To properly write a conclusion, an author should think about why he wrote the book in the first place. What goal is he trying to achieve?
I think the largest reason I wrote this book is as a result of one of the most formative experiences of my life. You see, a little more than a decade ago, the book Freakonomics came out. The surprise bestseller described the research of Steven Levitt, an award-winning economist at the University of Chicago mentioned frequently in this book. Levitt was a “rogue economist” who seemed to be able to use data to answer any question his quirky mind could think to ask: Do sumo wrestlers cheat? Do contestants on game shows discriminate? Do real estate agents get you the same deals they get for themselves?
I was just out of college, having majored in philosophy, with little idea what I wanted to do with my life. After reading Freakonomics, I knew. I wanted to do what Steven Levitt did. I wanted to pore through mountains of data to find out how the world really worked. I would follow him, I decided, and get a Ph.D. in economics.
So much has changed in the intervening twelve years. A couple of Levitt’s studies were found to have coding errors. Levitt said some politically incorrect things about global warming. Freakonomics has gone out of favor in intellectual circles.
But I think, a few mistakes aside, the years have been kind to the larger point Levitt was trying to make. Levitt was telling us that a combination of curiosity, creativity, and data could dramatically improve our understanding of the world. There were stories hidden in data that were ready to be told and this has been proven right over and over again.
And I hope this book might have the same effect on others that Freakonomics had on me. I hope there is some young person reading this right now who is a bit confused on what she wants to do with her life. If you have a bit of statistical skill, an abundance of creativity, and curiosity, enter the data analysis business.
This book, in fact, and if I can be so bold, may be seen as next-level Freakonomics. A major difference between the studies discussed in Freakonomics and those discussed in this book is the ambition. In the 1990s, when Levitt made his name, there wasn’t that much data available. Levitt prided himself on going after quirky questions, where data did exist. He largely ignored big questions where the data did not exist. Today, however, with so much data available on just about every topic, it makes sense to go after big, profound questions that get to the core of what it means to be a human being.
The future of data analysis is bright. The next Kinsey, I strongly suspect, will be a data scientist. The next Foucault will be a data scientist. The next Freud will be a data scientist. The next Marx will be a data scientist. The next Salk might very well be a data scientist.
Anyway, those were my attempts to do some of the things that a proper conclusion does. But great conclusions, I came to realize, do a lot more. So much more. A great conclusion must be ironic. It must be moving. A great conclusion must be profound and playful. It must be deep, humorous, and sad. A great conclusion must, in one sentence or two, make a point that sums up everything that has come before, everything that is coming. It must do so with a unique, novel point—a twist. A great book must end on a smart, funny, provocative bang.
Now might be a good time to talk a bit about my writing process. I am not a particularly verbose writer. This book is only about seventy-five thousand words, which is a bit short for a topic as rich as this one.
But what I lack in breadth, I make up in obsessiveness. I spent five months on, and wrote forty-seven drafts of, my first New York Times sex column, which was two thousand words. Some chapters in this book took sixty drafts. I can spend hours finding the right word for a sentence in a footnote.
I lived much of my past year as a hermit. Just me and my computer. I lived in the hippest part of New York City and went out approximately never. This is, in my opinion, my magnum opus, the best idea I will have in my life. And I was willing to sacrifice whatever it took to make it right. I wanted to be able to defend every word in this book. My phone is filled with emails I forgot to respond to, e-vites I never opened, Bumble messages I ignored.*
After thirteen months of hard work, I was finally able to send in a near-complete draft. One part, however, was missing: the conclusion.
I explained to my editor, Denise, that it could take another few months. I told her six months was my most likely guess. The conclusion is, in my opinion, the most important part of the book. And I was only beginning to learn what makes a great conclusion. Needless to say, Denise was not pleased.
Then, one day, a friend of mine emailed me a study by Jordan Ellenberg. Ellenberg, a mathematician at the University of Wisconsin, was curious about how many people actually finish books. He thought of an ingenious way to test it using Big Data. Amazon reports how many people quote various lines in books. Ellenberg realized he could compare how frequently quotes were highlighted at the beginning of the book versus the end of the book. This would give a rough guide to readers’ propensity to make it to the end. By his measure, more than 90 percent of readers finished Donna Tartt’s novel The Goldfinch. In contrast, only about 7 percent made it through Nobel Prize economist Daniel Kahneman’s magnum opus, Thinking, Fast and Slow. Fewer than 3 percent, this rough methodology estimated, made it to the end of economist Thomas Piketty’s much discussed and praised Capital in the 21st Century. In other words, people tend not to finish treatises by economists.
One of the points of this book is we have to follow the Big Data wherever it leads and act accordingly. I may hope that most readers are going to hang on my every word and try to detect patterns linking the final pages to what happened earlier. But, no matter how hard I work on polishing my prose, most people are going to read the first fifty pages, get a few points, and move on with their lives.
Thus, I conclude this book in the only appropriate way: by following the data, what people actually do, not what they say. I am going to get a beer with some friends and stop working on this damn conclusion. Too few of you, Big Data tells me, are still reading.