Analytics Januaryfebruary 2013

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ALSO INSIDE:
JANUARY/ FEBRUARY 2013 DRIVING BETTER BUSINESS DECISIONS
Pick the right
analytics partner
Big data, analytics
and elections
Software survey:
decision analysis
WORKSOCIAL
Analyze This!
Vijay Mehrotra
seeks to make
sense of the
analytics world
New approach blends data,
process and collaboration for
better, faster decision-making.
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Brave new mobile world
I NSI DE STORY
Pete Townshend of The Who was way
ahead of his time when he wrote “Goin’ Mo-
bile” more than 40 years ago, but the world
has fnally caught up. Today, seemingly ev-
eryone and their brother are goin’ mobile,
hooked on mobile communications.
In response to the growing number of
readers who prefer their reading material
served up via a mobile communication de-
vice, we begin the new year with a new look.
Instead of the landscape/horizontal format
we’ve employed for four years and which
worked well for desktop computer viewing,
we’ve switched to a portrait/vertical confgu-
ration more suitable for mobile apps, includ-
ing Apple iOS (iPad and iPhones) and the
Android (Google) platform, which includes
any Android device from phones to a variety
of tablets including Barnes & Nobles’ Nook,
Amazon’s Kindle Fire and Google’s Nexus.
(We’re investigating compatibility with the
new Windows phones.)
Keep in mind it takes a little time to
convert the finished issue and submit it
to Apple and Google for testing and ap-
proval, so give it a few weeks before you
try it. We look forward to your feedback.
The widespread use of mobile com-
munications, the social media it enables,
the enormous amount of data it produces
and the technology that drives it all has
given rise to a concept called “worksocial,”
the topic of this month’s cover story by
Samir Gulati. As Gulati explains, “work-
social starts with the processes and data
that drive the business, and overlays the
innovations of social collaboration and
mobile access directly over those busi-
ness engines.” The goal: better, faster
organizational decision-making.
Along with the usual variety of articles
designed to inform, enlighten and enhance
your analytical career (see, for example,
“Ten things to consider when evaluating
analytics and decision sciences partners”
by David Zakkam and Deepinder Singh
Dhingra or “Modeling experience yields key
insights” by Bruce W. Patty), this issue fea-
tures an article and a 20-plus page special
supplement on a new program that can ad-
vance your career: Certifed Analytics Pro-
fessional (CAP).
An important initiative of the Institute for
Operations Research and the Management
Sciences (INFORMS), CAP represents a
gold seal of approval for analytics profes-
sionals from the premier organization for
advanced analytics in the world. To fnd out
more about this ground-breaking program
and what it could mean for your career, visit
the CAP website. ❙
– PETER HORNER, EDITOR
peter.horner
@
mail.informs.org
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Dan Markowitz, Vice President Advance Programs at Kepler Research
www.aimms.com/kepler-research
KeplerResearch
Kepler Research provides government leaders with innovative
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advanced analytics.
“Technology from AIMMS is a strategic component of Viridity Energy’s unique software solution, VPower™.
VPower™ works within the existing operations of large energy users to give them the optimal demand
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With support from AIMMS, VPower™ can give our customers customized decision-making tools to align their
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Audrey Zibelman, CEO & Founder of Viridity Energy
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Viridity Energy works with large energy users to create
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Our mission is to bring the benefits of optimization to
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Contact us and let us show you how AIMMS can improve
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Bellevue, WA, USA Haarlem, the Netherlands Singapore Shanghai, China
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Contact us:
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ParagonDecision_Dec2012_ Shell 11/30/12 1:08 PM Page 1
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DRIVING BETTER BUSINESS DECISIONS
C O N T E N T S
FEATURES
WELCOME TO ‘WORKSOCIAL’ WORLD
By Samir Gulati
New approach, technology blends data, process and
collaboration for better, faster decision-making.
HOW TO PICK A BUSINESS PARTNER
By David Zakkam and Deepinder Singh Dhingra
Ten things to consider when evaluating analytics and decision
sciences partners.
BIG DATA, ANALYTICS AND ELECTIONS
By George Shen
Obama team’s all-front campaign leveraged Web, mobile, TV,
social media and analytics.
MODELING EXPERIENCE YIELDS INSIGHTS
By Bruce W. Patty
Lessons learned while working with the largest domestic
container fleet in North America.
SOFTWARE SURVEY: DECISION ANALYSIS
By William M. Patchak
Relationship between technology, thoughtful analysis remains
essential to success of any software tool.
SPECIAL SUPPLEMENT: CAP CANDIDATE HANDBOOK
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J ANUARY/ FEBRUARY 2013
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What does a typical analytics professional do?
What is his or her background?
Interesting questions – questions that would ben-
eft from the application of analytics. That’s what the
company Talent Analytics has done in cooperation
with the International Institute for Analytics.
Given that many people who practice analytics don’t
have “analytics” in their title, fnding an appropriate sam-
ple of practitioners wasn’t an easy task. Talent Analytics
put together its sample through business contacts and
through names gathered at analytics-oriented conferenc-
es. Individuals who said their only involvement with ana-
lytics was the use of spreadsheets were struck from the
list of names, leaving a sample of just over 300 people.
A dyed-in-the-wool statistician wouldn’t be satis-
fed with this approach, and any conclusions based
on the sample need to be placed in the context of
how it was obtained. Nonetheless, it provides the only
coordinated effort of which I’m aware to fnd out who
analytics professionals are and what they do.
Talent Analytics examined the data in a variety of
ways. I won’t seek to cover everything they shared
with me – it was quite extensive – but a number of
fndings did stand out.
The analytics professional
BY E. ANDREW BOYD
Professionals employed in
the field of analytics are
young and mobile. Of
those who responded, 45
percent had been in the
workforce for less than 10
years.
PROFI T CENTER
J A NUAR Y / F E BR UAR Y 2013 | 9 A NA L Y T I C S
Not surprisingly, analytics profession-
als are well trained. Roughly half indicat-
ed their highest level of education was a
master’s degree, 16 percent held doctor-
ates and virtually everyone had complet-
ed college.
The backgrounds, however, were
quite varied. Respondents were allowed
to check multiple boxes describing their
education. So, for example, an individual
who held an undergraduate degree in
liberal arts and then completed gradu-
ate work in business could check both
“liberal arts” and “business.” Not surpris-
ingly, mathematically oriented disciplines
were well represented, with 120 respon-
dents indicating they held degrees in the
category mathematics/statistics and an-
other 60 holding degrees in operations
research/engineering. Other degrees
made strong showings as well. Fully 108
respondents held degrees in business,
though degrees in fnance and econom-
ics were scant at 11. Interestingly, liberal
arts degrees edged out those in comput-
er science 71 to 69.
YOUNG AND MOBILE
As is the case in many technical dis-
ciplines, professionals employed in the
feld of analytics are young and mobile.
Of those who responded, 45 percent
had been in the workforce for less than
10 years, while only 9 percent had been
working for at least 30 years. A little more
than half had been with their current
employer for less than three years com-
pared with 7 percent for at least 10 years.
The data also supported the increasing
recognition of analytics as its own dis-
cipline, since the time respondents had
been employed as an “analytics profes-
sional” was, on average, far shorter than
the time they’d been employed.
To determine where analytics profes-
sionals devote their time, respondents
were asked how long they spent on
various activities, from analyzing data to
managing people. Based on the respons-
es and, of course, employing analytics,
Talent Analytics grouped respondents
into one of four functional clusters:
1. Data Preparation. Time largely spent
acquiring data and preparing it for
analysis.
2. Programmer. Time largely spent
developing software to perform data
analysis.
3. Manager. Time spent performing
general management and
administration, designing analyses,
interpreting results and presenting
conclusions.
4. Generalist. Time spent doing a little
bit of everything.
The clusters aren’t entirely surprising.
Anyone who’s worked with analytics knows
J A NUAR Y / F E BR UAR Y 2013 | 11
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that programming and data acquisition/
preparation are as vital as knowing the in-
tricacies of various mathematical tools. And
anyone who’s worked in a small frm knows
you get to do a little bit of everything.
EDUCATIONAL BACKGROUND
The educational background of peo-
ple in the different clusters provided
further insight into who analytics pro-
fessionals are. Individuals in the data
preparation cluster had more degrees in
business than anything else (33) followed
by mathematics/statistics (27), computer
science (17) and liberal arts (16). Gener-
alists were also led by business degrees
(57) with mathematics/statistics coming
in second (47) and liberal arts rounding
out the top three (35). The fact that busi-
ness degrees lead both of these clusters
helps emphasize that data acquisition/
preparation isn’t just about putting num-
bers in a database. It requires navigating
organizations to discover data availabil-
ity, uncovering the actual meaning of the
data (is a sale an order or receipt of pay-
ment?) and arranging for the data to be
prepared for analysis.
Interestingly, the No. 1 degree held
by managers is mathematics/statistics
(17) followed by business (10), with
operations research/engineering and
liberal arts tied for third (nine). Pro-
grammers are led by mathematics/
statistics (29), with computer science
taking second position (22) and op-
erations research/engineering and lib-
eral arts again tied for third (11). The
strong showing by liberal arts degrees
in all clusters begs the question of how
these individuals find their way into the
analytics profession.
The work done by Talent Analytics
has given us a glimpse into the make-
up of analytics professionals. And while
there are some surprises, the overall
picture that emerges isn’t a surprise at
all. Analytics professionals have a mix
of talents that span the technical to the
interpersonal. They’re not afraid of num-
bers, and they’re not defned by a unique
educational background. All in all, the
medley of skills enjoyed by analytics pro-
fessionals is a good thing. It’s just what
you want from a group of creative prob-
lems solvers. ❙
Andrew Boyd, senior INFORMS member and
INFORMS VP of Marketing, Communications
and Outreach, has been an executive and chief
scientist at an analytics frm for many years. He
can be reached at [email protected]. The
author thanks the senior management team at
Talent Analytics for taking the time to share the
results of the study.
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My wife and I recently hosted our annual holiday
party, in which a large and boisterous crowd of friends
and family members descended upon our house for
the evening. One part of this annual ritual is dropping
off fyers at nearby houses. The fyers serve both as
an invitation to join in the festivities and as a warning
to: (a) expect a lot of unfamiliar cars to appear, and
(b) anticipate a lot of noise coming out of our house
that night.
This year, a neighbor we had never met before
arrived at our house on the night of our party (“thank
you for the invitation”) and introduced himself to us
(“I’m Bill – I live a few houses up the street”). It turns
out that Bill is 65 years old and has lived his entire life
in his house on our (that is, his) street.
“I took piano lessons in this house as a boy,” he
explained, “but it has been a long time since I have
been inside.” And for the next hour, as we listened in
awe, Bill took us on a historical tour of our own house.
“This was originally Posey’s house,” he began. We
were dumbfounded to discover that apparently the
original owner of our house was George A. Posey [1],
the chief engineer for the tube connecting Oakland,
Calif. (where we live) with the neighboring island-city
Survey seeks to make sense
of analytics world
BY VIJAY MEHROTRA
What has perhaps been
most surprising about the
interviews is the sheer
variety of things that
people in the field of
analytics are concerned
about.
ANALYZE THI S!
J A NUAR Y / F E BR UAR Y 2013 | 13 A NA L Y T I C S
of Alameda, only the second underwater
tunnel ever built in the United States.
Later on, Bill told us, the house was
owned by a tool and die maker of some
local renown, whose machine shop was
on the second foor of the detached ga-
rage (this is now my home offce, the very
room in which I now sit writing this col-
umn). As we walked through the house,
he pointed out where additions had
been made, where moldings had been
replaced, and where walls had been
knocked down. When he left, we had a
much better sense of the place in which
we live.
WE NEED YOU … TO TAKE A
SURVEY
As it happens, I am also trying to get
a better sense of the professional world
that I live in, and I need you to help fll
out the picture. Under the umbrella of Ac-
centure’s Institute for High Performance,
I am partnering with Jeanne Harris (co-
author of “Competing on Analytics” and
“Analytics at Work”) on a detailed study
to examine the world of analytics today.
