Analytics Marchapril 2014

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H T T P : / / WWW. A N A L Y T I C S - MAGA Z I N E . O R G
MARCH/ APRI L 2014 DRIVING BETTER BUSINESS DECISIONS
BROUGHT TO YOU BY:
ALSO INSIDE:
ANALYTICS IN
THE OILFIELD
Executive Edge
Oversight Systems
CEO Patrick Taylor
on using operational
& strategic analytics
to achieve competitive
business advantage
• Exploration
& production
insights
• Hey, what’s the
fracking problem?
• Hottest analytics job markets in the U.S.
• Key attributes of analytics professionals
• Information decay: What to do about it
WWW. I NF OR MS . OR G 2 | A NA LY T I CS - MAGA Z I NE . OR G
Oil & analytics do mix
I NSI DE STORY
From the boardroom to the oil feld,
from healthcare to manufacturing, analyt-
ics continues to expand its reach and its
impact throughout the business world, but
as many of the articles in this issue point
out, the analytics profession and those
who practice it are only just now scratch-
ing the surface of their true potential.
Take the oil and gas industry, the
subject of this month’s cover, for example.
The industry in general, and exploration
and production (E&P) companies in par-
ticular, faces many complex problems that
could beneft big time from data-driven
analysis and solutions, yet the adoption
and application of analytics remains
sketchy at best.
In “Analytics in the oilfeld,” Warren
Wilson notes that while many E&P com-
panies have embraced business intel-
ligence and other analytics tools in their
back offces, they are way behind the
curve in terms of operations technology.
Wilson, a long-time IT analyst in the E&P
sector and an oil feld roughneck in a pre-
vious life, says drilling data is routinely
gathered in real time so that a rig can
be shut down if problems arise, yet the
data is then discarded, “foreclosing any
opportunity to look for patterns that could
enable earlier problem detection and
point the way toward better practices.”
Along with discarding potentially valu-
able data, Wilson says the E&P sector
also suffers from data fragmentation,
furthering hampering analytical efforts.
Atanu Basu follows Wilson’s article with
an article that looks at how prescriptive
analytics can reshape fracking in oil and
gas felds. Basu, CEO of a company
whose prescriptive analytics software
focuses on improving oil and gas explo-
ration and production, notes that current
fracking practices are quite ineffcient:
Horizontal drilling and hydraulic fractur-
ing recovers 20 percent or less of the oil
in the shale rocks. That means drillers
spent $31 billion in 2013 on suboptimal
frack stages across 26,100 wells in the
United States.
Given the sky-high cost of oil explo-
ration and production, and the potential
for analytics to boost operational effcien-
cy, E&P companies are leaving a lot of
money on the table. Sure, oil companies
make a ton of money, but why would they
spend it on ineffcient operations when
they don’t have to?
– PETER HORNER, EDITOR
peter.horner
@
mail.informs.org
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DRIVING BETTER BUSINESS DECISIONS
C O N T E N T S
FEATURES
EXECUTIVE EDGE: DATA POWER
By Patrick Taylor
Savvy execs make the most of data analytics by using insights on both
a strategic and operational level.
INFORMATION DECAY
By Dhiraj Rajaram, Krishna Rupanagunta and Aditya Kumbakonam
The value of information diminishes over time. Here’s what enterprises
need to do in response.
ANALYTICS IN THE OILFIELD
By Warren Wilson
Predictive and other forms of advanced analytics can yield crucial
insights for exploration and production companies.
WHAT’S THE FRACKING PROBLEM?
By Atanu Basu
Fracking is an inefficient means to capture oil and gas, but hybrid data,
big data and analytics can turn it around.
WHERE THE ANALYTICS JOBS ARE
By Scott Nestler, CAP
An analysis of data from LinkedIn and other sources reveals the best
job-hunting grounds in the U.S.
CORPORATE PROFILE: GENERAL MOTORS
By Jonathan H. Owen, David J. VanderVeen and Lerinda L. Frost
GM uses advanced analytics to meet auto industry challenges, provide
value to customers and company.
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MARCH/ APRI L 2014
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6 |
DRIVING BETTER BUSINESS DECISIONS
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President Stephen M. Robinson, University of
Wisconsin-Madison
President-Elect L. Robin Keller, University of
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Past President Anne G. Robinson, Verizon Wireless
Secretary Brian Denton,
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Vice President-
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Vice President-Practice Activities Jonathan Owen, General Motors
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and Professional Recognition Ozlem Ergun, Georgia Tech
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Analytics (ISSN 1938-1697) is published six times a year by the
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Analytics copyright ©2014 by the Institute for Operations
Research and the Management Sciences. All rights reserved.
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WWW. I NF OR MS . OR G 8 | A NA LY T I CS - MAGA Z I NE . OR G
In a recent column, Nicholas Kristof of the New York
Times decries the growing isolation of college and univer-
sity faculty members [1]. Notably, he quotes Will McCants,
a Middle East specialist at the Brookings Institution, as
saying “Many academics frown on public pontifcating as
a frivolous distraction from real research.”
Well, I have a long track record of public pontifcating,
and that I’m a big fan of both real research and frivolous
distraction. Indeed, this column has now been in every
issue of this magazine for the last four years. In addition,
I will be speaking at the upcoming Predictive Analytics
World 2014 Conference, which will be held on March 17-
18 at the Marriott Marquis Hotel here in San Francisco
[2] (and I’d love to see you there!).
This public pontifcating is particularly satisfying
when people respond to your ramblings (hint, hint).
Last month’s column was about a few odd interactions
with some technically oriented colleagues about what
“real” analytics actually was. In response, I received a
very thoughtful response from Fredrick Odegaard, a
former supply chain analyst and consultant who is now
on the faculty at the Ivey School of Business. Fred frst
proposed his own defnition of analytics (“combining
sources of information to create valuable insight that
is not readily apparent from the data alone”) and then
added, “for me, ‘descriptive statistics’ is NOT analytics.
The complexity of both
the data sources being
integrated and the
business problems being
addressed under the
banner of analytics is
continuing to grow, and
the breadth of capabilities
needed to implement
effective solutions is often
a very real challenge.
BY VIJAY MEHROTRA
Key attributes for analytics
professionals
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ANALYZE THI S!
Yet looking objectively at all the advertise-
ment and public manifestations of ‘analytics’
it is 99.9 percent descriptive stats with either
(A) fancy charts or (B) tables with a gazillion
descriptive statistics.”
Later in his e-mail, he made another
very interesting observation: “The hardest
part about analytics is not, as most people
think, the math. In fact, the math might actu-
ally be the easiest part. Analytics require a
lot of thinking and a lot of creativity, ingredi-
ents that require time and persistence, both
of which are in short supply in today’s world.
Most managers (and defnitely students)
cannot and do not want to spend more than
a few minutes (or is it a few seconds?!) be-
fore receiving gratifcation. Which means
that too often they will take a pie chart or
a summary table and rave about their
analytics!”
I often hear this kind of thing from ana-
lytics managers. Patience, persistence and
the ability to function effectively even un-
der a wide variety of pressures (including
a shortage of time) just might be the most
important attributes for successful analyt-
ics professionals. Given some foundational
programming and mathematical capability,
the knowledge of a particular coding lan-
guage or a specifc statistical technique can
be acquired more quickly and more cheap-
ly than ever before; however, there are
as yet no effective massively open online
courses for the business effectiveness skills
(including problem framing, relationship
management, effective communication with
non- and less-technical stakeholders) that
often determine how big an impact is made.
But don’t get me wrong; I’m not trying
to minimize the importance of what some
of my colleagues call “technical chops.”
The complexity of both the data sources
being integrated and the business prob-
lems being addressed under the banner
of analytics is continuing to grow, and the
breadth of capabilities needed to imple-
ment effective solutions is often a very
real challenge. With most of my MBA stu-
dents, I feel like there is a clear ceiling on
how much of the “solution stack” they will
ever truly be able to understand, and I am
frankly unclear on what career limitations
they may face as a result.
On this note, a company recently con-
tacted me because the number of data
scientists on staff had grown substantially
since we had last spoke and these people
had been identifed as key corporate assets
to be developed and retained. As part of
this initiative, a few analytics leaders within
the organization had sketched out compe-
tencies and job titles for two distinct career
paths – one that led to senior analytics man-
agement roles and the other culminating in
a highly esteemed (and very well compen-
sated) senior data scientist title.
When asked for my feedback, I had two
immediate responses. First of all, the very
MAR CH / A P R I L 2014 | 11
A NA L Y T I C S
NOTES & REFERENCES
1. Kristof, Nicholas D., “Professors, We Need
You,” New York Times, Feb. 15, 2014.
2. See http://www.predictiveanalyticsworld.com/
sanfrancisco/2014/agenda_overview.php for the
complete agenda for PAW 2014 SF.
existence of such imperfect but construc-
tive proposals for data science careers
was itself a huge, positive signal. Too of-
ten, business organizations view analyt-
ics people as high-priced commodities
to be acquired when clearly needed and
discharged casually when not. Knowing
this, the skilled professional is compelled
to make sure that their own fnancial and
intellectual needs are taken care of, even
when that means leaving the company
for better opportunities (and there are
typically many opportunities available to
skilled data scientists). In such cases, a
data scientist ends up leaving a relatively
good situation largely in order to feel ap-
preciated, while the company fnds that a
unique collection of broad analytics skills
and hard-earned domain knowledge has
just walked out the door.
Secondly, I was struck by just how
many different technical competencies
their proposed plan required, even for
people who wanted to pursue manage-
rial and leadership roles in data science.
When we discussed this, they were ada-
mant about the need for this broad and
deep set of capabilities, both in order to be
skilled in creating and assessing sources
and to be credible within the data scientist
community.
Not long after this discussion, a former
MBA student of mine came to visit me.
“Richie” had taken several courses with
me and had landed an interesting job as
data analyst for a large global organiza-
tion. After a year and a half on the job, he
turned down a good opportunity to move
into a line management position. Instead,
Richie had decided to go back to graduate
school again, this time to get a master’s
degree in analytics. “My company doesn’t
know how much it is leaving on the table,”
he told me, “but I do. I just need more
technical capabilities to be a real hero in
this kind of environment.” His fve-year
goal, however, was a senior analytic lead-
ership role, and both he and I were conf-
dent that he would get there, because of
the broad background from his MBA and
also because of his strong commitment to
learning and growing on all fronts.
When someone with that sort of attitude
gets enough technical chops, look out!
OK, I’m done pontificating for now.
More next time.
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.
WWW. I NF OR MS . OR G 12 | A NA LY T I CS - MAGA Z I NE . OR G
Consider what young people are learning in
school today. They are taught mean, mode, range
and probability theory in their freshman university sta-
tistics course. Today’s children have already learned
some of this math in the third grade! They are taught
these methods in a very practical way. If you have x
dimes, y quarters and z nickels in your pocket, what
is the chance of you pulling a dime from your pocket?
Learning about range, mode, median, interpolation
and extrapolation follow in short succession.
We are already seeing the impact of this learning
with Gen Y/Echo boomers who are getting ready to
enter the work force. They are accustomed to having
easy access to information and are highly self-suff-
cient in understanding its utility. The next generation
after them will not have any fear of analytics or look-
ing toward an “expert” to do the math.
Given that these analytical capabilities are becom-
ing commonplace, there is a broad range of problems
and opportunities that can be addressed that were
unimaginable to be tackled only a few years ago.
I am interested when the questions listed below
might be routinely answered with business analytics,
big data, and enterprise and corporate performance
management (EPM/CPM) software:
Provocative questions for
analytics to answer
BY GARY COKINS
Gen Y/Echo boomers
are accustomed to
having easy access to
information and are
highly self-sufficient in
understanding its utility.
The next generation
after them will not have
any fear of analytics
or looking toward an
“expert” to do the math.
FORUM
MAR CH / A P R I L 2014 | 13 A NA L Y T I C S
• Why can’t traffc intersection stoplights
be more variable based on street
sensors that monitor the presence,
location and speed of approaching
vehicles? Then you would not have
to impatiently wait at a red light when
there is no cross-traffc.
• Why can’t a call center route your
inbound phone call to a more
specialized call center representative
based on your phone number
and your previous call topics or
transactions? And once connected,
why can’t that call rep offer you rule-
based offers, deals or suggestions
most likely to maximize your
customer experience? Then you
might get a quicker and better
solution to your call.
• Why can’t dentists and doctors
synchronize patient appointment
schedule arrival times to reduce the
amount of wasted time that so many
people collectively have to idly sit while
in the waiting room? Then you could
show up just before your appointment.
• Why can’t airlines better alert their
ground crews for plane gate arrivals?
Then passengers don’t have to wait,
sometimes endlessly, for the jet
bridge crew to show up and open the
airplane’s door.
• Why can’t hotel elevators better
position the foors the elevators
arrive at to pick up passengers
based on when hotel guests depart
their rooms? Then you don’t have
to get stuck on a slow “milk-run”
elevator stopping at so many
foors while an “express” elevator
subsequently arrived and could
have quickly taken you to your
selected foor.
• Why can’t airport passport control
managers regulate the number of
agents in synchronization with the
arrivals of international fights? Then
you don’t have to wait in long queue
lines only to have the extra staff
show up (sometimes) much later.
• Why can’t retail stores partner with
credit card companies and their
transaction histories and use
algorithms like Amazon.com and
Netfix do to suggest what a customer
might want to purchase? Then you
might more quickly fnd what you are
shopping for.
• Why can’t water, gas and electrical
utility suppliers to home residences
provide instant monitoring and
feedback so that households
can determine which appliances
or events (e.g., taking showers)
consume relatively more or less?
Then households could adjust their
usage behavior to lower their utility
bills.
WWW. I NF OR MS . OR G 14 | A NA LY T I CS - MAGA Z I NE . OR G
• Why can’t personnel and human
resource departments do better
workforce planning on both the
demand and supply side? That
is, for the supply side, why can’t
they predict in rank order the most
likely next employee to voluntarily
resign based on statistical data
(e.g., their age, pay raise amount
or frequency) of employees who
have previously resigned? For
those who will retire, isn’t this
predictable? For the demand side,
why can’t improved forecasting
of sales volume and mix be
translated into headcount capacity
planning by type of skill or job
group? Then the workforce on
hand will match the needs without
scrambling when mismatches
occur.
• Why can’t magazines you subscribe
to print at the time of production a
customized issue for you that has
advertisements (and maybe even
articles) tailored to what you likely
care more about based on the
profle they may have about you?
Then the magazine’s content may
be more relevant to you.
• Why can’t your home’s refrigerator
and food pantry keep track using
microchips and barcode scanners
of what you purchased and the
rate of usage? Then you could
better replenish those items when
out shopping.
Are these a vision of the future? Not
in all cases. With business analytics
