October 20188 IN MY V EWBY VISWANATHAN IYER, VP OF ARCHITECTURES, CISCOWe are rapidly hurtling toward the reality of connecting more than a trillion smart and often autonomous devices through a global network we call the Internet of Things (IoT). These devices are being deployed everywhere--on factory floors, on public malls, on airports, on railway stations, on self-service kiosks, on hospitals. This is creating massive amounts of data at the edge and starting to redefine the way we store it, manage it, secure it, and make sense of it. The reality is that, today, only a very small percentage of IoT data (1 percent) actually gets analyzed and converted into business intelligence.In the meantime, in the data center and public cloud, companies are struggling to manage very complex environments and to gain visibility not just over their data, but also threats. No matter the environment--data center, public cloud or edge--it is all just beyond human control.As a result, Machine Learning (ML) and Artificial Intelligence (AI) are becoming critical for enterprises to be operationally and economically viable but, most importantly, more customer-oriented than ever. While data has been critical to businesses for the past years to drive personalization, with ML and AI there's the exponential opportunity to micro-personalize experiences, products and services in a way we never thought possible. Just imagine if your bank could proactively provide you support from the time you open your account, to when you buy your first car, first home, or open a business, caring for you the entire life cycle of your relationship based on digital interaction. This will be possible when AI and ML perfect the art of recognizing patterns and behaviors, and accelerate the value chain from conceptualization to delivery. The challenge is: how do you go about implementing and monetizing this value? The best place to start is your own data center, an environment easier to control and protect. There's often a perception that public cloud is the best starting point because of its flexibility and scalability capabilities. The reality is that many companies are realizing that not all data can be made public and that the pay-per-use model carries higher costs as data and consumption continue to ramp up. The trend now is to swing back to on-premises data centers for inference and leverage the public cloud for learning.ML & AI TO RESHAPE DATA CENTERS: HOW DO YOU MAKE IT WORK IN YOUR COMPANY?AS COMPANIES COME BACK TO ON-PREMISES, IT'S A GREAT OPPORTUNITY TO RETHINK HOW THEY CAN MODERNIZE THEIR DATA CENTERS FOR ML AND AI
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