Tuesday, August 07, 2012

The mad rush: Masters in Analytics programs

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:

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

  1. Statisticians are typically not the leaders of these programs. 
  2. 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.
  3. "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.


Unknown said...

Interesting article, and you raise an issue with which I've struggled as well. There are a number of what I think of as "para-statistical" programs, such as business analytics or marketing analytics, that are evolving without input from the greater statistical community. As an aside, when data mining approached statistics about 40 years ago, most statisticians (notable exceptions at Stanford) turned them away, and now data mining has a home in computer science. (This is a bit second- or third-hand talk, but I find it plausible based on interactions I've seen between data mining and statistics fields.) I wonder how much this is true of the analytics fields that are cropping up now.

Galit Shmueli said...

Thanks for your comment John. I think the same story is recurring. One thing that many academic statisticians seem to lack is interaction with business units and especially the managerial level (CIOs, CTOs and others who deal with data). There is a lot to learn from such interactions! Without relevant experience, it will be quite difficult (if not impossible) for statisticians to jump the analytics wagon in a meaningful way.

Sudhir Voleti said...

Nice food for thought there, Galit.

The broader Statistics field, it seems, has turned intensely towards the interface between the truly massive info processing and pattern recognition requirements in biology & genomics and the Statistical sciences. And to be fair, that's where a lot of the money (NIH and NSF grants for instance) is. The life sciences in fact are gaining enough muscle to nudge in into many other schools and departments in the typical US research university (for example, the Univ of Rochester) - from Statistics to Economics to Business (the Healthcare & HMO management vertical, for instance) and even (I hear) to law.

So yes, all in all, things are in flux (aren't they always?). Will have to wait and see where this trend goes.

-Sudhir Voleti

johnie said...

Can someone point out to me the difference between operations research/industrial engineering and course work of Analytics?

Galit Shmueli said...

I think that many analytics programs will include operations research methods such as simulation and optimization. Industrial engineering is different, since it is not a "tools" area but rather a domain area. In an industrial engineering degree you will also take courses on optimization and simulation, but they will be geared towards particular engineering application.

Anonymous said...

SO that was what business analytics is all about. I only read it in a leaflet for a suffolk business school and thought it was another computer science course.

Unknown said...

Interesting Article, but I think the MSA course at North Carolina is a campus oriented one - it isn't offered online !!

Galit Shmueli said...

Tejaswi - you are right. I removed "online" from the post. A program that does offer an online Masters degree in predictive analytics is The Northwestern School of Continuing Studies (again, not the statistics department).

Unknown said...

I am trying to determine if I should move forward with 'Business Analytics', 'Analytics', or 'Predecitive Analytics'. Can anyone help me with identifying the pros and cons or atleast the differences between these programs?

Galit Shmueli said...

My sense is that these terms are used sometimes interchangeably. The trick is to look at the syllabus and see what courses are offered and by faculty from which areas.