8/11/2019 Analytics Novemberdecember 2012
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H T T P : / / W W W . A N A L Y T I C S - M A G A Z I N E . O R G
ALSO INSIDE:
NOVEMBER/DEC EMBER 2012DRIVING BETTER BUSINESS DECISIONS
Image analytics:next really big data thing Distribution processing:math of uncertainty
Consulting & communication:achieving buy-in
DRILLING
with big dataDigital oil field helps oil & gas industryproduce cost-effective energy while addressingenvironmental concerns.
Executive EdMacys.com Kerem Tomaovercoming data, analychallenges
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1 | A N A L Y T I C S - M A G A Z I N E . O R G
Big Datas Big Daddy
I N S I D E S T O R Y
Can big data get any bigger?
The question reminds me of the
old joke about the bear in the woods.Of course big data is going to get
bigger. Today, and with apologies to
Sting, every breath you take, every
move you make, every bond you
break, every step you take seem-
ingly produces data. Multiply the
moves you make and the claims you
stake by the billions of other peo-
ple around the world and suddenly
youre talking really big data.
You might say Big Data has
many Big Daddies, all of whom are
prolic.
Wasnt it just a nanosecond ago
(in relative terms) that analysts every-
where were whining that if they only
had more data, they could solve all
manner of complex operational prob-
lems that were heretofore intractable?
Now those same analysts are drown-
ing in data and struggling to keep their
heads above the data deluge.
It turns out that irony really isa dish best served with cold, hard
facts. Just go easy on the side order.
Today, the analytics community
is basically scratching the surface in
terms of turning the deluge of data into
meaningful decision-making insight
on a widespread, corporate-world
scale. The sheer volume of availabledata is imposing enough, but then
the data has to be properly mined,
cleaned, analyzed and presented to
decision-makers or its going right
back on the scrap heap along with all
the other promising ideas that never
garnered C-level buy-in.
That, in a nutshell, is the theme
of several articles in this issue of
Analytics magazine. For example,
in his
cover story on the potential
of big data analytics and the digital
oil eld to revolutionize the oil and
gas industry, Adam Farris notes
that breaking into the oil and gas
industry is difcult for analysts be-
cause data scientists and petroleum
engineers not only dont speak the
same language, they dont appear
to be from the same planet. Yet the
potential for big data analytics to im-
prove energy production and safety
while protecting the environment isenormous.
Go gure.
PETER HORNER, EDITOR
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IMAGES & VIDEOS: SOME REALLY BIG DATABy Fritz Venter and Andrew Stein
Sizing up the potential impact of prescriptive analytics driven by
proliferation of images and video.
HOW BIG DATA IS CHANGING OIL & GAS INDUSTRY
By Adam FarrisAdvent of the digital oil field helps produce cost-effective energy
while addressing safety and environmental concerns.
DISTRIBUTION PROCESSING ADDRESSES UNCERTAINTY
By Sam L. SavageNon-profit organization promotes standards for making rational,
auditable calculations based on probability distributions.
SOFT SKILLS: ART OF EFFECTIVE COMMUNICATION
By Gary CokinsHow to achieve corporate buy-in during the Twitter-influenced,
short-attention-span era.
SUCCESSFULLY OPERATIONALIZING ANALYTICS
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Analytics (ISSN 1938-1697) is published six times a year bythe Institute for Operations Research and the ManagementSciences (INFORMS). For a free subscription, register athttp://analytics.informs.org. Address other correspondence tothe editor, Peter Horner, [email protected]. Theopinions expressed in Analyticsare those of the authors, anddo not necessarily reect the opinions of INFORMS, its ofcers,Lionheart Publishing Inc. or the editorial staff of Analytics.Analytics copyright 2012 by the Institute for OperationsResearch and the Management Sciences. All rights reserved.
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W W W . I N F O R M S . O R G
Its been more than a decade since the In-
ternet became a household shopping front.
We shop without leaving the sofa during a
commercial break due to the ease of a tablet
device. Our smartphone tells us how much anitem is on a competitive ecommerce site while
we are shopping in a retail store. If we like a
product we buy it instantly without waiting in a
checkout line.
One common theme behind all these ac-
tivities: we implicitly or explicitly create data
as we interact with these devices. We trans-
mit data to the cloud where it is stored. This
data (with our permission) then becomes part
of an analytic workow somewhere and comes
back to us with recommendations and/or of-
fers on what we should buy next, and the circle
of commerce continues.
Twenty years ago, 30MB of hard disk was
so immense that one didnt know what to do
with so much storage space. A gigabyte was
big data for an 8086 processor and DOS-
based Lotus 123 worksheets that were used.
The Internet did not exist, so the speed at
which data increased was contingent upon the
speed at which one could receive oppy disks
in the mail, 360KB at a time.
However, we still had the same workow
that we have today in relation to analytic ex-ercise. We sampled, ran descriptive statistics
and visualized the data. Based on our ndings,
we came up with a model or series of models
that best t the data, calibrated the model pa-
rameters based on simulations and completed
the version 0 of the analytics deliverable. As
we collected new data, we would revisit the
process and assess whether we needed a
new model or keep the existing one, making a
few parametric changes here and there. All the
data we had lled a spreadsheet back then.We could eyeball the data and see patterns
easily.
Similarly, when we sample data today, we
need efcient and fast visualization tools that
allow us to get to the nuggets quickly. Not
only is the data much larger, but the dimen-
sions over which the data is collected are nu-
merous. The belief that since we have more
data we do not need to sample is a awed
one. A critical assumption behind that thought
is that big data is accurately and comprehen-
sively capturing every known piece of informa-
tion there is to know about everything. Within
the modeling realm there is also the concept
of over-tting, data quality, etc., which still im-
plies sampling as a step in the analytic pro-
cess. However, a 1 percent sample of a 100TB
data is still large data.
RISING CUSTOMER EXPECTATIONS
As the time spans in which data is creat-
ed are compressed, customer expectations
of companies to provide information about
products and services such as availability, de-
livery, discounts in near real time, if not realtime, increase dramatically. To complicate
things even further, there is a new addition to
the data types that has added a twist to the
story: social media feeds. Semi- or un-struc-
tured data makes parsing, analyzing and in-
terpreting the data even more challenging, as
the data does not come in traditional columnar
setup. What is the value of a fans comment
on a businesss Facebook page? Who are the
social inuencers in a companys network of
fans and how can we use this information toreach to the right audience? How can a com-
pany understand which products are trendy or
what brands are in high demand from tweets?
After pre-processing and massaging the so-
cial data, these and similar questions can be
answered by using statistical tools and experi-
menting with ndings to see if any of those are
actionable.
Thanks to the cloud, we do not need to in-
vest a lot of money in hardware and software to
process all this data. Our ability of disseminat-
ing information quickly across different units
is constrained by the slowest link we maintain
in our network. If we are not comfortable with
moving and/or sharing a lot of data, we can
build our own cloud behind rewalls. Sophisti -
cated statistical and visualization software are
affordable as well. It can be only a matter of
4 | A N A L Y T I C S - M A G A Z I N E . O R G A N A L Y T I C S | N O V E M B E R / D E C E M B E R 2 012
Overcoming big datachallenges for analytics
BY KEREM TOMAK
As the time spans in
which data is created
are compressed,
customer expectations
of companies to provideinformation about products
and services increase
dramatically.
E X E C U T I V E E D G E
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5 | A N A L Y T I C S - M A G A Z I N E . O R G
days before a company obtains more
than simple analytical capabilities. En-
terprise class operations still require
signicant investment, but even these
are relatively cheap.
These affordable technological ca-
pabilities enable the possibility of build-
ing a successful analytics function as if
the unit is a startup company within a
larger organization. This is one of the
many scenarios in which an analytics
team can be established. With buy-
in from senior management already
achieved and seed funding ready, the
main starting point is to hire an expe-
rienced analytics leader and empowerhim or her to build the roadmap to es-
tablish a proactive team.
ANALYTICS LEADERSHIP
Analytics leaders need to speak
the language of at least one quantita-
tive eld such as mathematics, statis-
tics, operations research or economics.
