Tech Journal Overcome the Limitations of Today’s Storage

Innovation has fostered the emergence of new technologies like predictive analytics and artificial intelligence designed to find the insight in exploding volumes of data. But data storage hasn’t kept up.

Existing storage systems were designed in a different era to do a different job in a different environment.

Limitation # 1: It's still too complex

Traditional storage and even flash storage are not designed to handle the complexity of our edge-enabled and cloud-connected world. The challenge with these approaches is that they conceal or overlook your data value, force you into outdated support and ownership models and slow your ability to respond to business needs. As a result, much of storage administration is spent on:

• Reactive and time-consuming trouble-shooting of performance issues and outages
• Determining optimal workload placement across a hybrid IT estate 
• Navigating infrastructure complexity that hinders data mobility between edge, on-premises and public cloud
• Keeping up with the volume and pace of data creation
• Having too much data to process, store and copy, coupled with uncertainty about what data to discard, without compromising security and compliance regulations for different data types

Limitation # 2: It turns cloud into silos

Mobility, social media and the Internet of Things (IoT) have given rise to a new generation of applications. But these emerging applications didn’t necessarily emerge in the data center. Cloud-based apps gather and store data in the cloud.

And, data is increasingly collected and applied in remote edge locations such as factories, transportation hubs, oil fields and ships. Data spills across the entire hybrid cloud landscape in disjointed data silos rather than an integrated, continuous computing environment that lets IT run each app in the best place.

Limitation # 3: IT cost doesn't match its business value

Storage demands an ever-increasing slice of a not-so-increasing IT budget. Needs can outstrip capital depreciation schedules making it difficult for IT to upgrade and replace storage devices in support of business initiatives. Migration to new systems is not only the time consuming, it often requires operating and paying for old and new systems concurrently, sometimes for extended periods.

Because business demands and data growth no longer respect the capital budget process or even acquisition and implementation lead times, you’re left to guess high, overprovision and overpay to avoid being caught short.

Let's make storage smarter

When data isn’t available when and where it’s needed, its value is lost to the business. When managing and operating storage infrastructure is so complex it consumes excessive IT resources, it slows business progress. And when storage acquisition and operations cost exceed returns, it drains investment away from core business initiatives.

Those are the experiences of businesses trying to tap tomorrow’s opportunities with yesterday’s storage technology. And those experiences have led us to rethink what storage should be, how it should work and how it should be consumed.

Storage needs to be more than the place where data lives. Gaining intelligence from your data begins with more “intelligent storage“. Intelligent storage should deliver built-in artificial intelligence (AI), hybrid cloud interoperability and consumption-based IT.

This combination helps you manage, secure, grow and control data throughout its lifecycle, no matter where it lives, in order to extract insights from it. And, for IT organizations charged with driving the digital transformation businesses need, that’s a game changer.

Just as advanced analytics helps enterprise find business insight in mountains of business data, advanced analytics can find IT insight in the mountains of data generated in IT infrastructure to learn, adapt and react – a step towards autonomous IT.

Intelligent storage collects data not just from storage devices, but from servers, virtual machines, network interfaces and other infrastructure elements across the stack. It uses machine learning to develop models that reflect what’s right so it can spot what’s wrong. And it applies predictive analytics to anticipate and prevent issues across the infrastructure stack and to speed resolution when problems do occur.

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