The Mass Migration from Legacy to Virtual Storage in 2018

It’s been more than two decades since cloud computing and storage debuted, but it won’t be until 2018 that the big bang of private enterprise cloud environments occurs. There are some good reasons why corporations, healthcare systems, and government agencies have shied away from cloud in the past. But reasons like latency, cloud hacking, and loss of data caused by power outages or failovers have been resolved for several years. So, why wasn’t it 2015, 16, or 17 that private cloud for enterprise-level use exploded? Considering the expected exodus from legacy storage like servers and solid state drives (SSDs) to cloud forecasted for 2018, the biggest question is… why now?

Nine out of 10 businesses currently not using virtual space will be by the end of 2018, making it a mushroom cloud-like explosion on the storage and computing timeline. In 2012, Gartner predicted a mass adoption of cloud storage for enterprise. It never materialized, really. What is the big “it” that has more CEOs and others at the helm of multinational commercial ventures nodding “yes” for 2018 when they’ve been shaking their heads until now?

Awareness, understanding what virtual space is, and letting go of fear of trying new technology to store the most precious data a company owns are all reasons to not want change. Cost is another huge factor, as is the learning curve associated with new tech. But, more than any other year on the virtual storage timeline, 2017 was a year that laid to rest rumors, myths, and misunderstandings about cloud environments and virtualization. The result: a rush for virtual gold in 2018.

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Stop Biting Your Tail When it Comes to Your Data

We have many clients come to us saying, “I want a fancy dashboard that perfectly displays all of my data with sleek graphs that any user can have access to.”

That’s all fine and dandy, but what most don’t realize is that the pretty dashboard with the clean metrics is the very last part of a data project, the “tail end” if you will.

Data projects often run late, are over budget, and/or do not meet initial expectations. Here we will explain why this happens so often, and how you can avoid biting your tail with your data by taking the proper steps to ensure your project runs smooth:

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