The recent trend among mainstream business schools is opening a graduate program or a concentration in Business Analytics (BA). Googling “MS Business Analytics” reveals lots of big players offering such programs. A few examples (among many others) are:
- Carnegie Mellon’s Heinz College
- Michigan State’s Broad School of Business
- NYU Stern
- University of Connecticut’s School of Business
- Rutgers Business School
- Drexel’s Lebow College of Business
These programs are intended (aside from making money) to bridge the knowledge gap between the “data or IT team” and the business experts. Graduates should be able to lead analytics teams in companies, identifying opportunities where analytics can add value, understanding pitfalls, being able to figure out the needed human and technical resources, and most importantly — communicating analytics with top management. Unlike “marketing analytics” or other domain-specific programs, Business Analytics programs are “tools” oriented.
As a professor of statistics, I feel a combination of excitement and pain. The word Analytics is clearly more attractive than Statistics. But it is also broader in two senses. First, it combines methods and tools from a wider set of disciplines: statistics, operations research, artificial intelligence, computer science. Second, although technical skills are required to some degree, the focus is on the big picture and how the tools fit into the business process. In other words, it’s about Business Analytics.
I am excited about the trend of BA programs because finally they are able to force disciplines such as statistics into considering the large picture and fitting in both in terms of research and teaching. Research is clearly better guided by real problems. The top research journals are beginning to catch up: Management Science has an upcoming special issue on Business Analytics. As for teaching, it is exciting to teach students who are thirsty for analytics. The challenge is for instructors with PhDs in statistics, operations, computer science or other disciplines to repackage the technical knowledge into a communicable, interesting and useful curriculum. Formulas or algorithms, as beautiful as they might appear to us, are only tolerated when their beauty is clearly translated into meaningful and useful knowledge. Considering the business context requires a good deal of attention and often modifying our own modus operandi (we’ve all been brainwashed by our research discipline).
But then, there’s the painful part of the missed opportunity for statisticians to participate as major players (or is it envy?). The statistics community seems to be going through this cycle of “hey, how did we get left behind?”. This happened with data mining, and is now happening with data analytics. The great majority of Statistics programs continuously fail to be the leaders of the non-statistics world. Examining the current BA trend, I see that
- Statisticians are typically not the leaders of these programs.
- Business schools who lack statistics faculty (and that’s typical) are either hiring non-research statisticians as adjunct faculty to teach statistics and data mining courses or else these courses are taught by faculty from other areas such as information systems and operations.
- “Data Analytics” or “Analytics” degrees are still not offered by mainstream Statistics departments. For example, North Carolina State U has an Institute for Advanced Analytics that offers an MS in Analytics degree. Yet, this does not appear to be linked to the Statistics Department’s programs. Carnegie Mellon’s Heinz Business College offers a Master degree with concentration in BI and BA, yet the Statistics department offers a Masters in Statistical Practice.
My greatest hope is that a new type of “analytics” research faculty member evolves. The new breed, while having deep knowledge in one field, will also posses more diverse knowledge and openness to other analytics fields (statistical modeling, data mining, operations research methods, computing, human-computer visualization principles). At the same time, for analytics research to flourish, the new breed academic must have a foot in a particular domain, any domain, be it in the social sciences, humanities, engineering, life-sciences, or other. I can only imagine the exciting collaboration among such groups of academics, as well as the value that they bring to research, teaching and knowledge dissemination to other fields.