To date, we have done several one-
on-one interviews with analytics profes-
sionals from several different vertical
industries. What has perhaps been most
surprising about these interviews is the
sheer variety of things that people in the
feld of analytics are concerned about,
including such diverse topics as choos-
ing hardware platforms for data and
analysis, selecting software tools, clean-
ing data, integrating data from multiple
sources, setting up smart organizational
structures to support analytics, properly
prioritizing projects, collaborating effec-
tively with the IT department, impressing
business customers with insightful re-
sults, and dealing with the challenges of
employee retention amidst a perceived
shortage of analytic talent.
Based on these frank and thoughtful
conversations, we have put together a
survey that we are in the midst of rolling
out right now. We would like to invite you
to take our survey and tell us what your
view into the world of analytics looks like.
To take this survey, click here and follow
the instructions. Please forward this link
to everyone in your network who works
in the analytics feld. (You all know how
important getting good data is.)
Thanks in advance for your participa-
tion and for spreading the word about our
work. Jeanne and I look forward to gath-
ering the data, conducting our analysis
and sharing the results with the analytics
community in the near future.
BIG DATA, ADVANCED ANALYTICS
Along these same lines, the focus
of the October 2012 edition of the Har-
vard Business Review is big data and
J A NUAR Y / F E BR UAR Y 2013 | 15
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advanced analytics. Its cover reads “Get-
ting Control of Big Data: How Vast New
Streams of Information are Changing the
Art of Management,” and in a series of
three “Spotlight” articles by academic
and industry leaders, the journal makes
an admirable attempt at describing the
professional world that we live in for an
executive audience.
In “Big Data: The Management Revo-
lution,” MIT’s Andrew McAfee and Erik
Brynjolfsson point out that data and
analysis alone are not enough, asserting
that, in order to capitalize on the potential
of advanced analytics, companies must
also change the way in which the orga-
nization digests information and makes
decisions. The authors provide a descrip-
tion of many of the challenges of making
such changes, but also offer up a tanta-
lizing prize, citing some of their recent re-
search in which they show that “the more
companies characterized themselves as
data-driven, the better they performed on
objective measures of fnancial and op-
erational results.”
In “Data Scientist: The Sexiest Job
of the 21st Century,” Tom Davenport
(Harris’ co-author on “Competing on
Analytics” and “Analytics at Work,” and
also currently a visiting professor at
the Harvard Business School) and D.J.
Patil (data scientist in residence at ven-
ture firm Greylock Partners) note that
amidst all of the big data hype, there
is also significant confusion about the
people that turn all that data into value.
“There is little consensus on where the
role fits in an organization, how data
scientists can add the most value, and
how their performance should be mea-
sured,” they write. From here, they
go on to describe their prototype of a
successful data scientist (“a hybrid of
a data hacker, analyst, communica-
tor and trusted advisor”) and provide
high-level guidance on how to find, re-
cruit and manage data scientists.
Finally, McKinsey’s Dominic Bar-
ton and David Court open their article
“Making Advanced Analytics Work For
You” by asserting that “Big data and
analytics have rocketed to the top of
the corporate agenda,” and then pro-
vide a series of tips for executives anx-
ious to avoid making the same types
ANALYZE THI S!
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A NA L Y T I C S
of mistakes made in the adoption of
previously “hot” technologies such as
CRM.
All interesting articles and all worth
reading. Perhaps more notable, I believe,
is that HBR’s Editorial Board chose to
focus on this topic. For those who have
been working on this stuff for a long time,
it seems that (to quote an old cigarette
ad), you’ve come a long way, baby [2].
Looking forward to seeing what’s next
in 2013. ❙
Vijay Mehrotra ([email protected]) is
an associate professor in the Department of
Analytics and Technology at the University of San
Francisco’s School of Management. He is also an
experienced analytics consultant and entrepreneur,
an angel investor in several successful analytics
companies and a longtime member of INFORMS.
http://jps.informs.org
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REFERENCES
1. For more on George A. Posey and the Posey
tube, see www.alamedainfo.com/posey_tube.htm.
2. http://en.wikipedia.org/wiki/Virginia_Slims.
J A NUAR Y / F E BR UAR Y 2013 | 17 J A NUAR Y / F E BR UAR Y 2013 | 17 WWW. I NF OR MS . OR G 16 | A NA LY T I CS - MAGA Z I NE . OR G
In making election forecasts for the FiveThir-
tyEight blog (538) at the New York Times, Nate Silver
uses a statistical model that is subtle, sophisticated
and comprehensive. Real Clear Politics uses a shal-
low approach to forecasting that could have been
devised by a statistical Forrest Gump. But which fore-
caster better predicted the results in the 2012 presi-
dential election? Did the intellectual tortoise hold its
own against the hare?
From a conceptual standpoint, it should have been
no contest. In an approach that would make statisti-
cians shudder, Real Clear Politics (RCP) estimated
the Obama/Romney difference in a given state by the
simple average of differences in recent polls. Differ-
ences in sample sizes were ignored, the word “recent”
was defned differently in different states, undecided
voters were simply excluded, and evidence that some
polls skew toward Republicans and others toward
Democrats got no weight. The 538 model, in contrast,
avoided all these limitations, and took account of cor-
relations among outcomes in similar states and the
demographic makeup of each.
FROM THEORY TO PRACTICE
But how did the final state-by-state predictions
under the two approaches compare in accuracy?
Did Nate Silver beat the
tortoise?
BY ARNOLD BARNETT
The candidates and
everyone else recognized
that the outcome would be
determined by what
happened in about a
dozen “swing states.”
VI EWPOI NT
J A NUAR Y / F E BR UAR Y 2013 | 17 A NA L Y T I C S
RCP only made forecasts in 30 of the
51 states (including the District of Co-
lumbia), but these included all swing
states and all large states. And, at first
blush, it might appear that the race be-
tween the two methodologies in the 30
states was (in the familiar phrase) too
close to call.
The most obvious dimension for
comparison is the bottom line: Did the
forecast in a given state correctly iden-
tify the winner there? By that standard,
both methods did very well: In 29 of the
30 states, they agreed who the winner
would be and that candidate actually
won. (Complete data tables will ap-
pear in a longer version of this article
in the February 2013 issue of OR/MS
Today.) In Florida, neither forecaster
made a correct forecast: RCP errone-
ously projected a narrow Romney vic-
tory (1.5 percentage points), while 538
projected an exact tie (and thus ab-
stained from forecasting). Obama car-
ried Florida by 0.9 percentage points.
We can say, therefore, that 538 scored
a partial victory over RCP in one of
30 states, but that is hardly a decisive
advantage.
As for the absolute forecast errors
in the various states, the results were
once again similar. The mean absolute
error over the 30 states was 2.87 per-
centage points for RCP and 2.25 for
538. However, there is a “blue state
bias” among the 30 states: Romney
carried only 27 percent of them (eight
out of 30), while he captured 47 per-
cent (24 out of 51) in the entire nation.
When an adjustment is made for this
bias, the mean absolute error becomes
2.57 points for RCP and 2.33 for 538.
This revised difference of one-quar-
ter of one percentage point is hardly
decisive.
ON THE OTHER HAND
Yet this aggregate analysis is
oblivious to the central dynamic of the
2012 election. Given the realities of
the Electoral College, the candidates
and everyone else recognized that the
outcome would be determined by what
happened in about a dozen “swing
states” that either candidate could
plausibly win. In the other states, the
winner was a foregone conclusion so
there was little campaigning and little
interest in polling results.
Under the circumstances, a com-
parison between RCP and 538 should
focus primarily if not exclusively on their
Help Promote Analytics
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J A NUAR Y / F E BR UAR Y 2013 | 19
WWW. I NF OR MS . OR G 18 | A NA LY T I CS - MAGA Z I NE . OR G
accuracy in swing states. RCP identi-
fed 11 states as “toss up” just before the
election: Colorado, Florida, Iowa, Michi-
gan, New Hampshire, Nevada, North
Carolina, Ohio, Pennsylvania, Virginia
and Wisconsin.
Within these states, the two ap-
proaches differed markedly in per-
formance. 538 outperformed RCP in
absolute forecast accuracy in all but
one of the 11 swing states (Ohio).
Both forecasters were on average
more favorable to Romney than the
actual voters, but the net “bias” was
only 0.76 percentage points for 538
over the 11 states as opposed to 2.44
points for RCP. That difference of 1.68
(2.44-0.76) points is especially note-
worthy because regression analysis
makes clear that the 538’s estimates
about Obama’s performance were
consistently about 1.5 points higher
than those of RCP. Again and again,
this adjustment was vindicated by the
swing-state results: RCP underesti-
mated Obama’s actual vote share,
while 538 eliminated roughly 75 per-
cent of the underestimation.
In the 19 states out of the 30 origi-
nally compared that were not swing
states, 538 and RCP performed about
equally well, which is why statistics
based on all 30 states yielded less
disparity between the two approaches
than the swing states alone. It could be
that Obama outperformed the swing-
state polls upon which RCP relied be-
cause of the major voter-turnout drives
that his campaign undertook in those
states, which brought many people
to the voting booths whom pollsters
had not included in tabulations about
“likely” voters. In the other states, the
Obama campaign may not have waged
such efforts, so no comparable “surge”
occurred.
Nate Silver would be the frst to agree
that his state-by-state forecasts were cor-
related, and that circumstance stymies
assessments of whether his swing-state
victory over RCP was statistically signif-
cant. In effect, he made an all-or-nothing
bet on the premise that the polls under-
estimated Obama’s strength in swing
states: Had this premise been wrong,
his 11-1 victory over RCP could easily
VI EWPOI NT
Join the Analytics Section of INFORMS
For more information, visit:
http://www.informs.org/Community/Analytics/Membership
J A NUAR Y / F E BR UAR Y 2013 | 19
APRIL 7, JUNE 23, JULY 13, & OCTOBER 5, 2013
Conducted BY INFORMS, the leading professional society in advanced analytics
INAUGURAL ANALYTICS CERTIFICATION EXAMS

Be among the first to become a Certified Analytics Professional (CAP).
Make plans now to take the profession's first analytics certification exam.
Candidate Handbook now available at
www.informs.org/Build-Your-Career/Analytics-Certification.
BENEFITS OF CERTIFICATION
• Advances your career potential by setting you apart from the competition
• Drives personal satisfaction of accomplishing a key career milestone
• Helps improve your overall job performance by stressing continuing
professional development
• Recognizes that you have invested in your analytics career by pursuing this rigorous credential
• Boosts your salary potential by being viewed as experienced analytics professional
• Shows competence in the principles and practices of analytics
ELIGIBILITY
• BA/BS or MA/MS degree or higher
• At least five years of analytics work-related experience for BA/BS holder in a related area
• At least three years of analytics work-related experience for MA/MS (or higher) holder in
a related area
• At least seven years of analytics work-related experience
for BA/BS (or higher) holder in an unrelated area
APPLICATIONS
• Open in January, 2013
• Prepare to apply by reviewing Candidate Handbook now
• Arrange now to secure academic transcript and
confirmation of “soft skills” from employer
to send to INFORMS
COST
• $495 INFORMS Members
• $695 Non-Members
• Bundled rates with meetings available
QUESTIONS
• Email [email protected]
2013 CAP EXAM SCHEDULE
APRIL 7, 2013
INFORMS Conference on
Business Analytics & O.R.,
San Antonio, TX
DOMAINS OF ANALYTICS PRACTICE
Domain Description Weight*
Business Problem (Question) Framing
Analytics Problem Framing
Data
Methodology (Approach) Selection
Model Building
Deployment
Life Cycle Management
*Percentage of questions in exam
I
II
III
IV
V
VI
VII
15%
17%
22%
15%
16%
9%
6%
100%
OCTOBER 5, 2013
INFORMS Annual
Meeting,
Minneapolis, MN
JUNE 23, 2013
INFORMS Healthcare
Conference,
Chicago, IL
July 13, 2013
Booz Allen Hamilton
Offices,
McLean, VA
J A NUAR Y / F E BR UAR Y 2013 | 21 WWW. I NF OR MS . OR G 20 | A NA LY T I CS - MAGA Z I NE . OR G
have been a 12-0 defeat. Yet uncertainties about how
to defne statistical signifcance cannot obscure the
fundamental point: 538 did extremely well in 2012 in
those states where accuracy was most important.