This is necessary to build a credible
leadership vertically and across the or-
ganization. Think of them as interpret-ers between the quantitative types and
execution teams. An efcient analytics
leader needs to understand the busi-
ness and trends, anticipate the chang-
es in requests for information and plan
ahead to build required capacity to
respond to the changes. Many analyt-
ics projects fail as either the informa-
tion is too overwhelming or the model
is too complex for a non-quantitative
end-user to comprehend and take an
action.
One of the key early steps is to
have a dedicated systems team that
is given the right funding and flex-
ibility to build the analytics systems
and support. Without a clear roadmap
toward scalable and robust systems
and processes, an analytics team is
limited in capabilities. Analytics lead-
ership needs to pass requirements to
the systems team or teams in orderto put the building blocks in place.
This requires a comprehensive un-
derstanding, exposure and hands-on
experience with data and analytics
systems and tools.
What does this flexibility enable
an analytics team to accomplish?
They can rapidly prototype automat-
ed, data-driven solutions in reporting,
product recommendations, personal-
ized offers and more. Being on thecutting edge of tools and techniques
enables the right data scientist to
have the freedom to invent. Business
units benefit from not only improved
internal processes to acquire the in-
formation they need much faster, but
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A membership in INFORMS will help!
visit http://join.informs.org to join online
New in 2013! Certification for Analytics Professionals
Online access to the latest in operations researchand advanced analytics techniques
Subscriptions to online and print INFORMS Publications
Networking Opportunities available at INFORMS Meetingsand Communities
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E X E C U T I V E E D G E
they also start to find novel ways to serve
their customers, to improve their product of-
ferings, and to understand where the bottle-
necks are within the organization, and the
list grows.
TESTING AND PRODUCTION OF PROTOTYPES
Finally, the path to testing and production
of working prototypes needs to be smooth and
supported by technology teams across differ-
ent business units. An analytics team needs to
be able to build dashboards and disseminate
the information through centralized systems
for everyone who needs that information to
use. They need to be able to test new algo-
rithms live or by using simulations to see what
needs to be tweaked and/or improved. Butmost importantly they need to work hand in
hand with agile technology teams to turn pro-
totypes into products that pass strict SLAs and
requirements to meet the performance criteria
of the production systems.
The road to taming big data passes through
people who are trained to handle the intrica-
cies of data, understand their business, ar-
ticulate what they see and, most importantly,
are enabled to feed their intellectual curiosity
by learning new tools and thinking outside the
box. Aligned with testing and delivery teams,
an analytics team with a keen focus on the
end-goal can be a major driver of a successful
business.
Kerem Tomak([email protected])is vice president ofMarketing Analytics at Macys.com. He is a member of INFORMS.
6 | A N A L Y T I C S - M A G A Z I N E . O R G
An analytics team
needs to be able to
build dashboards and
disseminate the information
through centralized
systems for everyone who
needs that information.
Subscribe to Analytics
Its fast, its easy and its FREE!Just visit: http://analytics.informs.org/
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8/50W W W . I N F O R M S . O R G
How does an organization move from
practicing little or no analytics to becoming
a world leader? The answer isnt simple. But
much can be gleaned from taking a look at
companies and industries that now employ
analytics at the highest levels. And one ofthe great success stories is that of the airline
industry.
Prior to 1978 the Civil Aeronautics Board
(CAB) regulated where, when and at what
price every airline could y. If an airline wanted
to offer a new ight, it had to le the appro-
priate paperwork then wait for a decision from
the CAB. Prices, which were identical across
carriers, were set by the CAB to reect the
airlines reported cost of service. The environ-
ment didnt encourage the industry to operate
efciently.
That situation changed with the Airline De-
regulation Act of 1978. Airlines were free to es-
tablish their own routes and schedules and to
set prices however they saw t. It was an era
of tremendous upheaval as airlines sought to
adapt to the competitive environment in order
to survive. Analytics proved to be a corner-
stone of the adaptation process.
Where exactly did analytics fit in? One
area was that of choosing the routes air-
craft would fly. If an airline serves 100 cities
and a typical route involves a plane visitingthree cities per day, there are roughly a mil-
lion different routes a single plane can be
assigned to. Of course, the actual problem
is far more complicated. All of the planes in
the fleet must be routed and scheduled so
that their arrival and departure times are co-
ordinated, thus allowing passengers to make
connections.
The problem of nding a single, reason-
able schedule is in itself a difcult task. But
to be competitive, airlines need to nd goodschedules schedules that ll ights with pas-
sengers. In the wake of deregulation, airlines
developed analytical models to predict pas-
senger demand, demand that was in turn fed
into large optimization models to generate the
most protable schedule.
Routing and scheduling are only part of
the operational problem. Pilots and flight at-
tendants must be assigned to staff flights.
The question for airlines is who to assign to
various flights. Simply finding an assignment
can be difficult since union contracts and
government regulations place restrictions on
what crews are allowed to do. A pilot, for ex-
ample, cant fly for 24 hours without mandat-
ed rest breaks. But among the many potential
crew assignments, some are more cost ef-
fective than others for example, those that
require fewer crews, reduce overnight stays
in hotels and other items. For large airlines,
crew costs run well into the billions of dollars
annually, and large optimization models are
routinely used to find crew assignments with
the lowest possible cost.One of the more interesting practices to
spring from the Airline Deregulation Act was
the practice of dynamic pricing. Airlines quick-
ly realized there were two primary classes of
flyers: business passengers, who were rela-
tively price insensitive, and leisure passen-
gers, who cared a lot about price. Airlines
were able to segregate these two groups by
introducing fare restrictions. A $200 ticket
might be available up to three weeks in ad-
vance, after which the price would go up to$300. Segregation of this type worked be-
cause business travelers frequently booked
only a few days ahead of departure while
leisure travelers were willing to book their
vacations further in advance to obtain lower
prices.
The practice worked well, and once Pan-
doras Box was open airlines rushed to take a
look inside. If raising the price three weeks be-
fore departure was successful, why not raise it
again to $600 with one week to go? If a plane
is nearly full four weeks before departure, why
wait another three weeks to raise the price to
$600? Why not do so immediately? Over time
the practice incrementally evolved to a point
where future demand was being forecast by
price point and the interaction between differ-
ent fares on routes using shared ight legs was
7 | A N A L Y T I C S - M A G A Z I N E . O R G A N A L Y T I C S | N O V E M B E R / D E C E M B E R 2 012
Embracing analytics
BY E. ANDREW BOYD
The practice worked well,
and once Pandoras Box
was open, airlines rushed
to take a look inside.
P R O F I T C E N T E R
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being accounted for. Dynamic pricing in
the airline industry (revenue manage-
ment in industry jargon) is one of the
most advanced applications of analyt-
ics in use today.
The rise of advanced analytics in
the airline industry can be attributable
to many factors, but two stand out in
particular. One was Robert Crandall,
the CEO of American Airlines from
1985 to 1998, who believed in the
power of analytics. Crandall was no
lover of mathematics, but he was no-
toriously competitive and believed an-
alytics could be used as a competitive
weapon. Under his leadership Ameri-can embraced analytics and became
the most feared and revered airline of
the 1980s and 1990s, employing hun-
dreds of analytics professionals who
had their hands involved in every as-
pect of running the airline.
Americans innovations caught the
attention of other carriers who realized
the value of analytics. And this was the
second factor leading to wide-scale
adoption of analytics: airlines needed
it to remain competitive. The practice
of analytics had become necessary to
stay in business.
Most industries havent undergone
the analytics conversion experienced
by the airlines. While its true that de-
regulation helped serve as a catalyst
for the airline industry, earth-shaking
events arent required to embrace ana-
lytics. All thats needed is recognizing
the competitive advantage it provides
and nurturing a sustained effort to im-
prove over time. American Airlines
started with an analytics group of eight
people doing what they could in an or-
ganization devoid of analytics. It tooktime to grow in size and sophistication,
but American was ahead of its competi-
tors. And in a period that saw the de-
mise of dozens of established airlines,
American survived and thrived. Its one
of the great analytics success stories,
and one we have much to learn from.
Andrew Boyd, senior INFORMS member andINFORMS VP of Marketing, Communications and
Outreach, has been an executive and chief scientist atan analytics rm for many years. He can be reached [email protected].
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W W W . I N F O R M S . O R G
Not long after nishing graduate school,
I found myself working at what used to be
known as an operations research consulting
rm (today, this company would be called an
analytics services provider or some such),
working full-time on a project for a large client.I still have a lot of scars from that project.
