There's a reason why elected politicians seem to spend as much time fund-raising as governing (or misgoverning): Politics is, and always has been, a money game. This has never been more so than now, the era of Citizen United and Super PACs. Record amounts are being raised and spent in elections, and it's quite clear that money can buy a race. In the recent gubernatorial recall election in Wisconsin, incumbent Scott Walker easily defeated Democratic challenger Tom Barrett. He also reportedly outspent Barrett by a margin of at least 7 to 1, raising $30 million (to Barrett's $4 million) and benefiting from the huge influx of outside money from Super PACs. Maybe Walker would have won anyway without the huge spending advantage. We'll never know. But one thing we do know is that candidates who are outgunned financially must rely on other resources to compete. Traditionally that has meant volunteers, guerrilla tactics and free media. Slowly, though, political professionals are turning to data in order to find some kind of edge, whether it's discovering under-the-radar issues that arouse passion in voters or previously undetected connections between certain voter demographics and specific policy positions. An interesting article posted Friday on The Huffington Post explores the topic of data analytics in the political world. In the post, author Sam Stein focuses on Bill James, the legendary baseball statistical analysis guru whose theories single-handedly changed how many Major League franchises approach building a winning team. James's "sabermetrics" approach to baseball data-crunching created new metrics for assessing the true value of players and their impact on a team's ability to win. But what Stein really is talking about is Big Data, information analytics and their potential to transform the election process. However, there are obstacles slowing the adoption of data analytics in the political arena. Whereas baseball is a world in which every inning, game and season produces reams of specific data to be crunched, massaged and interpreted, politics is a bit more ephemeral. As one of Stein's sources says:
"There aren't any good databases" in politics, said Kevin Goldstein of Baseball Prospectus, an organization devoted to studying sabermetrics. "You would need like the last 50, 100 Senate campaigns. ... You would need the full books. Like this was the money. This is what they spent it on. You have to create categories: mail, personal appearances, television ads. And then you need to break up the television ads: positive ads, negative ads. How valuable was it? How valuable is going to the local diner? How valuable is the ad that says my opponent is a nimrod? There are so many things that you would need. ... I don't know anyone who is doing that."
Perhaps not yet -- or at least not fully -- but things definitely are moving in that direction. President Obama's re-election campaign, for one, has created an in-house analytics team at its Chicago headquarters to integrate and analyze online and offline data. Interestingly, Stein cites Texas Gov. Rick Perry as the "most innovative politician when it comes to adopting data-driven campaign theories." Perry's campaign to win the GOP nomination and replace Obama in the White House was an unmitigated disaster, but it had little to do with the failure of data analysis and pretty much everything to do with the candidate's unforced errors and on-camera debate meltdowns. In Perry's successful 2010 gubernatorial primary race, advisers used market research to reallocate funds away from activities deemed ineffective -- early television ads "framing" the candidate, lawn signs, direct mail, editorial board visits -- to a system of volunteers paid $20 to sign up friends and neighbors to vote for Perry. (Which, when you think of it, is a type of social networking strategy.) The point is that Perry was able to save $3 million, according to Stein. For cash-strapped campaigns, saving $3 million is like raising $3 million. It's all about maximizing return on investment and effective allocation of resources -- goals that data analytics is designed to help achieve. Washington may be "behind the curve" regarding data capture and analysis, as Stein writes, but eventually it will catch up. And when it does, data analytics may drastically alter the process of electing candidates. Of course, that doesn't mean it will necessarily save the rest of us from the childish partisan wars that are poisoning the political process, or the corrupting effects of money in Washington and at lower levels of government. After all, not even Big Data can't eliminate greed, corruption and stupidity. That's up to us.
Now read this: