Organizations need to dedicate the next three to six months to analyze their cloud spending and utilization patterns in detail, examining actual numbers instead of sanitized versions for executives. This will uncover inefficiencies, particularly in AI and ML workload projections which are expected to expand significantly beyond initial estimates.
Formulating a comprehensive workload placement strategy involves considering factors such as data gravity, performance requirements, and regulatory constraints. Decisions should reflect business realities rather than merely following trends, and explicit ROI models should be developed for hybrid and private cloud investments.
Optimizing technical architecture is essential, focusing on refining data pipelines, integrating edge computing, and satisfying the infrastructure needs for AI and ML. Multicloud connectivity has transitioned from an option to a necessity, yet it must align with robust security and compliance frameworks to ensure safety and efficiency.
Collection
[
|
...
]