Data analysts are in the driver’s seat, as demand for workers is spinning out of control and the stream of qualified applicants can’t keep up. According to the McKinsey Global Institute, by 2018 the demand for deep analytical talent in the U.S could be 50 to 60 percent greater than its projected supply.
Headline-grabbing stats like this have sent people scrambling for training, and colleges scrambling to provide it. The problem is that many schools are simply being wordsmiths by adding “analytics” into existing program titles without a curriculum overhaul. It’s a quick fix, but one that will not work.
You need to dig deeper to see exactly what kind of curriculum will equip graduates beyond bread-and-butter analytics. That requires direct work with corporate sponsors; and with so much riding on these skills, companies are eager for the collaboration.
A firm grasp of applied analytic techniques and database management is an obvious and necessary piece of the puzzle. But the more important skills are knowing how to interpret and communicate, how to incorporate data into the next strategic move, how to bridge it with other departments, how to deliver results.
For master’s degree students, the formula should start with strong hands-on exposure to key analytics methods typically complemented by courses in a particular area of application. In the new MS in Business Analytics at Bentley, for instance, a core of analytics courses and four computer science electives will train a data scientist. Students can also choose to focus on domain areas such as business processes, finance, management or marketing.
The message to higher education is clear: Analytics relies on academic work, but is essentially a corporate invention. Talk to the people entrenched in the daily grind if you’re serious about educating students with the propensity to work alongside them. Without business perspective infused into the curriculum, the best you will get from grads is knowledge of applied statistics, not analytics per se.
Unfortunately, many schools are used to teaching and research along strictly disciplinary lines in statistics or operations research. This often means a somewhat restrictive set of favorite journals, and a bias in favor of theory. Analytics in contrast is quintessentially trans-disciplinary and corporate colleagues do not agonize over journal rankings, so it’s not easy for top research universities to jump into the analytics training space.
Every day educators like me challenge students to reach beyond the obvious. Now it’s time for academia and the corporate world to join forces to train tomorrow’s analytics talent to excel in analytics careers.
Dominique Haughton is professor of mathematical sciences at Bentley University and an affiliated researcher at Université Paris 1 (Panthéon-Sorbonne) and Université Toulouse 1.