There are many things that I enjoy in life, but cooking and analytics are two of the things that excite me the most. In my free time, one of my favorite things to do is experiment in the kitchen to try to come up with something new and different to eat.
One of my favorite ways to innovate in the kitchen is to bring culinary techniques from different cultures to bear on my traditional, Southern style of cooking. What happens when I season my pot roast with some Italian flavors or spice up my macaroni and cheese with a little Mexican flair? My cooking technique may not change, but by bringing together flavors from different cultures, I can create lots of innovative new dishes in my kitchen.
Believe it or not, innovation in the analytics space often happens in much the same way. Innovation is not always about coming up with a brand new analytical technique. Sometimes it’s about taking a technique that’s been around for a while and applying it in an unexpected way.
For example, survival analysis provides methods for dealing with time-to-event data – also referred to as censored data. This technique has been used for decades in the analysis of clinical trials data where the time until a particular outcome occurs is of critical interest. With SAS 9.3, we’ve taken traditional predictive modeling techniques, sprinkled in a little bit of survival analysis, and provided our users with a new way of analyzing customer data that we call “survival data mining.”
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