Letter from the President - December 2017
Wouldn’t it be nice to be able to predict the future? People have been trying to for centuries, with varying degrees of success. Nostradamus, Leonardo da Vinci, and countless science fiction writers have all speculated on what the world would be like in times to come. Some suggestions, seen in hindsight, seem downright eerie. (Nostradamus, writing in 1555, apparently called the Great London Fire of 1666.) Others are so vague that almost any outcome could be interpreted as validation. (See: fortune cookies.) But really, the only way to evaluate a prediction is in hindsight, so maybe the less specific a prediction is, the more likely it is to come “true”!
We can’t literally predict the future, but in many ways we’re asked to try every day. How long will it take to get to work this morning? What’s for dinner? Will this project be completed on schedule? “Where do you see yourself in five years?”
That last question is one we hear all the time in performance reviews or job interviews, but that’s not the only time we’re asked to look ahead at work. We have to constantly be on the lookout for developments and innovations that can change our industry and affect our careers. The switch from paper drafting to CAD was one obvious one; the leap from 2D drawings to 3D modeling has been another. What’s the next one? Good question.
We all know people on each end of the “adoption continuum”—some sign up for every beta test and are the first to download a new product (they’re probably also the ones who stand in line overnight for new phone launches), while others practically have to be dragged away from their old methods and forced to use new ones.
Most of us fall somewhere in between. We don’t want to be left behind by innovations, but we can’t afford to constantly take risks on unproven solutions. How can we find a balance? In other words, how can we be most successful in our predictions of the future?
Two factors can contribute to a successful forecast. The first is time: the shorter the timeframe, the better luck we have with our estimations. The other component is data. The more data we have, the more accurate our evaluations will be. Checking traffic right before you leave the house provides a better prediction of your commute than looking it up the night before. And real-time traffic cams give more information than once-every-ten-minutes radio reports.
We don’t always have control over timeframes. Sometimes technology can take longer to develop than anticipated. (You’ll notice we don’t have flying cars yet.) Schedules can be unexpectedly compressed, too: clients may make requests that force us to adopt a new technique earlier than we might have otherwise, or our old hardware might suddenly prove to be incapable of handling the demands of a new project or software program.
That’s when we need data. The more research you’ve done in advance, the readier you’ll be with a solution when a challenge arises. Where do we get this data? We read blogs, articles, or books; we watch videos of other people sharing their experiences; we have conversations in person or on social media; we try things ourselves and record the results. In a lot of ways it’s similar to what scientists do, when they formulate a hypothesis and test it. (You didn’t know you were a scientist, did you?)
It’s normal to wish for a crystal ball, and the ability to see the future with certainty. But the more I think about it, the more I’m okay with not being able to predict everything. I think there would be downsides to infallible prognostications. We know that bad things will happen in life, but do we really want to know all the details ahead of time? And I wouldn’t want to lose the ability to be surprised by pleasant things.
So we stay in the middle, making our best guesses where we can and trying to stay ready for the things that come out of left field.
I will leave you with one solid prediction, though: If you read this month’s AUGIWorld, you’ll learn something! Maybe even more than one thing! Now that’s going out on a limb.