Securing E-Commerce Data Architectures: Advanced Frameworks for Accurate Analytics and Strategic Decision Support

Abstract

The rapid evolution of e-commerce platforms has transformed business landscapes, with data emerging as a central asset for strategic decision-making and competitive advantage. However, the vast amount of personal and transactional data generated by e-commerce activities has amplified concerns about data security and integrity. Ensuring secure data architecture is essential to protect against breaches, maintain consumer trust, and uphold regulatory compliance. This paper proposes an advanced framework for securing e-commerce data architectures, focusing on integrating security protocols with analytics accuracy and decision support mechanisms. The framework emphasizes secure data pipelines, real-time analytics, and encryption strategies, alongside data governance principles to ensure data quality, confidentiality, and usability. Key components such as robust encryption methods, access controls, and anonymization techniques are examined in the context of both traditional and cloud-based e-commerce infrastructures. The research also explores the trade-offs involved in balancing security and analytics accuracy, noting that poorly implemented security measures may degrade data quality and hinder data-driven insights.

A major contribution of this work is a layered approach to securing data at different stages of its lifecycle—from collection and storage to analysis and dissemination—coupled with mechanisms to ensure high-quality analytics. This research investigates advanced technologies such as homomorphic encryption, blockchain, and artificial intelligence (AI)-powered anomaly detection, assessing their applicability and effectiveness in e-commerce data security. Additionally, we examine regulatory frameworks, including GDPR and CCPA, that impose requirements on e-commerce platforms, stressing the importance of regulatory compliance as part of the data security architecture. By developing a framework that secures data while maintaining its analytical utility, this research seeks to guide e-commerce organizations in enhancing data-driven strategies without compromising security standards. Ultimately, this study contributes to the broader discourse on secure data architectures in e-commerce by addressing the specific needs of both operational and analytical data flows, thereby providing a model for strategic decision-making support.