The role of machine learning and automation in storage

Drivers, perceptions, readiness and practicalities

Published June 2020

The role of machine learning and automation in storage

There has been lots of hype around the increasing role that machine learning, and artificial intelligence more broadly, will play in how we automate the management of IT systems. Whether it's labelled as Intelligent Infrastructure, AIOps, “self-driving IT”, or even private cloud, the aim is the same: to embed machine learning (ML), workflow automation and infrastructure-as-code capabilities into systems, enabling them to automatically make changes in real-time to predict and adjust for future requirements – without human intervention. The claim is that this can both remove much of the manual drudgery associated with routine IT administration, and dramatically speed up the process, with the ultimate goal being continuous, automated, self-optimization.

Major concerns remain, however. Are the latest AI/ML-powered intelligent automation solutions trustworthy and ready for mainstream deployment, especially in areas under heavy resource, cost and SLA pressures, such as storage management? And more important, are we ready to trust in full hands-off automation when it comes to core business services, or is the hands-on involvement of admin staff still required?

To help answer these questions and understand the attitudes and perceptions of IT professionals towards automated IT, and in particular automated storage management, we conducted an online survey to which 171 people responded, from organizations large and small.

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