Big Data, it turns out, has become a mainstay of electioneering, in all countries. For this blog post, we take you across the Atlantic, to the United States of America.
Big Data comes into play even before the elections have begun. Voter registration is a big problem in the United States, where 1 in 4 eligible voters is not registered. The reasons mostly concern internal migration; 1 in 8 Americans move in any given year. People are unaware that they have to update their information and records are not synchronised between states. As a result many records are out of date, up to 12.7 million at any given time actually, and 2.7 million people are registered in multiple states. Additionally, when two records match in two different states, there is still some possibility that these are not duplicate records but are, in fact, two unique records. To tackle these issues, the Pew Charitable Trust called in an IBM worker, who thought of linking and correlating records through other sources, such as the Department of Motor Vehicles records. They were thus able to link records and contact the people who had to update their records, as well as those who were registered in various states.
The problem of registration was therefore solved by Big Data. Now onto the actual election campaigns.
Gone are the times when being a charismatic candidate was the cause for winning elections. The smiling and waving politician is now backed by a team of data scientists who know exactly who the voters and their interests are. Matt Damon’s character in The Adjustment Bureau says, “…this isn’t even my tie. This tie was selected for me by a group of specialists, who chose it over fifty six other ties we tested. In fact, our data suggests that I have to stick to either a tie that is red or a tie that is blue.”, and indeed, all candidacies are closely parameterized nowadays. One, if not THE, major reason for Obama’s re-election was his analytics department. In 2008, the department comprised 16 people and in 2012 it counted 165. The campaign allocated all of its resources based on predictive hypothesises of election outcomes.
Data analytics also explains why Ted Cruz rose to popularity in the 2016 elections though he was largely unknown before that. His data analytics team used a tool that defines five underlying traits in electors:
- Openness: How open a person is to new experiences
- Conscientiousness: Whether you prefer order and habits in your life
- Extraversion: how social you are
- Agreeableness: If you put other peoples’ needs, or society and our community before yourself
- Neuroticism: How much you worry
Hundreds of thousands of people took this survey, so the traits of citizens in America.
Based on the results, messages sent to potential electors were nuanced so as best to fit their personality traits. This strategy had an immediate and effective impact. Though Ted Cruz did not win, he was the runner up to Donald Trump during the 2016 Republican Party Presidential Primary.
We took you to the United States because it is particularly representative of the use of data on the field. Europe is far from exempt though. Big Data is everywhere.
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For our sources, follow these great links of various talks about elections and data:
TiEcon 2013 Big Data Wins Election with President Obama Campaign’s CTO
Obama Campaign Manager Jim Messina Talks Big Data at the Milken Institute’s 2013 Global Conference
Election 2016: The big data showdown – Google I/O 2016
Big Data & Analytics: Voter Registration and Elections
The Power of Big Data and Psychographics