James Kobielus is an analytics industry veteran and IBM’s Big Data Evangelist where he drives thought leadership activities in big data, data science, advanced analytics, and data management. He has published several business-technology books and is a big voice in Big Data when it comes to published articles, podcasts, technology press publications and social media.
icrunchdata speaks with analytics thought leaders to discuss their career in big data, what they are currently working on and who they are outside of technology. We spoke to James recently about his work at IBM and what his interests are outside of the data.
James, thanks for speaking with us today. Let’s get started…
My role has steadily deepened and broadened. I’ve taken on a significant operational responsibility in IBM Analytics’ performance management organization. I manage the technical marketing sector in IBM Big Data & Analytics Hub, which is our principal content marketing site and channel. This is our single largest content-marketing sector, spanning our portfolio from business intelligence to big data, advanced analytics, data integration, database platforms, cloud data services, and beyond. In this capacity, I direct, oversee, and manage publication of thought-leadership content on data and analytics that is authored by subject matter experts in IBM, our customers, business partners, and independent analysts and bloggers.
Just as important, I direct the amplification, promotion, and engagement of all of this fresh content (blogs, videos, infographics, slideshares, etc.) across diverse social media channels. Also, I remain one of the principal thought-leadership subject matter experts at IBM on these topics. As big data evangelist, I publish regularly in BD&A Hub, as well as in other IBM and external blogs, publications, and other channels, including InfoWorld, Dataversity, a daily LinkedIn Pulse blog, and so forth. Not just that, but I regularly speak at IBM and external events in the big data analytics industry on these topics.
Hidden persuaders will go well beyond their traditional sphere, advertising, into every aspect of customer engagement. It won’t even feel like persuasion, but simply like your own shopping list that continues to evolve moment by moment to keep pace with the dynamic flow of your daily existence. Persuasion will be so embedded in the experience of shopping, buying, and using products that we consumers won’t even perceive it as marketing or sales. Whether one regards this new order benignly as “trusty advisors” or “devious manipulators” reflects your personal, cultural, or ideological predisposition toward the concept of hidden persuaders. More of the actual “products” we’ll all be using will in fact be online services, many of which we’ll access 24x7 through our mobile, wearable, and embedded devices (i.e., smart car, smart home, smart appliances, etc.).
The entire marketing and sales process won’t involve any direct human contact—not even an outbound call center—but rather will be driven by back-end predictive recommendation engines. These will chug away constantly and silently behind the scenes, presenting us with continuously and algorithmically personalized options so “in the ballpark” that we’ll follow those recommendations more often than not. Some people will demand full transparency into the algorithmic machinery that’s driving the discrete algorithmic persuaders that permeate their lives. But most people won’t care or bother for an audit trail. Most people will tacitly accept the presence of these persuaders as a fact of life in the 21st century.
The data scientist will become the core application developer in this new order of things. The assets they build and maintain—big data clusters, statistical models, machine learning algorithms, and so on—are becoming the chief intellectual property that drives recommendation engines, decision automation, next best actions, and so forth within cloud, mobile, social, Internet of Things, marketing automation, and other business and consumer apps. Consequently, the data scientist is rapidly evolving away from a high-skilled R&D function performed by premium university-educated talent toward an operational function that will need to be scaled and automated to a high degree by less pricey data-center IT staff positions who’ll need to be on call 24x7. Data scientist skills will rapidly become commoditized, just as low-level programming and system administration jobs became years ago. Like it or not, data scientists will be grown in the future through trade schools, vocational education programs, and other channels that will certify large numbers of freshly minted personnel who won’t require a 4-year college degree in mathematics, statistics, or some highly statistically oriented domain specialty.
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My wife and I left Madison in 1985, the year after we got married and earned our respective masters degrees. We’ve been back exactly once, in the late 90s, on vacation with our children. It looked much the same as it had been, but with a few new buildings. We’ve been in northern Virginia for 30 years, come September. We would have stayed in the Midwest if there’d been job opportunities at the time. We’ve never once regretted leaving. It was a nice enough place, though. My father was from Wisconsin and I still have family there and in the Chicago area. I’m from the Detroit suburbs originally. I consider myself more a Wolverine than a Badger. My undergraduate degree was from University of Michigan Ann Arbor. I grew up 25 miles east of there in Livonia.
I’ve had three graduations: high school, college, and grad school. Here’s what I’d advise younger me on each of those occasions:
High school graduation in June 1976: “You’re 17. You cannot possibly know what the job opportunities will be in your 20s, 30s, and beyond into the 21st century. So just acquire the broadest, most diverse education that you possibly can. You may very well end up making your career in a field that doesn’t yet exist doing a job using technologies that won’t be invented until several decades from now. So stay flexible.”
College graduation in May 1980: “You’re 21. You have just completed the 4 most demanding academic years of your life, as an honors student in economics at a top public university. Prepare now for several years of soul-numbing underemployment before you find the job that actually launches you onto your eventual career. It won’t be in any field that you’ve studied. Every last thing that you just learned, both in college and in K-12, will be utterly useless in your career, or incredibly important. You cannot possibly know which is which yet. So stay flexible.”
Grad school graduation in May 1984: “You’re 25. You’ve just earned a master’s in journalism that’s really quite irrelevant to your ultimate career. You didn’t need the diploma, because you had all the requisite talents practically from birth. Besides, you’re not going to become a journalist, in any strict sense, but you will become someone whose career success will depend on having a journalistic discipline as a well-published author. But you cannot possibly know what subject matter you’ll make your career writing about. In fact, you haven’t begun to acquire the knowledge base upon which your ultimate reputation in your career will depend. You’ll teach all that to yourself as you go along. So stay flexible.”
If my musical options were limited to just three artists, I would never listen to music again. I think it’s clear from my tweeting behavior that what gives me pleasure is a diversity of artists, genres, and styles. If I don’t have access to diverse music, I’d rather sit on my desert island and listen to tropical breezes. But if you want to know who/what I own and CURRENTLY listen to as my default comfort-music in most circumstances, here are the top three: Beatles boxset, best of Dandy Warhols, Stuart Moxham “The Huddle House.”
James, that’s all I’ve got. Thank you very much for taking the time to answer our questions today!