Tiered storage is a strategic approach to data management that allocates different types of data across multiple storage media to make the best use of performance, cost efficiency, and accessibility. By categorizing data according to its importance and how frequently it’s used, organizations put their most critical information on the highest-performing storage, while less critical or infrequently accessed data resides on more economical, slower storage media. Tiered storage forms a foundational component of information lifecycle management, which manages data from creation through archival for effective use of resources and regulatory compliance. The concept of tiered storage originated in the mainframe era, primarily driven by IBM's innovations in managing complex storage requirements efficiently. Automated storage tiering is a technique that dynamically moves data between different tiers of storage based on pre-defined policies and real-time usage patterns. Optimized tiering takes automation to the next level by developing a well-defined taxonomy for data, categorizing it by importance, how often it’s used, and retention requirements, so each data type is assigned to the most appropriate storage tier. Tiering and caching are distinct data management strategies with different purposes and behaviors; while tiering involves moving entire datasets between storage layers based on usage patterns, caching creates a temporary high-speed copy of frequently used data in fast memory.