This week’s roundup discusses how to stay ahead of the AI boom, transitioning from the “technological age” to the “data age”, leveraging blockchain technology for predictive analytics, and what businesses really think about data analytics, predictive analytics, and machine learning.
by Alec Shirkey, Associate Editor of Partner Marketing for CRN
Solution providers looking to take advantage of emerging artificial intelligence technologies should tap into young talent that already possesses the relevant skills, according to vendors with AI expertise. As machine learning and deep learning platforms become more accessible than ever before, IT industry executives believe gifted graduates will eventually fill the skills gap that exists around data science and smart machines.
by Tommaso Chiorino, CEO at DCG.NET, Arabian Business
Data is now the most valuable resource owned and how data is used will decide the leaders of tomorrow. Data has enabled companies to shift their focus from a mass-marketing approach to personalized communication concept, where the type of marketing you come across is the result of your online behavior, demographics, psychographics, personality archetype and much more.
by Ralph Tkatchuk, Freelance Data Security Consultant, featured on Dataconomy
Until recently, it took a degree in statistics, data science and maybe computer programming to fully succeed when using analytics suites. Even so, new technological forces are slowly tearing down the existing barriers. Blockchain-based startups have leveraged the technology’s many advantages to create predictive analytics suites that resolve two of the largest issues limiting regular users’ ability to enter the field: technological capability and accessibility.
by Janet Morss, Marketing Director at Dell EMC Ready Solutions, featured on CIO
In March 2018, Dell EMC surveyed 315 IT executives, in primarily mid-size and enterprise organizations across a wide range of industries, to investigate various issues around the current and future use of use of data analytics, predictive analytics, and machine learning. Findings reveal insights into general satisfaction with data analytics activities, interest in automation for predictive analytics, and key benefits and deterrents associated with the use of machine learning.
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