software and communication technol-
ogy some, if not all, of these questions
are already solvable. Analytics not
only proves or disproves an analyst’s
hypothesis, but its truth-seeking tests
also reveal cause-and-effect relation-
ships. Understanding causality serves
for making better decisions by reducing
uncertainty.
It is a complex world that we live in.
It is now time that gut-feel, intuition and
guessing be replaced with applying an-
alytics to better manage organizations
and better serve their customers.
Gary Cokins [email protected], CPIM,
is the founder of Analytics-Based Performance
Management LLC, an advisory firm. He is an
internationally recognized expert, speaker
and author in advanced cost management
and performance improvement systems. He
previously served as a principal consultant with
SAS. For more of Cokins’ unique look at the
world, visit his website at www.garycokins.com.
He is a member of INFORMS. A version of this
article appeared in Information Management.
FORUM
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WWW. I NF OR MS . OR G 16 | A NA LY T I CS - MAGA Z I NE . OR G
BY RAJIB GHOSH
Two years ago, Vinod Khosla, the luminary ven-
ture capitalist and the co-founder of Sun Microsystems,
shook the technology and the medical communities with
his highly talked about article, “Do We Need Doctors
Or Algorithms?” In the article Khosla argued that given
the level of service that we seek and eventually receive
from 80 percent of physicians, we might be better off
receiving that care from a computer with sophisticated
algorithms. Khosla fondly named that system “Dr. Algo-
rithm” or “Dr. A,” for short.
Later in his follow-up talks, including the recently
concluded Rock Health CEO Summit in San Francis-
co, he ignited the debate further by saying that 80 per-
cent of physicians in the United States can be replaced
with machines, and that day is not very far away. The
medical community responded with the argument that
healthcare is not about technology – it is about the in-
tersection of technology, science and human emotions,
along with the therapeutic touches and listening abilities
of a doctor. David Liu, M.D., did a balanced rebuttal in
The Healthcare Blog.
As healthcare analytics continues to evolve in 2014,
let’s pause for a few moments and think about the de-
bate at hand. There are some big ideas embedded in
it that we as data scientists and big data technologists
need to consider seriously. If Khosla is right in his pre-
diction that clinical data analytics will usher in a new era
in U.S. healthcare – a sea change that will transform
Vinod Khosla argued
that given the level of
service that we seek and
eventually receive from
80 percent of physicians,
we might be better
off receiving that care
from a computer with
sophisticated algorithms.
Khosla fondly named that
system “Dr. Algorithm.”
Algorithm is the new doctor
and data is the new drug
HEALTHCARE ANALYTI CS
A NA L Y T I C S MAR CH / A P R I L 2014 | 17
healthcare like never before – what Kho-
sla is actually predicting is that healthcare
in the future will essentially become a data
game! Data is the new drug!
In theory, the more data available, the
more precise the diagnosis, and the effcacy
of the treatment will also improve, which, in
Khosla’s words, is far less complex than the
problem of autonomous driving. With the
deluge of data coming from multiple sourc-
es, such as wearable and ambient sensors,
gene sequencing and digitized encounters,
diagnosing a problem in the human body
will become a matter of pattern recognition.
There could be billions of possibilities, but
searching a large set of possibilities with so-
phisticated algorithms, image processing,
machine learning and artifcial intelligence is
what machines do well. Machines are doing
it now and in real time!
Humans create algorithms tapping into
their own “knowledge” of today. Machines
take that knowledge and develop new
knowledge based on the emerging pat-
terns in the data. That’s the whole premise
of IBM’s (Dr.) Watson, which is using can-
cer knowledge created by oncologists plus
related data to fne-tune cancer treatments
for patients [1]. So why do we need doctors
to tell us what ails us when machines are
capable of doing this?
CAN MACHINES REALLY REPLACE
PHYSICIANS?
The 2010 National Ambulatory Care
Survey reveals that out of 1 billion physi-
cian offce visits, the average number of
visits per person is approximately four per
year. The most common reason for the visit
is for a cough, and the most commonly di-
agnosed condition is “essential hyperten-
sion” [2]. Algorithms are available today for
diagnosing hypertension. But legal liability
and regulatory hurdles play a big role in
preventing software developers from de-
claring a confrmed diagnosis.
Decision support software, therefore,
seeks confrmation from the clinicians. Ma-
chines also don’t have access to the huge
data in healthcare that is needed to gener-
ate the desired precision in diagnosis. Ge-
nomic data is sporadic, and the majority of
the clinical encounter data is still not digi-
tized. Further complicating matters, when
Will data, analytics and computers replace physicians?
Probably not, but they can help improve healthcare by
augmenting human capabilities.
WWW. I NF OR MS . OR G 18 | A NA LY T I CS - MAGA Z I NE . OR G
HEALTHCARE ANALYTI CS
electronic data is available, the absence of
data liquidity and interoperability within and
among healthcare organizations makes it
harder to get a holistic view of any patient.
IBM’s Watson, therefore, is not only just an
“advisor” it is an incredibly expensive “advi-
sor” that takes too long (18 to 24 months)
to understand how care pathways work [3].
The key here is that physicians have to
let the machines learn from their decisions
or mistakes, and as IBM is fnding out, that
is non-trivial. How do you scale when ev-
ery project is custom built, takes a long time
to complete and yet you are at the mercy
of the physicians who fear that they are
training their replacement? Moreover, even
when personal medical data is available
patients are concerned that seamless data
fow among healthcare stakeholders will
destroy their privacy and make them more
vulnerable to insurance payers and em-
ployers. Not an easy problem – is it?
Developing algorithms and technology
for the purpose of replacing physicians is
the wrong premise to begin with. Having
said that, I have to admit that the future
of medicine will no doubt embrace a larg-
er role of data and analytics. The barriers
that face Dr. Watson today will eventually
come down. Business models will emerge.
Privacy will be addressed through legisla-
tion. Treatments will be personalized in real
time. But human beings are social animals
– we want to hear from other humans that
no matter what the current situation is, we
will be OK! A sick patient wants to go back
home with assurance from a human minus
the “confdence levels of 90 percent.”
Armed with the data and algorithms,
doctors of the future will be able to triage
patients far more effectively and preemp-
tively, spend more time with those that they
need to see, and be the listener, healer and
collaborator that a patient expects. This is
how Dr. A will help to augment, not replace,
the human capabilities to take care of an
increasingly aging population that will con-
tinue to live longer.
Rajib Ghosh ([email protected]) is an
independent consultant and business advisor with
20 years of technology experience in various industry
verticals where he had senior level management
roles in software engineering, program management,
product management and business and strategy
development. Ghosh spent a decade in the U.S.
healthcare industry as part of a global ecosystem
of medical device manufacturers, medical software
companies and telehealth and telemedicine solution
providers. He’s held senior positions at Hill-Rom,
Solta Medical and Bosch Healthcare. His recent work
interest includes public health and the feld of IT-
enabled sustainable healthcare delivery in the United
States as well as emerging nations. Follow Ghosh on
twitter @ghosh_r.
NOTES & REFERENCES
1. Laura Nathan-Garner, “The future of cancer
treatment and research: What IBM Watson means for
our patients,” MDAnderson.org.
2. CDC.gov, “Ambulatory Care Use and Physician
Visits,” http://www.cdc.gov/nchs/fastats/docvisit.htm.
3. Spencer E. Ante, “IBM Struggles to Turn Watson
Computer Into Big Business,” http://online.wsj.com/
news/articles/SB100014240527023048871045793068
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WWW. I NF OR MS . OR G 22 | A NA LY T I CS - MAGA Z I NE . OR G
hen it comes to analytics, or-
ganizations can use data in-
sights on a strategic as well
as an operational level. The
2011 flm “Moneyball,” based on the best-
selling book by Michael Lewis, tells the sto-
ry of how Major League Baseball’s Oakland
Athletics, on a limited budget, compiled an
outstanding team of players using deep data
analysis to drive their team-building strat-
egy. The team then furthered their use of
data to make day-to-day playing decisions
based on analytic insights. This combina-
tion of strategic and operational analysis
led the A’s to an outstanding performance
– making the MLB playoffs and nearly beat-
ing the Yankees – with one of the lowest
payrolls in baseball.
Many businesses face the daunting
challenge of building analytics programs
within their organizations, yet become so
wrapped up with the system and technol-
ogy that they fail to realize the full value of
the insights. Some kick-start the process
focused entirely on the strategic value of
the data. Others implement analytics on the
operational side, using it to fag exceptions
or identify anomalies in the way processes
are followed, but never use the data to re-
veal massive, game-changing fndings. The
How savvy execs
make the most of
data analytics
BY PATRICK TAYLOR
W
EXECUTI VE EDGE
most successful businesses do both, just
like the Oakland A’s. Whether knee-deep
in a big data implementation or just starting
to explore the options, companies should
consider some tips, pitfalls and best prac-
tices for getting the maximum value from
their data. A good way to start is to make
data analytics decisions with eyes wide
open about what is truly required for set-
up, which tools are most effective for the
organization, and how to maximize always-
limited resources.
NOTE TO SELF: DATA IS NOT PERFECT
Anyone who has ever worked with
data understands that no data set is ever
“clean.” The situation becomes even more
complicated when organizations are pulling
data from multiple production applications.
A few examples highlight the enormous,
unavoidable challenges associated with
data inconsistencies.
Consider an international company
looking to identify fraud in offces world-
wide. The company may start with a data-
base of countries with the highest risk of
corruption, and then evaluate transactions
for those countries. In different production
applications, countries may be noted in
multiple different ways depending on the
system, the purpose for which the informa-
tion was captured, and the individual who
entered the data. For example, South Ko-
rea may be entered as a standard two-letter
abbreviation such as “KR” in one system,
and specifed in various other standard text
formats such as “South Korea,” “Korea,
South” or “Republic of Korea.”
MAR CH / A P R I L 2014 | 23 A NA L Y T I C S
Anyone
who has ever
worked with data
understands that
no data set is
ever “clean.”
WWW. I NF OR MS . OR G 24 | A NA LY T I CS - MAGA Z I NE . OR G
Similar issues exist for person names. Taking the
United States only as a simple case, generally names
are straightforward with a frst and last name such as
“John Smith.” However, sometimes middle names are
captured such as “John James Smith” or the names
are entered in an alternate format such as “Smith,
John.” In a simple text comparison, “John Smith,”
“John James Smith” and “Smith, John” do not match;
however, they could be the same person. It gets more
complex internationally where people may use up to
fve or six name components. To accurately identify
activities associated with a particular person, the ana-
lytics tool must be fexible and intelligent enough to
allow for various name formats.
There are many possible solutions, such as nor-
malizing names to remove special characters and
standardize formats; breaking the names down into
components and matching on various combinations
of the name components (tokenizing); and cross ref-
erencing known alternate spellings into standardized
names such as ISO country names. The important
thing is to ensure that the analytics solution being
used is capable of effectively handling variances. A
brittle solution that only accommodates a single nam-
ing convention will likely have issues.
CLOUD-BASED ANALYSIS VS. ANALYTICS
The benefts of moving to the cloud are widely
recognized. Scalability, accessibility and expandable
horsepower and storage provide resources precisely
when and where they are needed. As a result, many
companies are turning to cloud-based analytics: ana-
lytics tools available in the cloud. While cloud-based
analytics solutions present all of the familiar benefts
EXECUTI VE EDGE
The benefits of moving
to the cloud are widely
recognized. Scalability,
accessibility and
expandable horsepower
and storage provide
resources precisely when
and where they are
needed.
MAR CH / A P R I L 2014 | 25
A NA L Y T I C S
associated with the cloud, they still require
the same data scientist prowess needed
to power in-house analytics solutions. Sta-
tistical knowledge, business understand-
ing and analytical savvy are all required to
use cloud-based analytics programs to ef-
fectively bridge the gap between business
questions and meaningful data insights.
Cloud-based analysis is a new breed
of solutions that encompass much of the
“heavy lifting” when it comes to analysis.
With cloud-based analysis, domain exper-
tise is resident in the solution. Rather than
exclusively serving as an analytics tool in
the cloud, cloud-based analysis also offers
pre-confgured analytic queries to apply
to the data sets found in a given industry.
Companies can upload their data, which
is then analyzed using a series of vetted,
tried and true statistical analyses and al-
gorithms that instantly reveal actionable
insights for that particular industry. These
cloud-based analysis solutions are best
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For example, in travel and expense
management, companies rely on catego-
rization of expenses to help classify and
report for trending purposes, but also to
prepare tax flings, which may include dif-
ferent deduction rates depending on the ex-
penditure category. Employees may either
inadvertently or purposefully misclassify ex-
penses. Cloud-based analysis can analyze
T&E expenses for miscategorization, fre-
quent offenders and merchants associated
with multiple misclassifed expenses. With
this insight, the company can investigate to
determine if there is fraudulent activity tak-
ing place, if certain inappropriate merchants
(i.e., dating services expensed as “meals”)
should be blocked, or if a process or policy
change needs to be implemented to guard
against problems.
Cloud-based analysis puts available
data to work immediately, asking key
questions and delivering business-critical
insights on day one. Minimal ramp-up time
is required and enterprises can start see-
ing trends immediately. These new solu-
tions enable companies to see immediate
beneft from analytics and also avoid the
lead-time and resources required to prog-
ress through the learning curve of which
questions to ask, which queries to con-
fgure and how to deliver meaningful re-
ports. Companies should consider if there
are areas where cloud-based analysis
can deliver immediate operational value,
allowing analytics gurus to focus on “deep
dive” strategic issues.
ACCOUNT FOR NUANCES OF THE
BUSINESS
While some expenses may be a red fag
for most any business (i.e., dating services)
beyond the most obvious examples, deter-
mining what kinds of transactions represent
a possible risk for a particular company is
a critical frst step to ensuring the analytic
reports delivered are valuable.
For every industry and every business,
there are differences in what qualifes as
“typical” or “atypical.” For example, a large
invoice to a plumbing vendor may represent
a red fag to a pharmaceutical company, but
be quite typical for a construction company.
Likewise, a $500 dinner expense at the Ritz
in New York may not be uncommon for a
company with all East Coast clients, but the
same dinner expense in Robert’s Restau-
rant (part of the Scores strip club) may be
a red fag. The type of business, number
of daily transactions and specifc situations
combine to make each company different.
Managers typically understand these ex-
ceptions and anomalies, but they may not
come to mind when initiating an analytics
program.
There are a couple of ways to capture
and integrate this information. One is to
start off with a questionnaire prior to imple-
menting an analytics solution. A survey may
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WWW. I NF OR MS . OR G 28 | A NA LY T I CS - MAGA Z I NE . OR G
queue managers to think of anomalies
about their business. The following ques-
tions may encourage managers to think
along the right lines:
• What types of vendors indicate
possible risk for your business?
• Is there a typical size/number of
transactions per week/month that
are typical of your business?
• What policies/guidelines are in place
that you typically fnd employees
