Article’s

Cloud computing platforms have become a foundational environment for storing, processing, integrating, and delivering digital services. Yet the value of cloud adoption depends not only on the availability of infrastructure but also on how data assets and application workloads are governed, secured, orchestrated, monitored, and optimized throughout their lifecycles. Data management and application management are often treated as separate operational domains: the former emphasizes storage, consistency, access, quality, protection, and lifecycle controls, while the latter focuses on deployment, configuration, scaling, reliability, observability, and service-level objectives. In practice, these domains are tightly coupled. Data locality affects application latency; application architecture shapes data consistency and recovery requirements; security controls influence both throughput and operational complexity; and autoscaling decisions alter cost, contention, and data access patterns. This paper presents an analytical review and conceptual framework for evaluating data and application management in cloud computing platforms. It synthesizes established cloud reference concepts and research on cloud data management, storage, security, autoscaling, and performance-aware resource management. The study organizes the problem into six dimensions: data governance and lifecycle, application architecture and deployment, performance and elasticity, security and compliance, resilience and interoperability, and financial/operational governance. A multi-criteria evaluation model is proposed to support platform assessment, with indicators such as latency, throughput, recovery time, availability, data durability, policy coverage, portability, and cost per workload unit. Rather than reporting unperformed experiments, the paper specifies a reproducible validation protocol that future empirical work can apply across public, private, hybrid, and multi-cloud settings. The analysis indicates that no single platform configuration is optimal for every workload; successful management requires workload-aware trade-offs, measurable service objectives, automation with guardrails, and explicit ownership of data and application controls. The proposed framework can guide researchers and practitioners in selecting, designing, and evaluating cloud management strategies.