Last year, I spoke about Data Mining and how my interest in it developed over the early stages of my career. It’s been amazing to see the evolution of Data Mining, and more specifically, the uptick in what has become known as Data Science.
Now, although I’ve never been in the Data Science field directly even though I took no shortage of Math, Stats and Computer Science courses (the Chase building was one of my homes at Dalhousie back in the day), I am a bit amused at how the nomenclature seems to have evolved over the years. Because of my background, I’ve always tended to use IT systems and data to help me make operational decisions. It’s been a comfort zone for me. Data Analysis existed well before my professional career started. Data Mining came to my attention in the early 2000s, and over the course of the next decade, I’ve seen additional terms such as Big Data, Machine Learning, Data Science, and Data Engineering, all sometimes used interchangeably (yes, even by myself). Just as we often use the term ‘analysis paralysis’ when describing wading too much into data or overthinking a problem, are we not doing the same with the terminology?
While I’m not one to question the usefulness of data (far from it), having to create a dictionary for all the terms applicable in the field seems to be going a bit too far. I’ve read a number of articles over the last year, where Data Scientists, Computer Scientists and Statisticians debate whether the field of Data Science is really something new, or a nuance of something that’s been around for a while. My opinion….the science isn’t new, but the applications are. No matter how much you analyze data, you’re still using Computer Science to design, store, track and pull the data, just as you’re using Mathematics and Statistics to analyze the trends and potential outcomes (in my day it was called combinations, permutations and probabilities). Is the intersection of CS and Math something new? I don’t think so. Did we create new terminology when data was used to help determine lunar orbits or map DNA sequences? Not widely, although maybe in specific arenas. They are merely new applications of the existing science(s). And in today’s age of new, ever-evolving technology, let’s not get mired in what the technology and data is called vs what it can do for us.
It might not be as glamorous, but whether you call it Computer Science or Math & Statistics, the technology and tools we have today are going to help us analyze data and make businesses, and our lives, more effective, with better quality, going forward. And that’s really all that matters.