Data, data, everywhere: we’re drowning in it

We are drowning in data. We create more of it daily—so much it seems impossible we’ll ever make sense of it.

Data—or, as it was known back in the 20th century, “paperwork”—has been a challenge for decades. As Frank Zappa wrote in 1989:

“It isn’t necessary to imagine the world ending in fire or ice. There are two other possibilities: one is paperwork, and the other is nostalgia.”

Data: the horse manure of the 21st century?

One of the most memorable stories in Steven Levitt and Stephen Dubner Freaknomics revolves around a seemingly insoluble problem of 19th century urban life. As traffic increased, so did the emissions generated by vehicles. But since the vehicles in question derived their horsepower from, well…horses, the emission in question was manure. Tons of it.

Cities struggled to keep their streets navigable. Pedestrians crossed at their own risk. Civic leaders despaired of handling the exponential growth of excrement as the population increased and commerce flourished.

In one sense, horse manure was the 19th century equivalent of our data problem
Henry Ford and his Quadricycle, 1896

No one saw the solution on the horizon. No one, that is, except Henry Ford. His gasoline-powered “horseless carriage”—he called it a “Quadricycle”—produced a different kind of emission.

The proliferation of vehicles powered by internal combustion engines solved the horse manure problem. Of course, it created new problems whose effects we’re dealing with today. But that’s beyond the scope of this blog.

Now obviously it’s an imperfect analogy. Data is much easier to brush off the sole of your shoe, should you step it in. Also it doesn’t stink, not even in the heat of summer.

Does data proliferation matter?

As Moore’s Law predicted more than half a century ago, ever-smaller devices now store ever-greater amounts of data. But what’s the point? Sometimes I wonder if companies aren’t collecting data for the same reason British mountaineer George Mallory pursued his passion.

In 1923, before he returned for his third and final trip to Mt. Everest, a journalist asked him, “Why did you want to climb Mount Everest?” Mallory’s admirably terse reply:

“Because it’s there.”

It’s worth noting that the 1924 trip was Mallory’s final visit to Everest not because he reached the summit, but because he died trying.

As a writer, I worry about the role I play—admittedly a small one—in the increasing amount of data bombarding our world. These bits and bytes assembling themselves into daily blog posts add to the world’s data stockpile. So does everything you’ll write today, and into the foreseeable future.

Every PowerPoint presentation, every white paper, every boring employee newsletter (and the few interesting ones, too)—every communication we produce gets socked away into the digital storage bin. So let’s make those words count, shall we? Let’s produce good work. Work worth reading.

That’s our Everest; I climb it daily. Come join me.

Making data interesting: Advice from a word-alchemist

A while back, I wrote about facts being inherently less interesting—and less memorable—than stories. But you can’t avoid data forever; sometimes you have to talk about it. And when that happens, you need a way of making data interesting.

Enter your best friend, the Story. Yes, stories can include data. In fact, journalist Stephen Dubner says they must.

Why should we care what Stephen Dubner says? Because he’s not just a writer. I believe he’s actually an alchemist. A word-alchemist.

Alchemists in the Middle Ages searched for a way to turn base metals like lead into gold. Dubner and his co-author, award-winning economist Steven Levitt, have actually done that. They’ve taken the “dismal science” of economics and turned its principles—and data—into best-sellers with stories so compelling you can’t stop reading.

If you haven’t read Freakonomics, Super Freakonomics, Think Like a Freak, or When to Rob a Bankperhaps because you assumed that any book about economics had to be dull and boring—I forgive you. That kind of thinking kept me away from them for so long. But, boy was I wrong.

Here’s the opening paragraph of the Bookrags study guide for Freakonomics:Freakonomics, a case study in making data interesting

What trait is shared by both Ku Klux Klan members and real-estate agents? In what way do the working worlds of Chicago schoolteachers and Japanese sumo wrestlers intersect? These questions might seem puzzling at first glance, but the answers provided in Freakonomics: A Rogue Economist Explores the Hidden Side of Everything reveal that fundamental notions of economics can be used to interpret just about everything in modern society.

I’m sorry—anyone who can put KKK members and Realtors in the same group has my attention. Talk about making data interesting. And teachers and sumo wrestlers? Can you imagine? It doesn’t matter if you can’t, because Dubner and Levitt have imagined it for you.

Making data interesting: Tell stories

Tim Ferriss interviewed Stephen Dubner on his podcast a couple of years ago—I just got around to listening to it this week—and Dubner’s description of a good story fascinated me:

But to me what a story is it’s got the narrative but it does include…data and time series. Data, because you need to know the magnitude of the story—is it really important? And time series because you need to know if it was a kind of blip or if it really persisted. And that to me are the elements of a good story: data, a time  series and a narrative with characters that people can identify with. And by the way, it needs to all be true.

It all needs to be true. The best stories always are.

So don’t think you have to bombard people with data to get them to trust you. Facts—even millions of them—can’t build trust on their own. And people won’t remember them, anyway, unless they’re set in a story. A true story. Told by you, from your heart, with memorable characters that people can identify with.

It’s simple. Even when you’re talking about a complex subject like economics.