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Top 10 Influencers in Hybrid Multicloud Storage You Should Follow in 2019

The data center is undergoing a massive structural shift as organizations attempt to scale with the flexibility hybrid multicloud provides - PragmaEdge

Top 10 Influencers in Hybrid Multicloud Storage You Should Follow in 2019

If you had a chance to read my post, you know that digital transformation and disruption is a complex strategy for many companies. The data center is undergoing a massive structural shift as organizations attempt to scale with the flexibilityhybrid multicloudprovides.

Breaking down silos and easily accessing data across different clouds is important, and IT leaders need to consider hybrid multicloud as a solution to their data challenges. According to IBM Institute for Business Value research, most organizations aren’t ready for hybrid multicloud. Research shows that by 2021, 98 percent of organizations plan to adopt multicloud architectures, but only 41 percent have a multicloud management strategy and just 38 percent have procedures and tools to operate a multicloud environment.”

I’ve identified several storage experts to help guide your journey. Their backgrounds are vast and interests vary. Engaging with these key opinion leaders will help you stay alert to the evolving industry.

Here are the top 10 people inhybrid multicloudstorage you should follow on Twitter (in alphabetical order):

1.Data first: Starting off by collecting all the available data and then determining how to use it can be tempting. The problem is that any organization may have petabytes of data, only a fraction of which has true value to the business. Placing it all in a large data lake, for example, may not necessarily lead to failure, but it can use a great deal of energy without any assurance of positive outcomes.

2. Candy store:Pursuing several opportunities at a time is tempting because of the large number of AI opportunities available. Unfortunately, pursuing many opportunities this way dramatically increases the risk of failure because it dilutes effort, and may increase the complexity of implementation.

3. Clarity later:Starting out by building out an AI capability with the intent to gain clarity on the question or objective later can lead to avoidable risk. If the insight to be gained by the analytics project doesn’t clearly demonstrate how it’s expected to provide business value, hitting the target is all the more challenging.

4. Payoff later:After investing substantial time and money in an initiative, only to realize that the question went unanswered or the gains unrealized can court failure. For example, you may risk loss of support from key stakeholders who needed a more expedient return. Getting sign off for future projects might become an uphill battle.

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Top 10 influencers in hybrid multicloud storage you should follow in 2020

If you had a chance to read my post, you know that digital transformation and disruption is a complex strategies for many companies.

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A conversation with Thomas Taroni: Data Containers & Availability

IBM Systems Content Director Jim Braden sat down recently with Thomas Taroni, CEO and Senior Software Architect of Phoenix Systems in Zurich, Switzerland.

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4 Ways AI Analytics Projects Fail – How to Succeed​

“How do I de-risk my AI-driven analytics projects?” This is a common question for organizations ready to modernize their analytics portfolio. Here are four ways AI analytics projects fail

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