The exponential growth of data, and in particular unstructured data, is a problem enterprises have been wrestling with for decades. IT organizations are in a constant battle between ensuring that data is accessible to users, one the one hand, and that the data is globally protected and in compliance with data governance policies, on the other. Added to this is the need to ensure that files are stored in the most cost-effective manner possible, on whichever storage is best at that point in time. The problem is there is no such thing as a one-size-fits-all storage platform that can serve as the shared repository for all of an organization’s data, especially across multiple locations. Instead, there are myriad storage choices available from as many vendors, each of which is best suited for a particular performance requirement, access protocol, and cost profile for each phase of the data’s life cycle. Users and applications simply want reliable, persistent access to their files. But data policies inevitably require files to move to different storage platforms or locations over time. This creates additional cost and complexity for IT and disrupts user workflows. The explosion of AI and machine learning applications has sparked a new explosion of data that is only making this problem worse. Not only is the creation of data growing even faster, AI applications need access to legacy data repositories for training and inferencing workloads. This typically requires copying data from lower-cost, lower-performance storage systems into much higher-cost, higher-performamce platforms.
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