I hate data. I’ve always hated data. As a programmer right out of school, I worked in “technical programming” as opposed to “business programming.” Business was about accounting mostly, and technical about, well, technical stuff. One would think that technical would involve data and numbers while business would be about less precise things. Nevertheless, the fact was that it was the business side that worried about data and about uploading huge amounts for backup every night. Therein, of course, lies my initial dilemma. It happened that I was fortunate enough to focus on my passion, which was computer graphics. This was back in the days when we were figuring out how to make circles look round and to get rid of the “jaggies.” At the time, I had little respect, if not outright disdain for business, but then, I was much younger and living in my 3-d graphics world. We on the technical side focused on what you could program computers to do. I wrote code that made every graphics device I could find sing and dance, and the input devices like mice and early tablets and joysticks, too. Imagine the thrill of making one of the first joysticks move cursers and move through 3D spaces rendered on 2D screens! Compare that to the dubious activity of staring at tons of numbers on countless reports. Those guys debugged things by studying numerical data, while I had the joy of detecting bugs with the screen looking like a war zone of odd shapes, colors, and flashes, or simply by crashing the computer altogether. Who wouldn’t choose the latter?
Sometime later, after my phases of programming robots and working with pattern recognition algorithms, I began working with refinery modeling and programs to perform mathematical optimization (LPs). These programs combined data from the refinery operations, from laboratories, inventories, planning systems and such, along with current economic information. Lots of data was involved, but the huge impediment was getting the data from ten different sources in a manner that aligned all of it meaningfully. Often the end users would manually enter some of the data and they would pretend that running a very precise optimizer would give just as good results with some of last month’s data. Wrong! Why couldn’t the data just work smoothly in the background?
I hate data. It’s hard to deal with. Too many problems. That’s not what I want to focus on – there are much more interesting things to think about. That’s whyEnterprise Enabler® just had to come along – so that I wouldn’t have to deal with all the idiosyncrasies of disparate data. It was very selfish of me. Let a computer handle all that craziness. Hide everything behind the scenes and automate everything that has to be done more than a couple of times. But then Enterprise Enabler unexpectedly swept me into “business” and all kinds of things I never imagined. I have to be careful, now that the headaches of data are managed, I might start liking it. I can’t admit it, but I’m starting to think data may be what it’s all about. Big Data, little data, virtual, bi-virtual, octy-virtual, and numberical, too.