The core of the model that we were build-
ing was coded and implemented on a main-
frame computer. This meant that I often had
to struggle with writing a few crucial and con-
fusing lines of Job Control Language (JCL)
something that I never did quite master
and every time one of my compute jobs
crashed, I would receive a late-night phone
call at home.
Also, upon joining the project team, I had
inherited a largely undocumented SAS pro-
gram from a departing colleague, a mess of
spaghetti code that contained the guts of the
model that we were implementing. I spent
countless hours trying to sort it out and clean it
up without causing our nightly production runs
to crash (see late-night phone call above).
THE BIGGEST PROBLEM
By far the biggest problem, however, was
the lack of clarity about the projects purpose.
The project was sponsored and funded by the
clients IT organization. The actual business
groups who were expected to use the modelswere not at all clear on what the value proposi-
tion was for them, and we could see that our
project deliverables were being shoved down
their throats.
Not surprisingly, the business users we
worked with were motivated to nd problems
with what we were doing and they often
did. Some instability in the networking infra-
structure often prevented us from delivering
updated results, which generated one set of
complaints. Even when everything worked onschedule, our results (based on a daily snap-
shot of the systems state) would inevitably be
out of synch with the latest data that was avail-
able, which in turn produced a whole other set
of complaints. At core, there was a fundamen-
tal disconnect between the business users
(who believed they had asked IT for a tactical
reporting tool) and the IT organization (who
believed that we had been asked to deliver a
more sophisticated decision support system).
Meanwhile, we were pushed for political
reasons to get the model into production as
soon as possible, while the business users
kept nding reasons not too sign off on the de-
liverables and refused to use our solution at all
until it had been formally accepted. As such, a
huge amount of effort was spent designing the
perfect user interface, right down to the choice
of colors to be used to represent differen t kinds
of outputs, while the models core logic and
functionality was never seriously examined by
the clients who would ultimately have to use it.
PROJECTS VALUE PROPOSITIONThough nearly 20 years have passed since
then, this project came to mind again the other
day when an old friend of mine (lets call him
Doug) told me a familiar tale about one of
his recent projects. From the beginning, our
understanding was that the purpose of the
project was to build an alpha version, Doug
explained, something to demonstrate the
potential of the application while giving us a
chance to establish data connectivity, get a
bunch of technologies talking to each other,and use a sample of the operational data to
show that the optimization could actually pro-
vide signicant savings.
So far, this made great sense to me. In fact,
of late I have had conversations with many
people in the analytics eld about the value of
rapid prototyping for engaging potential stake-
holders, for demonstrating business value, and
for ensuring that the team really understands
the problem domain.
So what went wrong for Doug?
Turns out his project was also being led by
the IT organization, and that those folks did
not have any sense at all about the projects
value proposition. In addition, not long after
Doug and his team had begun working, some-
one somewhere in the IT chain of command
made an ad hoc decision to roll out the alpha
9 | A N A L Y T I C S - M A G A Z I N E . O R G A N A L Y T I C S | N O V E M B E R / D E C E M B E R 2 012
Even tragic projects canhave happy endings
BY VIJAY MEHROTRA
The business users we
worked with were
motivated to find problems
with what we were doing
and they often did.
A N A L Y Z E T H I S !
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version of the software to a group of busi-
ness users around the county. Within the
clients IT group, this was interpreted as
a decision to treat this alpha version as a
production system and as such to hold
the consultants feet to the re as if tak-
ing delivery on commercial enterprise
software.
Doug and his team were perplexed
and distracted from what they thought
their focus should be. A great deal of time
was spent on minute details associated
with the graphical user interface, includ-
ing long discussions about the layout and
coloring of various output values, even
though there was no established accep-tance criteria (since the GUI had not been
viewed as a signicant part of the original
projects scope). Meanwhile, despite re-
peated attempts by Doug, no one on the
client side was willing to even look at the
results of the optimization until the entire
user interface design was signed off and
fully functional.
In fact, with the possible exception of
the original executive sponsor (who was
extremely busy and far removed from
the reality of the project), it seemed to
Doug that no one really understand how
the systems pieces (including the GUI,
a conguration rules engine, the optimi-
zation model, a relational database, and
java code for developing and deploying
the application) t together, or why the
project was being done in the rst place.
