It comes in voters’ own words, often registered onto the clipboards of canvassers, during a call-center phone conversation, in an online signup sequence or a stunt like “share your story.” As part of the Dreamcatcher project, Obama campaign officials have already set out to redesign the “notes” field on individual records in the database they use to track voters so that it sits visibly at the top of the screen—encouraging volunteers to gather and enter that information. And they’ve made the field large enough to include the “stories” submitted online. (One story was 60,000 text characters long.)

What can the campaign do with this blizzard of text snippets? Theoretically, Ghani could isolate keywords and context, then use statistical patterns gleaned from the examples of millions of voters to discern meaning. Say someone prattles on about “the auto bailout” to a volunteer canvasser: Is he lauding a signature domestic-policy achievement or is he a Tea Party sympathizer who should be excluded from Obama’s future outreach efforts? An algorithm able to interpret that voter’s actual words and sort them into categories might be able to make an educated guess.

In other words, person for person:

Asians are the most desired racial group in the country.

Then Latinos. Then whites, sort of. As we’re seeing it now, the data is being distorted: a huge part of the country is white, and white people mostly like to talk amongst themselves. Intentionally or not, minorities are left out in the cold.

Nonetheless, people prefer their own race

Given equal choice, every race strongly prefers itself: And white people actually prefer themselves the least, but right now there’s just so many of them.