Expert answer:Business Intelligence EBTM 446
Answer & Explanation:Please answer the Homework file (Parts 1, 2 and 3)homework 1 fall 2015(1).pdfBig Data Analytics and Elections.pdfIMG_7106.JPGIMG_7107.JPGIMG_7108.JPG
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Homework #1
EBTM 446, Fall 2015
You may submit your solutions individually or in groups of no more than three.
The assignment is due at the start of class on Thursday, September 3. Hand in typed solutions in
class.
Part One
Read the article “Big Data, Analytics, and Elections” that is posted with this assignment. Answer
the following questions:
1. Explain what the author means by integrated system.
2. What did the mega database allow the Obama team to do? What types of predictive
models were built?
3. Identify one analytics lesson that can be learned from the 2012 Presidential Campaign.
Part Two
Read the Opening Vignette in section 1 on pages 3-‐5 in the textbook. Answer the following
questions:
1. What information is provided by the descriptive analytics employed at Magpie Sensing?
2. What type of support is provided by the predictive analytics employed at Magpie
Sensing?
3. How does prescriptive analytics help in business decision-‐making?
Part Three
Other than the cases discussed in Chapter 1, find an example of a company using Big Data or
analytics in their operations. Identify the application and provide a link or reference to support
your answer.
CAM PAIG N ST RAT E GY
Big data, analytics
and elections
BY GEORGE SHEN
The 2012 U.S. presidential
election is over, and from
a statistical viewpoint, the
winner was a small group
of people armed with analytics who
out-predicted many so-called political
T
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A N A LY T I C S – M A G A Z I N E . O R G
experts (who relied mostly on gut instinct and experience). The election
demonstrated that analytics fueled by
big data and advancement in computing
technology has become an integral part
of the presidential campaign process.
W W W. I N F O R M S . O R G
The real winner of the 2012 election is
analytics.
While most people thought the
election would be very close (as many
politicians and pundits wanted us to
believe), prior to the election, a number
of quants and statisticians begged to
differ and predicted it was anything but
a “nail biter.” In the last few days before
Election Day, their models and simulations predicted that Obama would prevail with close to 99 percent certainty
based on aggregated poll data. For example, Nate Silver at FiveThirtyEight, a
popular political blog published by The
New York Times, predicted not only
Obama that would win but by exactly
how much. Simon Jackman, professor
of political science at Stanford University, accurately predicted that Obama
would win 332 electoral votes and that
North Carolina and Indiana would be
the only two states that Obama won in
2008 that would fall to Romney.
Others, including Drew Linzer (assistant professor of political science at
Emory University), Sam Wang (a neuroscientist at Princeton University) and
Josh Putnam (visiting assistant professor of political science at Davidson
College) also correctly predicted the
presidential race and many congressional races with great accuracy [1]. It
is worth noting that some of them had
A NA L Y T I C S
an outstanding track record in predicting the 2008 election results as well.
Most of these models were based on
poll aggregation. Accurate predictions
usually factored in the latest polls just
before the election. However, Moody senior economist Cheng Xu took a different
approach. His model, made in February
2012, used both state economic and political data and predicted Obama winning
303 electoral votes vs. Romney’s 235.
It’s difficult to model nine months ahead
of time, especially given the economic
uncertainty in terms of the length and
depth of the recession in every state. According to Xu, his model also took into
account voter sentiments – “the grumpy
voter effect” [2]. Had Obama lost Florida,
which has 29 electoral votes, Xu would
have been spot on. (Obama won Florida
by a razor-thin margin).
Of course, many journalists, pundits
and politicos who are ill-equipped to interpret data were not short of opinions prior
to the election. Some of these “political experts” disdained and ridiculed the analyticsdriven predictions while others attacked the
data scientists and statisticians. Geoffrey
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ANALY TIC S & E LE C T I O NS
Norman at The Weekly Standard called Xu
a “bad economist” and Joe Scarborough on
MSNBC’s “Morning Joe” called Silver an
“ideologue” and a “joke” (Scarborough later
offered a post-election apology to Silver).
In the end, data-driven analytics triumphed
over hunches and experience. Vindication
and respect are due for the quantitative
minds.
IMPORTANT ROLE FOR ANALYTICS
Analytics played a bigger and more important role in the election than just predicting the outcome. Analytics was an integral
part of the 2012 political campaign. In recent
elections, Republican and Democratic campaigns have employed data-driven analytics and social-media data to stay ahead of
the competition, but the Democrats clearly
had the competitive advantage in the 2012
presidential. In June of last year, Politico
reported that Obama had a data advantage and went on to say that the depth and
breadth of the campaign’s digital operation,
from political and demographic data mining
to voter sentiment and behavioral analysis,
reached beyond anything politics had ever
seen [3]. Obama’s 2012 data-crunching
operation was far more sophisticated and
more efficient at a large scale than its muchheralded 2008 social media juggernaut.
(Note that Facebook was 10 times bigger in
2012 than it was in 2008).
