We recently sat down with this year’s Marketing Analytics & Data Science speaker Steve Weiss, who is the Content Manager for the Data Science & Business Analytics course libraries at LinkedIn Learning/Lynda.com. In his role, he develops learning resources that support LinkedIn’s mission to connect the world’s professionals and make them more successful.
What is the state of the data science and analytics industry in 2017?
Weiss: Healthy, and growing and complexity. It’s complex because of the multi-directional growth we’re seeing (disciplines, markets, skills, business models) and also in the new industries being spun up, e.g. the Industrial IoT (Industry 4.0). “AI and “Machine Learning” are interchangeable to many business and institutional leaders, and are in turn spawning their own significant sub-categories such as NLP, computer vision, and machine intelligence. In short--as we're all aware--everybody and their dentist wants to know more about data science and analytics.
Not to get all meta, but things are moving so quickly in what has become to a large degree an AI-driven technology sphere, that you have to acknowledge the threat of quick obsolescence of individual skills areas. Topics for specialty flare up, glow brightly with hot demand, and will then be obviated by machine learning, and this is on top of the exposure that skillsets in, say, various areas of statistics—long-running go-to areas for career training—are now experiencing.
There’s a sense among some in the data science and business analytics space who I interact with that far from being secure in having one of the sexiest jobs in the world (or what have you; pardon the hyperbolic phrase-cribbing), they’re feeling more pressure than ever to keep ahead of the market trends. Or at least to try to divine what data science and analytics knowledge will be needed next, and to acquire and market those skills quickly before technology gobbles those up.
What have been the biggest changes in data science and analytics since you started your career?
Weiss: The commoditization of jobs and career opportunities. There was a time not all that long ago when “data scientist” was often considered merely a needlessly upgraded 25-cent term for “statistician with a computer.” Now we have millions of people around the work working on data science teams, or doing their own analytics in their small business. People do data science as an avocation.... a hobby, something they love (see 538.com; NCAA March Madness; FanDuel & DraftKings, among many other examples). From my perspective, it's kind of amazing and it's pretty cool.
How is data science and analytics transforming every industry right now?
Weiss: It is truly astounding to watch the effect data science and analytics is having on every market vertical you might choose to consider. Consider any of these areas below (and I’m expanding beyond strictly “industry” as a commercial reference), and know that data science and analytics have already brought tremendous change to them, and promise long-term disruption to degrees we’re incapable of fully seeing:
Finance; Healthcare; Retail; Scientific Research; Agriculture; Hospitality; National Defense; Marketing; Manufacturing; Media; Sports; Non-Profits; Law Enforcement; Justice System; Government; Entertainment; Education. Among others.
What I wonder at is how this affects the balance between the roles of governments and of markets. It may be the key question of our time.
Why is data science considered the “sexiest job of the 21st century?”
Weiss: Because clickbait. <grin>
Kind of like we’d mentioned earlier about the technology generating market demand—for skills, for content, for insights—and then obviating the need for many of these skills in short order, due to so many smart people getting into the industry, developing even-smarter software and more efficient processes. Actually, the clickbait analogy sort of works anyway, where evolving tech = a piranha-like appetite for creating buzz as well as tools for feeding off of and creating more buzz.
What is the biggest challenge in data science and analytics today?
Weiss: Today, as in just now: Data security, data privacy, good governance, and verifiability. There’s no need to sell anyone on the advantages—even the sheer necessity—of harvesting insights and even wisdom from the observable, measurable world around us. New businesses and industries are cropping up all around us showing the value in this…. but the world is quickly seeing what comes with this Pandora’s Box of tech-driven innovation: people’s lives and the welfare of institutions on which they depend can be jeopardized if we don’t understand and mitigate the risks, building models of behavior that enable us to effectively control what’s happening. Some people will assert to you true control is just wishful thinking anyway.
Where do you see data science and analytics moving in the next 5 years?
Weiss: Again, largely toward more automation of tasks, including the creative component. And that includes the stuff that entrepreneurs and artists do. For example, why won’t technology begin spinning up viable business ideas? Or, look at how formulized pop music and country music—huge industries, mind you--have become: there’s no reason machine intelligence can’t create the music much of the world listens to every day. Or imagine the day when someone lets it slip that one of our culture's most popular TV shows is in fact generated by software: story lines and scripts that have everyone buzzing are algorithmically generated.
The performing artists and actors will still be bigger than ever, but I wonder about writers of songs and stories. Imagine the John Henry story but replace the battle between the pile-driving steam machine and John the railroad worker with machine intelligence-driven software and a human poet. What will it mean when art—that which makes us feel more human—is created by artificial means, and consumed and enjoyed by people? Will anyone care as long as we’re more satisfied? It’s anyone’s guess until we see the mass effect, isn’t it?
We'll want to remain the bosses of the tech, won't we? We get it spinning, and we sit in judgment of the value of what the tech produces.