skirting to avoid hassle or make
transactions easier? (For example,
breaking expenses in half to avoid
expense limits that require a long
pre-approval process)
With the answers to these questions
in mind, the analyst can gain a better un-
derstanding of what to be looking for, and
perhaps more importantly, what not to be
looking for in results.
Sometimes an even easier way to get
to this information is for the analyst to de-
liver the frst set of reports and then collect
feedback in real time. Managers typically
don’t understand statistical calculations,
but they do understand well-delivered re-
sults and have a keen eye for identifying
when something is amiss. Based on reac-
tions to initial reports, the analyst can adjust
the queries/algorithms to take into account
the newly shared insights. For example, a
retail analyst may identify that 75 percent
more cash refunds for product were issued
at register No. 4 than at any other register.
This is a potential red fag for fraudulent
returns perpetrated by the cash register
operator. However, in looking at the report,
the manager may know that to keep cus-
tomers moving quickly through check-out,
refunds are directed to the customer ser-
vice desk (home to register No. 4), where
these transactions are handled whenever
possible to prevent delaying other cus-
tomers. This operational policy needs to
be taken into account in the analysis so
that the “normal” volume for register No. 4
refunds is appropriately adjusted.
By spending some time up-front and in
the frst few cycles of analysis to account
for nuances in the business, analysts can
set up much more valuable reports and
avoid time and energy spent on mislabeled
red fags.
LOOK BEYOND OPERATIONAL
ANALYSIS
Leveraging analytics for operational
analysis is a great place to start due to the
quick ROI and powerful insights yielded in
a short time. However, as in the example of
the Oakland A’s, the savviest organizations
should use analytics for both operational
and strategic insights. Once organizations
become comfortable with operational anal-
ysis to deliver insights for better day-to-
day decision-making, it is easy to fall into
EXECUTI VE EDGE
A NA L Y T I C S
MAR CH / A P R I L 2014 | 29
a pattern of contentment. However, once
cloud-based analysis gets rolling, it should
leave in-house talent with the bandwidth to
explore strategic-level queries that could
lead to the next “ah-ha” discovery that will
reshape the business.
If cloud-based solutions can be lever-
aged for some day-to-day analysis, then
analysts with true domain expertise can
focus their energies on coming up with the
next big discovery. Companies often know
the questions they would like to have an-
swered. Big, game-changer questions like:
How can we know which past customers of
one product are the most likely customers
of a new product? Or, which new markets
are the most potentially lucrative?
Data analytics hold the answers to
these questions, but it often requires some
lead time and many interim answers be-
fore arriving at the ultimate answer. It can
take months or even years to investigate
these questions. Therefore, companies
should begin applying their analytic man-
power to those big questions as soon and
as effciently as possible.
Strategic-level insights may also be
conducted at different times of year and at
different intervals than continuous monitor-
ing. For example, at the end of the year,
managers may be making strategic sourc-
ing plans and may wish to identify vendors
that cause the most problems over the last
year, requiring a different kind of analysis of
the data with comparison against a differ-
ent baseline. Likewise, larger trends may
require a comparison of a full year’s results
over those of the last several years to iden-
tify operational challenges or sales trends.
Analysts also need to focus on deliv-
ering information in a consumable format
that is understandable and usable by their
“customers.” Sets of data in tables may not
be as understandable to the typical busi-
ness user as a chart or graph. A chart may
also be enhanced with accompanying ex-
planatory text. Delivering the information
in a way that is too challenging may leave
critical insights unaddressed.
It is when continuous monitoring is com-
bined with strategic data analysis that com-
panies fully realize the value of analytics.
The Oakland A’s went beyond convention-
al baseball statistics like batting averages
and stolen bases to perform much deeper,
more rigorous statistical analysis to under-
stand and select players. Then they sought
to outperform their opponents at each and
every game by using more tactical insights
such as having batters take more pitches
to tire the opposing pitcher. The combina-
tion led to astounding success. In the same
way, savvy executives can outperform their
competitors with a combination of strategic
and continuous analytics.
Patrick Taylor is CEO and founder of Oversight
Systems, a provider of business analytic software.
He is a member of INFORMS.
WWW. I NF OR MS . OR G 30 | A NA LY T I CS - MAGA Z I NE . OR G
or those of you who still re-
member high school chem-
istry, you may recall that
radioactive decay is an in-
herent property of all matter. And as a
quantum physicist would tell you, while it
is impossible to predict when a particular
atom will decay, the chance that a given
atom will decay is constant over time. We
believe that the same principle holds true
for information within an organization as
well: While it is diffcult to predict when a
particular information entity (e.g., a set
of data records) will lose its relevance
for a decision-maker, it is certain that all
F
information loses value over time.
The parallels are striking – so much
so that we believe that every information
entity should have an attribute called
“information decay” that describes how
the value of this information decreases
over time, much like the half-life captures
the rate of decay for all matter. As far as
the information entity is concerned, this
phenomenon has accelerated in recent
times as advances in analytics, data and
technology have transformed the way or-
ganizations leverage information to drive
decisions. Thanks to an explosion in
data, there is not only a lot of information,
DATA ASSETS
BY (l-r) DHIRAJ RAJARAM,
KRISHNA RUPANAGUNTA
AND ADITYA KUMBAKONAM
Information decay
WWW. I NF OR MS . OR G 30 | A NA LY T I CS - MAGA Z I NE . OR G
How the value of information diminishes over time.
A NA L Y T I C S MAR CH / A P R I L 2014 | 31
but the rate of information accumulation
is accelerating as well. Combine that
with a highly dynamic business environ-
ment, and it starts becoming clear that
the value of each information entity is
decreasing at an ever-faster rate.
There is no better example of informa-
tion decay than that of the Oakland A’s from
1996-2004, famously storied in the book
“Moneyball.” Starting in 1996, the team ad-
opted a novel approach to scouting, driven
by using an analytical, evidence-based
(“sabermetric”) approach. The results were
dramatic, as the A’s made it to the playoffs
four straight years starting in 2000. Other
teams then caught on, and the A’s lost their
advantage rapidly – a case of rapid infor-
mation decay.
An example of information decay that
marketers would recognize is the half-life
associated with GRPs/TRPs or impres-
sions to create a derived metric – ad-stock,
used to quantify the impact of marketing.
The delayed effects of marketing cam-
paigns have been well understood and
have been successfully leveraged to mea-
sure short- and long-term effects on rev-
enue and brand equity.
WHY DOES INFORMATION DECAY?
Information decays for several reasons,
and, as is usually the case, more than one
of the following reasons is typically at play:
Information becomes outdated: In
many situations, information has a tempo-
ral value that decays unless refreshed on
a regular basis. Consumer credit scores,
for instance, need to be continuously
refreshed in order to retain their value,
which is directly impacted by the refresh
frequency. In a world where a combina-
tion of data and technologies make near
real-time refreshes possible, the informa-
tion decay of consumer credit scores is
increasing.
Natural decay of information: As
technologies evolve, some information el-
ements begin to lose relevance. Tradition-
ally, surveys were the preferred (often, the
only) method for companies to get a pulse
of their customer base. However, with
the explosion of e-commerce and social
Every information entity should have an attribute
called “information decay” that describes how the
value of this information decreases over time.
WWW. I NF OR MS . OR G 32 | A NA LY T I CS - MAGA Z I NE . OR G
I NFORMATI ON DECAY
media, companies are increasingly tapping
multi-channel data sources to better under-
stand the moments of truth in the customer
lifecycle. They are tapping into these newer,
richer sources for better customer insights,
and in the process the information value of
surveys is diminishing.
The “effcient information hypoth-
esis”: In fnance, the effcient market hy-
pothesis posits that the prices of traded
assets refect all the available information.
As the access to information increases, its
value decays. This information decay is ac-
celerating at an unprecedented rate, thanks
to technology.
A case in point is competitive pric-
ing. Once upon a time, not very long ago,
competitive pricing strategies kept scores
of managers busy in organizations. And
then came the Internet and with it, web-
site scraping, which has given organiza-
tions the ability to track real-time changes
to competitor prices. Big data technolo-
gies allow multiple retailers to dissect
every price change in the ecosystem in
near real time, sucking away any possible
arbitrage opportunity. In other words, the
Internet has accelerated the information
decay rate of competitive pricing.
Another situation that is all too familiar
for city dwellers is traffc information. As
real-time information about traffc fows
(or more likely snarls) becomes avail-
able, this triggers a bandwagon effect
of redirecting the traffc to the hitherto
unclogged routes, sucking them to the
gridlock as well. The value of the traffc
information comes down, and the speed
with which this information is distributed
determines its rate of information decay.
The “observer effect”: One of the
more esoteric concepts in quantum phys-
ics, this refers to the changes that the very
act of observation cause when any phe-
nomenon is being observed. This is well
known in stock markets – often, the very
act of an analyst initiating coverage of a
relatively unknown stock brings attention to
the stock. And more eyes on the stock can
change the dynamics of the stock, altering
the information decay of the stock price.
Until, of course, the effcient market hypoth-
esis kicks in and brings the stock back to its
natural levels.
WHY DOES INFORMATION DECAY
MATTER?
Data is the “new oil” of the 21st cen-
tury, and companies are fast accumulating
data assets. Organizations need to invest
in extracting the true value from data by
institutionalizing a culture of data-driven
decision-making. As they embark on this
journey, managers would do well to recog-
nize that the value of data, like any asset,
depreciates over time.
To begin with, any data governance
process should have a strong data value
A NA L Y T I C S MAR CH / A P R I L 2014 | 33
of information decay into their data DNA.
Over time, we expect information de-
cay – which we now refer to as “µDecay”
– to be formalized as an attribute of every
information entity. And once organizations
begin to measure information decay and
drive corrective actions, the value they
can extract out of data assets will grow.
Dhiraj Rajaram is the CEO of Mu Sigma, an
analytics services provider that provides services to
more than 75 of Fortune 500 companies. Krishna
Rupanagunta is the “geography head” and Aditya
Kumbakonam is the “delivery head” at Mu Sigma.
audit process. This should be a structured
process that, on a pre-defned frequency,
takes a critical look at every data element
(from raw data to derived metrics) and
evaluates its value. If it turns out that the
value derived by business from that data
element is decaying, follow-up with cor-
rective action – either refresh the compu-
tation method to revise the data element
or replace the data element completely.
This alone should set companies well
along the way to incorporate the concept
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WWW. I NF OR MS . OR G 34 | A NA LY T I CS - MAGA Z I NE . OR G
redictive analytics software
has become increasingly at-
tractive as it has increased in
capability and fallen in price.
It is becoming so powerful that many en-
terprises consider it a must-have tech-
nology. As with any investment decision,
however, mounting enthusiasm tends to
mute the critics and skeptics who may
have valid questions about the rationale.
The importance of predictive and
other forms of advanced analytics is
self-evident. Properly deployed, they
can yield crucial insights obtainable
DATA I NTEGRATI ON
Analytics in the
oilfield
Properly deployed, predictive and other forms of
advanced analytics can yield crucial insights for
exploration and production companies.
BY WARREN WILSON
in no other way. Accordingly, explora-
tion and production (E&P) companies
should develop an analytics strategy as
quickly as possible, if they have not
already done so.
E&P companies should also be mind-
ful that investments made without clear
goals and implementation plans risk
wasting critical resources and not achiev-
ing the desired results. As E&P compa-
nies formulate their plans, they can boost
the likelihood of success by taking care
to address three basic questions: What
business value do you expect to gain,
P
what data is required to realize that value,
and which analytics tools are best suited
to your goals? Only when those questions
have satisfactory answers can an enter-
prise move forward with confdence.
Many E&P companies have already
embraced business intelligence and other
analytics tools in their back offces, partic-
ularly for fnancial management and en-
terprise resource planning. But they are
signifcantly further behind in operations
technology. Many are using only rudimen-
tary IT in the operations that defne their
industry: exploring for oil and gas, devel-
oping reserves and managing production
for maximum lifetime value.
Drilling data, for example, is routinely
gathered in real time so that the rig can be
shut down if key measurements such as
torque on the drill pipe and bit, or pressure
in the mud circulation system, move out-
side of established limits. But this drilling
data often isn’t saved. It is simply discard-
ed, foreclosing any opportunity to look for
patterns that could enable earlier problem
detection and point the way toward better
practices. So the starting point must be to
identify gaps in data capture and plug as
many of them as possible.
The next challenge is to minimize,
or at least significantly reduce, data
fragmentation. Typically, exploration, de-
velopment and production departments
have maintained separate data reposito-
ries, each with its own data types. These
repositories may be further fragmented
geographically, for example, if companies
organize and store data on the basis of
MAR CH / A P R I L 2014 | 35
Drilling data often isn’t saved. It is simply discarded, foreclosing any opportunity to look for patterns that could
enable earlier problem detection and point the way toward better practices.
WWW. I NF OR MS . OR G 36 | A NA LY T I CS - MAGA Z I NE . OR G
OI LFI ELD ANALYTI CS
national boundaries or oil-producing regions.
Such fragmentation refects the way IT has
typically been adopted: in piecemeal fashion, by
local or departmental managers, to address a
specifc problem. Today, however, the E&P indus-
try (like many others) has become so data-driven
that the limitations of piecemeal adoption are all
too evident. For one thing, the resulting data frag-
mentation makes data management less effcient
and more expensive than necessary. In addition,
it prevents the company from analyzing its data
in a comprehensive, unifed and forward-looking
manner. This, in turn, poses two main problems.
One is that fragmentation reduces the value of
each type of data – exploration, development and
production – individually. The other is that frag-
mentation makes it impossible to analyze the
three types holistically, denying the company the
insights that can come only from an integrated
approach.
The best path forward can vary considerably
from one enterprise to another, because piece-
meal adoption means that no two companies
start from the same place. One E&P company
might be getting suboptimal results from its seis-
mic tests and exploration drilling, but not realize
it because it lacks the tools to analyze historical
data and identify the factors degrading its results
(to say nothing of predictive tools). Another com-
pany might be drilling too many or too few devel-
opment wells, or not siting them correctly – again,
due to lack of analytical insight. Yet another com-
pany might be deferring too much or too little pro-
duction because it lacks the predictive analytics
The E&P industry has
become so data-driven
that the limitations of
piecemeal adoption are all
too evident. The resulting
data fragmentation
makes data management
less efficient and more
expensive than necessary.
In addition, it prevents
the company from
analyzing its data in a
comprehensive, unified and
forward-looking manner.

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THE INTERNATIONAL JOURNAL OF APPLIED FORECASTING
5 FORECASTING REVENUE
in Professional Service Companies
14 FORECAST VALUE ADDED:
A Reality Check on Forecasting Practices
19 S&OP AND FINANCIAL PLANNING
26 CPFR: Collaboration Beyond S&OP
39 Progress in FORECASTING RARE EVENTS
50 Review of "GLOBAL TRENDS 2030:
ALTERNATIVE WORLDS"
THE INTERNATIONAL JOURNAL OF APPLIED FORECASTING
Fall 2012 Issue 27
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40 New Texts for Forecasting Modelers
THE INTERNATIONAL JOURNAL OF APPLIED FORECASTING
Summer 2012 Issue 26
5 Setting Internal Benchmarks Based on a Product’s FORECASTABILITY DNA
18 Regrouping to Improve Seasonal Product Forecasting
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WWW. I NF OR MS . OR G 38 | A NA LY T I CS - MAGA Z I NE . OR G
OI LFI ELD ANALYTI CS
capabilities needed to make better deferment de-
cisions. Or it might not have adequate insight into
why production in a given well, feld or region is
declining, and how future production might be op-
timized using various intervention methods.
In addition to different starting points, E&P
companies have unique assets. As a result, the
key problem for one company may lie in its seis-
mic exploration methods, while for another the
main challenge might be production manage-
ment. Such differences dictate different strate-
gies with regard to data integration and analytics,
leading individual E&P companies toward differ-
ent vendors and applications.
Furthermore, E&P companies’ exploration,
development and production operations histori-
cally have operated as separate departments.
They support each other, of course, and informa-
tion is routinely shared among departments. But
integrated approaches have been diffcult to im-
possible because the necessary information has
typically been fragmented, housed in separate
databases that are isolated from one another.
This isolation takes two basic forms. Similar
types of data may be isolated geographically. For
example, production data may be stored in re-
gional databases that cannot talk to each other.
Production data also may be stored in different
formats, and/or with different technologies, that
prevent holistic analysis.
Still, regardless of different starting points
and unique challenges, E&P companies share
common goals – reducing the drag on business
performance that stems from the lack of unifed
Regardless of different
starting points and
unique challenges, E&P
companies share common
goals – reducing the drag
on business performance
that stems from the lack of
unified data and analytics
capabilities.
SAS and Hadoop take on
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WWW. I NF OR MS . OR G 40 | A NA LY T I CS - MAGA Z I NE . OR G
OI LFI ELD ANALYTI CS
data and analytics capabilities. The solution
starts with creating a platform for analytics by
consolidating and integrating data by type (ex-
ploration, development or production) over as
broad a geographic area or portion of the com-
pany as possible.
Next is to deploy analytics tools that can op-
timize the value of historical data in each of the
three main categories, while laying groundwork
for real-time and predictive tools that can holisti-
cally analyze the three main categories of data.
Ovum primary research shows that E&P and
oilfeld services companies are taking up this chal-
lenge. In its 2013 ICT Enterprise Insights survey,
Ovum interviewed more than 400 IT decision-
makers in E&P (among more than 6,500 across
17 industries). Asked about their priorities in infor-
mation management, the respondents said data
warehousing and data management/integration
technologies are among their top priorities for in-
vestment this year. Both technologies are impor-
tant steps in laying the groundwork for broader
use of analytics tools.
Important though it is, data integration should
not be undertaken all at once. Depending on the
degree of fragmentation of its existing data, an
E&P company may face a complex challenge ex-
tracting all of this data, transforming it into a con-
sistent format, and loading it into a new, unifed
database. Most enterprises will want to rely on an
outside company – the analytics software vendor,
a systems integrator or both – to do that, rather
than build or hire for such skills internally.
Pragmatism dictates tackling the problem in
Respondents said data
warehousing and data
management/integration
technologies are among
their top priorities for
investment this year. Both
technologies are important
steps in laying the
groundwork for broader
use of analytics tools.
MAR CH / A P R I L 2014 | 41 A NA L Y T I C S
smaller bites – for example, focusing
on just one data type across a small to
medium-sized geographic area. The ap-
proach will depend on what challenges
the company is trying to address.
If offshore seismic data is a key prob-
lem, the company might focus frst on
analytics that allow it to monitor the data
coming back from the seismic vessel
in real time. That will allow it to identify
poor-quality data immediately and have
the operator correct it under its current
contract, rather than waiting weeks to
discover the problem and having to en-
gage the contractor again under a new
contract.
If the company’s main challenges
involve development drilling, analytics
tools can help determine the optimal
number and spacing of wells to optimize
yield and production costs. Similarly, pre-
dictive and prescriptive analytics tools
can help an E&P company maximize the
value of a well feld’s lifetime production.
Such tools also can help to minimize the
cost of replacing submersible pumps
March 30-April 1, 2014
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WWW. I NF OR MS . OR G 42 | A NA LY T I CS - MAGA Z I NE . OR G
OI LFI ELD ANALYTI CS
(which fail with some regularity, bringing produc-
tion to a halt), or to choose the best procedures
to “work over” a well whose production has fallen
due to causes such as sand accumulation or cas-
ing deterioration.
Still, while analytics software can deliver sig-
nifcant value in each of these cases, it is impor-
tant to keep in mind that these examples address
the three domains – exploration, development and
production – separately. Companies that unify
their data to enable holistic analysis of all three
domains will understand each of the three much
more deeply than they do today. In addition, they
will likely fnd hidden interrelationships that can
only be guessed at today. Ultimately, these new
and deeper understandings will improve explora-
tion success, bring new effciencies to the devel-
opment phase, and increase the lifetime return on
their assets and investments.
Warren Wilson ([email protected]) leads Ovum’s
energy team, focusing primarily on IT for upstream oil & gas.
His research focuses on the ways in which leading-edge IT
such as analytics, information management and mobile/wireless
technologies can enable better practices and results throughout
the upstream industry. Wilson brings a unique combination of
skills to his oil & gas research. He holds a degree in geology, has
direct experience working in the oilfeld, and spent several years
as a journalist covering the exploration and production industry.
An IT analyst for the past 15 years, his research has focused
on mobile business applications and enterprise applications
including ERP, CRM, supply chain management and analytics.
Wilson joined Ovum in 2006 when Ovum acquired his former
employer, Summit Strategies, where he had worked for the
previous eight years. Before becoming an IT analyst, he was a
reporter and editor for U.S. newspapers including the Seattle
Post-Intelligencer and The Miami Herald. He majored in geology
at Carleton College in Northfeld, Minn., and later worked in the
oilfeld as a roughneck and in well logging.
Companies that unify their
data to enable holistic
analysis of all three
domains will understand
each of the three much
more deeply than they do
today. In addition, they
will likely find hidden
interrelationships that can
only be guessed at today.
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WWW. I NF OR MS . OR G 44 | A NA LY T I CS - MAGA Z I NE . OR G
he United States is re-
emerging as an energy su-
perpower. According to the
International Energy Agen-
cy, by 2016 the U.S. will surpass Saudi
Arabia and become the world’s largest oil
producer.
The domestic energy industry’s re-
cent rise is the result of lower demand
through energy effciency and the rise in
production of unconventional oil and gas
discovered in underground shale forma-
tions. Horizontal drilling and hydrau-
lic fracturing have made it possible to
economically produce oil and gas from
tight rocks. In October 2013, U.S. oil
production reached its highest month-
ly total in the last 25 years. In Texas,
with crude oil production of more than
2.7 million barrels per day, two shale oil
felds alone – Eagle Ford and Permian
Basin – are on target to produce nearly
BY ATANU BASU
T
How prescriptive
analytics can
reshape fracking in
oil and gas fields
HYBRI D DATA
WWW. I NF OR MS . OR G 44 | A NA LY T I CS - MAGA Z I NE . OR G
A NA L Y T I C S MAR CH / A P R I L 2014 | 45
2 million barrels of oil equivalent a day
in 2013.
However, while abundant, shale oil
and gas can be diffcult to locate and
extract. Horizontal drilling and hydraulic
fracturing processes are expensive and,
some say, potentially harmful to the en-
vironment. Another relatively unknown
fact – especially to industry outsiders –
is that fracking is quite ineffcient today:
80 percent of the production comes
from 20 percent of the fracking stages.
Today, horizontal drilling and hydraulic
fracturing recover about 20 percent,
probably less, of the oil in the shale
rocks. According to PacWest, drillers
will spend $31 billion in 2013 on subop-
timal frack stages across 26,100 wells
in the United States. In response, some
of the largest oil and gas companies
are using big data analytics technolo-
gies to improve their exploration and
production.
Big data analytics includes three cat-
egories: descriptive analytics, which tells
you what happened and why; predictive
analytics, which tells you what will hap-
pen; and prescriptive analytics, which
tells you what will happen, when, why
and how to improve this predicted future.
Marketers, operations experts, fnan-
cial offcers and other business leaders
Horizontal drilling and
hydraulic fracturing
have made it possible
to economically
produce oil and gas
from tight rocks.
WWW. I NF OR MS . OR G 46 | A NA LY T I CS - MAGA Z I NE . OR G
RESHAPE FRACKI NG
have already used prescriptive analytics to
improve customer experience, reduce churn,
increase up-selling and cross-selling revenue,
streamline logistics and enhance other impor-
tant applications. For the oil and gas industry,
prescriptive analytics can help locate felds
with the richest concentrations of oil and gas,
make pipelines safer, and improve the fracking
process for greater output and fewer threats to
the environment.
About 80 percent of the world’s data today
is unstructured – videos, images, sounds and
texts. Until recently, most big data analytics
technologies looked only at numbers. The oil
and gas industry looked at images and num-
bers, but in separate silos. However, the abil-
ity to analyze hybrid data – a combination
unstructured and structured data – provides
a much clearer and more complete picture of
the current and future problems and opportuni-
ties, along with the best actions to achieve the
desired outcomes. For example, to improve
hydraulic fracturing performance, the following
datasets must be analyzed together:
• images from well logs, mud logs, seismic
reports,
• videos from down-hole cameras of fuid fow,
• sounds from fracking recorded by fber optic
sensors,
• texts from drillers’ and frack pumpers’ notes,
and
• numbers from production and artifcial lift
data.
Taking hybrid data into account is critical
For the oil and gas industry,
prescriptive analytics can
help locate fields with the
richest concentrations of
oil and gas, make pipelines
safer, and improve the
fracking process for greater
output and fewer threats to
the environment.
MAR CH / A P R I L 2014 | 47 A NA L Y T I C S
because of the multi-billion dollar in-
vestment and drilling decisions that are
being made by the energy companies
regarding where to drill, where to frack
and how to frack. It calls for combining
disparate computational and scientifc
disciplines to be able to interpret differ-
ent types of data together. For example,
to algorithmically interpret images (such
as well logs), machine learning needs to
be combined with pattern recognition,
computer vision and image processing.
Mixing these different disciplines pro-
vides more holistic recommendations re-
garding where and how to drill and frack,
while reducing the chances of problems
that could emerge along the way.
For example, by developing detailed
analytical signatures – using data from
production, subsurface, completion and
other sources – one can better predict
performing and non-performing wells in
a feld. This process is supported by the
prescriptive analytics technology’s abil-
ity to automatically digitize and interpret
well logs to create depositional maps
of the subsurface. With a better idea of
where to drill, companies save invalu-
able resources by skipping wells that
shouldn’t be drilled in the frst place. At
the same time, they minimize damage
to that particular landscape.
Prescriptive analytics can be used in
other areas of oil and gas production.
In both traditional and unconventional
wells, by using data from pumps, pro-
duction, completion and subsurface
characteristics, one can predict failures
of electric submersible pumps and pre-
scribe actions to mitigate production
loss. Apache Corp., for example, is us-
ing analytics to predict failures in pumps
that pull oil out from subsurface and
preempt the associated production loss
from these pump failures.
Another potential application of pre-
scriptive analytics is that it can possibly
predict corrosion development or cracks
in pipelines and prescribe preventive
and preemptive actions by analyzing
video data from cameras along with
other data from robotic devices called
“smart pigs” inside these pipelines.
Smarter decisions equal fewer re-
sources, lower environmental impact
and greater yields. Successful compa-
nies will be the ones that know how to
prioritize resources to extract, produce
and transport oil and gas in the most ef-
fcient and safest manner. Look for big
data and prescriptive analytics to play a
much bigger role in this space over the
coming years.
Atanu Basu is CEO of AYATA, a software
company headquartered in Austin, Texas. AYATA’s
prescriptive analytics software focuses on
improving oil and gas exploration and production.
Basu is a member of INFORMS. A version of this
article appeared in DataInformed.
WWW. I NF OR MS . OR G 48 | A NA LY T I CS - MAGA Z I NE . OR G
ecause that’s where the
money is.”
— Willie Sutton
(on why he robbed banks)
CONSIDER THE OBVIOUS
In pondering an upcoming change
of career (or at least employer), a good
start is to follow Sutton’s Law, para-
phrased in the apocryphal quote above
and taught to medical students learn-
ing about diagnosis. In a more general
form, it states something like, “frst con-
sider the obvious.” With regard to where
jobs in analytics can be found, the obvi-
ous might include considering a mix of
Where the
analytics jobs are
BY SCOTT NESTLER, CAP
B
CAREER ADVANCEMENT
WWW. I NF OR MS . OR G 48 | A NA LY T I CS - MAGA Z I NE . OR G
MAR CH / A P R I L 2014 | 49
A NA L Y T I C S
large, metropolitan cities and smaller,
but technology-centric areas. While
this proves to be a good approach, ad-
ditional research yields results that are
interesting, informative and potentially
useful to anyone looking for a job in the
analytics feld.
THE REALM OF POSSIBILITIES
One line of investigation is to use
LinkedIn to see where jobs related to
analytics can be found. While there
are many other job boards available
(e.g., general sites like Monster, Sim-
plyHired and Indeed, as well as more
focused options like the INFORMS Ca-
reer Center), this study used LinkedIn
as a proxy for the universe of analyt-
ics job postings. Using the job search
capability, it is possible to do a key-
word search for all currently listed po-
sitions within a distance of a zip code.
While poking around using the Sutton’s
Law approach might be a useful start,
a more systematic approach seems
appropriate.
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Questions?
Analytics Connect
March 30–April 1, 2014 | The Westin Boston Waterfront | Boston, Massachusetts
Applying Science to the Art of Business
USINESS ANALYTICS &
PERATIONS RESEARCH
INFORMS CONFERENCE ON
CAREER
CENTER
WWW. I NF OR MS . OR G 50 | A NA LY T I CS - MAGA Z I NE . OR G
WHERE THE JOBS ARE
The idea of a metropolitan area seemed to
be a good place to start, but what does that in-
clude (or leave out)? The U.S. government’s Of-
fce of Management and Budget (OMB) defnes
a number of statistical areas that might provide
a useful framework. There are 388 metropolitan
statistical areas (MSAs) with population greater
than 50,000, and 541 micropolitan statistical ar-
eas (mSAs), with population between 10,000
and 50,000. There is also a grouping of adjacent
MSAs and mSAs based on social and economic
ties and incorporate commuting patterns; these
169 combined statistical areas (CSAs) seemed
a good place to start, but initial exploration re-
vealed that this list does not include MSAs that
have only one urban core and therefore omits
places like San Diego, Calif., Phoenix, Ariz.,
Tampa, Fla., and San Antonio, Texas. As these
locations may be of interest to jobseekers in the
analytics feld, another approach is warranted.
Further searching revealed a list of 574 (unof-
fcial but commonly used) groupings called pri-
mary statistical areas (PSAs), which include all
169 CSAs, 122 (of the 388) MSAs, and 283 (of
the 541 mSAs). As this assemblage seems to
have been developed for studies like this one, the
569 PSAs in the United States (but not Puerto
Rico) were considered in this analysis.
LET’S COLLECT SOME DATA
While LinkedIn provides a straightforward
search capability (for people, groups and jobs),
there is also an advanced search capability.
Exploring the advanced query indicates that a
Using the job search
capability, it is possible
to do a keyword search
for all currently listed
positions within a
distance of a zip code.
While poking around
using the Sutton’s Law
approach might be a
useful start, a more
systematic approach
seems appropriate.
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DATA
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June 22-24
2014
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Chief Analytics Officer
Teradata Corporation
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the gap between decision makers and analytics professionals like no one else.
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A.J. Mobley, Kaiser Permanente | Govind Nagubandi, JP Morgan Chase |
Alan Papir, Analytics Media Group | Lee Paries, Aster Data | Ion Stoica, UC Berkeley |
Marina Thottan, Bell Labs, Alcatel-Lucent | Simon Zhang, LinkedIn |
Conference Co-Chairs: Margery H. Connor Diego Klabjan
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meetings. informs.org/bigdata2014
WWW. I NF OR MS . OR G 52 | A NA LY T I CS - MAGA Z I NE . OR G
WHERE THE JOBS ARE
customized query can be produced by
editing the uniform resource locator
(URL) entered into a Web browser. The
simplest representation of a query for
searching within 25 miles (a reasonable
commuting distance that incorporates
a city and surrounding suburbs without
overlapping nearby areas) of Washing-
ton, D.C., (zip code 20005) looks like
this:
http://www.linkedin.com/vsearch/j?k
eywords=analytics&postalCode=20005
&countryCode=us&distance=25
While the standard user interface
only allows limited selections (e.g.,
distances of five, 10, 25, 50 and 100
miles), it is possible to customize the
search (e.g., a distance of 12 miles)
if desired. Using a few dozen lines of
Python code, this can be automated
to repeat an identical query (modifying
only the zip code) for all 569 PSAs. The
script took about an hour to run, due
to the addition of some random delays
between queries to emulate human be-
havior and possible LinkedIn account
suspension. While the actual number
of job listings changes from one day to
the next, and even during the course
of a day, the results included here are
likely representative of the relative job
markets applicable to those looking for
jobs in analytics.
BIGGER IS GENERALLY BETTER
On Feb. 1, 2014, there were a total
of 11,584 jobs containing the keyword
“analytics” on LinkedIn. A total of 339
of the 569 PSAs had no analytics job
listings on this day. Quants looking for
work can probably skip Fresno, Calif.;
Vernon, Texas; and a few hundred other
locations. As might be expected, larger
cities in general have more analytics
jobs than smaller towns. Table 1 shows
the 10 largest cities, their population (in
millions) and the numbers of analytics
job listings. The correlation between
population and jobs is quite high (0.85),
but even a cursory look at Table 1
shows that some large cities (e.g., Chi-
cago and Miami) might not be “pulling
their weight” in terms of providing jobs
in analytics.
Metropolitan
Area
Populaton
(M)
Jobs
New York 23.3 2122
Los Angeles 18.2 512
Chicago 9.9 166
Washington, D.C. 9.3 660
San Francisco 8.4 1330
Boston 8 854
Philadelphia 7.1 253
Dallas 7.1 443
Miami 6.4 93
Houston 6.4 202
Table 1: Analytics jobs in the 10 largest U.S.
metropolitan areas.
November 9-12, 2014
Hilton San Francisco & Parc 55 Wyndham
San Francisco, California
Submission Deadline: May 15, 2014
Submit Early, Capacity Limited!
Organizing Committee
General Chair
Candace A. Yano
University of California-Berkeley
Program Chair
Philip Kaminsky
University of California-Berkeley
Plenary/Keynotes Chair
Shmuel S. Oren
University of California-Berkeley
Invited Sessions Co-Chairs
Hyun-Soo Ahn
Damien Beil
University of Michigan
Sponsored Sessions Co-Chairs
Alper Atamturk
Zuo-Jun Max Shen
University of California-Berkeley
Contributed Sessions Co-Chairs
Rachel Chen
University of California-Davis
Steven Nahmias
Santa Clara University
Practice Program Co-Chairs
Vijay Mehrotra
San Francisco State University
Warren Lieberman
Veritec Solutions
Thomas Dag Olavson
Google, Inc.
Interactive Sessions Co-Chairs
Hari Balasubramanian
Ana Muriel
Univ. of Massachusetts-Amherst
Tutorials Co-Chairs
Alexandra M. Newman
Colorado School of Mines
Janny Leung
Chinese University of Hong Kong
Arrangements Co-Chairs
Julia Miyaoka
Theresa M. Roeder
San Francisco State University
meetings2.informs.org/sanfrancisco2014
WHERE THE JOBS ARE
WWW. I NF OR MS . OR G 54 | A NA LY T I CS - MAGA Z I NE . OR G
BIGGER ISN’T ALWAYS BETTER
Table 2 shows the 20 PSAs with the
greatest number of jobs. Not surpris-
ingly, nine of the 10 largest metropolitan
areas (indicated by grayed-out text) are
also included in this list. The “Top 10” ac-
counted for 7,274 (or 73 percent of all)
jobs, while the “Top 20” covered 6,264
(or 80 percent of all) jobs. Not surpris-
ingly, the distribution of analytics jobs is
skewed toward a small number of loca-
tions. Figure 1 shows a Pareto diagram
of the frst 50 (sorted by number of jobs)
PSAs. Note that these locations account
for 94 percent of all listed positions.
We compute a “persons per job” met-
ric in an attempt to determine which ar-
eas are “punching above their weight,”
and identify smaller areas that have an
unusually high (relative to their total
population) number of analytics jobs.
This is computed by dividing the total
population by the number of analytics
job listings within the CSA. As shown in
Table 3, Platteville, Wis., leads in this
category with roughly one analytics job
opening for every 800 people; keep
in mind that this includes people of all
ages, including children too young to
work and retirees, not just those eligible
for work. This compares to an overall
average (for those areas with at least
one advertised position) of one opening
per 122,000 people. So, some areas are
a more “target rich environment” than
others for job hunters. While looking at
some of these smaller areas that pro-
vide more “bang for the buck” (in terms
of the number of analytics jobs rela-
tive to the population) may seem wise,
Rank Metropolitan Area Jobs

