Free DownloadData Storage Management: What is it and Why is it Important?
With this guide, explore what data storage management is, who needs it, advantages and challenges, key storage management software features, security and compliance concerns, implementation tips, and vendors and products.
Organizations can use AI for storage management and AIOps to learn where to make improvements in their storage infrastructure, but note the benefits and drawbacks.
The rise of AI has moved storage management forward, from storage resource management software to AIOps tools that automate much of the process.
AIOps enables monitoring, diagnostics, predictive analysis and prescriptive functions for storage infrastructure and applications. Essentially, AIOps can tell an organization what is happening with storage and why, what could happen and what to do about it.
In recent years, AIOps capabilities have been augmented with large language models and generative AI capabilities. This means that some platforms enable storage admins to use natural language queries to verify storage health, generate reports and even receive guidance on how to fix problems.
Some platforms are also incorporating agentic AI, meaning that the AIOps platform can perform various actions on the administrator's behalf. As these capabilities mature, they will cause storage management to become less labor-intensive. Agentic AI tools also have the potential to shorten the mean time to resolution when problems occur by executing policy-based remediation workflows.
By removing much of the manual work from storage management, AIOps drives efficiency and frees IT staff to focus on other tasks. A variety of vendors provide AI for storage and storage management, which has advantages and disadvantages.
AIOps for storage uses machine learning to collect and analyze telemetry data. This information is used to generate predictive analytics.
AIOps includes automation, performance management and service management, automating many of the decisions involved in scaling and securing storage systems. Additionally, AI can help with tasks such as storage planning, storage lifecycle management, root-cause analytics and storage optimization.
Telemetry data is a key piece of AI storage management tools. This data is information collected through sensors from storage, servers and networking systems.
AI and machine learning analyze information gathered from these systems on hardware devices, OSes, applications and hypervisors. This helps to detect anomalous activity, such as poorly configured devices, unexpected capacity growth or unusual throughput demands. These activities can be used for resource planning and optimizing storage performance.
AI storage management systems are often SaaS applications that perform the analysis on a public cloud. This enables those applications to compare that data to information collected from a wide range of systems, increasing their predictive capabilities. Several vendors, however, offer products specifically designed to run on-premises. It is also worth noting that there are products that are specifically designed for localized use. These products are intentionally air-gapped and may be a good fit for use in regulated industries or in situations in which Internet connectivity is lacking.
The differentiation in AI-based storage management tools is in their functionality. Many such tools use AI to help with troubleshooting, root cause analysis and storage optimization. Some tools provide additional functionality, such as help with power consumption, application deployment and even hardware lifecycle management. Likewise, some products are designed to look for anomalies that may signal a ransomware attack. Vendors are also focusing on cyber-resilience and using analytics to help ensure availability.
Most AI-enabled storage tools remain aligned with a vendor's storage ecosystem. However, nearly all such tools support API use and telemetry ingestion, thereby allowing them to work with third-party storage. However, levels of interoperability still vary from one vendor to the next.
Before investing in an AI storage tool, consider how the system delivers alerts. Most tools display alerts on dashboards, but some use other mechanisms, such as text messages or email messages. Whatever the delivery method, the tool should filter out noise alerts so the organization can focus on what is important.
AI storage management options
Several vendors offer AI storage management software that is compatible with various systems and applications. Vendors also use AI storage management with their storage-as-a-service offerings.
Vendors and the systems and applications they cover include the following:
Dell AIOps. Dell AIOps is an AI-driven observability tool and operations platform for Dell products, including storage, servers, networking and hyperconverged systems.
HPE InfoSight.HPE InfoSight is an AI-based platform for monitoring, optimizing, and analyzing HPE storage, servers, and virtualized resources.
IBM Storage Insights. A cloud-based SaaS platform designed to provide visibility, capacity planning, and alerting capabilities for IBM and select third-party storage.
Infinidat InfiniVerse. A portfolio-wide services architecture that uses AI to analyze telemetry to deliver storage insights, predictive anomaly detection, and resource optimization.
NetApp Active IQ. An AI-powered advisor that provides guidance and recommendations for NetApp storage.
Pure Storage Pure1. An AI-driven tool that offers real time insights into Pure Storage arrays through natural language queries.
Advantages of AI storage management
AI-driven storage management removes much of the complexity and manual tasks associated with traditional storage resource management. Benefits include automated provisioning, intelligent data tiering and workload optimization. Dedicated storage personnel can spend less time monitoring and managing systems as a result. AIOps can be a big help for MSPs because it enables them to manage many customers' storage remotely.
By predicting future events based on current usage patterns, management systems can prevent problems that users may not otherwise anticipate. They can also advise users to add storage capacity and compute resources and complete other upgrades before performance becomes a problem. Users can configure the applications to automatically take action to prevent device failures or performance degradation, although IT shops might prefer to receive recommendations and then make those changes themselves.
Challenges of AI storage management
One issue with AI storage management systems is that they are often proprietary and usually work with only one vendor's products. While some third-party integrations might be possible, if an organization has a SAN from one vendor, another vendor's AI storage management system probably will not be compatible with it.
Another issue is that data collection and analysis generate more data -- a case of storage management creating a need for more storage. Over time, organizations must decide which data to safely discard.
Not all organizations can allow third parties to connect to their data centers. So-called dark sites cannot use SaaS-based analytics that collect and store data in public clouds or at the vendor's site. Vendors can use analytics software on local servers so the telemetry data doesn't travel, but these users lose some benefit of the analytics when their data isn't compared to that of their peers.
AI-based storage management will evolve as a direct result of AI technology reaching maturity.
Dark site or not, all users should ask their vendors how much information they collect outside of storage and how they guarantee that data remains anonymous and protected.
As AI adoption matures, organizations must carefully consider model transparency, explainability and governance. This is especially true for organizations in regulated industries. In such environments, it is important to have insight into why AI has recommended a particular action be taken. These types of organizations also require transparency regarding data residency and how telemetry information is actually being used.
The future of AI in storage management
The last few years have seen tremendous advances in AI and machine learning technologies. As such, AI-based storage management will evolve as a direct result of AI technology reaching maturity.
While generative AI capabilities have already been integrated into some storage management tools, this trend will likely accelerate. AI assistants capable of summarizing storage health, recommending configuration changes or forecasting growth will eventually become standard.
The trend of storage vendors using AI as a security tool will undoubtedly also gain momentum. Ransomware detection capabilities are already becoming the norm. Vendors will likely also, at some point, begin using AI to look for signs of other types of nefarious activity.
Cyber resilience will continue to be another area of focus, with AI being increasingly integrated into processes such as snapshot creation or workload isolation.
AI-based storage management tools will likely support intelligent backup and recovery capabilities. For example, AI could automatically identify the organization's most critical data, ensure that the data is backed up and prioritize this high-value data in the event a restoration becomes necessary.
AI in storage systems could also deliver self-healing capabilities. In doing so, AI might detect failing disks, corrupt sectors or similar problems and take corrective action to prevent data loss or system outages.
Over time, AI in storage management will evolve from being a differentiating factor to becoming a baseline requirement. As this occurs, storage management will become part of a much broader, AI-enabled IT operations strategy.
Brien Posey is a 22-time Microsoft MVP and a commercial astronaut candidate. In his over 30 years in IT, he has served as a lead network engineer for the U.S. Department of Defense and as a network administrator for some of the largest insurance companies in America.