FINAL REMARKS
So how does it all add up? Under the Occam’s Razor
principle, there is a clear starting preference for simple
models over more complicated formulations. A complex
model must justify its intricacy by offering more accurate
information than a simpler counterpart; moreover, this
added information should arise in places where it is most
needed. In the present setting, the question is whether
Nate Silver’s 538 model outperformed the straightfor-
ward RCP method to an extent that makes 538 the wiser
choice, even if the less transparent one.
Readers can reach their own judgments, but be-
cause of the results in the swing states, the author
believes that 538 met the test for superiority just
posed. While the tortoise catches up with the hare in
the nursery stories, it seems here that the hare won
hands down. But the outcome does not contradict Ae-
sop’s fable because, far from being lazy, the 538 hare
ran the race as hard as it could. And, if the evidence
is any guide, it is very much a world-class runner. ❙
Arnold Barnett ([email protected]) is the George Eastman
Professor of Management Science at the MIT Sloan School of
Management. His research specialty is applied mathematical
modeling with a focus on problems of health and safety. Barnett is a
senior member of INFORMS.
A complex model must
justify its intricacy by
offering more accurate
information than a simpler
counterpart; moreover, this
added information should
arise in places where it is
most needed.
VI EWPOI NT
Request a no-obligation INFORMS Member Benefits Packet
For more information, visit: http://www.informs.org/Membership
J A NUAR Y / F E BR UAR Y 2013 | 21
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Certified Analytics
Professional
I NFORMS I NI TI ATI VE
Big data is creating a big demand for
data scientists and analytics profession-
als, but it’s also creating a big problem for
organizations looking for analytical help.
The problem: Given the growing demand
for analytics professionals and a short-
age of highly qualifed candidates, how
do you fnd the right person with the req-
uisite skills and capabilities to do the job?
The Institute for Operations Re-
search and the Management Sciences
(INFORMS), the premier source for ad-
vanced analytics (and the publishers
of Analytics magazine), has a solution:
Certifed Analytics Professional (CAP).
A year in the making and set for launch
this spring, the CAP program is designed
to be the industry’s gold seal of approval
for analytics professionals. The program
will beneft not only organizations looking
for analytical expertise, it will also beneft
individuals who earn the certifcation by
setting them apart from the crowd, driv-
ing personal satisfaction, and improv-
ing overall job performance by stressing
continuing professional development.
The CAP program consists of sev-
eral elements, including a 100-question,
multiple-choice exam that will be admin-
istered for the frst time on April 7 in con-
junction with the INFORMS Conference
on Business Analytics & O.R. in San An-
tonio, Texas. Three other exam sites are
also scheduled for 2013: June 23 in Chi-
cago (preceding the INFORMS Health-
care Conference), July 13 in McLean, Va.
(Booz Allen Hamilton offces) and Oct. 5
in Minneapolis (preceding the INFORMS
Annual Meeting).
Exam questions will cover such do-
mains as problem framing, data man-
agement, methodology selection, model
building, deployment and lifecycle man-
agement. Sample questions and other
details regarding the program can be
found in a special supplement of the CAP
Candidate Handbook included in this is-
sue of Analytics.
According to Jack Levis, direc-
tor of Process Management for UPS,
vice president of Practice Activities for
INFORMS and a driving force behind
CAP, more than 200 analytics profes-
sionals from the U.S., Europe and Asia/
Pacifc offered input into the structure of
the test’s sections. Adds Bill Klimack, a
J A NUAR Y / F E BR UAR Y 2013 | 23 A NA L Y T I C S
decision analysis consultant for Chevron,
INFORMS board member and Levis’ co-
chair on the certifcation task force: “My
personal perspective is that certifcation
is becoming more important in many
felds. This seems to be a result of accel-
erating change. Certifcation programs
help cope with the environmental change
by providing a clear model for standards
and advancement” [1].
In order to be eligible to take the
exam, candidates must have a bache-
lor’s degree or higher, between three and
seven years of analytics work-related
experience (depending on degree) and
verifcation of communication and other
“soft skills,” such as effective partnering
with business clients, framing problems
with stakeholders, working in project
teams and communicating results to
decision-makers.
Interested individuals are encour-
aged to review the CAP website and
its Frequently Asked Questions sec-
tion. For more information on CAP,
contact INFORMS. ❙
Join the Community for inside information.
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J A NUAR Y / F E BR UAR Y 2013 | 25 WWW. I NF OR MS . OR G 24 | A NA LY T I CS - MAGA Z I NE . OR G
Beyond Facebook: New approach, technology blends
data, process and collaboration for better, faster
organizational decision-making.
Welcome to the
‘worksocial’ world
BY SAMIR GULATI
I NFORMATI ON TECHNOLOGY
J A NUAR Y / F E BR UAR Y 2013 | 25 A NA L Y T I C S
The pervasive infuence of
Facebook in the consumer
market has prompted a ris-
ing tide of “enterprise social
platforms” trying to bring social media
into the workplace. Almost all the market
entrants taking advantage of this trend
are missing the point. The golden ticket
for business value is not bringing exter-
nal social media into the workplace. It
is bringing work into the realm of social
technology.
What comes after Facebook is an
approach and technology increasingly
known as “worksocial.” Worksocial starts
with the processes and data that drive the
business, and overlays the innovations
of social collaboration and mobile access
directly over those business engines.
BUILDING ON BPM
Enterprise information technology
(IT) exists to improve how work functions
are performed. From e-mail and calen-
daring applications to large enterprise
software systems, the goal of technology
is to make business information available
to the people who need it to do their jobs.
These systems include enterprise
resource planning (ERP), customer re-
lationship management (CRM), busi-
ness intelligence (BI), databases and
data warehousing, network infrastructure
and middleware and a host of others too
extensive to list here. Over the past 10
years or so, business process manage-
ment (BPM) software has gained consid-
erable attention because it extends this
concept to the automation and improve-
ment of the actual business processes
that are executed against that enterprise
data. More recently, the most advanced
BPM suite technologies have worked to
bridge the gap between the “what” and
“how” by incorporating both data and
process. Taken in total, these two com-
ponents defne what “work” is, covering
both structured straight-through process-
ing (STP) and unstructured knowledge
work.
The recent advent of the social and
mobile revolution in IT has brought these
problems even more clearly into focus.
Now, social and mobile capabilities in
the enterprise can either exacerbate the
problems or fnally provide a solution that
takes IT and business into a new world
of effectiveness and opportunity. It all
depends on whether they embrace the
game-changing worksocial approach.
PERSISTENT PROBLEMS
In addition to major ERP, CRM, BI
and other systems, the typical company
has deployed hosts of smaller packaged
and custom applications across its vari-
ous departments. Depending on the size
of the company, that number could be in
T
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the hundreds or even the thousands for
global organizations.
This level of application sprawl is
expensive and divisive. Every one of
the applications must be individually
maintained, monitored, customized
and upgraded by IT. The average IT
department spends almost 90 percent
of its annual budget maintaining and
enhancing existing system, simply
“keeping the lights on.” That leaves
only about 10 percent for the innova-
tion required to deliver new solutions
to the business.
What’s more, most of those hundreds
or thousands of applications do not play
well together. They don’t cross depart-
mental boundaries. They aren’t visible to
a wide range of employees. They don’t
share data. This can be detrimental to
business performance. End-to-end busi-
ness processes that touch multiple sys-
tems and departments cannot be seen
and managed in total. Employees that
could potentially add value to drive better
business outcomes are excluded. Data
inconsistencies wreak havoc on the em-
ployee and customer experience.
The divisiveness of application sprawl
manifests itself directly in the “state of
separation” that characterizes today’s
enterprise. Complaints about “applica-
tion silos” are not new, but the pain aris-
ing from these silos is becoming more
acute.
If employees across an organization
cannot see important business events that
arise in disparate enterprise systems, poor
decisions get made. A sales person attempt-
ing a customer up-sell will be hard-pressed
to achieve success if she isn’t aware of the
trouble ticket that customer submitted last
week … and escalated that morning due to
inaction. If employees cannot collaborate
with the users of other systems, valuable
insights are lost.
When subject matter experts and
employees with particular insight into a
given issue are excluded from collabo-
ration, the resulting business action will
be less than optimal. If the organization
cannot provide a consistent, high-quality
customer experience due to the siloed
nature of data and systems, customer
churn increases and revenue decreases.
This separation affects an organiza-
tion’s ability to operate at the speed of
success. The typical nature of a com-
plete business process today is a point
of action followed by a delay, and a sub-
sequent point of action followed by an-
other delay, with the cycle repeated until
WORKSOCI AL
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J A NUAR Y / F E BR UAR Y 2013 | 27
A NA L Y T I C S
the full process is fnally executed. These
rampant spans of inaction manifest in
market delays and operational delays.
Market delays can be highly visible to
the outside world. New product introduc-
tion is a prime example. When you are
late to market with a new product, cus-
tomers can see it and won’t be happy.
Competitors can see it, too. They will
pounce on your customer base, touting
the superior qualities and capabilities of
their already-available new product. On
the fip side, delays in your ability to react
to competitive changes will cause you
to miss new opportunities, or worse, put
you behind the competitive eight ball.
THE WORKSOCIAL CONNECTION
Typical enterprise social platforms
deliver better communication – but com-
munication about what? They don’t hear
about or post system-generated business
events in real-time. They don’t track col-
laboration in the context of an auditable
business process. They are no better than
e-mail at enforcing business rules, ensur-
ing quality task completion and measur-
ing process improvement. Where’s the
highly touted social advantage?
Worksocial starts with the processes
and data that drive the business, and
overlays the innovations of social col-
laboration and mobile access directly
over those business engines. The best
business outcomes are achieved when
everyone swarms to collaborate and re-
solve a business event without losing
process context and secure governance.
Worksocial changes each individual
business decision point into an immediate
“collaboration moment.” Business action is
then taken right from the same social inter-
face, no matter what underlying software
applications are required, with no additional
manual effort.
Take the insurance industry as an ex-
ample. A system-generated feed post of
a high-value customer’s claim rejection –
seen simultaneously by the head of the
business unit, his staff and the indepen-
dent agent who brought in the customer
– will spur action, provide a place for col-
laboration and enable rapid response all
in the same interface. In that case, bring-
ing work into a social environment reduc-
es the chance of the customer taking his
business elsewhere. Add mobility to the
mix, where those alerts are visible and
accessible from any smartphone, tablet
or laptop, and you begin to understand
the transformative power of worksocial
for business.
Worksocial drives business value by
automatically structuring, recording and
governing social collaboration with process
context – bridging the worlds of structured
enterprise processes with collaboration. It
brings social collaboration into business
J A NUAR Y / F E BR UAR Y 2013 | 29
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context, enabling measurement of how col-
laborations rendered results.
Analysts are closely following this
trend. Anthony Bradley, a group vice
president at Gartner, advocates combin-
ing social media and business process
transformation to drive organizational
success. Doing so, Bradley says, allows
companies to leverage the massive reach
and infuence of social media, while main-
taining an open channel for communica-
tion and collaboration. People and social
process included in a peer-to-peer net-
work structure take actions faster than in
a top-down approach, he maintains.
GATEWAY TO THE APP INTERNET
The value an organization can realize
today through worksocial is clear:
1. Measurements and visibility.
Worksocial brings measurable
meaning to social collaborations and
their impact on work.
2. Access. Not only access to any
corporate data, but also access to
open conversation strings and from
any device (mobile, tablets and
desktop/laptops).
3. Effciency. The overall experience of
integrated social collaboration and
work in a single user experience and
from any device drives measurable
effciency gains throughout the
enterprise.
What may be less obvious about
worksocial is its importance as a gate-
way to future evolutions in how work
is done and how IT supports it. IT in-
dustry thinkers talk about a concept
called “The App Internet.” Instead of
thinking about the Internet bringing
you to a discrete “location” where you
download a discrete “thing” (a product,
a service or a specific piece of infor-
mation) onto a device, imagine a world
where everything you need and want
is floating in the ether all around you,
and you simply grab those things – or
pieces of those things – at any time,
use them as you need them, and then
let them float back into your orbit.
Heady stuff, but it is in essence what
the industry is delivering today in terms
of how an organization’s employees can
access and make use of the various ap-
plications and application components
they need to do their jobs. Worksocial is
the tipping point, the confation of BPM
and enterprise social platforms in a way
that sets the stage for the future of busi-
ness analytics, collaboration, process
and business value. ❙
Samir Gulati ([email protected]) is the
vice president of marketing at Appian Corporation,
an innovative leader in the BPM software market.
Previously, Gulati served as vice president of global
marketing for Pegasystems, a large, publicly traded
software company. He holds a master’s degree in
computer science from University of Pennsylvania
and an MBA from University of Chicago.
WORKSOCI AL
J A NUAR Y / F E BR UAR Y 2013 | 29
The Roadmap to Align Your IT Strategy
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April 29 – May 1, 2013
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Leslie Zun
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Joel Taylor
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Princy Thomas
Director, Data Analytics
Preferred Health Partners
Dennis Price
Executive Vice President,
Financial Affairs and New
Business Development
Preferred Health Partners
Jennifer Bordenick
CEO
eHealth Initiative
Indranil Ganguly
Vice President and Chief
Information Officer
CentraState Medical Center
Ben Patel
Executive Director
Yale New Haven Health
System and Yale School of
Medicine
Jonathan Everett
EHR Manager
Chinese Community Health
Care Association
Jay Simmons
Director, Physician information
services
IASIS Healthcare
Thomas Ortiz
Chief Medical Officer
Reliance Medical Group
Doug Mitchell
Associate Vice President
IASIS Healthcare
Daniel Morreale
Vice President and Chief
Information Officer
Kingsbrook Jewish Health
System
Ali Alsanousi
Fellow of Clinical Informatics
Harvard Medical School
Joseph Carr
Chief Information Officer
New Jersey Hosptial
Association
Steve Peskin
Medical Director
Horizon Innovations
Blue Cross Blue Shield
Steve Eisenberg
Chief Medical Scientist
LifeView
J A NUAR Y / F E BR UAR Y 2013 | 31 J A NUAR Y / F E BR UAR Y 2013 | 31
The information explosion
has led organizations to le-
verage data to improve the
overall decision-making pro-
cess. Organizations are looking to deploy
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How to pick a
business partner
BY DAVID ZAKKAM (LEFT)
AND DEEPINDER SINGH DHINGRA
T
BUI LDI NG BUSI NESS
data-driven strategies across their busi-
ness processes and functions, such as
marketing, risk, supply chain and fnance.
It’s critical now more than ever to have the
right people, processes, methodologies,
Ten things to consider when evaluating analytics and
decision sciences partners.
J A NUAR Y / F E BR UAR Y 2013 | 31 A NA L Y T I C S
platforms and infrastructure that enable
the institutionalization of analytics. In re-
sponse to this a wide a range of players
have come forth to help organizations
with analytics.
However, working with an analytics
partner should not be seen as a short-
term, project-based decision. Data-driv-
en decision-making is a key muscle that
all organizations need to develop, and it
requires an ongoing effort. The choice of
an analytics and decision sciences part-
ner therefore is one where an organiza-
tion is entering into a joint collaboration
and journey. So how should organiza-
tions go about choosing the right analyt-
ics and decision sciences partner?
Following are 10 key factors to consid-
er in the search for an analytics partner:
Core decision sciences
and analytics DNA.
Every company has an un-
derlying DNA that informs its culture and
all parts of its organization – recruitment,
training, talent management, engage-
ment model, processes and infrastruc-
ture. Look for a partner that has built its
organization from the ground up around
the application of analytics and decision
sciences to solve business problems. For
example, recruitment and training pro-
cesses should focus on hiring and pre-
paring the right profle of employees who
not only have the required quantitative
skills but can combine that with the right
consultative, communication, structured
problem-solving and business skills.
The culture should emphasize infer-
ential learning, culture of experimenta-
tion, innovation, thought-leadership and
craftsmanship, while at the same time
creating an environment to scale the art
of problem-solving using analytics. Pro-
cesses should be in place that can ac-
count for the unique needs of analytics,
such as the iterative nature of analysis
and the need to balance the creativity
and agility required for business with the
rigor of math and science. The analytics
industry is still nascent. Companies with
their core focus and experience in analyt-
ics will be able to create, evolve and sus-
tain the right operating culture and DNA.
Interdisciplinary approach.
Helping organizations make
better decisions via analytics
requires a combination of business, math
and technology skills. The appreciation
of business domains, such as marketing,
risk and supply chain, keeps analytics rel-
evant. The ability to apply multiple math
disciplines such as statistics, economet-
rics and operations research ensures
the partners can address a wide range
of business problems of varying levels of
complexity. Enabling technologies, which
1
2
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include statistical tools, databases, BI
platforms, visualization tools and big
data technologies, helps develop, opera-
tionalize and scale analytics solutions.
Without this mix of math-business-tech-
nology, the partner will not be effective.
Further, a number of the business
problems that organizations need to
tackle start off muddy and fuzzy before
evolving to be clear for which the scope,
objective, analytical solution requirements
and methodology can be clearly defned.
Addressing such problems needs a syn-
thesis of approaches, for example, the
left-brain/algorithmic along
with right-brain/heuristic ap-
proaches, a mix of art and
science, a mix of analytical
thinking and design thinking.
The ideal analytics partner
should be able to appreciate
the fact that different prob-
lems require different ap-
proaches and should be able
to traverse the journey from merely the
analytics process to decision sciences via
integrating multiple disciplines such as
business, math, technology, behavioral
sciences and design thinking.
Analytics across the
descriptive to inquisitive to
predictive to prescriptive
(DIPP™) spectrum.
Counter to conventional thinking that or-
ganizations evolve from descriptive to
inquisitive to predictive to prescriptive
analytics, all four kinds of analytics need
to be utilized in the right mix to enable the
holistic creation of insights.
Briefy, descriptive analytics an-
swers the question, “What happened
in the business?” It is looking at data
and information to describe the current
business situation in a way that trends,
patterns and exceptions become appar-
ent. Inquisitive analytics answers the
question, “Why is something happening
ANALYTI CS PARTNERS
3
Figure 1
Figure 2
J A NUAR Y / F E BR UAR Y 2013 | 33
A NA L Y T I C S
in the business?” It is the study of data
to validate/reject business hypotheses.
Predictive analytics answers the ques-
tion, “What is likely to happen in the fu-
ture?” It is modeling to determine future
possibilities. Prescriptive analytics is
the combination of the above to provide
answers to the “so what?” and the “now
what?” questions.
Different business problems will need
different levels of all four kinds of analyt-
ics. Being able to effectively navigate the
DIPP spectrum is essential to generating
the right insights and recommendations
and enabling better consumption. Orga-
nizations should be cautious of analytics
companies that focus on only one or par-
tial aspects of the DIPP spectrum.
Enabling creation,
translation and
consumption of analytics.
Analytics is not just about data science.
The goal of analytics is to solve business
problems and enable better decisions.
To institutionalize analytics, organiza-
tions need to industrialize creation, be
very smart about translation and greedy
about consumption. To be effective, busi-
ness problems need to be articulated
and translated into analytical problems.
Analytical problems need to be solved.
Analytical solutions then need to be
translated back into business solutions.
These business solutions then need
to be communicated, socialized, imple-
mented and consumed by the organiza-
tion to realize the beneft from data-driven
decisions. While consumption of analytics
will primarily be the responsibility of your
organization, don’t just look for partners
who are good at the creation of analytics.
Look also for partners who are able to
translate across the business and math
worlds and for partners who will help you
derive value from the insights and outputs
by enabling consumption. Drill down into
case studies and engagements where
the analytics partner demonstrated the
ability to not only analytically solve the
business problem but also enabled the
right translation and consumption.
Integrated ecosystem.
Today there is a lot of focus
on having data scientists that
can combine applied math and computer
4
Figure 3
5
J A NUAR Y / F E BR UAR Y 2013 | 35
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science skills to address complex busi-
ness problems. However, having data
scientists is just one part of the equation.
As previously noted, decision science re-
quires an interdisciplinary approach that
combines business, math, technology,
design thinking and behavioral scienc-
es. Institutionalizing decision sciences
requires a complete ecosystem where
people, processes, tools, reusable in-
tellectual property and asset solutions
come together as bionics helping convert
data scientists into decision science pro-
fessionals. This ultimately provides the
foundation for institutionalizing decision
support via analytics. This integrated de-
cision support ecosystem should support
the creation of a better art for problem-
solving rather than be designed to only
solve specifc problems.
Ask yourself these questions when
you’re evaluating a partner: Does the
partner have frameworks for structured
problem definition, analytical matu-
rity assessment analytical process
guides and Q&A processes designed
for analytics? Has the company de-
veloped tools for productivity improve-
ment, knowledge management and
infrastructure for supporting analytics
delivery? Has the partner invested in
developing assets that address both
the art of problem-solving as well as
address specific problems?
Obtaining answers to these questions
will put you one step closer to identifying
the analytics partner that fts your busi-
ness needs.
White-box and
collaborative approach.
Since analytics is a journey it
is very important to have a collaborative
approach with the partner. The partner
should have a white-box engagement
model in which every part of the analyti-
cal process (problem defnition, solution
design and insight generation steps) is
transparent, and the organization has the
ability to participate, audit and enhance
the process. Building trust behooves the
ANALYTI CS PARTNERS
6
Figure 4
J A NUAR Y / F E BR UAR Y 2013 | 35
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J A NUAR Y / F E BR UAR Y 2013 | 37
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ANALYTI CS PARTNERS
partner to leverage the relevant business
context and share the key assumptions
with relevant stakeholders.
A white-box approach is critical to en-
suring the consumption of analytics as
the ability to explain the methodology, as-
sumptions and insights will lead to better
buy-in. Organizations should be cautious
of companies that offer a black-box ap-
proach, as it is unlikely that you will be
able to translate or leverage that work
effectively in ensuring consumption and
implementation of the insights.
Sustainability.
Analytics is an ongoing initia-
tive rather than a one-time
project or a set of projects. One of the
barriers to the institutionalization of ana-
lytics in organizations has been the sup-
ply shortage and the consequent high
cost of partnering. Sustainability requires
the ability to scale to the volume demand
of analytics in your organization in a cost-
effective manner. To ensure this, organi-
zations should look for partners that have
optimized their operating and models to
be sustainable partners of analytics.
Look for partners that have applied levers
such as global delivery, use of “assetized”
platforms and ongoing engagement mod-
els rather than short-term, project-based
engagements. Further, in today’s environ-
ment partners that can scale well are those
that are able to create their own talent rather
than depend on talent acquisition. When
evaluating analytics partners, pay special
attention to the recruitment, training and tal-
ent management activities that helps create
analytics professionals.
Convergence via cross-
industry experience.
Many organizations prefer
to choose partners who have deep ex-
pertise in their own industry or with their
set of problems. However, it is important
to look for cross-industry and cross-do-
main experience. In a world of continu-
ous business transformation and blurring
of value-chain boundaries, organizations
can learn more from best practices from
across domains and industries rather
than from the same industry.
7
8
Figure 5
J A NUAR Y / F E BR UAR Y 2013 | 37
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In an era where the next big idea is
increasingly becoming harder to come
by one of the ways companies are tack-
ling this is to learn how other industries
are solving critical business challenges.
For example, online advertising net-
works learn how to manage ad space by
learning how airlines manage seats, and
health care organizations could become
more effcient by learning from the as-
sembly chain. In addition, studies have
shown that creativity thrives in conditions
where there is a constant exchange of
ideas and concepts. To generate cre-
ative ideas as well as bring in learning
and cross-pollination from other indus-
tries, it is very important for the partner to
have solved a variety of problems across
several industries.
Scale.
By scale we mean the sheer
breadth and depth of busi-
ness problems that the partner has
solved in its experience. In a feld like an-
alytics and decision sciences scale has
immense learning benefts. The more
problems the partner has solved, the bet-
ter it gets at the art of problem-solving
and the better it will be able to help you
with your current and emerging needs.
Organizations should look for part-
ners that have achieved scale not only
from a particular domain and industry,
but also from working with a diverse
client base solving a variety of busi-
ness problems.

Applied research and
innovation.
The feld of decision sci-
ences and analytics is evolving in re-
al-time. Data explosion has allowed
for the emergence of new techniques,
technologies and applications that can
expand the breadth and depth of ana-
lytics across the enterprise. A partner
that is constantly researching these
new developments and can offer solu-
tions for new technologies, techniques
and applications is invaluable.
Innovation is no longer the result of one
big idea but the coming together of a combi-
nation of changes that lead to disproportion-
ate returns. A partner that can help generate
ideas and provide the ability of low cost ex-
perimentation in a fail fast mode will help ac-
celerate the innovation cycle.
ANALYTI CS PARTNERS
9
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A NA L Y T I C S
Analytics is a key differentiator for
companies that seek to be more com-
petitive in the new reality of fast chang-
ing business and an economy that
looks for cost efficiency. The choice of
an appropriate decision sciences and
analytics partner is a very important
part of making analytics work. Serious-
ly considering the points mentioned in
this article will help you maximize the
chance for success as you take this
important decision. ❙
David Zakkam is a senior delivery manager for Mu
Sigma (www.mu-sigma.com). He has 10 years of
experience working in the analytics industry. His current
focus areas include rapid impact analytics, search
engine monetization, big data and customer lifecycle
analytics. He has an MBA from the Indian Institute of
Management, Calcutta, and received an engineering
degree from Indian Institute of Technology, Delhi.
Deepinder S Dhingra is head of Products & Strategy
for Mu Sigma. He has more than 12 years of experience
and is responsible for recommending solutions that can
scale and meet customer requirements with the help of
assets and products that are developed in Mu Sigma. He
holds a master’s degree in science from the University
of Massachusetts, Amherst, and a bachelor’s degree in
chemical engineering from Indian Institute of Technology,
Kanpur.
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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
experts (who relied mostly on gut in-
stinct 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.
Big data, analytics
and elections
BY GEORGE SHEN
T
CAMPAI GN STRATEGY
J A NUAR Y / F E BR UAR Y 2013 | 41 A NA L Y T I C S
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 simula-
tions predicted that Obama would pre-
vail with close to 99 percent certainty
based on aggregated poll data. For ex-
ample, 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 Univer-
sity, 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 (as-
sistant professor of political science at
Emory University), Sam Wang (a neu-
roscientist at Princeton University) and
Josh Putnam (visiting assistant pro-
fessor of political science at Davidson
College) also correctly predicted the
presidential race and many congres-
sional races with great accuracy [1]. It
is worth noting that some of them had
an outstanding track record in predict-
ing 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 se-
nior economist Cheng Xu took a different
approach. His model, made in February
2012, used both state economic and po-
litical data and predicted Obama winning
303 electoral votes vs. Romney’s 235.
It’s diffcult 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. Ac-
cording 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 inter-
pret data were not short of opinions prior
to the election. Some of these “political ex-
perts” disdained and ridiculed the analytics-
driven predictions while others attacked the
data scientists and statisticians. Geoffrey
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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 im-
portant role in the election than just predict-
ing the outcome. Analytics was an integral
part of the 2012 political campaign. In recent
elections, Republican and Democratic cam-
paigns have employed data-driven analyt-
ics 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 advan-
tage 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 effcient at a large scale than its much-
heralded 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, lever-
aging Web, mobile, TV, call, social me-
dia and analytics to directly micro-target
potential voters and donors with tailored
messages. Compared to previous presi-
dential campaigns in 2004 and 2008, the
2012 campaign was going digital and an-
alytical across all channels. The Obama
campaign management hired a multi-dis-
ciplinary team of statisticians, predictive
modelers, data-mining experts, math-
ematicians, software programmers and
quantitative analysts. It eventually built
an entire analytics department fve times
as large as that of its 2008 campaign.
In an interview with Time magazine, a
group of Obama senior campaign advis-
ers revealed an enormous data effort to
support fundraising, micro-targeting TV
ads and modeling of swing-state voters.
They frst went through a data integration
process to consolidate many disparate
databases and create a single, massive
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J A NUAR Y / F E BR UAR Y 2013 | 43
A NA L Y T I C S
system that merged information collected
from pollsters, fundraisers, feld workers
and consumer databases as well as so-
cial-media and mobile contacts with the
Democratic voter fles in the swing states
[4]. The advantage of the integrated sys-
tem is that analytics could be performed
effectively across multiple datasets from
multiple channels – the ability to connect
the digital dots. Furthermore, the infor-
mation 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 segmen-
tation 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 percent-
age probability that an individual would
back the Democratic candidate [5]. The
advisers ran experimental campaigns,
and analysts factored the results into the
models to refne and improve them. The
campaign rarely made assumptions with-
out numbers to back them up, according
to Obama’s campaign manager Jim Mes-
sina who had promised a totally different,
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 maxi-
mize the star powers. Fund-raising e-mail
and text messages targeting certain demo-
graphics were tested frst among supporters
with different subject lines and contents on a
small scale and subsequently achieved bet-
ter results among potential voters on a larg-
er scale. Fund-raising metrics were carefully
gauged and analyzed between executions.
Big data and analytics also helped
drive the campaign’s ad-buying deci-
sions, 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. Ulti-
mately, 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 frst election where
big data and analytics played a crucial
J A NUAR Y / F E BR UAR Y 2013 | 45
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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 busi-
nesses draw from the 2012 election?
The answer is plenty. First and foremost,
businesses need to rely more on a da-
ta-driven approach and measured per-
formance 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, sen-
timent and purchase pattern, predict-
ing the next sales opportunity and most
proftable 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 in-
telligence across a variety of channels
to identify, target and win customers
similarly to how 2012 election was won
on the digital front. ❙
George Shen ([email protected]) 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 fnancial 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.
ANALYTI CS & ELECTI ONS
REFERENCES
1. Plumer, Brad, “Pundit Accountability: The
Offcial 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 refect the
views and opinions of Deloitte Consulting. Deloitte is not, by means of this article, rendering
business, fnancial, 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.
J A NUAR Y / F E BR UAR Y 2013 | 45
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After 20 years of consulting
in the freight transportation
arena, I joined Pacer Stack-
train as AVP of Equipment
in 2003. One of the key responsibilities
of our group was to determine how many
chassis of each size (20 feet, 40 feet, 48
feet and 53 feet) needed to be positioned
at each location across North America
where Pacer containers would arrive on
trains. At the time, Pacer had the largest
domestic container feet in North America
with more than 27,000 containers. It also
had contracts with its rail partners that al-
lowed Pacer to provide its own chassis
at rail terminals across North America.
[Note: In the domestic intermodal market-
place, containers are designed to move
around North America on trains, then be
mounted on chassis at rail terminals in or-
der to be transported from the rail termi-
nal to the destination by trucks.]
In the years preceding my arrival, Pac-
er had developed a spreadsheet model to
estimate the number of chassis of each
size that would be needed at each equip-
ment supply point (EQSP). This analytic
model used traditional inventory planning
inputs such as turn-time (estimated num-
ber of days that an arriving container
would use a chassis), forecasted num-
ber of containers arriving on a train each
Modeling
experience yields
key insights
BY BRUCE W. PATTY
A
LESSONS LEARNED
J A NUAR Y / F E BR UAR Y 2013 | 47 A NA L Y T I C S
day and the number of days each week
that trains arrived or departed. In gener-
al, this model did a good job at estimat-
ing the number of chassis that would be
needed in “steady state” conditions. And
yet, more often than was desirable, the
number of chassis actually needed far
exceeded the projection. We needed to
identify what was causing the model to
be so far off.
PROBLEM APPROACH
Since the model was developing accu-
rate projections at about 90 percent of the
EQSPs, we believed the fundamentals of
the model must be working properly. Given
that, our initial guess was that one or more
of the inputs to the model were off. The most
likely possibilities were that inbound freight
had surged, turn-times had signifcantly
increased or the number of trains oper-
ated each week had dramatically dropped.
However, when we analyzed updated mea-
surements for these values, we found that
actual numbers were quite close to those
used in the model! With our frst hypothesis
proven wrong, we needed to consider other
possibilities.
We decided to step back from the
problem and see if we could identify any
business conditions that consistently
were present at EQSPs where the actual
number of chassis needed exceeded the
projections. We set up conference calls
with both the Equipment team and the
Operations team to discuss what was
happening at the terminals that were “in
trouble.” After several calls it became
evident that we needed to conduct some
historic analyses prior to the calls or we
would get bogged down with anecdotal
discussions about what happened on
one particular day when some unusual
situation took place. This made it virtually
impossible to move the discussion to the
underlying fundamentals.
After using these analyses to dis-
credit some theories that were driven
by these one-time occurrences, we real-
ized that EQSPs where we were running
short of chassis tended to be locations
where empty containers would build up
until they were repositioned out on trains.
That is, inbound loaded container volume
exceeded outbound loads and empties
were building up at the terminal.
We then went back and looked at the
model to see how it handled this situa-
tion. We found out that turn-times were
being measured from when the container
and chassis left the terminal after arriving
on an inbound train to when the contain-
er and chassis “ingated” the terminal af-
ter being released by the customer. The
time between when the container ingat-
ed the terminal and when the container
was taken off the chassis and placed on
the outbound train was not included in
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this measurement, often because those
events were not transmitted to Pacer by
the rail carrier. However, this time was
not included for both loaded and empty
containers. Why was its omission only
causing problems at terminals where
empties accumulated?
To answer this question, we arranged
another round of conference calls with the
Operations team. We found out that a key
difference in the way that loaded containers
and empty containers were handled by the
railroads was that, if there was limited space
on the trains, the loaded containers would
get priority. So, empty containers would be
left behind. While this worked fne in terms
of meeting delivery promises for the loaded
containers, it caused situations where emp-
ty containers would stay mounted on chas-
sis for days. And since these days were not
being captured in our measurement of turn-
time, the model was not accounting for this
in the chassis projection. In short, we dis-
covered that under certain and occasional
conditions, our modeling assumptions did
not refect operational practice.
We ended up modifying the model
that estimated chassis requirements by
using historic chassis usage trends that
did include chassis on terminal, and then
looking at averages, maximums and
variances from the norm to develop de-
mand projections. With this change, we
were able to dramatically improve the
accuracy of the model. The change in
our modeling approach was one of the
key reasons that Pacer was able to meet
chassis needs with an industry low chas-
sis-to-container ratio of 85 percent, but
I’ll save that story for another article.
BEST PRACTICE INSIGHTS
What can be gleaned from the pro-
cess described above that can be ap-
plied to many business problems? Below
are just three key insights:
1. Confrm the assumptions behind
a model. Analytic models are just
that, an attempt to model a real-world
phenomenon. These models are
based on fundamental assumptions
such as the probability distribution
of arrivals, linearity of a cost function
or limitations on supply. Often
when models are developed and
subsequently used, assumptions are
glossed over and attention is paid
to getting the inputs as accurate
as possible, or ensuring that all
of the constraints are accurately
represented. But, in situations where
the results from the model are not
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accurately refecting the real world
phenomenon, it is often best to
start with confrming that the model
assumptions are truly valid for the
situations where the model is failing.
In our case, the assumption that the
chassis requirements were driven by
inbound loaded container volumes
did not hold for locations where
empties could build up, requiring
signifcant quantities of chassis.
That said, the original modeling
assumptions were reasonable for 90
percent of the actual situations!
2. Diagnose causes of problems
by identifying similarities or
commonalities. Often, there will
be situations where models are
working well for a majority of cases
and not working for only a few. In
these situations, one of the quickest
ways to diagnose the problem is to
identify what the few “problem” cases
have in common and then determine
how the model behaves or handles
those similarities. In our process,
by identifying that the locations
where the model was not performing
well were locations where empties
built up, we were able to focus our
attention on how the model handled
empties.
3. Understand how measures are
being calculated. In school, we’re
often presented problem descriptions
where the values (costs, supplies,
demands, times, etc.) are provided
to us and we are then responsible for
building a model or solving a system
of equations. We don’t spend much
time questioning how the values were
calculated or derived. In practice,
determining how to come up with
these parameters is often the most
challenging aspect.
I’ve never encountered a situation
where my manager or my client came to
me with a table of numbers and asked me
to solve for the correct answer. Often, we
are limited in our ability to come up with
the most accurate set of values by the
data that is captured in our systems. To
develop accurate and useful models, we
must understand how these limitations
will impact our solutions and make allow-
ances for these impacts. In our situation,
the fact that turn-times did not include the
on-terminal time after a container came
back into the terminal on a chassis until
the container was loaded onto the train
became a serious shortcoming, especial-
ly at EQSPs where empties could build
up and this time became signifcant. ❙
Bruce W. Patty ([email protected]) is
vice president of transportation analytics at Veritec
Solutions (www.veritecsolutions.com). Patty is
a member of INFORMS. A version of this article
appeared in Analytics Lens.
BEST PRACTI CES
J A NUAR Y / F E BR UAR Y 2013 | 51
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Survey highlights new features and trends, but the
relationship between technology and thoughtful
analysis remains essential to the success of any
software tool.
Two years have passed
since OR/MS Today, the
membership magazine of
INFORMS (Institute for Op-
erations Research and the Management
Sciences) last surveyed the landscape of
decision analysis software, a landscape
continuing to evolve to meet the needs of
decision analysts and the clients/decision
makers they serve. When Don Buckshaw
introduced the vendors featured in 2010’s
survey, he observed that they were “pro-
ducing a richer set of analysis tools and
better visualization and analysis options”
than when the frst survey debuted in
1993 [1]. Dennis Buede, in his article in-
troducing that inaugural survey, defned
decision analysis as “the discipline of
Decision Analysis
BY WILLIAM M. PATCHAK
T
SOFTWARE SURVEY
J A NUAR Y / F E BR UAR Y 2013 | 53 A NA L Y T I C S
evaluating complex alternatives in the
light of uncertainty, value preferences
and risk preferences” [2]. Almost 20 years
later, decision analysts have access to
software products supporting a range of
purposes, from the softer skills of struc-
tured value elicitation to high horsepower
predictive analytics. Indeed, the results
of this year’s survey confrm the grow-
ing diversity of tools available, each with
increasing numbers of features to assist
the decision analyst in an array of indus-
tries and problem sets.
THE SURVEY
This year’s survey process followed
those of previous years closely, with an
online questionnaire sent to vendors
based on either past survey participa-
tion or the authors’ awareness of existing
software tools. As with previous surveys,
vendors who did not receive the original
questionnaire have the ability to add their
information to the online survey results by
contacting Patton McGinley at patton@
lionhrtpub.com. And, as with previous
surveys, results are provided verbatim
from the responses sent by the vendors.
Yet, the compiled results are not meant
to separate the software tools in terms of
quality or cost effectiveness – they are
merely meant to catalogue existing prod-
ucts and to make potential users aware
of new features and releases.
The responses this year featured 13
returning vendors from 2010, with 10 ad-
ditional vendors submitting information
as well. Two of the “newer” vendors are
actually affliated with products featured
in 2010 – an indicator that some software
ownership may have changed in the past
two years. While the number of vendors
responding actually decreased slightly
from 2010 (from 24 to 23), individual soft-
ware entries increased. Compared to the
36 products listed in the 2010 survey re-
sults, 47 products appear this year, with
many vendors submitting multiple en-
tries. Such an increase in product diver-
sity from a small number of vendors may
indicate that certain vendors are branch-
ing out from previous releases to offer a
broader range of tools.
2012 RESULTS
The product price range has expand-
ed since 2010, with some versions of the
software tools offered for free and certain
commercial licenses reaching $49,000.
Likewise, this year’s results feature ad-
ditional international vendors from the
Survey Results & Directory
For results of the decision analysis software
survey and a directory of decision analysis
software vendors, click here.
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United Kingdom and Belgium to comple-
ment returning vendors from the United
States, New Zealand, Finland, Sweden
and the U.K.
In terms of applications for the prod-
ucts, this year’s survey maintained the list
from 2010: selection of a best option using
multiple competing objectives, analysis of
uncertainty, analysis of probabilistic depen-
dencies, risk preference, sequential deci-
sion-making, portfolio decision-making and
multiple stakeholders’ collaboration. The
response this year also featured several
software tools specifcally citing their predic-
tive analytic capabilities, such as ensemble
decision trees and multivariate adaptive
regression splines. And while Bayesian
belief networks are a way to represent and
analyze uncertainty, this year’s survey in-
cludes a specifc option for vendors to se-
lect if they included Bayesian networks in
their products. The result of this change
was that, while almost all of the vendors
said their software products could analyze
uncertainty, only six responses explicitly
listed a Bayesian network capability. Over-
all, and as expected, the featured vendors
run the gamut in terms of application and
functionality.
Another addition to this year’s
survey was a catch-all free response
question to capture additional features
and methods not explicitly stated else-
where in the survey. The diversity of
featured software packages neces-
sitated the question, and responses
captured nuances in software capabili-
ties ranging from the simulation of time
series processes to group to surveys
to methodologies for handling of miss-
ing data.
Another change for the 2012 survey
was to include questions related to train-
ing available for each product. Following
the 2010 survey, Buckshaw challenged
vendors to “build in some form of coach-
ing into their products so that even a
novice can be confdent that their mod-
els are producing sensible results” [1].
While recognizably different from built-in
coaching, the training opportunities pro-
vided by either the vendor themselves
or by a third party is one way of steering
new users toward effective implementa-
tion of the software tools. As anticipated,
the vast majority of this year’s survey re-
spondents provide training for the tools
themselves in a classroom environment.
DECI SI ON ANALYSI S SURVEY
Join the Analytics Section of INFORMS
For more information, visit:
http://www.informs.org/Community/Analytics/Membership
J A NUAR Y / F E BR UAR Y 2013 | 55
A NA L Y T I C S
However, some training is also available
via third party courses, and some is avail-
able online.
BEYOND 2012
As it has in the past, this year’s sur-
vey serves as a helpful benchmark and
snapshot in time for the decision analy-
sis software community. Looking out be-
yond the current year, it’s easy to see
certain trends continuing into the fu-
ture. For example, Buckshaw observed
in 2010 that “10 packages [were] web
implementations, up from six packages
two years ago [2008]. This trend will like-
ly continue” [1]. Indeed, the 2012 survey
lists 12 web implementations available,
seven of which are returning packages
from 2010. There’s very little reason to
expect this trend to reverse.
According to Gartner’s Hype Cycle for
Business Intelligence, 2011, collabora-
tive decision-making (CDM) is just enter-
ing its “rise” in the business intelligence
community, and many of the software
tools highlighted in this year’s survey
Limited print copies are available to purchase for $45.00
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INFORMS 2012 edition of the TutORials in Operations Research
series is available online to registrants of the 2012 INFORMS
Annual Meeting. It will be made available online to all 2013
INFORMS members on January 1, 2013.

To access the 2012 TutORials log in at:
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J A NUAR Y / F E BR UAR Y 2013 | 57
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appear postured to ride the CDM wave.
As Gartner terms it, the method and its
applicable software packages have had
their “technology trigger” and are now
well on their way toward the “peak of
infated expectations” by focusing on
bringing high level decision-makers to-
gether in a transparent way to facilitate
a group decision and to capture the pro-
cess and best practices [3]. In 2010, only
12 (33 percent) of the featured software
products supported group elicitation and
nine (25 percent) supported decentral-
ized elicitation. In this year’s survey, 21
(45 percent) of the featured products
have group elicitation capabilities with
17 (36 percent) supporting decentralized
elicitation. Twenty-eight (60 percent)
have multiple stakeholder collaboration
applications. For those software vendors
poised to support CDM, the increas-
ing trends of Web integration could be
extra benefcial in connecting disparate
decision-makers.
However, while the technology itself
continues to evolve, the need for thought-
ful analysis remains essential to the suc-
cess of any software tool. Increasing
computational power and the expand-
ing capabilities of “data warehouses” are
now tempting more and more senior de-
cision-makers to have their organizations
enter the world of analytics. Notably,
several new software tools for predic-
tive analytics are featured on this year’s
survey results. Yet, many analysts esti-
mate that data cleanup accounts for 80
percent of the cost of data warehousing
projects [4], and in my experience, these
cleanup efforts are heavily reliant on hu-
man judgment.
Choices made on how to deal with
data inconsistencies and data errors can
make or break a decision model. In other
words, no matter how automated an ana-
lytic software product may be, an analyst
must make the right decisions on how
to prepare the data. Even once the data
is cleaned, the choice of algorithms and
modeling methods remains as much an
art as a science, and as much an act of
refnement and revision as an act of fun-
damental principle.
The symbiotic relationship between
decision analyst and software tools is
tested further in certain government or
defense industries where information
security can trump the ability to lever-
age sophisticated modeling software. An
analyst working in these industries must
transfer his or her knowledge of a soft-
ware’s underlying principles to whatever
DECI SI ON ANALYSI S SURVEY
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J A NUAR Y / F E BR UAR Y 2013 | 57
INFORMS second conference
on the HEALTH SECTOR,
bringing together researchers
and stakeholders around the
most current work in healthcare
operations research, systems
engineering and analytics.
• The best, most current research and applied work in
healthcare operations research, systems engineering
and analytics in one highly-focused conference.
• Top-quality presentations by leading researchers
and practitioners, selected through a review of
extended abstracts.
• Cross-cultural view of healthcare systems and
analysis of operational impacts.
• Structured networking opportunities, including
birds-of-a-feather discussion groups and facilitated
networking over lunch.
• Collegial, small-scale setting in a great hotel in a
dynamic city.
http://meetings2.informs.org/healthcare2013
INFORMSHEALTHCARE 2013
Call for Papers
and Posters
Abstract deadline:
March 1, 2013
All submissions will be reviewed
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notified of acceptance at specified
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conference, and we encourage you
to submit early.
June 23-26, 2013
Chicago Marriott Downtown
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J A NUAR Y / F E BR UAR Y 2013 | 59
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tool is available – a pen and paper if nec-
essary – in order to complete the tasks
required. On these occasions, software
products have the opportunity to be more
than “black boxes” outputting an answer.
Rather, they can inspire analysts to ap-
proach decision analysis problems in new
ways, perhaps with different techniques
for visualization and elicitation. Instead
of replacing a living, breathing analyst,
well-designed software has the ability to
impact business practices beyond the
immediate application of a product.
FINAL THOUGHTS
Where will the decision analysis
software community be in 2014 when
the next survey is issued? If I were a
financial analyst, I would start with the
disclaimer that past performance is
not an indicator of future results. But
despite several entries from newcom-
ers to the survey, the 2012 results saw
many returning vendors, albeit with
updated features and new tools. Many
vendors continue to build on the fun-
damental underlying decision analysis
principles and previous software re-
leases to refine the user experience
for decision analysts. For the next
survey, I would expect to see a similar
number of respondents, but perhaps
with more Web implementations and
more integration opportunities.
