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2018 Executive Round Up – Part 3

Welcome to insideBIGDATA’s annual Executive Round Up designed to give our readers a sense for what the upcoming year is going to look like with respective to our focus technologies: big data, data science, machine learning, AI and deep learning. Weighing in with their thought-leadership predictions are some of the industry’s biggest players. We’re honored to pass along these comments to our valued audience.

In this article, Part 3 of the series, we’ll review the evolutionary process of digital transformation and how IDC is predicting that by 2020, 60% of all enterprises will have climbed aboard this bandwagon. The notion of digital transformation dominates many C-suite and boardroom agendas. At the same time, it remains unclear what “digital transformation” actually means. Many definitions are rather shallow, even while expectations for success are optimistic at least on the surface. CTOs, CDOs and CIOs struggle to define cohesive digital transformation strategies when faced with these disparate viewpoints and demands.

2018 is poised to be the year when business leaders and technology innovators come together to avoid the hype trap by focusing less on the capabilities of specific digital transformation enabling technologies, and more on embracing the strategic business transformations that new digital technologies make possible.

Daniel D. Gutierrez, Managing Editor and Resident Data Scientist – insideBIGDATA.com

 

insideBIGDATA: IDC believes that by 2020, 60 percent of all enterprises will have fully articulated an organization-wide digital transformation strategy. How do you see 2018 in shaping this view?

John (“Jay”) Boisseau, Ph.D., HPC & AI Technology Strategist, Dell EMC

Just 60%? I think it’ll be a bit higher, and there is no doubt that machine learning and AI will accelerate this. There is so much potential for AI that no enterprise can ignore it completely. If you are not using all of your data for the deepest insights, you’re at risk of being left behind–out-innovated, outperformed, and outcompeted. When a transformation of this game-changing magnitude comes along–like web, mobile, and now AI–you embrace or you lose. Because AI only produces results if an enterprise has taken great care to collect and manage it’s data, make it available without barriers, etc., one can only leverage the power of AI if one executes an organization-wide digital transformation strategy. AI will be necessary to win, and it will be part of,  enabled by, and motivation for, that organization-wide digital transformation strategy. Organizations will take better care of their data, and use that data to accelerate their business efficiencies and innovation. (If not, they’ll die, and thus won’t count against that 60%!)

Pankaj Goyal, VP, AI Business & Data Center Strategy, Hewlett Packard Enterprise

In 2017, AI became a CEO/CIO level topic. In 2018, AI will become a Board level topic.

Most enterprises will have no choice, but to start thinking about an organization-wide AI first digital transformation strategy. This is a huge disruption. I recommend an ‘Explore-Experiment-Expand’ approach – start simple, prove and then scale. Finally, we should not forget the human-angle of this disruption – how enterprises change culture, mindset and behavior in an AI-driven world will be critical.

Roy Kim, Director of Product Marketing for FlashBlade, Pure Storage

The entire industry has been on this long journey of digital transformation. Up to now, it’s been about storing raw digital data ​in something called a data lake in the happen-chance that some killer application will come along to unlock insights in the sea of data. The killer application is here, and it’s called Deep Learning. The fork in the this digital transformation journey is the realization that, now that we have to use this data, it’s now stuck in the data lake. I see 2018 being that fork in the road, where some enterprises will chose the path of building the right data infrastructure for AI while others will stay on the path of locking more and more data into the data lake.  ​

 

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