ABANDON SHIP
After more than a year on my project ,
I had become fed up. The partners in my
rm had made a great deal of money
from my billable hours on this project, but
I had come to understand that this was
clearly just part of the standard profes-
sional services business model. Howev-
er, I had nothing to show for my efforts
but lost sleep, a bunch of unhappy people
within the client organization, and a deep
sense that I was wasting their time and
my own. I left the project, and the rm,soon thereafter.
For many years, I felt quite smug about
this decision to abandon ship. Indeed, I
have since been told by several people
that I respect and trust that the willing-
ness to put your job on the line for what
you believe every single day should be
a core value for successful project lead-
ership. In any case, I was young, single
and free of debt, and walking out of my
employers ofces on that nal day, I felt
that I had very little to lose by leaving it all
behind.
After talk ing to Doug, however, Im
not so sure. Far older and wiser now
than I was then, Doug has worked
through this challenging project calmly
despite the many frustrations, commu-
nicating his concerns to his own man-
agement and doing his best to educate
people throughout the client organiza-
tion all the way along. Though the ini-
tial project was ultimately cancelled, the
clients executive sponsor has recently
re-engaged with Dougs company, rec-
ognizing her own part in the projects
failure and still believing in the poten-
tial business value that the optimization
might be able to provide.
While his initial project had appeared
to be a tragedy, or at least a black come-
dy, Dougs story may yet turn out to have
a happy ending. In any case, I plan to
stay tuned, and I hope to learn something
along the way.
Vijay Mehrotra([email protected]) is an
associate professor in the Department of Analytics andTechnology at the University of San Franciscos Schoolof Management. He is also an experienced analytics
consultant and entrepreneur, an angel investor in severalsuccessful analytics companies and a longtime memberof INFORMS.
A N A L Y Z E T H I S !
prizecall for
nominations
The Institute for Operations Research and the ManagementSciences annually awards the INFORMS Prize for effectiveintegration of Operations Research/Management Science(OR/MS) and advanced analytics into organizational decision
making. The award is given to an organization that hasrepeatedly applied the principles of OR/MS and advancedanalytics in pioneering, varied, novel, and lasting ways.
2013DEADLINE FOR APPLICATIONS IS DECEMBER 1, 2012
2013 COMMITTEE CHAIR:
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voice:+1 303.328.6389 e-mail: [email protected]
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log onto: www.informs.org/informsprize for more information
Which organization has the best O.R. department inthe world?
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was spent in discussions with senior man-
agement across various groups including
QA & Testing, Engineering, Development
& Engineering, Product and Release Man-
agement, among others. These discus-
sions are the rst opportunity to establish
priorities and set realistic expectations. In
addition to developing a set of key ques-
tions and project objectives, these discus-
sions provide valuable historic context on
the problem and can identify sensitive top-
ics that a consultant has to tread very care-
fully around.
For this project, the learning agenda
was distilled down to three key questions.
1. What is the likelihood that a customerwill encounter a bug as a function of
time in usage?
2. How do characteristics of the bug
impact that likelihood?
3. Which bug types are the most likely
to be encountered by a customer?
DATA COLLECTION & AGGREGATION
Collecting and aggregating all the
needed data can be one of the most chal-
lenging and time-consuming aspects of
an analytics initiative. The data needed for
this effort was spread across myriad data-
bases requiring many different resources
to fully source. Resources worked on this
stage for over two months, often requiring
multiple data extractions.
The denition of data should be broad-
ened to include information not contained
within a database. Resources intimate
with the software and how the customer
uses it can provide a wealth of knowledge
that can often be translated into quantita-
tive variables providing additional dimen-
sions to the analysis.
QA, ANALYSIS & DATA CLEANSINGOnce all the data has been extract-
ed, its important to plan for a proper
amount of time and effort to validate
and clean the data. This step is often
underestimated in analytics projects but
is one of the most critical as misleading
results can be produced if this work is
not done thoroughly. Almost all data will
have issues that need to be resolved.
