Yes, that allow a number of databases to be

    Yes, I think Obama’s election team had used
Big Data Analytics to help winning the US Presidency Re?election in 2012.

    In fact, Big Data refers to high-volume, high-velocity and high-variety
information assets that demand cost-effective, innovative forms of information
processing for enhanced insight and decision making. In the following
paragraphs, I would like to explain in details how Obama’s team made use of Big
Data Analytics in the 2012 election campaign.

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    Since half year preceding the election, the
Obama’s team had started to carry out a full-scale and all-front campaign, exerting
influence on Web, mobile, TV, call and social media. Relatively, the 2012
campaign was moving more digital and analytical throughout all channels
comparing to former presidential campaigns in 2004 and 2008. This showed that
there were data from multiple types and natures of information used for analytics
and data delivery, so high-variety of
big data was proved, and it helped micro-targeting potential voters and donors directly
with tailor-made messages.

    Actually, the Obama campaign management in
2012 built a powerful team possessing of a 100-strong analytics staff employed
multi-disciplinarily, including statisticians, predictive modelers, data-mining
experts, mathematicians, software programmers and quantitative analysts, who
made use of the HP Vertica MPP (massively parallel processing) analytic
database as well as predictive models with R and Stata. The MPP system that
employed by the Obama’s team is considered better than a symmetrically parallel
system (SMP) for applications that allow a number of databases to be searched
in parallel. This enabled high speed of data to be generated and processed
for analytics, so high-velocity of
big data was proved, and it eventually helped the Obama’s team to gain a competitive
edge.

    According to Obama senior campaign advisers,
it was said that immense data effort had been put in supporting fundraising,
micro-targeting TV ads and modeling of swing-state voters. Huge amount of
data amassed from pollsters, fundraisers, field workers and consumer
databases as well as social-media and mobile contacts with the Democratic voter
files in the swing states were combined to form a single, enormous system under
data integration process. This allowed large quantity of generated and stored
data information can be shared throughout the whole team seamlessly, without
multiple types of the identical data or potential data quality issues, which ultimately
not only facilitated the high-velocity of
big data, but also proved the existence of high-volume of data being utilized for analytics, and this
integrated system enabled the analytics to perform effectively across numerous
datasets from different channels – the ability to join the digital dots.

    Therefore, other than backing campaign
operations, with the high-volume, high-velocity and high-variety of data, it was found by George Shen (2013) that the
big data allowed “data scientists and number crunchers to build analytical
models predicting swing voter segmentation with high persuadability based on
demographic and socioeconomic data and voting record generated, incorporating
the results from micro-targeting models that analyze hundreds of data points to
generate support scores”. Finally, this Big Data Analytics method was very
successful in reaching the messages out to the targeted voters and driving the
turnout in swing states by encouraging them to register, persuading the
voters who did not decided to vote for Obama, and motivating them to show up to
vote on the Election Day. At the end, Obama’s election team won.

    From the above, we can see that high-volume, high-velocity and high-variety
of data had been used for Big Data Analytics to help Obama’s election team winning
the US Presidency Re?election in 2012.

 

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