Labor for the Data

Happy Labor Day! My last post on Google Correlate is still on my mind. So is Buck Woody’s (Blog|Twitter) post on being a Data Professional … yes, this is at least the third time I’ve mentioned Buck’s post, but I think the message is that important to grasp. Anyway, what’s on my mind is two encounters I had that, at least for me, indicated I should not get so involved in technology that I forget about the data.

The first encounter was seventeen years ago, the second was ten years ago. So I have two stories, and since it’s a holiday you probably feel like reading just one. Okay, I’ll go with the more recent story for now and save the older one for a follow-up blog post.

It was my first day at a new job. The previous day I graduated with a computer science degree (I know, I should have taken some time off in between, but I was excited about the new job). My boss came over to my desk to welcome me, and the exchange went something like this:

  • Boss: Hi Noel, we’re glad you’re finally here.
  • Me: Same here, I received my degree yesterday, so I’m ready to get to work!
  • Boss: You were the one who wanted to wait until you graduated to start work. I was ready for you to start working here months ago. Quite frankly, I don’t care about your computer science degree, it’s the skills from your previous graduate study that are interesting.

Ouch, talk about a backhanded compliment. I’d just spent over two years of time and boatloads of SQLCruises in dollars to study algorithms, programming languages, relational algebra, software engineering techniques, etc. How could he not value that? How could he find the five years of grad school I did beforehand more interesting? Especially when half of those five years didn’t even result in a degree.

After a few years, I not only understood his viewpoint but even agreed with it. Well, mostly agree with it. I’d never want to give up the computer science study (especially the algorithms material), and if I try to think of what parts I could have cut out, I don’t come up with much.

So what was it about the five years of non-computer science grad school that made my boss interested in me?

The answer: data. I spent all that time rolling around in data.

The first graduate degree was in social and applied economics. The focus was on applying data, statistics and economic theory to public policy and business issues. I was also a research assistant. So days and nights revolved around loading data tapes on mainframes, crunching away at them with SAS, then taking the resulting greenbar printouts to a professor’s office. That’s where your real education started: pour over scatter plots and regression lines, dig into data rows to find points that didn’t fit, figure out what was missed, what the data told us that we didn’t know, adjust models, then head back to the computer center and repeat.

After that, I taught economics for a few years, then I headed back to graduate school to work on a doctorate (I became bored with being in a small college town, plus my interests changed, so I left without a degree during my third year). Once again I was a research assistant while taking courses in quantitative methods, economics and accounting, so my experience was similar to the above. Similar but not the same; by that time, PCs had become powerful enough that you no longer needed a mainframe to run SAS on large data sets. Which meant you spent more time with data… it was right there on your desktop, so you didn’t have to run back and forth to the computer center!

That’s the end of the story. My take-away: it occurs to me that in recent years I’ve spent disproportionately more time learning about the technology side than the data side. So I’m consciously going to try to balance that out in the coming months. With that, I guess it’s time to find my copy of Hogg and Craig. Oh there it is, underneath my monitor stand 🙂

About Noel McKinney

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