Rank Metropolitan Area Jobs
1 New York, NY 2122 11 Raleigh, NC 220
2 San Francisco, CA 1330 12 San Diego, CA 214
3 Boston, MA 854 13 Houston, TX 202
4 Washington, DC 660 14 Minneapolis, MN 187
5 Seatle, WA 651 15 Columbus, OH 182
6 Los Angeles, CA 512 16 Chicago, IL 166
7 Dallas, TX 443 17 Richmond, VA 164
8 Atlanta, GA 394 18 Denver, CO 145
9 Austn, TX 308 19 Detroit, MI 130
10 Philadelphia, PA 253 20 Phoenix, AZ 127
Table 2: 20 metropolitan areas with the most analytics jobs.
A NA L Y T I C S
MAR CH / A P R I L 2014 | 55
Figure 1:
Pareto diagram
of jobs in
50 PSAs.
Primary Statstcal Area Jobs Populaton Per Job
Plateville, WI 65 51087 786
Dubuque, IA 65 95097 1463
Platsburgh, NY 19 81654 4298
Austn, TX 308 1834303 5956
San Francisco, CA 1330 8370967 6294
Winona, MN 8 51629 6454
Seatle, WA 651 4399332 6758
Richmond, IN 13 92375 7106
Richmond, VA 164 1231980 7512
Raleigh, NC 220 1998808 9085
Bennington, VT 4 36697 9174
Huntngdon, PA 5 45943 9189
Boston, MA 854 7991371 9358
Burlington, VT 21 213701 10176
New York, NY 2122 23362099 11009
Columbus, OH 182 2348495 12904
Washington, DC 660 9331587 14139
San Diego, CA 214 3177063 14846
Atlanta, GA 394 6092295 15463
Lewistown, PA 3 46773 15591
Table 3:
10 Micropolitan
(and metropolitan) areas
with greatest “jobs per
capita.”
A “persons per job”
metric in an attempt
to determine which
areas are “punching
above their weight,”
and identify smaller
areas that have
an unusually high
(relative to their total
population) number of
analytics jobs.
WWW. I NF OR MS . OR G 56 | A NA LY T I CS - MAGA Z I NE . OR G
WHERE THE JOBS ARE
some caution may be in order. Taking
one of the three available positions in
Lewistown, Pa., might be appealing to
someone who prefers a more rural set-
ting, but if the situation didn’t work out,
there might not be any other opportuni-
ties in the area, potentially necessitat-
ing an unexpected relocation.
A PICTURE IS WORTH 11,584 JOBS
Figure 2 shows an info graphic view
of the 230 areas with at least one analyt-
ics job. The circles are scaled to repre-
sent the number of jobs in each location;
however, the largest circle (New York) is
only 200 times larger than the smallest,
not 2,000 times larger if it were true to
scale. As you can see, they are spread
out across the U.S., with opportunities
in many geographic regions and climatic
zones. All in all, it appears to be a good
job market for those with skills in analyt-
ics. However, the majority of the available
positions appear to exist in a relatively
small number of metropolitan and micro-
politan areas. After all, 90 percent of the
listed jobs are in only 35 locations. Happy
job hunting to those who are looking!
Scott Nestler, Ph.D., CAP®, PStat®, is an
Army operations research analyst, a member of
INFORMS and chair of the INFORMS Analytics
Certifcation Board (ACB).
Disclaimer: The views expressed in this article
are those of the author and do not necessarily
refect the offcial policy or position of the Army, the
Department of Defense or the U.S. Government.
Figure 2: Info graphic view of analytics jobs. Circles are scaled to represent the
number of jobs in each location.
to join online visit
http://join.informs.org
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▪ A FREE Community Membership
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WWW. I NF OR MS . OR G 58 | A NA LY T I CS - MAGA Z I NE . OR G
n today’s world, where glo-
balization is a fact of busi-
ness life, where competition
is fercely intense and where
concerns such as energy security and
climate change are global in scope, a
science-based approach to decision-mak-
ing and problem-solving is essential.
CORPORATE PROFI LE
I
General Motors
Using operations research and other advanced
analytics to meet auto industry challenges and
provide value to customers and company.
BY (l-r) JONATHAN H. OWEN
DAVID J. VANDERVEEN
AND LERINDA L. FROST
At General Motors, operations re-
search provides that framework, particu-
larly for complex issues and systems that
involve multiple objectives, many alter-
natives, trade-offs between competing
effects, large amounts of data and situa-
tions involving uncertainty or risk. In truth,
for an entity the size of General Motors,
WWW. I NF OR MS . OR G 58 | A NA LY T I CS - MAGA Z I NE . OR G
2014 Chevrolet Corvette Stingray
MAR CH / A P R I L 2014 | 59
A NA L Y T I C S
these are the only kind of challenges the
company faces because GM is huge and
no issue is simple!
With products that range from elec-
tric and mini-cars to heavy-duty full-size
trucks, monocabs and convertibles, GM
offers a comprehensive range of vehi-
cles in more than 120 countries around
the world. Along with its strategic part-
ners, GM sells and services vehicles un-
der the Chevrolet, Buick, GMC, Cadillac,
Opel, Vauxhall, Holden, Baojun, Wuling
and Jiefang brand names. GM also has
signifcant equity stakes in major joint
ventures in Asia, including SAIC-GM, SA-
IC-GM-Wuling, FAW-GM and GM Korea.
GM has 212,000 employees located
in nearly 400 facilities across six conti-
nents. Its employees speak more than
50 languages and touch 23 time zones.
The work they do demonstrates the depth
and breadth of the auto business – from
developing new vehicles and product
technologies to designing and engineer-
ing state-of-the-art plants, organizing
and managing the company’s vast global
Alliance for Paired Donation, for "Kidney Exchange"

