The opinions were varied and equally passionate on both sides of the argument. One community member wrote, for example, that an MBA has been a disappointment:
I'm a generalist and my MBA has not done much for me. I'm beginning to think that I would have done better if I had specialized. I have noticed that the generalists are passed over for more specialized skills in different areas. And even I am finding it hard to decide the best jobs for me.
Another community member, on the other hand, pointed out that pursuit of an advanced degree wasn't necessarily warranted (or maybe even advisable): "You don't need to get a degree to learn advanced statistics or business management. Indeed, university professors will teach you outdated material."
I was thinking about this thread as I reviewed notes from a recent conversation I'd had with Radhika Kulkarni, vice president of advanced analytics R&D at SAS (this site's sponsor) and our latest Women in Analytics featured professional. (Read our Q&A with her.) Kulkarni shared some great advice for professional development that's applicable to this discussion.
What you do as an academic, as you start learning in the particular field that you specialize or do your PhD in, at the end of the day you may end up using that throughout your career... or you may not. The most important lesson learned as you pursue an advanced degree is the ability to craft new things, to learn new things, and to apply them... That's a lesson that will stand you in good stead anywhere.
Focusing too narrowly is ill-advised, Kulkarni added. "You might specialize in a particular area, and go deep. But always listen and look at all the surrounding disciplines and learn about them."
Perhaps you're working on hard-core mathematical programming and learning deterministic optimization and mixed-integer programming as your thesis topic. "Know that there are other aspects of operations research, other analytical areas, all of which are going to be needed to solve any business problem," she said.
You might apply one algorithm very well, but that won't guarantee project success, Kulkarni said:
You need to be able to handle data, you need to be able to create the model that's going into your optimization problem, and you need to be able to implement it in the environment that the business needs it to be implemented in. All of these aspects need to be considered, and you need to have an understanding of the big picture.
Don't panic. Kulkarni isn't suggesting you need to be an expert in each of these areas. What she does say, though, is that you need to be able to work with the experts and bring all the pieces together. "So at the end of the day, a successful project is not done by a single hero. It's done by a team of people working together."
That, Kulkarni said, is a lesson that everybody must learn:
You need to understand that a multidisciplinary approach is important for success. You should be able to work with others and explain your technique to others in ways that they can understand. And you should be able to recognize the value of what others bring to the table and determine how all the pieces can fit together.
Always remember, she added, "The whole is bigger than the sum of its parts!"
Does Kulkarni's advice resonate with your experience? Tell us why or why not below.