Forget Data Science — Here’s Why Decision Scientists Will Be the Most Sought After Career in the Coming Decades
The ‘sexiest job of the 21st century’ is looking a little tired.
Nothing builds hype quite like predictions of vast shortages. Add to these predictions an ocean of big data, with confusion around what the term means, and you have a recipe for the “sexiest job of the 21st century” — data scientists. That’s what happened following McKinsey’s now infamous 2011 report that claimed that by 2018, the United States would be short 190,000 skilled data scientists.
Businesses and startups took off on a mad dash to recruit and hire them. Universities were sent scrambling too, as, at the time of the report’s publication, there were few data science graduate degree programs in the US. Five years later, the hype lives on, with conferences, webinars, and much ink spilled over the scientist’s importance in the “big data economy.”
The obsession with the data scientist shortage is short-sighted
As data science gets its 15 minutes of fame, it’s actually decision science that will be the most sought after career in the decades to come. Yes, data scientists will remain important in the next several years, but their attractiveness will quickly fade given the ambiguity around what data scientists actually do and how to train someone to do the job successfully. At the highest level, it’s important to remember that data scientists are not all one uniform thing, ready to be plucked from the graduate courses and online certificate programs that are cropping up to fill the void. Whether data scientist or decision scientist, there’s a lot of training involved to use data to meet a particular business’s needs.
There are bigger reasons data scientists are fast becoming a band-aid fix for most businesses. Generally speaking, data scientists apply math and technology to solve a particular problem, or set of problems, that are clearly defined and/or relate to one business activity. In other words, data scientists are “problem-specific” solvers. But as most people know from experience, business problems are rarely laid out and defined from the get-go. Most of them start out murky at best. Adding to this, as pointed out in a recent Fortune article, increasingly sophisticated software may soon obviate the need for entry-level data scientists and number crunchers.
Decision science — what businesses today really need to compete
Given the knotty, risky, and ambiguous nature of businesses problems today, what the world actually needs are not more data scientists, but decision scientists. Solving complex business problems requires breaking down silos that separate programmers, analysts, mathematicians, and business leaders from one another. A decision scientist functions across these disciplines — turning data into context-specific, objective insights that can foster better, quicker decision-making throughout an organization. As Dhiraj Rajaram, CEO of Mu Sigma, said: “While data scientists are about creating analytics, decision scientists help companies consume them.”
Decision sciences are being pioneered right now, and their impacts are felt across Fortune 500 companies like Microsoft and Walmart. These companies realize that decision science is not old wine in new bottles to satisfy the talent pipeline. They recognize that scaling the use of analytics will institutionalize a culture of informed decision making, and that requires deep investments in developing talent — far more significant than certificates in data science or US university programs can currently provide.
Decision scientists are renaissance problem solvers, culling insights from across disciplines
These decision scientists, numbering more than 6,000 today, have cut their teeth at Mu Sigma University, the requisite training program for the analytics firm’s clients and employees. Best described as part design studio, part research lab, and part data factory, the program constantly evolves to meet the needs of businesses today, with 7 percent of annual revenue reinvested into the program each year. The most recent outgrowth is the Columbus Program, which takes its name from the prototype of discovery Christopher Columbus.
College graduates in the US are brought offshore for six months of intensive training in decision sciences at the company’s headquarters in Bangalore, India. Participants are immersed in a curriculum that bears little resemblance to what is required for a degree in data science. From the basics of design thinking, to storyboarding, to the art of problem solving, to behavioral science and psychology, Columbus grads are equipped with the cross-disciplinary expertise that lies at the intersection of analytics and the (very human) decision-making process. Adding to this, Columbus students also work directly with clients, getting hands-on experience with some of the business and tech giants in their respective fields.
New employees are exposed to the fast-paced, dynamic business-technology environment in India, which is fast becoming a hotbed for global startups. India is unlike Silicon Valley in several key ways: young entrepreneurs there are incredibly hungry to solve problems, working in markets with huge demand, and in certain ways have a greater appetite for risk than in the US. The impetus behind the founding of Mu Sigma, and the reason its headquarters are in Bangalore, was to solve a critical business challenge — making the use of analytics scale. Decision scientists get an inside look into where it all began: the “lab” where mathematics, business, and technology unite.
In a broader sense, decision scientists will completely bypass the next iteration of the “data scientist shortage” as declared by McKinsey and propagated by others. That’s because their training was not designed in response to a shortage, but was rather a long-term investment that prioritizes learning over knowing; heterodox approaches to testing and creating new solutions; and a systems-wide approach that doesn’t focus too squarely on a certain problem or fix. These are the core attributes of the next generation of decision scientists — the people who will provide real, measurable value to businesses and enterprises. Now, that’s sexy.
Tom Pohlmann (@tpohlmann) is CMO and Head of Values & Strategy at Mu Sigma, where he is responsible for marketing, communications, service development, and the development of programs to ensure that employees and client engagements alike reflect the company’s unique values and belief system.