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How can cloud-based predictive analytics advance your enterprise storage?

StorageExperts

As storage grows at unpredictable and rapid rates, enterprises need tools that can help them plan for this growth. Cloud-based predictive analytics can fill that need.

HPE_hybrid cloud_predictive analytics_blog.jpgThese days, the enterprise is incorporating cloud-based predictive analytics across a wide range of systems and processes. But perhaps the most compelling use cases are emerging from storage.

As data environments get bigger and more complex, legacy storage systems are struggling to keep up with the load. In the past, no matter what the challenge, the path to growing storage was always to add more of it.

This approach is no longer viable, though, as modern storage infrastructure must do more than store data. It must also organize, classify, analyze, and retrieve data, and it must do so in real-time. Storage is now an integral component in turning raw data into valuable, actionable information.

Why predictive analytics in the cloud?

Cloud-based predictive analytics is a relatively new class of technology that grew out of remote monitoring platforms and other data-intensive use cases. However, three distinguishing characteristics set it apart from earlier solutions.

First, it draws on large, diverse data sets in order to provide highly accurate pictures of operational states. Second, it brings a broader scope to the management process compared with the single-system or single-process tools of earlier solutions. And, finally, it leverages the latest advances in artificial intelligence and machine learning to produce highly intuitive and highly adaptive data interpretation.

Making storage smarter

When applied to storage environments, cloud-based predictive analytics becomes invaluable in the drive to deliver positive customer experiences, particularly when coupled with high-speed, flash-based storage environments. While flash-based storage has worked wonders because of its ability to support the volume of data generated by today's digital ecosystem and enable the speed at which users prefer to interact with applications, it's often far too sophisticated for traditional storage management models. Most legacy solutions emphasize risk reduction at the expense of performance enhancement, and this leads to the complex and time-consuming practice of fixing known problems with scheduled, intermittent software and hardware upgrades.

Cloud-based predictive analytics reinvents this process by stressing continuous, automated optimization and the incorporation of numerous metrics targeting the customer experience. For instance, it can leverage processes such as machine learning and artificial intelligence to extend the window of predictive fault identification, CIO writes. Not only does this provide an early warning system for impending failures; it also enables operators—or the system itself—to take corrective action before users encounter noticeable performance degradation. At the same time, cloud-based predictive analytics provides a more holistic view of system health by continuously tracking and comparing metrics such as utilization, availability, and throughput to identify conflicts that would otherwise go unnoticed in more fractured monitoring environments.

Introducing new efficiencies

This same expansive approach to data collection and analysis can also streamline support processes when users encounter performance problems. By employing predictive analysis, modern automated help systems can manage issues that would generate a level-one or level-two support ticket, freeing up human tech support to focus on concerns that grade a level three or higher. And even these more complex issues will be resolved more quickly and more thoroughly with real-time diagnostics and a more holistic view of the data ecosystem.

Another advantage of cloud-based predictive analytics is its ability to streamline deployment and upgrade processes by using artificial intelligence and machine learning to conduct pre-validation. By analyzing relevant information in the host vendor's data store and the data available from other vendors, industry groups, development communities, and other sources, the system is able to mask much of the complexity surrounding maintenance and integration. This, in turn, benefits IT staff and users by simplifying the introduction of new resources, cutting-edge tools, and security updates—all while lowering overall costs.

Choosing the right cloud-based predictive analytics solution

Not all solutions are created equal, however. Key differentiators are already starting to emerge on leading platforms, which will likely produce significant competitive advantages—and disadvantages—from one enterprise to another. When making a selection, the enterprise should look for the following features:

  • Multisite synchronous replication—This should be tied to automatic failover to ensure that systems remain up and running even in the most trying of circumstances.
  • Autonomous monitoring and management—Storage environments are so complex these days that their management requirements exceed what even an army of administrators can accommodate. Automating cloud-based solutions to handle routine matters allows skilled technicians to concentrate on higher-level strategic objectives.
  • Native cloud integration—Look for cloud-native APIs for Amazon, Microsoft Azure, or the provider of your choice to avoid costly and time-consuming deployment headaches.
  • Global vision and orchestration—The entire data ecosystem should function as a single entity, regardless of how many clouds, data centers, or edge facilities it inhabits.
  • High availability—Downtime is the enemy of today's digital business model. Any system that does not provide six-nines availability probably isn't going to cut it.

Arguably the most important factor when selecting a vendor for cloud-based predictive analytics is experience. Companies with long track records of successful technology development and innovation will likely provide a superior platform that supports advanced data environments. Part of this is because of the ingrained technical knowledge they have of storage and storage systems, but successful companies also boast the advantage of having accumulated broad data sets with which to train their intelligent algorithms on the proper techniques of modern storage management.

In this day and age, when a simple mobile app is enough to upend an entire industry, the last thing any enterprise needs is a storage environment that can't deliver a reliable customer experience from the start. Cloud-based predictive analytics is the proven way to remain relevant in a rapidly changing digital economy.

For more on the topic, check out these articles on AI-driven intelligent storage:

 

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Arthur Cole.pngMeet Around the Storage Block blogger and freelance content developer Arthur Cole. Arthur is a longtime industry journalist with more than 25 years of experience covering high-tech fields, ranging from professional audio and video to IT infrastructure, cloud, telecommunications, and B2B applications and services.

 


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