Adventures in Analytics

SCENE I: I recently received a voice mail from my old friend “Elk.” The tone of his message was urgent: “I’ve got a court date on Friday to fight this speeding ticket, and I need your help with some serious math!”

As I listened to his message, I figured the dude was almost surely guilty as charged. Elk drives fast cars and he drives them fast. A well-preserved memory from college: Elk racing his sports car down a two-lane country road at 90 mph with me in the passenger seat, the windows open, rock-n-roll blaring from the stereo. I immediately envisioned that I would be stuck solving some kind of differential equation problem, a much less well-preserved collegiate memory. Finally, I was afraid that I would get sucked into spending precious time that I didn’t have to help him out, and inevitably end up even further behind on my mounting to-do list.

I called him back anyway (I am, alas, that kind of friend). Turns out he was driving a minivan and he swore to me that he was not speeding and that the police officer had grossly exaggerated his speed “to meet his monthly quota.” In just a few minutes, looking at the map together online, we managed to put together a simple spreadsheet that, even with conservative assumptions, demonstrates that the officer’s version of the facts literally did not add up.

The next day, he brings printouts from three different scenarios into court with him, and calmly explains our logic to the judge. Elk is rewarded with a full acquittal.

SCENE II: Last winter I received an e-mail from a nursing professor across campus who is a consultant to a local research hospital. The focus of her efforts is improving the hospital’s pediatrics operations, and she has been trying to understand how to improve the flow of patients. Groping around for ideas, she had come to believe that queueing and simulation models might be the key to improving patient flow.

I agreed to meet with her to learn more (I am, alas, that kind of colleague). She explains that the goal of her work is to reduce the number of patients who are turned away due to lack of available beds and also to minimize the number of sick patients stranded in waiting rooms for long periods of time while waiting for a bed. Given the wide range of patients, illnesses, treatments, equipment and outcomes, the pediatrics facility that she describes is a very complex system. Though I was told that there is a huge amount of historical data, when I ask about very specific information, the most common answer is, “I think we can probably get that.” After spending time observing the folks who actually decide which patient gets a bed in which room in which unit at which time, I realized that we are a long, long way from being able to create a simulation model that will have any business value at all.

Along the way, however, what I do come to understand is that these folks could benefit greatly from a forecasting model that could help them anticipate when different types of patients are likely to arrive. Along with another quant colleague, we’re working on a prototype of a data-driven forecasting system for them now.

SCENE III: A few weeks ago, I received an e-mail from the director of customer care analytics at a local company with a big national profile with a reputation for excellent customer service (referred to herein as “Slick”). She had contacted me because of my background in call center operations, both as a consultant and as a researcher, though the conceptual problem that she outlined in her e-mail appeared to be quite straightforward to me.

I responded immediately to her e-mail, thinking that I could probably learn something from talking to folks who are considered to be on the cutting edge (I am, alas, that kind of professor). And I definitely did. Although Slick had acquired a leading commercial system for call forecasting and agent scheduling some years ago, they had only just recently begun using it on a regular basis. The customer care analytics team at Slick had also recognized that, because of some very innovative data-driven management practices their customer service group has adopted, they also have a unique need for another layer of prescriptive analytics that no commercial vendor was likely to address. I am going to visit them next week with some ideas about how they might proceed; it turns out to be quite a challenging problem.

CONCLUSION: My business is studying and applying data-driven models and teaching my students to do the same. Somehow each of these three encounters with other people’s problems has helped me understand my own world a little bit more clearly. Sometimes, as in the case of Elk, a simple model gives you all the insight that you need for the situation you find yourself in. Other times, as in the case of the pediatrics group at the hospital, the complexity of your environment means that you have to start with something really focused and simple to get immediate value and generate ideas about what might come next. And finally, as in the Slick situation, one set of analytics-driven innovations may end up requiring you to come up with another more challenging one as well.

Anyway, these are a couple of my recent adventures. I am looking forward to hearing some of these types of stories from you as well. Drop me an e-mail or give me a call. You can be confident that I’ll respond. (I am, alas, that kind of guy.)