Right versus wrong, science versus business

201601-unsplash-photo-1446329360995-b4642a139973-rulerWhen you think of the difference between science and business, what do you think of?

  • Rigid versus flexible?
  • Exact versus inexact?
  • Empirical versus heuristic?

Maybe you think of something else. The point is, you get down to something along the lines of science having a “right” answer, while business doesn’t.

That’s kind of true, but the distinction is more subtle. I’ll explain.

Think of product design. We’re building a widget. We need that widget to support a mechanical load. We know, given the constraints at hand, what kind of material we need to use.

What do I mean by constraints? The widget has to be a certain size and shape. It has to weigh less than a certain amount. And so on.

The key to having a “right” answer is knowing the requirements. We know what load the widget has to support. We can use simulation or experimentation to determine whether our newly designed widget will support that load. The physics of the situation tells us whether we succeed or fail.

If you specify the constraints, we can design almost anything you need. We might have to use exotic materials. Or we might have to use exotic machines to actually manufacture the widget. But we can likely get it done. And we’ll know it’s the “right” answer, because it’s clear what problem we’re trying to solve.

What about business? Instead of building a widget, maybe we’re trying to enter a new market. Rather than supporting a mechanical load, we’re trying to grow our revenue base. The fundamental difference between these scenarios isn’t science versus business. The fundamental difference is the precision of our question.

Can the widget support the load? I can answer “yes” or “no”, as long as I have the proper equipment. Will entering this new market meet our expectations for revenue growth? Maybe. Maybe not. It depends on what products and services we’re offering to this new market, what customers we’re targeting, how our new competitors respond to our entry, etc.

Here’s the unfair part of the comparison I just made, between widget design and new market entry. Widget design is a low level activity. It comes after we know what the larger mechanical assembly looks like. It comes after we know what technical challenges we’re trying to solve, and which we’ve chosen to ignore. It comes after we’ve moved from a blank slate to having a conceptual direction.

You can imagine similar low level tasks in business. If your marketing spend is X, and you want to reduce it by Y%, you know whether a proposal is “right” or “wrong”. You’ll either outspend your target or you won’t. Any precisely-defined goal works that way, as long as you control all the parameters.

The moment you start working with things outside of your control, you’ll have uncertainty. And that uncertainty means you won’t strictly have “right” or “wrong” answers. You’ll have answers that are more likely to be right, or more likely to be wrong, but you won’t know for sure in advance.

The same thing happens in science. The researcher doesn’t know if she’ll win a particular grant. She can study proposals that have won in the past. She can consult with other researchers who have won similar grants. But she can’t guarantee the committee will approve her proposal.

Science and business both have high-level challenges that involve uncontrollable parameters. Often these uncontrollable parameters are the behaviors of other people. You just can’t predict what decisions people will or won’t make. And when you can’t perfectly predict the behavior of people, you can’t perfectly predict the behavior of businesses, or committees, or governments, or school boards, etc.

You can, though, perfectly predict how a material will respond to a mechanical load. You can perfectly predict how your marketing spend will respond to specific cut.

Don’t get so caught up in perceived differences between science and business. The more important distinction is between what’s controllable and what’s not. Then you get into what’s predictable and what isn’t. And for stuff that’s predictable, we’ll have more confidence in some predictions than others. Those are the distinctions that are most important.

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