U.S. Centers for Disease Control and Prevention, for "Using Integrated Analytical
Models to Support Global Health Policies to Manage Vaccine Preventable Diseases:
Polio Eradication and Beyond"

The Energy Authority, for "Hydroelectric Generation and Water Routing Optimizer"

Grady Health System, for "Transforming Emergency Department Workflow and Patient Care"

NBN Company, for "Fiber Optic Network Optimization at NBN Co."

Twitter, for "The ‘Who to Follow’ System at Twitter: Strategy, Algorithms, and Revenue Impact"

http://meetings.informs.org/analytics2014
Be there at the Edelman Gala, March 31 when the 2014 winner is announced!
WWW. I NF OR MS . OR G 6 0 | A NA LY T I CS - MAGA Z I NE . OR G
CORPORATE PROFI LE
supply chain and logistics systems, build-
ing new markets and creating new busi-
ness opportunities.
The work is multifaceted, but wheth-
er in Detroit, Frankfurt, Sao Paulo or
Shanghai, the goal is straightforward:
offer products and services that estab-
lish and maintain a deep connection with
customers around the world while simul-
taneously generating revenue and proft
for the company.
Considering the complexity of the
challenges in the auto business and
the speed at which change is occurring
in every arena – technology, business,
materials and resources, governmental
policies and regulations – it is critical to
employ a scientifc approach in think-
ing about and attempting to understand
problems and implement viable solu-
tions. Today, no area of GM is untouched
by analytical methods.
THE EARLY YEARS
Even before the industry entered the
current period of globalization and pro-
found technological change, operations
research was valued within GM. As early
as the 1960s and 1970s, GM employed
analytical techniques for transportation
studies and traffc fow analysis. In the
1980s, GM developed analytical princi-
ples and used mathematical optimization
methods to improve assembly line job
sequencing. In the 1990s, it patterned
warranty cost reduction analyses after
Centers for Disease Control epidemiol-
ogy studies.
GM Chairman and CEO
Dan Akerson (fourth row,
third from left), GM CTO
and Global R&D Vice
President Jon Lauckner
(fourth row, fourth from left)
and Global R&D Executive
Director Gary Smyth
(top) with the GM R&D
Operations Research team.
A NA L Y T I C S
MAR CH / A P R I L 2014 | 61
In 2005, GM won the Franz Edel-
man Award from INFORMS for its work
on production throughput analysis and
optimization. Even when overall industry
production capacity is above demand, it
is usually the case that demand for cer-
tain “hot” vehicles exceeds planned plant
capacities. In such cases, an increase in
production capacity will generate larger
profts via more sales revenue and/or
overtime cost avoidance.
GM’s operations research team
analyzed production throughput using
math models and simulation, identifed
cost drivers and bottlenecks, and de-
veloped a throughput improvement pro-
cess to increase productivity and reduce
costs. The resulting software has been
enhanced over a 20-year period to ex-
tend GM’s capabilities, enabling it to ac-
commodate product and manufacturing
fexibility, variable control policies and
more complex routing. The software is
used globally in GM plants, as well as
to design new production systems and
processes.
THE DANIEL H. WAGNER PRIZE
Excellence in Operations Researc Practice
Apply to win this prestigious practice prize that rewards professionals
who devise innovative analytical methods, utilize those methods is a
verifiably successful O.R./analytics project, and describe their work in
a clear, well-writen paper.

Two-page abstract is due by May 1, 2014.

This top INFORMS practice prize spans all O.R. and analytics disciplines
and application fields. Any work presented in an INFORMS section or
society practice-oriented competition is eligible as long as the work did
not result in a published paper.