Buckshaw acknowledged in 2010
that the software “is a tool to support
smart analysis, not to replace it [1],”
and as the software increases in so-
phistication every year, challenges re-
main for the decision analysts using it.
Data cleanup and innovative applica-
tions of the products and underlying
principles continue to require careful
thought and analysis. Technology has
yet to replace the decision analyst,
and a decision analyst cannot function
to full effect without an ever-improving
suite of software tools. There’s no in-
dication this symbiotic relationship
will change any time soon, but I look
forward to seeing how it continues to
evolve in the years to come. ❙
William M. Patchak (wpatchak@
innovativedecisions.com) is a decision analyst
with Innovative Decisions, Inc., a management
consulting frm serving business and government
clients and specializing in the disciplines of decision
and risk analysis, operations research and systems
engineering. A version of this article appeared in the
October 20012 issue of OR/MS Today.
DECI SI ON ANALYSI S SURVEY
REFERENCES
1. Buckshaw, Don, “Decision Analysis Software
Survey,” OR/MS Today, October 2010.
2. Buede, Dennis, “Decision Analysis Software:
Aiding the Development of Insight,” OR/MS
Today, April 1993.
3. Sallam, Rita L. and Andreas Bitterer, “Hype
Cycle for Business Intelligence, 2011,” Gartner,
Inc., August 2011.
4. T. Dasu and T. Johnson, “Exploratory Data
Mining and Data Cleaning,” John Wiley & Sons,
Inc., 2003.
J A NUAR Y / F E BR UAR Y 2013 | 59
A membership in INFORMS will help!
visit http://join.informs.org to join online
▪ New in 2013! Certification for Analytics Professionals
▪ New in 2013! A FREE Community Membership
▪ Online access to the latest in operations research
and advanced analytics techniques
▪ Unsurpassed Networking Opportunities available in INFORMS
Communities and at Meetings
▪ Subscriptions to online and print INFORMS Publications
▪ Education programs around the world to enhance your
professional development and growth!
How will you stand out from the crowd?
2013
INFORMS Renewals
available online
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and easy. Renew online
to help us stay green.
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Two senior executives from marquee
companies – Nicole Piasecki, VP of Busi-
ness Development and Strategic Inte-
gration at Boeing Commercial Airplanes,
and Sandy Carter, VP of Social Busi-
ness Evangelism and Sales at IBM – will
headline the 2013 INFORMS Conference
INFORMS analytics &
O.R. conference
CONFERENCE PREVI EW
Marketing Analytics
• Neil Biehn, Ph.D., vice president of
Science and Research, PROS, Inc.
• Nathan Brixius, Ph.D., senior vice
president, Analytics Development,
Nielsen Marketing Analytics
• Allen Crane, M.S., executive director,
Applied Analytics, United Services
Automobile Association (USAA)
• Maher Lahmar, Ph.D., manager R&D,
Merchandising Business Intelligence,
Target
• Sorin Patilinet, M.Sc., marketing
services manager, Mars Inc.
• Damon Ragusa, chairman & chief
strategy offcer, ThinkVine
• David Schweidel, Ph.D., associate
professor of marketing, co-director of
the Emory Marketing Analytics Center,
Goizueta Business School, Emory
University
• John (Jeff) Tanner Jr., Ph.D., professor
of marketing, associate dean, Baylor
University
Big Data
• Jeff Butler, M.S., director, Research
Databases, U.S. Internal Revenue
Service
• Michael Cavaretta, technical leader,
Predictive Analytics, Research &
Advanced Engineering, Ford Motor Co.
• Arnol N. Ghoting, Ph.D., research staff
member, IBM TJ Watson Research
Center, IBM
• Priyank Patel, director, Product
Management, Teradata
Analytics Process
• Don N. Kleinmuntz, Ph.D., executive
VP and chief analytics offcer, Strata
Decision Technology LLC
• Kathy Lange, M.S., senior director,
Business Analytics Practice, SAS
• Steve Sashihara, A.B., president &
CEO, Princeton Consultants Inc.
• Sam L. Savage, Ph.D., consulting
professor, Management Science &
Engineering, Stanford University
on Business Analytics & Operations Re-
search, April 7-9, in San Antonio, Texas.
Piasecki and Carter will deliver thought-
provoking talks, highlighting two days
of intensive, real-world education on
descriptive, predictive and prescriptive
analytics.
Conference Focused Tracks
J A NUAR Y / F E BR UAR Y 2013 | 61 A NA L Y T I C S
• Dirk Van den Poel, Ph.D., professor
of Marketing Analytics, Faculty of
Economics & Business Administration,
Ghent University
• Sameer Vittal, Ph.D., manager of
Advanced Analytics, General Electric-
Power & Water
• Sasha Bartashnik, M.S., strategic
analyst, Strategic Planning and
Innovation, Memorial Sloan-Kettering
Cancer Center
Supply Chain Management
• Stan Aronow, MBA, supply chain
research director, Gartner
• Chris Caplice, Ph.D., executive director,
MIT Center for Transportation & Logistics
• Lora Cecere, MBA, CEO, Supply
Chain Insights
• Thomas Rucker, Ph.D., director,
Technology Development, Intel Corp.
Decision Analysis
• William Haskett, MBA, senior principal,
Energy Strategy, Decision Strategies Inc.
• Max Henrion, Ph.D., chief executive
offcer, Lumina Decision Systems, Inc.
• Freeman Marvin, executive principal,
Innovative Decisions, Inc.
• Col. Brian K. Sperling, Ph.D., senior
military advisor/military deputy director,
Center for Army Analysis, U.S. Army
Forecasting
• Dieter Ambruster, Ph.D., professor,
Mathematical & Statistical Sciences,
Arizona State University
• Scott A. Bernhardt, president, Planalytics,
Inc.
• Michael Gilliland, M.A., MSE, product
marketing manager, SAS Institute
Soft Skills for Analysts
• William K. Klimack, Ph.D., decision
analysis consultant, Chevron USA, Inc.
(Col. U.S. Army, retired)
• Randy Krum, BSME, president,
InfoNewt, LLC
Human Resources Analytics
• Dean Abbott, president, Abbott Analytics Inc.
• Aleksandra Mojsilovic, Ph.D., manager,
Predictive Modeling & Optimization, IBM
TJ Watson Research Center, IBM
The conference program is being de-
signed by a 27-member committee of ana-
lytic practitioners and academics, chaired
by Jim Williams, manager of Advanced
Analytics at Land O’Lakes. Two expanded,
10-session tracks will focus on marketing
analytics and the analytics process. Other
tracks will cover big data, supply chain man-
agement, forecasting, decision analysis,
soft skills and HR analytics.
The program will feature presen-
tations by more than 100 speakers
representing a broad range of industries
and applications areas. Speakers already
lined up include executives and analysts
from Target, Intel, MIT, Gartner, Nielsen,
the IRS, Ford, GE and other companies
across sectors and industries (see ac-
companying sidebar story).
The Franz Edelman Competition, show-
casing the best in high-impact analytics, will
feature presentations by six fnalists: Bao
Steel, Chevron, Dell, Delta Commissioner
of Holland, Kroger and McKesson. A new
J A NUAR Y / F E BR UAR Y 2013 | 63 J A NUAR Y / F E BR UAR Y 2013 | 63
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track for 2013 will highlight applied work that
has received recognition in other competi-
tions, including fnalists for the Innovative
Applications in Analytics Award and winners
of the Wagner Prize, INFORMS Prize, UPS
George D. Smith Prize and Spreadsheet
Guru Prize.
PRESENT A POSTER, SAVE ON
REGISTRATION
While most speakers are hand-picked
by the committee to deliver talks on specifc
topics, interested professionals can submit a
poster proposal and receive a 35 percent to
40 percent discount off regular registration
rates. The submission deadline is Feb. 1.
The poster format lends itself to
works-in-progress on which the presenter
is looking for feedback; successful proj-
ects that may not be extensive enough
for a 50-minute talk; and corporate or
consulting work subject to nondisclosure
restrictions that can still be presented at
a high level. Poster presentations will be
scheduled in standalone sessions on two
days of the conference.
SPECIAL PROGRAMS
Two special programs within the con-
ference are designed for future analytics
leaders. The Early Career Connection,
for junior faculty and young industry re-
searchers, provides participants with in-
sights into the critical problems facing
business, helping them to broaden their
research agendas and providing impor-
tant networking contacts.
The INFORMS Professional Colloqui-
um offers a full-day, interactive workshop
on career guidance for master’s and Ph.D.
students who are interested in practice ca-
reers. The colloquium will be held April 7,
and students will then stay for the remainder
of the conference.
Participants in both programs can
register for the full conference at a
steep discounted rate of $395; they
must be nominated and selected to at-
tend. Limited financial aid is available
for the colloquium. The nomination
deadline is March 1.
For conference information, visit http://
meetings2.informs.org/Analytics2013.
CONFERENCE PREVI EW
INFORMS Healthcare 2013
In 2011 the Institute for Operations
Research and the Management Sci-
ences (INFORMS) introduced a new,
highly focused brand of conference
with a three-day meeting on healthcare.
That meeting was such a success both
in terms of attendance and quality of
presentations that a second INFORMS
J A NUAR Y / F E BR UAR Y 2013 | 63
A NA L Y T I C S
conference on healthcare is being
planned for June 23-26 this year at the
Marriott Downtown in Chicago.
The conference aims to bring a di-
verse group of researchers and stake-
holders together to share information
and insights that can improve the long-
term effciency, effectiveness and qual-
ity of healthcare delivery. Presentations
will span a wide range of problems in the
healthcare continuum that are amenable
to quantitative approaches. The program
will also feature contributions from syner-
gistic felds, including healthcare policy,
clinical decision-making, global health,
therapy and treatment delivery, public
health and data-driven modeling.
General Chair Sanjay Mehrotra,
professor of Industrial Engineering and
Management Sciences at Northwest-
ern University’s McCormick School of
Engineering, has formed an Organiz-
ing Committee of respected OR/MS
professionals including Steven Shech-
ter, University of British Columbia;
Vedat Verter, McGill University; Hari
Balasubramanian, University of Mas-
sachusetts-Amherst; and Cerry Klein,
University of Missouri.
Several INFORMS subdivisions
have agreed to be technical co-spon-
sors of the meeting, including the
INFORMS Health Applications Soci-
ety, Computing Society, Optimization
Society, Simulation Society and Data
Mining Section. Other organizations
outside INFORMS will also participate.
With support from these groups, the
conference is expected to draw re-
searchers and practitioners from the
United States, Canada, Europe and
around the world, offering a cross-
cultural and multi-disciplinary view of
healthcare systems.
The conference will offer a small-
scale, collegial environment where
high-quality talks are presented by
leaders in the field. All submissions
will be reviewed by the committee and
chairs.
In a new feature, the INFORMS
Health Applications Society will hold a
Student Paper Competition in conjunc-
tion with the meeting. The award will
call out papers judged to be the best
in the field of health applications, both
in terms of methodology and potential
for impact.
The conference venue, the Mar-
riott Downtown Magnifcent Mile, is a
renowned Chicago hotel, situated at
the best address in a great city, with
world-class dining and shopping, top at-
tractions, and the best in theatre and mu-
seums within walking distance.
For complete information and to sub-
mit an abstract visit: http://meetings.in-
forms.org/healthcare2013/. ❙
J A NUAR Y / F E BR UAR Y 2013 | 65 WWW. I NF OR MS . OR G 64 | A NA LY T I CS - MAGA Z I NE . OR G
In January Frontline Systems
(www.solver.com) is expected to be-
gin shipping Version 12.5 of its Solv-
ers for Excel, including a new flagship
integrated product, Analytic Solver
Platform. V12.5 also includes new ver-
sions of Risk Solver Platform, the lead-
ing tool for simulation risk analysis,
conventional and stochastic optimiza-
tion in Excel; subsets Premium Solver
Platform, Premium Solver Pro and Risk
Solver Pro; and XLMiner, the leading
data mining add-in for Microsoft Excel.
