Enabling AI-Driven Decision-Making through Scalable and Secure Data Infrastructure for Enterprise Transformation - Toluwanimi Adenuga; Amusa Tolulope Ayobami; Uchenna Mike-Olisa (International Journal of Scientific Research in Science, Engineering and Technology, 2024)
Enabling artificial intelligence (AI)-driven decision-making within modern enterprises requires a foundational shift toward scalable and secure data infrastructure. This paper explores the central role that robust data architecture plays in powering intelligent, agile, and autonomous decision systems across organizational ecosystems. As industries become increasingly data-intensive, especially in regulated domains such as finance, healthcare, and defense, ensuring that data is reliable, integrated, and interoperable becomes paramount. At the core of this transformation is the alignment of data governance frameworks with enterprise-wide data strategies to support ethical, compliant, and efficient AI deployment. The discussion highlights how cloud-native technologies, including containerized environments, microservices, and serverless computing, provide the flexibility and scalability needed to manage dynamic data flows and support AI workloads in real time. The integration of intelligent data lakes and data warehouses enables unified data views, promoting consistency, transparency, and contextual accuracy essential for predictive and prescriptive analytics. Moreover, by embedding real-time analytics and streaming data pipelines, enterprises can facilitate near-instantaneous decision-making processes driven by machine learning and other AI models. The paper also underscores the importance of designing data infrastructure with security and compliance by design, addressing concerns around data privacy, sovereignty, and ethical use in AI-driven operations. Interoperability across systems, platforms, and departments is emphasized as a key enabler of unified intelligence, empowering enterprises to unlock insights from disparate datasets without compromising performance or control. Ultimately, the paper argues that the success of enterprise transformation initiatives hinges on the ability to architect data ecosystems that are not only scalable and secure but also strategically aligned with business objectives and AI capabilities. Organizations that invest in resilient data infrastructure, strong governance policies, and seamless integration capabilities are best positioned to unlock the full potential of AI, drive innovation, and gain competitive advantage in complex, fast-evolving markets.