Daniel H. Wagner
The Wagner Prize competition is high-profile, with its own track at the INFORMS Annual Meeting.
Presentations are widely distributed via streaming video. Finalist papers are published as a special issue
in INFORMS respected practice journal Interfaces.
The 2014 competition will be held at the INFORMS Annual Meeting, November 9-12, in San Francisco,
California. First-place prize of $1,000 will be awarded at the Edelman Gala, during the April 2015
Conference on Business Analytics and O.R. in Huntington Beach, California.
pply for 2014
www.informs.org/wagnerprize
WWW. I NF OR MS . OR G 62 | A NA LY T I CS - MAGA Z I NE . OR G
CORPORATE PROFI LE
This long-term effort is just one
example that demonstrates how GM
has applied operations research (O.R.)
methods to change the way it lever-
ages O.R. and advanced analytics on
a continuing basis. The importance
of activities like this in a company the
size of GM cannot be fully measured.
For plant throughput alone, the savings
are estimated at more than $2 billion
over the past two decades. But just as
important as the economic benefts is
the mindset – the scientifc approach
to problem-solving, decision-making,
scheming the business, and identifying
new opportunities.
O.R. AT GM TODAY
Given the success of the work de-
scribed above, the R&D operations re-
search team broadened its mission
about fve years ago and today provides
a research capability within the company
focused on tackling long-term strategic
challenges. With the wide-ranging scope
of potential assignments, the O.R. team
is composed of Ph.D. and master’s-level
technical experts, along with subject-
matter experts with hands-on and exec-
utive leadership experience in key areas
of the business, such as manufactur-
ing, supply chain, engineering, quality,
planning, marketing, and research and
development.
Projects are aligned with top company
priorities, which are based on a combina-
tion of business performance drivers and
senior leadership input. The work may
start with targeted questions, e.g., what’s
the opportunity of (fll in the blank), or it
can focus on improving operational effec-
tiveness through process improvements
in areas such as manufacturing produc-
tivity, capital or supply chain manage-
ment, or dealer inventory management.
Many opportunities to improve revenue
management exist through the applica-
tion of tools and systems that help deci-
sion-makers optimize portfolio planning,
reduce complexity, target incentives, or
optimize content and packaging. In ad-
dition, given the large new data streams
coming from the intelligence available in
today’s vehicles, new emphasis is be-
ing put on improving vehicle effciency,
quality and diagnostics, as well as more
deeply understanding customers so GM
can provide differentiated value through
new automotive products and services.
The team’s implementation model com-
prises a mix of:
• analysis by internal consultants to
understand the issue,
• capability development, including
analytical principles, math models
and tools, and
• partnering with stakeholders and
decision-makers early to scope and
A NA L Y T I C S
MAR CH / A P R I L 2014 | 63
maximize the potential impact of
implemented solutions.
O.R. DRIVES TRANSFORMATION
The R&D operations research team
recently received two “teamGM Trans-
former” Awards for developing business
tools that use “big data” and analytics
to improve decision-making. This is an
internal award that rewards employees
who are leading change across the com-
pany by fnding signifcant and innovative
ways to drive GM business priorities.
One of the O.R. team’s Transformer
Awards recognized new analytic tools to
support GM’s Product Development ac-
tivity. These include a range of tools that
help guide engineering decisions to re-
duce complexity in the vehicle and pow-
ertrain, apply market research to vehicle
attribute balancing and optimize portfolio
planning in light of greenhouse gas per-
formance objectives. The other award
was for development of a new approach
INFORMS is the foremost association of O.R. and analytics experts. Our
members literally wrote the book on how analytics and the principles of
operations research are used to improve organizational decision making.
To find an
expert to help
you, log onto
INFORMS
Find An
Analytics
Consultant
Database
informs.org/Find-Analytics-Consultant/Search
WWW. I NF OR MS . OR G 64 | A NA LY T I CS - MAGA Z I NE . OR G
CORPORATE PROFI LE
to optimizing inbound logistics. This tool
is being expanded to support its use by
all vehicle programs early in the vehicle
development process.
With the exponential growth in data,
the ever-expanding digital connection to
customers and the introduction of excit-
ing new vehicles technologies, this is an
exciting time for operations research at
GM. With so many research-rich oppor-
tunities, the team is always mindful of the
characteristics that are key to successful-
ly applying O.R. methods and achieving
organizational excellence, including the
ability to:
• choose the right problem to address;
• see and convince others that a
complicated problem is important
and solvable;
• work as part of a team toward a
common and well-defned goal;
• have tenacity in chasing down details
and data, and then equal tenacity in
the implementation of a solution;
• get the model to the right level of detail
for the purpose at hand so it is not
too complex, nor too data intensive,
but suffciently detailed to capture the
salient characteristics and trade-offs;
• engage the key stakeholders in
the process of development and
implementation, in order to gain joint
ownership. Technology transfer is
something that takes place between
consenting adults; and
• deliver an O.R. solution to decision-
makers in a form or format that they
can understand and act upon.
O.R. practitioners who embody these
characteristics can have a profound im-
pact on their organization, help their com-
pany rise above the competition, and most
importantly provide increased value to
customers. As the world goes global – as
innovation strives to create more, faster,
better and at less cost; as new business
and technology paradigms emerge – end-
less opportunities abound to take advan-
tage of operations research and reap the
substantial good that can be realized from
its practice.
Jonathan H. Owen is director of Operations
Research at GM R&D. David J. VanderVeen,
now director of analytics in GM Global Product
Development, was formerly director of GM R&D
Operations Research. Lerinda L. Frost leads
executive communications and business support
at GM R&D. Owen and Vander Veen are members
of INFORMS.
Request a no-obligation INFORMS Member Benefits Packet
For more information, visit: http://www.informs.org/Membership
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WWW. I NF OR MS . OR G 66 | A NA LY T I CS - MAGA Z I NE . OR G
CONFERENCE PREVI EWS
Davenport, Kilmer to keynote March 30-April 1 event in Boston.
INFORMS Conference on
Business Analytics & O.R.
For the past several years the INFORMS Analyt-
ics Conference has been setting records for atten-
dance and presentation submissions. Now is your
chance to see what all the buzz is about. The 14th
Annual INFORMS Conference on Business Analyt-
ics and O.R., set for March 30-April 1 in Boston, will
feature presentations by more than 100 speakers
representing a broad range of industries and applica-
tion areas. INFORMS expects more than 900 attend-
ees this year, making it the largest analytics-focused
event in the world. With special events ranging from
career-building programs to world-class prize pre-
sentations, this conference is sure to offer valuable
content for practitioners and academics alike.
KEYNOTES: ANALYTICS PIONEER, DISNEY
SENIOR EXECUTIVE
Keynote speakers Tom Davenport and Kathy
Kilmer will headline this year’s conference. Daven-
port will speak on Monday, March 31, while Kilmer
will speak on Tuesday, April 1.
Davenport is the President’s Distinguished Pro-
fessor of Information Technology & Management at
Babson College, a Fellow of the MIT Center for Digi-
tal Business, co-founder of the International Institute
for Analytics and senior advisor to Deloitte Analytics.
Tom Davenport
Kathy Kilmer
A NA L Y T I C S MAR CH / A P R I L 2014 | 67
Davenport’s 2006 Harvard Business
Review article and best-selling 2007 book,
“Competing on Analytics” (co-authored
with Jeanne Harris), launched the revolu-
tion that has made analytics the hottest
business trend and “data scientist” the
sexiest job profle. His most recent book,
“Keeping Up with the Quants: Your Guide
to Understanding and Using Analytics,”
with Jinho Kim, has been called “the
quantitative literacy guide” for managers.
He has written or edited 16 other books
and more than 100 articles for Harvard
Business Review, Sloan Management
Review, the Financial Times and many
other publications.
Kilmer, director of sales planning and
analytics at Walt Disney Parks and Re-
sorts, oversees the teams responsible for
providing analytical and technology inte-
gration support across the sales teams
and contact centers. Prior to her current
role, she was director of industrial engi-
neering at Disney, overseeing more than
120 industrial engineers who serve as
internal business consultants. She is on
Purdue University’s Engineering Adviso-
ry board and INFORMS Analytics Certif-
cation Board, and she has been featured
on the “Today in America” show.
Kilmer is a recipient of IIE’s Fellows
Award, Purdue University’s Outstand-
ing IE Alumni Award and Distinguished
Engineering Alumni Award, and is in
Purdue University’s Engineering Coop-
erative Education Hall of Fame.
HAND-PICKED TOPICS AND SPEAKERS
The key to the conference’s ongo-
ing success is its program committee,
chaired this year by Freeman Marvin,
CAP, vice president and executive princi-
pal analyst at Innovative Decisions, Inc.
“Attendees will have the opportunity
to experience frsthand how the appli-
cation of analytics is changing the way
business is conducted across many in-
dustries and transforming everyday life
for billions of people around the world,”
Marvin says.
The 36 members of the committee
include analysts and managers from
companies such as Google, Target, IBM,
Chevron, Amtrak, SAS, Intel and UPS,
as well as leading universities and gov-
ernment agencies. The committee de-
velops the topic tracks, selects speakers
and organizes the presentations that
comprise the heart of the conference.
This year the speakers will present
talks that are organized into the following
focused tracks: The Analytics Revolution,
Healthcare Applications, Big Data, Market-
ing Analytics, Decision Analysis, Soft Skills
and Supply Chain Management. New this
year is a track organized by the INFORMS
Roundtable that will feature world-class
O.R. projects in established and mature
WWW. I NF OR MS . OR G 68 | A NA LY T I CS - MAGA Z I NE . OR G
CONFERENCE PREVI EWS
analytics companies. The program will be
rounded out by six tracks of contributed
talks, plus tracks on software solutions.
Sixty-eight poster presentations will
augment the oral presentations. These
visual presentations include case studies,
best practice examples and academic
research with a practitioner orientation.
AWARD-WINNING ANALYTICS
On the evening of Monday, March
31, the winner of the 2014 Franz Edel-
man Award will be announced at the
Awards Gala and Banquet. The Edelman
Award is the highest international award
for achievement in operations research.
This year’s fnalists are Twitter, The U.S.
Centers for Disease Control and Preven-
tion, The Energy Authority, Grady Health
System, Alliance for Paired Donation and
NBN Company.
Other high-impact work will be show-
cased throughout the meeting in talks by
fnalists and winners of the INFORMS
Prize, Daniel H. Wagner Prize, UPS
George D. Smith Prize, Innovative Appli-
cations in Analytics Award, Gary L. Lilien
Marketing Science Practice Prize and the
coveted Spreadsheet “Guru” Prize.
SPECIAL PROGRAMS
The conference also offers the follow-
ing special programs for graduate students
and young researchers, as well as learning
and networking events for all attendees:
• INFORMS Professional Colloquium:
Intensive career guidance for master’s
and Ph.D. students interested in
practice, held on March 30.
• Richard E. Rosenthal Early Career
Connection: Exclusive networking
program for junior faculty and young
industry practitioners.
• Soft Skills Workshop: Full-day workshop
on the “soft” skills needed to partner
with decision-makers and users.
• Technology Workshops: In-depth training
from leading solution providers. Free
to conference registrants.
MARCH 17 DEADLINE FOR EARLY
CONFERENCE RATES
Early rates of $965 for INFORMS
members and $1,200 for nonmembers
are available until March 17. Organiza-
tions can take advantage of the $827
team discount rate when they send three
or more attendees. All meals for two days
are included in the fees.
The conference venue, the new Wes-
tin Boston Waterfront, is located in Bos-
ton’s exciting waterfront area. This area
is abundant with great restaurants, shops
and public green space – plus easy ac-
cess to all the history and attractions that
make Boston such a fascinating city.
For additional conference informa-
tion, click here.
MAR CH / A P R I L 2014 | 69 A NA L Y T I C S

INFORMS Continuing Education
These two-day, in-person courses
presented by INFORMS provide real-
world value in skills, tools and methods
that can be implemented in your work.
Two courses will be offered just before
the INFORMS Analytics Conference,
both held on March 28-29 in Boston:

Essential Practice Skills for Analytics
Professionals: Participants will learn
practical tools for integrating their
analytical skills into real-world problem-
solving for businesses and other
organizations. The course provides
approaches that can be applied
immediately to a wide variety of settings,
whether within a participant’s own
organization or for an external client.