Analytic Solver Platform integrates all
the capabilities of all these products
and adds new features such as visual
data exploration and data mining meth-
ods applied to Monte Carlo simulation
trial data.
Frontline’s Solvers for Excel allows us-
ers to solve problems hundreds to thou-
sands of times larger than the basic Excel
Solver at speeds anywhere from several
times to hundreds of times faster. Analyt-
ic Solver Platform is the frst software to
bring the power of data mining and visual
data exploration to the analysis of Monte
Carlo simulation trial data.
I NDUSTRY NOTES
Frontline Systems, Inc. is a leading
developer of optimization and simulation
software, and the leader in spreadsheet-
based software for both conventional and
stochastic optimization.
SMALL BUSINESS SCORING
SOLUTION
FICO (www.fco.com), a leading pro-
vider of predictive analytics and decision
management technology, announced the
availability of FICO Small Business Scor-
ing ServiceSM solution version 7.0, which
brings expanded data and analytics to
small business lending. This new version
of the small business scoring solution
enables small business credit grantors
to assess credit risk, comply with regu-
latory requirements and offer faster re-
sponses to small business applicants
through process automation and instant
risk assessment.
The new service offers a suite of em-
pirically derived, multi-data sourced pre-
dictive models. The predictive models
include Equifax commercial and consum-
er data, and through Equifax’s relation-
ship with the Small Business Financial
Exchange, Inc., banks will now have ac-
cess to the largest source of small busi-
ness fnancial payment information in the
industry, including business loans, cards,
leases and lines of credit reported by
more than 400 SBFE members.
Analytics for
Excel users
J A NUAR Y / F E BR UAR Y 2013 | 65 A NA L Y T I C S
CMOS INVEST IN MARKETING
ANALYTICS
Chief marketing offcers are investing
in integrated marketing management so-
lutions with advanced, real-time analytics,
according to a recent report sponsored by
SAS (www.sas.com). The analytics power
consistent, personalized customer experi-
ences via traditional and digital channels.
“More organizations are realizing that
modern marketing is a data-driven disci-
pline,” says Wilson Raj, global director of
Customer Intelligence at SAS. “CMOs are
poised to not only drive great marketing
but drive broader business changes and
enhance alignment across the C-suite.”
According to the study, eight of the
12 areas CMOs identifed for marketing
investment revolve around technology
such as customer analytics, CRM, social
media, marketing automation, collabora-
tion and optimization tools.
SAS is a leader in business analytics
software and services, and the largest in-
dependent vendor in the business intel-
ligence market. ❙
J A NUAR Y / F E BR UAR Y 2013 | 67 J A NUAR Y / F E BR UAR Y 2013 | 67 WWW. I NF OR MS . OR G 66 | A NA LY T I CS - MAGA Z I NE . OR G
Each year, the beginning of the Christmas
shopping season, “Black Friday,” seems to start
earlier. Black Friday is currently the day immedi-
ately following “Mobile Thursday” [1], although this
year some businesses stated their holiday sales
at 8 p.m. on Mobile Thursday. I am interested in
the so-called “Christmas Creep,” where lights and
sales begin earlier. Consider the following from
Google Trends for searches on “Black Friday” and
various retailers who are known for Black Friday
sales (see Figure 1).
Clearly, search volumes for these retailers are
strongly correlated with the Black Friday search-
es, which themselves are strongly correlated with
Black Friday itself. For a comparison of Black Fri-
day sales against other holiday shopping dates,
see Figure 2.
Black Friday has been joined recently by oth-
er shopping dates such as “Cyber Monday” and
“Mobile Thursday,” although the results in Trends
for these two items are completely dwarfed by the
Black Friday results. There are most likely two
reasons for this:
1. There’s no reason to search for “Cyber Monday”
shopping – you simply go and do it directly from
your favorite Web site.
Black Friday
BY HARRISON SCHRAMM
Christmas creep:
Comparing the impact of
other shopping dates such
as “Cyber Monday” and
“Mobile Thursday.”
FI VE-MI NUTE ANALYST
J A NUAR Y / F E BR UAR Y 2013 | 67 A NA L Y T I C S
2. Because of overhead, the stakes
are much higher for retailers with a
physical presence. Bringing in your
employees after dinner on Mobile
Thursday represents a risk.
There’s an interesting dynamic to
opening times and “Christmas Creep.”
Holiday season sales are estimated to
represent 20 percent to 40 percent of
annual sales for many retailers. If the
Figure 1: Google Trends results for searches on “Black Friday” (blue) and selected retailers with
physical presence.
Figure 2: Google Trends results for searches on “Black Friday” (blue) and other shopping dates
of interest. Internet searches for Black Friday dwarf both Cyber Monday and Mobile Thursday.
The small peak in early 2012 is correlated to a “Spring Black Friday” sale [2].
Request a no-obligation INFORMS Member Benefits Packet
For more information, visit: http://www.informs.org/Membership
J A NUAR Y / F E BR UAR Y 2013 | 69
WWW. I NF OR MS . OR G 68 | A NA LY T I CS - MAGA Z I NE . OR G
holiday season begins on “Black Friday,”
this is because retailers (at least implic-
itly) agree to begin it then. As soon as
one retailer decides to begin his sales
“a little early,” then all of the other retail-
ers in that particular sector need to move
their sales up as well. Like an arms race,
this problem is unstable. Consider the
payoffs in Table 1.
Table 1 does not include any “limits”
on the beginning of the holiday shop-
ping season; for example, it would be
strange for Santa to arrive before, say,
the Fourth of July, but there is nothing
to prevent this solution. The limits are
sometimes set by law, such as work-
ing hours on holidays, as well as what
customers are willing to consider as
the “Christmas Season.” It would seem
that there is a disincentive for being the
“leader” of making the shopping sea-
son start sooner; however, once one
retailer decides that they should open
earlier (and thereby begin the holiday
season earlier), it is optimal for other
retailers to follow suit. ❙
Harrison Schramm ([email protected])
is a military instructor in the Operations Research
Department at the Naval Postgraduate School in
Monterey, Calif. Schramm is a member of INFORMS.
FI VE-MI NUTE ANALYST
Join the Analytics Section of INFORMS
For more information, visit:
http://www.informs.org/Community/Analytics/Membership
Store 1 / Store 2 Open Early Open at Last Year’s Time
Open Early (0,0) (+1, -1)
Open at Last Year’s Time (-1, +1) (0,0)
Table 1: Payoffs for a hypothetical game between two retailers. From the starting position of
“open at last year’s time,” “open at last year’s time,” either side can unilaterally increase his
payoff by deciding to open early by moving in the direction of the arrows. With the knowledge
that your competitor may do better by opening early, the best strategy is to open early.
NOTES
1. Some of the older readers of this column may
ask, “But isn’t Thanksgiving on a Thursday?” It
is. Thanksgiving is celebrated between tweets on
Mobile Thursday.
2. Forbes Magazine, April 10, 2012.
J A NUAR Y / F E BR UAR Y 2013 | 69
NEW in 2013
All new members who join INFORMS at the regular member rate receive one FREE Community
membership!
INFORMS Communities are where you make your best contacts and form your best relationships to help
your career and give back to the profession. You’ll gain:
» Access to specialized knowledge through Community newsletters, list serves, discussion
boards and meetings
» Increased ability for you to learn about the specific “tools of the trade”
» Ability for compete for specialized awards
» Access to sometimes hidden analytics job opportunities
» Volunteer leadership opportunities
» Opportunities to make a positive contribution to the profession
www.informs.org/Subdiv
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· Analtyics
· Artificial Intelligence
· Aviation Applications
· Behavioral Operations Management
· CPMS: The College on the Practice of Management Science
· Data Mining
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· Financial Services
· Group Decision and Negotiation
· Location Analysis
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J A NUAR Y / F E BR UAR Y 2013 | 71 WWW. I NF OR MS . OR G 70 | A NA LY T I CS - MAGA Z I NE . OR G
As a farmer, the decisions you make when plant-
ing crops infuence whether your year will end with
signifcant profts or in bankruptcy. As the owner of
1,000 acres, you need to make the following deci-
sions for your upcoming planting season:
1. Plant corn or soybeans
2. Buy crop insurance
3. Use fertilizer
Corn has a proft of $190 per acre, but you will
lose $190 per acre if your crop fails. Corn fertilizer
costs $30,000 and corn insurance costs $35,000.
Soybeans have a proft of $170 per acre, but you will
lose $170 per acre if your crop fails. Soybean fertilizer
costs $10,000 and soybean insurance costs $20,000.
Table 1 shows the probability of reaping a full har-
vest for either crop. For simplicity, assume that you
will either successfully reap all 1,000 acres or there
will be a total crop failure.
QUESTION: What is your expected proft when the
best decisions are made?
Send your answer to [email protected] by
March 7, 2013. The winner, chosen randomly from
correct answers, will receive an “Analytics - Driving
Better Business Decisions” T-shirt. Past questions
can be found at puzzlor.com. ❙
John Toczek is the manager of Decision Support and Analytics for
ARAMARK Corporation in the Global Risk Management group. He
earned a bachelor’s degree in chemical engineering at Drexel University
(1996) and a master’s degree in operations research from Virginia
Commonwealth University (2005). Toczek is a member of INFORMS.
Farm O.R.
BY JOHN TOCZEK
THI NKI NG ANALYTI CALLY
Probability of reaping
a full harvest (%)
Buy crop insurance and
use fertilizer
100%
Use fertilizer only 95%
Buy crop insurance only 90%
Do not buy insurance
and do not use fertilizer
85%
Table 1: Farmers face a tough
decision.
J A NUAR Y / F E BR UAR Y 2013 | 71
OPTIMIZATION
www.gams.com
Europe
GAMS Software GmbH
P.O. Box 40 59
50216 Frechen, Germany
phone
+49-221-949-9170
fax
+49-221-949-9171
mail
[email protected]
web
http://www.gams.com
USA
GAMS Development
Corporation
1217 Potomac Street, NW
Washington, DC 20007, USA
phone
+1-202-342-0180
fax
+1-202-342-0181
mail
[email protected]
web
http://www.gams.com
GAMS Integrated Developer Environment for editing,
debugging, solving models, and viewing data.
0.04
0.03
0.01
0.02
0
0
0
20
20
40
40
60
60
80
80
100
100
Interfacing GAMS with MATLAB
®
and R
GDXMRW (for MATLAB) and GDXRRW (for R) are utilities to exchange data
between GAMS and MATLAB or R and to call GAMS models from MATLAB or R:
• Give MATLAB or R users access to all the optimization capabilities of GAMS
• Allow visualization and analysis of GAMS data directly within MATLAB or R
• Extend the existing GDX data utilities
• GDXMRW is included with GAMS distributions
• GDXRRW is freely downloadable
High-Level Modeling
The General Algebraic Modeling System (GAMS)
is a high-level modeling system for mathemati-
cal programming problems. GAMS is tailored for
complex, large-scale modeling applications, and
allows you to build large maintainable models that can
be adapted quickly to new situations. Models are fully
portable from one computer platform to another.
Wide Range of Model Types
GAMS allows the formulation of models in
many different problem classes, including
• Linear (LP) and Mixed Integer Linear (MIP)
• Quadratic Programming (QCP) and
Mixed Integer QCP (MIQCP)
• Nonlinear (NLP) and Mixed Integer NLP (MINLP)
• Constrained Nonlinear Systems (CNS)
• Mixed Complementary (MCP)
• Programs with Equilibrium Constraints (MPEC)
• Conic Programming Problems
• Stochastic Linear Problems
State-of-the-Art Solvers
GAMS incorporates all major commercial and
academic state-of-the-art solution techno-
logies for a broad range of problem types,
including global nonlinear optimization solvers.
For more information please visit: http://support.gams.com/doku.php?id=gdxrrw:interfacing_gams_and_r
MATLAB is a product of MathWorks, R is a free software environment for statistical computing and graphics.