Automating Storage Analytics with New Advances in AI
Effective storage management may be an everyday IT deliverable—but the strategy behind it has become anything but mundane. Fast-advancing artificial intelligence (AI) and machine learning (ML) capabilities are sparking all-new strategic value for storage. One of several promising new trends in storage and data management technology, AI-powered storage brings powerful predictive analytics to the challenge of managing voluminous data.
Equipped with cutting-edge, AI-backed storage analytics, your IT team could automate identification and resolution of potential hardware failures and compliance threats, staving off downtime and optimizing performance. You could also more accurately predict future needs based on analyses of current and historic data-generation rates—while potentially reducing operational costs and freeing up IT time for more strategic, innovative work.
Plus, predictive storage analytics could drive day-one automated deployment and alert you when data should be moved to different storage tiers, whether on-premise or in the cloud.
Vendors have been turning out an array of sophisticated AI-storage solutions. For example, Imanis Data Management Platform 4.0's SmartPolicies can optimize backup schedules based on user specifications, while Igneous DataDiscover and DataFlow index and categorize unstructured data. Pure Storage also has an AI-enabled platform powered with Nvidia graphics cards that accelerate visibility and automation for multi-cloud management, orchestration, and automation.
HPE Nimble Storage uses predictive flash storage technology powered by AI to drive availability and application uptime. Coupled with HPE’s InfoSight analytics technology, which it acquired with Nimble Storage in 2017, the solution helps IT teams predict, prevent, and auto-resolve problems from storage to VMs before they cause any disruption to business.
The potential strategic value is huge. However, it may take time for AI and ML storage solutions to become a viable option for many organizations.
Are you ready for the AI-storage revolution?
Every organization can theoretically benefit from these advances, but investing in large-scale machine learning and AI environments is no simple feat. With new solutions still emerging, scaling out from a traditional server and architecture can be expensive and the roadmap unclear.
You’d be wise to weigh carefully the benefits—which can be tremendous across the organization—with the costs of new and quickly-evolving infrastructure. You should also consider not only general IT challenges and benefits, but also how AI capabilities would affect multiple functions, from developers, data scientists, and code-writers to the CIO and CEO offices.
With thoughtful consideration, your team can then plot out its own storage strategy revolution. Whether it’s an ambitious all-out AI initiative or a smaller-scale solution will depend on your organization’s unique needs and goals.
This blog was sponsored by Hewlett Packard Enterprise (HPE).