Data Exploration & Visualization:
Participants can expect to be
re-introduced to approaching data in
a powerful, yet playful manner.
They will see and experience how
exploration and visualization can be
used to answer existing questions,
thereby corroborating or invalidating
hunches and preconceptions.
For more information and to register,
click here.
Analytics Certifcation
Analytics certifcation is offered
by INFORMS to provide analytics
professionals with a means to distinguish
themselves and demonstrate to
employers, colleagues and the public
that they are competent analytics
professionals. Those attaining certifcation
will be able to list “CAP®” after their
names. INFORMS is pleased to offer the
CAP certifcation exam at this conference.
The exam is offered on Saturday, March
29, at the conference hotel. Attendees of
this conference qualify for a discounted,
bundled rate of Conference + Certifcation.
Also, don’t miss a special session
on March 31 providing information
and guidance on the benefts and
requirements of the CAP certifcation.
For more information, click here.
Analytics Maturity Model
How well does your organization use
analytics? What can you do to progress
from good to great? Attend this debut of the
new INFORMS analytics maturity model to
see how you can score your organization,
set target goals, and attain the enormous
advantage of analytics leadership. This
special session will be on Tuesday, April 1.
For your career & your company
The following special events will be held in conjunction with
the INFORMS Conference on Analytics & O.R. in Boston:
WWW. I NF OR MS . OR G 70 | A NA LY T I CS - MAGA Z I NE . OR G
How do you get from data discovery
to return on investment and real busi-
ness value? The INFORMS Big Data
Conference, set for June 22-24 in San
Jose, Calif., aims to help you discover
just that. This newly launched topical
conference will put the focus squarely
on the business of big data. A major
component of the conference will be
case studies of big data projects that il-
lustrate the complete journey from busi-
ness problem to analytics solution.
The conference committee is be-
ing spearheaded by Margery Connor,
CAP, senior operations researcher-Ad-
vanced Analytics at Chevron, and Diego
Klabjan, CAP, professor and director
of the Master of Science in Analytics
Program at Northwestern University.
Other conference committee mem-
bers are practitioners in the data are-
na, hailing from organizations such as
Humana, IBM, Booz Allen Hamilton,
SAIC, Intel, SAS, Alcatel-Lucent, and
UPS. The talks are being arranged into
tracks on Case Studies, Big Data 101,
and Emerging Trends. Other talks will
address topics such as:
• expediting the journey from business
problem to analytics solution;
• bridging the gap between decision-
makers, IT managers and analytics
professionals; and
• selecting and using the right big data
technologies.
Speakers are handpicked by the
committee from an impressive list
of “who’s who” in the feld of big data.
Anthony Goldbloom of Kaggle, Ion
Stoica of UC- Berkeley, Paul Kent of
SAS and Simon Zhang of LinkedIn
have all confrmed they will share their
best practices, success stories and les-
sons learned on real implementation of
big data analytics. The Big Data 101
track will offer tutorials on how to navi-
gate the big data ecosystem, how to se-
lect and use the right technologies, as
well as the challenges of building data
New event, set for June 22-24, to focus on the business of big data.
INFORMS Big Data
Conference
CONFERENCE PREVI EWS
WWW. I NF OR MS . OR G 70 | A NA LY T I CS - MAGA Z I NE . OR G
MAR CH / A P R I L 2014 | 71
A NA L Y T I C S
science teams. The speakers will also
address critical topics such as ethics
and privacy requirements.
Bill Franks will deliver the keynote
address, “Putting Big Data to Work.”
Franks is the chief analytics offcer at
Teradata Corporation. At Teradata and
throughout his career, Franks has fo-
cused on translating complex analytics
into terms that business users can un-
derstand and then helping organizations
implement the results effectively within
their processes. He is author of the
book, “Taming the Big Data Tidal Wave,”
and his work has spanned clients in a
variety of industries.
The INFORMS Big Data Conference
will be held at the San Jose Convention
Center. Rooms are being held for con-
ference attendees at the Marriott San
Jose, which is connected to the center.
For more information, click here.
Job Seeker Benefits
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CENTER
WWW. I NF OR MS . OR G 72 | A NA LY T I CS - MAGA Z I NE . OR G
FI VE-MI NUTE ANALYST
Lego Brickbox
Computing the
distribution of random
bricks tossed into a box
is difficult, because each
layer is dependent on the
one below it. Also, real
children do things that
real children do, such as
shake the box to make the
Legos settle.
At the holidays, many children received spe-
cial promotional “Brick Boxes” from Lego, which
may be taken back to the store and filled from the
brick repositories on the back wall in the store. Af-
ter Christmas, one child, “Norah,” saw one of her
friends, “Tyler,” meticulously build a shape to fit
exactly in his box. She asked me, “How much bet-
ter do you think that Tyler did by building an exact
shape than I did just by tossing what I wanted into
the box?” Like many things, this turned out to be a
much simpler question to ask than to answer! For
the remainder of the article, we will use the natural
unit of “Lego cubes” (LC), which are the size of a
1x1 Lego brick, as shown in Figure 1. So, a 2x4
brick has an area of 8LC, and so on.
First, an easy problem: The promotional brick-
box is 11x11x9 LC, and has a capacity of 1,089
squares. Packing square bricks into a square box
is very easy. This turns out to be the only easy
thing about this problem.
Computing the distribution of random bricks
tossed into a box is difficult, because each layer is
dependent on the one below it. Also, real children
do things that real children do, such as shake the
box to make the Legos settle. A few minutes with
a paper and pencil convinced me that this was
not the proper approach. So, I decided to simu-
late. Now, computer simulation has some of the
same difficulties – imagine playing a 3-D version
of Tetris – but fortunately, this is not the only way
WWW. I NF OR MS . OR G 72 | A NA LY T I CS - MAGA Z I NE . OR G
BY HARRISON
SCHRAMM, CAP
to simulate. It is possible, for small
problems, to simulate the system it-
self, which entails actual Legos, actual
children and an actual box. And here’s
where I stopped being in control and
the problem took over.
I went to the store and purchased a
(non-promotional) Lego brickbox. This
one is different than the promotional ver-
sion, because it is a large round cup, and
now things get really interesting. Because
while packing square Legos in a square
box is easy, packing square Legos in a
round cup is hard. My idea was to have
a set of Lego bricks, 1x1, 1x4, 2x2 and
2x4 of different colors for a group of chil-
dren to toss into the promotional (square)
box. We could then determine what an
“average” random fll of bricks might be.
I didn’t concern myself too much with
optimally packing the cup; I reasoned that
it was so much larger than the box (946
vs. 670 cubic centimeters) that I wouldn’t
need to worry too much about optimizing.
Naïvely, based solely on volume, one
might estimate that the large cup holds
1,678 bricks. This is a naïve measure be-
cause it simply divides the volume of the
box by the volume of the bricks.
I turned out to be dead wrong;
my brick purchase that haphazardly
filled the cup only filled the box (when
optimally stacked) a little more than
half way! This is because it’s difficult
to pack squares into a round container,
even more so when you don’t try.
Figure 2: Lego promotional brickbox
(left) and for-purchase brickbox cup
(right). Which holds more?
Figure 1: A Standard Lego brick, measur-
ing 8 mm square (1LC in this article’s mea-
surements), with a standard U.S. quarter
and Darth Vader for size comparison.
MAR CH / A P R I L 2014 | 73
WWW. I NF OR MS . OR G 74 | A NA LY T I CS - MAGA Z I NE . OR G
FI VE-MI NUTE ANALYST
When analyzing putting bricks in
round cups, there are two approaches
one may take: The frst is to consider
how many squares may be packed in
a circle in any arrangement, which is
known as Square Packing [1]. The other
approach is to ask how many integer lat-
tice points may be contained in a circle
of radius r. We usually think of building
Legos with a lattice because we want
to build layers on top of layers, so we
choose this method. It turns out that a
similar problem was studied by Gauss
and is known as Gauss’ Circle Prob-
lem [2]. The key idea is to realize that
the number of lattice points inside a
square is the number pairs of integers
( , ) m n
such that which is,
of course, the equation of a circle. In
calculus we just take the limit as the
area of the boxes tends to zero and
arrive at the well known . However,
requiring

makes the problem
much more complicated. Fortunately,
Hilbert et al. come to the rescue, and
the number of lattice points in the cir-
cle may be found by evaluating:

Where is the Gauss bracket or
Floor function, which means “round
down to the nearest integer.” If you
restrict yourself to integer values of
r, this formula will generate a named
sequence [3]. Here we have used non-
integer values for the radius of the
circle because the cup does not have
an exact radius in terms of our foun-
dational unit (bricks). This will still
tend to over-esti mate the number of
bricks that will fit in a cup because it
assumes that the lattice points have
zero dimensions, and we know that
our bricks have finite dimension; there-
fore, the calculations that follow are an
upper bound.
Figure 3: Proposed bottom and top lay-
ers next to the brickbox cup. These two
layers were the only ones built in the
course of this analysis, and are smaller
than the theoretical maximum layers that
would ft in from equation (1).
(1)
MAR CH / A P R I L 2014 | 75
A NA L Y T I C S
This equation may be readily im-
plemented in Excel. Because there is
a “floor” function on the summands,
for our purposes need only be evalu-
ated up to . The base of the cup
has a radius of approximately 4.4 LC
and has a theoretical maximum of 61
bricks. I was able to achieve a base
layer of 58, but this is probably be-
cause I’m not a great builder. The top
of the cup has a radius of approximate-
ly 6 LC and has a theoretical maximum
of 113 Bricks. I was able to achieve 98
in my build. Using these and assuming
a linear trend in the cup (the sides of
the cup look smooth and straight), we
estimate that a theoretical maximum
of 1,364 Lego bricks could fit in the
round cup, with a more likely number
being approximately 1,250. See Fig-
ure 4 for three different calculations of
bricks-per-layer.
So the round cup holds about 200
more bricks than the box if you take the
time to pack it. Real Lego enthusiasts
use a greedy heuristic to fill their cups,
Figure 4: Upper bound on bricks per layer, conical Lego cup, computed three differ-
ent ways. The blue line is the theoretical maximum, using equation (1) and is a strict
upper bound. The red line considers the size of the largest square that could be ft at
each layer and should be considered a strict lower bound. The green line is the linear
trend line of the brick counts of the “bottom” and “top” layers, pictured in Figure 3.
WWW. I NF OR MS . OR G 76 | A NA LY T I CS - MAGA Z I NE . OR G
FI VE-MI NUTE ANALYST
putting large pieces in first, then filling the rest
of the space with smaller pieces (“elements”),
which for tractability were excluded from this
analysis. The conclusion of this article is that
while they didn’t really look like much, the pro-
motional brickbox was a really nice gift.
As a final note, we observe that achieving
an optimal fill of the round cup will be much
more difficult than achieving an optimal fill of
the square box. In fact, the amount of time that
it takes a child to optimally fill a box is about
the amount of time that it takes an adult to cre-
ate the bottom level of the round box.
Next time: We answer our original question
of how much better off one is by packing both
the round and square cups than by randomly
tossing bricks in.
Harrison Schramm ([email protected]) is an
operations research professional in the Washington, D.C.,
area. He is a member of INFORMS and a Certifed Analytics
Professional (CAP).
NOTES & REFERENCES
1. There is a community of people interested in this problem,
for starters, see Erich’s Packing Center: http://www2.stetson.
edu/~efriedma/squincir/
2. There is a very nice description of the problem at mathworld:
http://mathworld.wolfram.com/GausssCircleProblem.html.
Additionally, Hilbert discusses the problem in “Geometry and
the Imagination,” which I purchased during the course of
writing this article.
3. Sloane’s A000328: 1, 5, 13, 29, 49, 81, 113, 149, 197, 253…
Achieving an optimal fill
of the round cup will be
much more difficult than
achieving an optimal fill
of the square box. In fact,
the amount of time that it
takes a child to optimally
fill a box is about the
amount of time that it
takes an adult to create
the bottom level of the
round box.
Join the Analytics Section of INFORMS
For more information, visit:
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Conducted BY INFORMS, the leading professional society in analytics
UPCOMING ANALYTICS CERTIFICATION EXAMS

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(CAP
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Candidate Handbook available at
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Drexel University James E. Marks
Intercultural Center
Philadelphia, PA
JUNE 21, 2014
Precedes INFORMS Conference
on The Business of Big Data,
June 22-24
San Jose, CA
MARCH 29, 2014
Precedes INFORMS Conference on Business
Analytics and Operations Research,
March 30-April 1, 2014
Boston, MA
MARCH 30, 2014
Precedes Gartner’s Business
Intelligence & Analytics Summit,
March 31 – April 2
Las Vegas, NV
MAY 22, 2014
University of Cincinnati
Lindner College of Business
Cincinnati, OH
APRIL 15, 2014
Queens University School of Business
Toronto, Ontario, Canada


The INFORMS Analytics Certification Program is positioned to be
the defacto standard for Analytics Professionals worldwide. It will
be a must-have for the analytics field in the same way PMP is for
project managers.
~ Greta Roberts, CEO Talent Analytics, Corp.
www.informs.org/Build-Your-Career/Analytics-Certification
WWW. I NF OR MS . OR G 78 | A NA LY T I CS - MAGA Z I NE . OR G
THI NKI NG ANALYTI CALLY
As the owner of a pizza delivery restaurant, you are
constantly looking for ways to keep costs low while
maintaining quality service. Because your proft margins
are thin, you’d like the number of delivery drivers to be as
low as possible.
You receive orders from customers at an inter-arrival
time of six minutes exponentially distributed. A driver can
pick up one order, deliver it to a customer and return back to
the restaurant in 20 to 60 minutes, equally distributed.
The corporate offce mandates that you must have
an average order delivery time of less than 60 minutes.
Some deliveries can be more than 60 minutes and some
can be under 60 minutes, but on average they must be
below one hour.
You may hire as many drivers as you like. But hiring
too many drivers will cause your payroll to be unnecessar-
ily high and too few drivers will put you over the 60-minute
delivery requirement. Assume that a driver can only deliver
one order per round trip.
Question: How many drivers are needed in order to
keep average delivery times under one hour?
Send your answer to [email protected] by May 15.
The winner, chosen randomly from correct answers, will
receive a $25 Amazon Gift Card. Past questions can be
found at puzzlor.com.
John Toczek is the senior director
of Decision Support and Analytics for
ARAMARK Corporation in the Global
Operational Excellence group. He
earned a bachelor of science degree
in chemical engineering at Drexel
University (1996) and a master’s
degree in operations research from
Virginia Commonwealth University
(2005). He is a member of INFORMS.
BY JOHN TOCZEK
Pizza delivery
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