Retrieval-Augmented Enterprise Analytics with Privacy-Aware Cloud Data Pipelines
DOI:
https://doi.org/10.22399/ijcesen.5133Keywords:
Retrieval-augmented analytics, privacy-aware data pipelines, enterprise analytics, cloud-native architectures, data governanceAbstract
The rapid adoption of cloud-native data platforms has intensified the need for enterprise analytics systems that are both contextually intelligent and compliant with strict privacy and governance requirements. Traditional analytics pipelines often struggle to deliver meaningful insights from heterogeneous enterprise data while simultaneously safeguarding sensitive information. This study investigates a retrieval-augmented enterprise analytics architecture integrated with privacy-aware cloud data pipelines to address these challenges. A design-science–oriented methodology is employed to evaluate analytical quality, system efficiency, and governance effectiveness across multiple pipeline configurations. The results demonstrate that retrieval augmentation significantly improves contextual accuracy, relevance, and explainability of analytics outputs, while privacy-aware mechanisms reduce policy violations and unauthorized data exposure without severely impacting system performance. Sensitivity and interaction analyses further reveal that balanced tuning of retrieval depth and privacy thresholds is critical for maximizing overall system effectiveness. The findings highlight that embedding retrieval intelligence and privacy controls as first-class architectural components enables scalable, trustworthy, and regulation-ready enterprise analytics in modern cloud environments.
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