The enterprise has come to accept the fact that it cannot exert direct control over cloud infrastructure, but it does have a lot of influence when it comes to governing its resource consumption and data management. All the more surprising, then, that more organizations are not building the necessary mechanisms to ensure that what happens in the cloud does not stay in the cloud.
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A few years ago, it appeared that the public cloud was going to take over the enterprise. That didn’t happen, but neither did the enterprise fall completely to the private cloud or the hybrid cloud. In fact, it seems that the data center industry is still feeling its way through the conversion from hardware-centric to software-centric infrastructure, leaving open the very real possibility that no single architecture will dominate the future.
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The purpose behind IT technology development is to make work processes simpler and the knowledge worker more efficient. But if that is the case, why is so much of today’s workforce so frustrated with the tools it uses to meet its objectives?
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Containers are changing the way the enterprise develops and deploys applications, but this is affecting more than just the operational side of IT. Some leading experts are saying that the technology is so revolutionary that it threatens to supplant the operating system as the linchpin of the data stack.
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Public and hybrid cloud deployments need the flexibility and management benefits provided by software-defined networking.
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It’s a given that artificial intelligence (AI) requires a high level of computing power in order to produce a worthwhile return on the investment. But the question remains whether this calls for specialized hardware and integrated software, or is the enterprise better off with a commodity systems approach.
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Enterprise executives are rightly intrigued by the operational advantages that artificial intelligence (AI) brings to the modern data environment, but questions remain as to how this technology will be deployed into legacy infrastructure and the ways it can best be applied to existing and future workflows.
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The public cloud is drawing the bulk of enterprise workloads these days, but in terms of application importance, the private cloud is likely to support the heart of data operations for some time to come.
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The enterprise has come to rely on the cloud for an increasing share of its mission-critical workloads. This means cloud infrastructure needs to be available and reliable as never before.
But there is a disconnect of sorts as to who is responsible for what when it comes to uptime, or even what to do when infrastructure fails and business activity comes to a halt.
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Artificial intelligence (AI) in the enterprise is the source of both enthusiasm and trepidation as the knowledge workforce comes to grips with what the future of IT automation has in store.
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Enterprise Illumination 