Ever since my days as an industrial and operations engineering student I've been messing around with data, mathematical models and information technology. I have equally fond memories of learning stochastic processes, Bayesian statistics, C, and dBase III+. In the 1990's while working as a "management engineer" and "operations analyst" for a few big healthcare systems, I learned about the unique world of healthcare data. We wrestled with trying to use various classifications schemes like DRGs, ICD9, CPT4, and CCS to try to make sense of hospital operations. We built big databases and wrote tons of code to deal with the huge amounts of data we would get from the myriad of information systems in a typical multi-hospital system. Sounds like "healthcare informatics". Yes, but we also built simulation models for capacity planning (MedModel) and optimization models (AMPL+CPLEX) for staff scheduling. We did all kinds of data analysis, becoming Excel and Access wizards in the process. When someone needed a tool to play "what if?" for some thorny management decision making problem, we jumped at the challenge. Sounds like, to use the latest term, "business analytics".

When I left industry in 1999 and become an academic at Oakland University, I almost immediately launched a spreadsheet based modeling class I called Business Analysis and Modeling. It was pretty clear to me that what business students needed was a combination of modeling, data analysis, and practical computing savvy if they were to be useful as business analysts. So, my class had (and still has) everything but the kitchen sink in it: basic modeling principles, @Risk, Solver, uber power Excel, VBA, SQL, data analysis and visualization, data warehousing and even a little regex (if I could cram some Python in there I would). Many of my former students are out there doing this stuff for a living. In fact, about ten of them are at one healthcare analytics consulting firm run by my good friend and long time collaborator, Steve Littig.

Flash forward a decade or so and big data and business analytics is all the rage. Healthcare analytics and healthcare informatics especially so. Lots of universities creating business analytics programs of various flavors. However, as pointed out by a recent article in the NY Times, it's hard. The range of skills needed - math, stats, modeling, databases, computer programming, and some sort of domain knowledge (i.e. healthcare, finance, whatever, ...) make it tough to design good academic programs and find people (both students and faculty) this geeky. It's even difficult to figure out what to call this stuff. Personally I've often struggled to figure out what to call myself, especially within academia. I don't fit in one neat discipline. Similarly, "Big data" and "data science" are, well, data centric. "Business analytics" sounds like it's all about analysis. So, the next time someone asks me what I do, "infolytics" is my answer. Umm, I really hope someone can come up with something better than that.