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Why climb out of the app-data gap? Soar over it instead.

Jason Nickerson, Supplier Business Executive HPE Storage, Tech Data

Jason Nickerson, Supplier Business Executive
HPE Storage, Tech Data

Many, if not most of your customers’ environments are susceptible to the app-data gap – defined as the inability to enjoy immediate access to data all the time and without interruption. The gap creates a bottleneck that can impact productivity and even the company’s overall operations.

Once in the gap, companies try to do whatever it takes to climb out of it, focusing most of their efforts on storage. That’s only natural since that’s where the data resides. However, in 54% of the cases, that’s not where the problem resides. The issue could just as well be related to configuration or interoperability, as well as to host, compute, or VM.

Reacting to the gap

Because the causes of the gap are multiple and complex, they usually cannot be quickly identified and addressed. This leads to a wide range of consequences, including time wasted chasing the wrong issue, extended downtime, user frustration, and missed business goals.

The same factors also mean that the gap cannot be eliminated simply by buying new and “better” storage. Even fast flash cannot address the 54% of the gap issues that are non-storage related.

The best way to help your customers climb out of the app-date gap is to not let them fall into it at all. That is something you can easily do by providing them with a storage solution that makes use of machine learning and predictive analysis.

Avoiding the gap completely

Predictive analysis helps your customers avoid the app-data gap by identifying and addressing issues before they can cause issues in the environment. As a result, customers probably will not know the gap even exists.

Specifically, predictive analysis provides:     

  • Downtime prediction, identifying potential causes of slowness and downtime well before they occur.
  • Downtime automatic prevention, done automatically thanks to machine learning. This approach is much superior to traditional reactive monitoring, which provides little relief other than flagging the problem.
  • Prescriptive resolution, leading to a clear and prescriptive resolution on the rare occasion where the infrastructure cannot automatically prevent an issue. This eliminates the need for customers to call support or pore through documentation/online forums
  • Rapid root-cause analysis rapidly identifies the root cause so that the problem can be quickly resolved. Again, this is for the rare occasions where no automatic prescription is available.
  • Cross-stack application of analytics, providing an intimate knowledge of and the ability to collect information across the infrastructure stack.
  • Analytics-driven tech support that can eliminate the need for frontline, level-1 and level-2 support engineers.

Closing the gap for customers. Widening it for the competition. 

HPE InfoSight has proven its ability to deliver all the benefits of a machine learning and predictive analysis solution. Pioneered by Nimble Storage, it is now available to your customers in both HPE Nimble and HPE 3PAR storage. These solutions deliver on the impressive promise of predicative analysis by predicting and resolving 86% of problems before customers even know there is an issue. These include problems throughout the environment, not just in storage, that is why both HPE Nimble and HPE 3PAR offer guaranteed six-nines (99.9999%) availability.

Predictive analysis also offers you a clear benefit since it is exclusive to HPE storage and therefore not available from any other vendor.

More information 

Information about HPE InfoSight and predictive analysis is available on the storage page of the HPE Zone. I highly recommend the following white papers, which you can also share with customers:

Your customers can find information on the HPE InfoSight page on the HPE website. Encourage them to read the “Faster applications with predictive flash” e-book for a quick overview of the power and benefits of predictive analysis.  

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