During the six months leading up to
the election, the Obama team launched
a full-scale and all-front campaign, leveraging Web, mobile, TV, call, social media and analytics to directly micro-target
potential voters and donors with tailored
messages. Compared to previous presidential campaigns in 2004 and 2008, the
2012 campaign was going digital and analytical across all channels. The Obama
campaign management hired a multi-disciplinary team of statisticians, predictive
modelers, data-mining experts, mathematicians, software programmers and
quantitative analysts. It eventually built
an entire analytics department five times
as large as that of its 2008 campaign.
In an interview with Time magazine, a
group of Obama senior campaign advisers revealed an enormous data effort to
support fundraising, micro-targeting TV
ads and modeling of swing-state voters.
They first went through a data integration
process to consolidate many disparate
databases and create a single, massive
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W W W. I N F O R M S . O R G
system that merged information collected
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
[4]. The advantage of the integrated system is that analytics could be performed
effectively across multiple datasets from
multiple channels – the ability to connect
the digital dots. Furthermore, the information could be shared across the entire
organization seamlessly, without multiple
versions of the same data or potential
data quality issues.
In addition to supporting campaign
operations that simply pull data points,
the mega database allows 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, incorporating
the results from micro-targeting models
that analyze hundreds of data points to
generate “support scores” – a percentage probability that an individual would
back the Democratic candidate [5]. The
advisers ran experimental campaigns,
and analysts factored the results into the
models to refine and improve them. The
campaign rarely made assumptions without numbers to back them up, according
to Obama’s campaign manager Jim Messina who had promised a totally different,
A NA L Y T I C S
metric-driven kind of campaign in which
politics was the goal but political instincts
might not be the means.
Big data and analytics played a critical
role in fund raising too. Fund-raisers, such
as George Clooney and Sarah Jessica
Parker, were picked by number crunchers
through data-mining discovery to match
their appeals to certain donors and maximize the star powers. Fund-raising e-mail
and text messages targeting certain demographics were tested first among supporters
with different subject lines and contents on a
small scale and subsequently achieved better results among potential voters on a larger scale. Fund-raising metrics were carefully
gauged and analyzed between executions.
Big data and analytics also helped
drive the campaign’s ad-buying decisions, which resulted in purchasing ads
during unconventional programming and
time slots. Here again the team relied on
big data analytics rather than on outside
media consultants and experts to decide
where and when ads should run. Ultimately, this data-driven approach proved
very successful in getting the messages
out to the targeted viewers and driving
the turnout in swing states.
IMPACT ON THE ELECTION
Perhaps the 2012 election will be
remembered as the first election where
big data and analytics played a crucial
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role and had a tremendous impact on
the outcome of the presidential election.
Time will tell if it may have revolutionized
the institution of politics, similarly to how
Billy Beane of “Moneyball” fame and his
data-driven approach changed the game
of baseball and made a profound impact
on the institution of professional sports.
Nonetheless, the 2012 election will be a
classic case of big data analytics and its
applications for many years to come.
What analytics lessons can businesses draw from the 2012 election?
The answer is plenty. First and foremost,
businesses need to rely more on a data-driven approach and measured performance and less on gut instinct when
data and analytics are available. It may
require a cultural change and paradigm
shift in some organizations. Second,
understanding consumer behavior, sentiment and purchase pattern, predicting the next sales opportunity and most
profitable customer, segmenting and
micro-targeting the right population with
tailored messages that resonate with
customers are the challenges faced by
almost every business. Businesses of
all types and sizes should start building
a solid, big data knowledge base and
mastering the new social and digital intelligence across a variety of channels
to identify, target and win customers
similarly to how 2012 election was won
on the digital front. ❙
George Shen (geshen@deloitte.com) is an
information management specialist master with
Deloitte Consulting. An information strategist
and consultant with 17 years of experience
advising, designing, and implementing business
intelligence and data management solutions for
many Fortune 500 clients in financial services,
telecommunications and life sciences industry,
Shen’s expertise spans across information strategy
and architecture, business analytics, performance
management and a variety of emerging
technologies.
REFERENCES
1. Plumer, Brad, “Pundit Accountability: The
Official 2012 Election Prediction Thread,”
WONKBLOG, The Washington Post, Nov. 5, 2012.
2. Cooper, Michael, “9 Swing States, Critical to
Presidential Race, Are Mixed Lot,” The New York
Times, May 5, 2012.
3. Romano, Lois, “Obama’s Data Advantage,”
Politico, June 9, 2012.
4. Scherer, Michael, “Inside the Secret World of
the Data Crunchers Who Helped Obama Win,”
Time, Nov. 7, 2012.
5. Issenberg, Sasha, “Obama Does It Better”
(from “Victory Lab: The New Science of Winning
Campaigns), Slate, Oct.29, 2012.
Disclaimer
The opinions expressed here are the views of the author and do not necessarily reflect the
views and opinions of Deloitte Consulting. Deloitte is not, by means of this article, rendering
business, financial, investment or other professional advice or services. This article is not a
substitute for such professional advice or services, nor should it be used as a basis for any
decision or action that may affect your business.
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W W W. I N F O R M S . O R G
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