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Intelligence changes everything

Data is at the heart of everything your business does. You must make it smarter if you want to transform your enterprise. Here’s a look at what that means, why it matters, and what you need to do next.

The days of unintelligent data are over. Enterprises can no longer afford to think about data the way they’ve done for years. They need an intelligent data platform that can help them unlock business benefits and deal with an increasing, non-stop data deluge.

The value of intelligent data was brought home to me when I started using Waze. Waze automatically alerts me to traffic, directs me around accidents and optimizes my driving routes. With it, I save time and avoid disasters. Waze relies on vast amounts of traffic data that change every second of the day. It’s built on a sea of sensors and uses artificial intelligence (AI) in the backend. It’s a powerful example of how intelligence applied to data changes everything.

Using intelligence in this way doesn’t just change everything for consumers, but on the business side as well. 

Dawn of the Intelligence era

For a long time, advances in storage were focused on speed and agility. The flash era brought the speed of flash to storage to speed up applications, and delivered the majority of the gains. We’re not likely to see game-changing speed gains like that again, though, because with NVMe, or storage-class memory, we are now talking about incremental gains.

The cloud era has unlocked new levels of agility. Because of this, everyone expects to have an as-a-service experience going forward for everything.

The real game changer now is the AI-driven experience. With AI, you can predict and proactively resolve issues before they occur as well as unlock hidden insights from your data to deliver better customer experiences, uncover new revenue streams, or improve business models.

Applying AI to storage is particularly important in an era in which we need to store and use almost unimaginable amounts of data. By 2025, there will be 175 zettabytes of data in the world, according to IDC. (To give you a sense of the size of that, a single zettabyte is roughly a trillion gigabytes.) Much of that growth will come as a result of IoT devices.

So, how do you store all of this data? More importantly, how do you unlock hidden insights from the data to transform your business and develop a competitive advantage? You need an intelligent data strategy, where data is always on, always fast, and delivers an automated, on-demand and self-service experience that your developers expect they can only get from the public cloud.  It enables hybrid cloud by design and it brings global intelligence.

HPE advances its intelligent data strategy enabling companies to gain more insights from their data and catapult their business forward.

Intelligence is the key

We’ve all heard about the intelligent enterprise, one that is fast, agile, and built for innovation. Applying intelligence is vital for companies that want to achieve that vision.

For a start, intelligence is key for storage and infrastructure management. Reliable, fast storage is useful, but it solves only a fraction of infrastructure problems. According to a recent IDC survey of IT leaders, more than 90 percent of the issues that can disrupt or cause performance variability with application workloads arise above the storage layer. They can be caused by complex combinations of hundreds of variables that require hundreds of thousands or even millions of simulations to correlate. That’s a problem that’s far too complex for humans to solve. 

Intelligence is also key for taming hybrid complexity. Smart CIOs realize they need to build a hybrid cloud. But while hybrid clouds solve many problems, they also cause problems of their own. On-premise infrastructure is already complex, and that complexity multiplies exponentially when you deploy a hybrid infrastructure. How do you decide what workload to put where? How do you know how a new workload is going to interact with existing workloads on any of the multiple infrastructures you use in a hybrid cloud? Imagine this challenge at scale across hundreds of workloads, hundreds of systems and multiple clouds. 

Intelligence is key for data management as well. Data has a fragmented lifecycle; it runs the continuum from primary storage to secondary storage to backup and archival. It’s stored across a variety of siloed systems and tools. In addition, you have copies of data made for development testing and data analytics. How do you determine where your data should live at every step of the life cycle so that it's optimized? How do you even know how many copies of the data you have in your backup or for data sharing? 

Finally, intelligence is key for extracting value from your data. There’s a massive data storm coming, primarily generated by IoT. Every company will effectively become an IoT company, with data produced and collected from edge devices. How do you store that data, but, more importantly, how do you unlock hidden insights from it to drive a competitive advantage and drive your business forward? 

In short, data is at the heart of everything that your business does and drives your applications. You need an intelligent data strategy that solves all the problems outlined above. 

Intelligent data platform requirements

Recognizing that you need intelligent data is only a starting point. Next, you need to build an intelligent data platform. The platform should have workload-optimized composable systems that span mission-critical, general purpose, secondary, and big data/AI workloads. All this should be connected with bi-directional data mobility so that data can flow seamlessly across the data lifecycle, and have workload mobility in addition to data mobility. This structure allows for the greatest number of use cases, including data lifecycle management and hybrid development and testing where you can develop in the public cloud, and then deploy production applications on premises. That lets you unlock the power of hybrid storage or disaster recovery as well as modernize data protection, so you have rapid recovery on premises with cost effective long-term data retention in the public cloud.

Your intelligent data platform should be imbued with enough intelligence so it can predict and proactively resolve issues before they occur. It should also create workload fingerprints that recognize the unique characteristics of individual workloads, and then deliver recommendations about how each should be handled. For example, it should be able to provide your virtual machine (VM) administrators with intelligence about their VM farm, such as which VMs are sitting idle but still consuming terabytes of capacity or when a given VM is sapping performance of a node, and how moving to another node can elevate the performance of the entire VM farm.

Delivering value to everyone

What does an intelligent data platform do for those who work with data, applications and infrastructure? Line-of-business owners get accelerated application workloads. VM and cloud administrators can effortlessly deploy and scale VM farms and embrace the hybrid cloud. Developers can deploy continuous integration and continuous delivery (CI/CD) pipelines on containers or VMs and accelerate parallel builds. Data scientists can shrink the time it takes to deliver insights from months to minutes. Infrastructure administrators can get effortless administrative tools so they can spend their time innovating, not administrating. 

The Intelligence era is upon us. It’s no longer about speeds and feeds. Intelligence is the game-changer. An intelligent data strategy is a must, if you don’t have one in place. Finally, scrutinize your storage infrastructure purchases, and ensure you’re investing your precious dollars in infrastructure that enables AI-driven experience and intelligence.

Intelligent data platform: Lessons for leaders

  • Every business will become an IoT company, deluged by data produced and collected from edge devices. 
  • AI will need to be applied to that torrent of data, to manage it more effectively, handle its fragmented life cycle and glean business insights from it.
  • An intelligent data platform should be able to predict and proactively resolve issues before they occur, and deliver recommendations about how to handle specific workloads.

Related links:

HPE redefines mission-critical storage with new platform designed for the intelligence era

Introducing: True hybrid cloud for containers

This article/content was written by the individual writer identified and does not necessarily reflect the view of Hewlett Packard Enterprise Company.