The data warriors and the electoral wars they wage

One of the most interesting issues raised by the rise of data science in party politics is how to untangle corporate rhetoric from social reality. I have much time for the argument that we risk taking the claims of a company like Cambridge Analytica too seriously, accepting at face value what are simply marketing exercises. But the parallel risk is that we fail to take them seriously enough, dismissing important changes in how elections are fought as marketing hype propounded by digital charlatans.

Perhaps we need to focus more on the data scientists themselves. As much as there is something of the Bond villain about Alexander Nix, CEO of Cambridge Analytica, it’s important that we don’t become preoccupied with corporate leaders. Who are the rank-and-file data scientists working on campaigns? What motivates them? How do they conceive of the work they do? There were interesting hints about this in the recent book Shattered, looking at Hilary Clinton’s failed election campaign. Much as was the case with Jeb Bush’s near entirely stalled campaign, there had been much investment in data analytics, with buy-in right from the top of the campaign. From pg 228-229:

These young data warriors, most of whom had grown up in politics during the Obama era, behaved as though the Democratic Party had come up with an inviolable formula for winning presidential elections. It started with the “blue wall”—eighteen states, plus the District of Columbia, that had voted for the Democratic presidential nominee in every election since 1992. They accounted for 242 of the 270 electoral votes needed to win the presidency. From there, you expanded the playing field of battleground states to provide as many “paths” as possible to get the remaining 28 electoral votes. Adding to their perceived advantage, Democrats believed they’d demonstrated in Obama’s two elections that they were much more sophisticated in bringing data to bear to get their voters to the polls. For all the talk of models and algorithms, the basic thrust of campaign analytics was pretty straightforward when it came to figuring out how to move voters to the polls. The data team would collect as much information as possible about potential voters, including age, race, ethnicity, voting history, and magazine subscriptions, among other things. Each person was given a score, ranging from zero to one hundred, in each of three categories: probability of voting, probability of voting for Hillary, and probability, if they were undecided, that they could be persuaded to vote for her. These scores determined which voters got contacted by the campaign and in which manner—a television spot, an ad on their favorite website, a knock on their door, or a piece of direct mail. “It’s a grayscale,” said a campaign aide familiar with the operation. “You start with the people who are the best targets and go down until you run out of resources.”

Understanding these ‘data warriors’ and the data practices they engage in is crucial to understanding how data science  is changing party politics. Perhaps it’s even more important than understanding high profile consultancies and the presentations of their corporate leaders.

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