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Inside Obama’s Data Machine

Here is one of the ways that Barack Obama won in 2012

In late spring, the backroom number crunchers who powered Barack Obama’s campaign to victory noticed that George Clooney had an almost gravitational tug on West Coast females ages 40 to 49. The women were far and away the single demographic group most likely to hand over cash, for a chance to dine in Hollywood with Clooney — and Obama.

So as they did with all the other data collected, stored and analyzed in the two-year drive for re-election, Obama’s top campaign aides decided to put this insight to use. They sought out an East Coast celebrity who had similar appeal among the same demographic, aiming to replicate the millions of dollars produced by the Clooney contest. “We were blessed with an overflowing menu of options, but we chose Sarah Jessica Parker,” explains a senior campaign adviser. And so the next Dinner with Barack contest was born: a chance to eat at Parker’s West Village brownstone.

For the general public, there was no way to know that the idea for the Parker contest had come from a data-mining discovery about some supporters: affection for contests, small dinners and celebrity. But from the beginning, campaign manager Jim Messina had promised a totally different, metric-driven kind of campaign in which politics was the goal but political instincts might not be the means. “We are going to measure every single thing in this campaign,” he said after taking the job. He hired an analytics department five times as large as that of the 2008 operation, with an official “chief scientist” for the Chicago headquarters named Rayid Ghani, who in a previous life crunched huge data sets to, among other things, maximize the efficiency of supermarket sales promotions.

Exactly what that team of dozens of data crunchers was doing, however, was a closely held secret. “They are our nuclear codes,” campaign spokesman Ben LaBolt would say when asked about the efforts. Around the office, data-mining experiments were given mysterious code names such as Narwhal and Dreamcatcher. The team even worked at a remove from the rest of the campaign staff, setting up shop in a windowless room at the north end of the vast headquarters office. The “scientists” created regular briefings on their work for the President and top aides in the White House’s Roosevelt Room, but public details were in short supply as the campaign guarded what it believed to be its biggest institutional advantage over Mitt Romney’s campaign: its data.

Heading back to Saskatoon…

Trip two to Winnipeg is almost done.  We are checked out of the Polo Park Canad Inns a while ago and I am spending the next couple of hours in workshops discussing the ins and outs learning about HIFIS and how we can use empirical data more effectively in Saskatoon (and across Western Canada).

Believe me, driving 32 hours to and from Winnipeg on two separate drips isn’t my idea of fun which means that I better have gotten something out of his.  Now I did pick up three different Foursquare badges (Crusty’s Pizza, Old Spaghetti Factory, Aaltos Garden Inn) but more important than that, I figured out some better ways of communicating poverty and the narrative of homelessness using empirical data both locally but also Saskatoon wide).

One of the things that was said in a workshop is that the data that we collect says more about the shelter (occupancy, turn aways) than it does about those who are homeless which I immediately agreed with.  It was the bit that tied together some other themes I have been working on.  On one hand I hate anecdotal evidence about homelessness in Saskatoon.  Several organizations have tossed around the number that there are 3000 homeless in Saskatoon which is preposterous yet at the same time I know there are guys sleeping in abandoned buildings and condemned houses.  In the summer there are several homeless encampments in the city but to be honest, what I know about them is just anecdotal and isn’t based on anything factual.  So now I need to figure out a way to document and tell that story with data, not just wild figures.  I have some ideas but I need to freshen up on statistical models a bit before I talk about them.

Until then I have an eight hour drive back to Saskatoon which will put me back in the city just before midnight, depending on how long we stop in Regina and if it’s at Boston Pizza or at McDonalds.