In fact, there are even some who predict that the job of data scientist will disappear in the not-too-distant future, replaced by powerful algorithms. Gartner is already saying, “more than 40 percent of data science tasks will be automated by 2020.” That’s only three years from now! We’ll all be out of our jobs! It’s stirring up dystopian visions of machine intelligence taking over the world. There is no spoon.
But this is over the top. Despite the name, “data science” is not just science — it’s both a science and an art. And real, live humans are uniquely positioned to juggle both of these competing skill sets — to provide the expertise, judgment and, most importantly, creativity needed to tackle a non-trivial, real-world business problem and provide meaningful recommendations. It’s one thing for a tool to automatically send a follow-up email when an online cart is abandoned, or filter an email campaign list based on a preconfigured model score. But these are small things — what about the big, strategic challenges we regularly face in our marketing organizations?
A predictive model provides one piece of information to apply to a strategic business problem. Sure, it’s one significant piece of information based on rigorous scientific methodology and fueled by factual data about actual customers, not theoretical entities. But what’s the context?
Human intelligence and creativity is still vital to interpret what the model produces as output and consider it in the broader context of the business question. A model can’t know the people that will be affected — both customers and employees. A model can’t take into account the political implications of the decision to be made, and the steps needed to smooth the way. A model might be able to predict a customer’s need state based on their historical behavior, but what if the customer doesn’t view their current interaction in the same way as the model does? There are upper bounds to how much artificial intelligence is capable of when it comes to context, now and for the foreseeable future.
In summary, predictive modeling can be automated — but true predictive intelligence will never be. Predictive modeling — and all of machine learning — should play a role that empowers, not replaces, the person responsible for using models to take real action in the business. Despite the hype around machine intelligence, you'll get the best results if there's a real, live human being in the mix to interpret the data science outputs correctly — and to apply them in a meaningful way within the context of your organization.
Learn even more about the convergence of technology and branded content at the FUSE Enterprise summit. Artificial intelligence and personalization will be featured among many other techniques and technologies.
Jim Sawyer is Chief Scientist at Elicit. The company's resident savant, Jim is responsible for the artistic application of Elicit’s customer science. From evaluating the state of customer data and analytics systems to developing customized segmentation, Jim leads a team of data scientists to bring customers to life through data. He has over 20 years of experience in analytics, a Stanford B.A.S. in Mathematical and Computational Sciences and a Georgia Tech M.S. in Industrial and Systems Engineering. Elicit's Fortune 500 clients include Southwest Airlines, Fossil, GameStop, Sephora, BevMo!, HomeAway, Best Buy and Pier 1 Imports.