA PIPELINES FOR SCALABLE AUDIO ANALYTICS AND SECURE ENTERPRISE APPLICATIONS

Abstract

This study responds to a critical gap in the empirical understanding of how cloud-native architectural capabilities directly and indirectly contribute to analytics performance outcomes and enterprise business value in production-scale audio analytics environments. While industry discourse frequently asserts that cloud-native maturity enhances pipeline efficiency, resilience, and innovation velocity, systematic evidence quantifying these relationships—particularly in the context of audio data pipelines with stringent real-time processing, compliance, and observability requirements—remains limited. The central purpose is to estimate both the individual and joint effects of cloud-native maturity, pipeline automation and observability capabilities, and security and data governance frameworks on analytics performance and downstream business outcomes, reflecting the hypothesis that technical maturity and organizational governance jointly determine enterprise readiness for value extraction from audio intelligence workflows. The study employs a quantitative, cross-sectional design using a case-based survey administered across six enterprise contexts representing cloud-first and hybrid-cloud environments. A total of 198 role-verified practitioners including DevOps engineers, data architects, product leads, and security officers—from multiple industries such as telecommunications, media, healthcare, and finance participated in the study.  The analysis plan follows a rigorous sequence beginning with descriptive statistics to characterize the maturity distribution of participating organizations, followed by reliability and validity assessments using Cronbach’s alpha and confirmatory factor analysis. Correlation matrices establish preliminary relationships among constructs, while hierarchical multiple regression models test theoretical expectations regarding the incremental explanatory power of each architectural and operational domain. Moderation and mediation effects are explored using PROCESS-based algorithms and structural estimation logic to evaluate whether cloud-native maturity moderates the impact of automation and observability on performance, and whether analytics performance mediates the path to business value. Robustness checks include cluster-robust standard errors to account for case-level dependencies and mixed-effects modeling to re-estimate coefficients under alternative assumptions of nested hierarchies. The findings reveal a clear pattern: automation and observability capabilities demonstrate the strongest unique association with analytics performance, suggesting that operational excellence in pipeline management yields direct gains in processing quality and reliability.  The performance-to-value pathway is the dominant mechanism through which technical capabilities generate strategic benefits, affirming the mediating role of analytics effectiveness.