Errors, incorrect values, unusual ob-
servations, extreme outliers and data
inconsistencies are quite common; ad-
dressing these issues will benefit both
the project at hand as well as other
applications that these data are being
used for. For this particular project, the
technical teams uncovered a host of
problems with a few of the databases
revealing that an uncomfortable level of
inaccurate data was being used to cre-
ate various reports distributed across
the business. A separate project was
kicked off to fix these issues and im-
prove the accuracy of these reports.
Validating and cleaning data gener-
ates some very rich conversations among
stakeholders and technical teams. This
stage is also helpful to set expectations
with stakeholders when the gaps and
limitations of the data can be more clearly
shown. These discussions help the stat-
istician better connect how the business
views the problem to how the available
data can be used to produce actionable
business metrics. Valuable insight into
the nature of the data is gleaned as the
statistician is able to examine the vari-
ability and correlation structure identify-
ing issues that may impact the statistical
modeling and analysis techniques to be
used.
F O R U M
Limited print copies are available to purchase for $45.00http://tutorials.pubs.informs.org
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INFORMS 2012 edition of the TutORials in Operations Researchseries is available online to registrants of the 2012 INFORMSAnnual Meeting. It will be made available online to all 2013INFORMS members on January 1, 2013.
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STATISTICAL MODELING
Once the data has been adequately
cleaned and prepared, the statistical mod-
eling work begins. By this point, enough
analysis work and data examination should
have been done so the statistician has a
very clear idea of the technique and ap-
proach that will be used. The goal here is
to reduce the data to a mathematical ex-
pression with a component that provides
a description of the overall structure in the
data and a component that accounts for
the variability and uncertainty around that
structure. Its important to remember that
the goal of statistical modeling is to build
as simple of a model as possible that ad-equately describes the key features in the
data allowing the hypotheses/questions of
interest to be addressed without adding un-
necessary complexity.
A great quote that most statisticians
keep top of mind during this process to
help strike this balance is from one of the
pioneers of the science, George Box: All
models are wrong, but some are useful.
For time-to-event analyses, the mod-
eling technique needs to account for
the censored nature of the data. Many
statistical model forms that are common
in time-to-event analyses can handle
censored data and a variety of statistical
software that contains these techniques.
The author used the Minitab Statistical
Software, which is a software package
common among reliability engineers.
RESULTS
In most analytics projects, the more
advanced statistical analyses and models
are not shared beyond the core techni-
cal team doing the analysis work. These
models and analyses need to be trans-
lated into a variety of summary statistics
and graphical displays that communicatethe features in the data and are easy
to share across a broad range of audi-
ences. For this project, a technical report
containing a variety of graphical displays
and data tables was produced. Figure 1
is an example of one of the graphical dis-
plays produced in this project, and one
thats commonly used in time-to-event
analyses.
The graph displays the likelihood that a
customer will encounter a bug as a func-
tion of time (Note: the probability values are
not shown to protect the condentiality of
the work). This approach shows the rate at
which that likelihood increases over time.
The likelihood for ve different bug types is
displayed (A, B, C, D and E), allowing for a
comparison across the bug types. For ex-
ample, bug type E has the greatest chance
of being found by a customer while bug
type A and B have the least chance.
Management can use graphical dis-
plays such as these to help determine
the time in usage at which certain bug
types would have a likelihood of being
encountered beyond desired. In this proj-
ect, a certain level of likelihood was de-
cided upon by senior management and
displayed on the graph (shown by the
grey horizontal line). As can be seen, bug
types A and B dont reach that likelihood
until almost three years in usage, indicat-
ing that xing these bugs can be of lower
priority. Bug types D and E, on the oth-
er hand, reach that likelihood within the
rst few months of usage indicating that
these bugs have a high chance of being
encountered and should be top priority to
x before too many customer encounter
them.
Kevin Potcner([email protected]) isa director at Exsilon D ata & Statistical Solutions. Astatistician, Potcner has provided analytics consulting andtraining for a variety of industries including automotive,
biotech, medical device, pharmaceutical, nancialservices, software, e-commerce and retail. He holds amasters degree in applied statistics from the RochesterInstitute of Technology.
F O R U M
Figure 1: Graph displaying likelihood that a customer will encounter a bug as a function of time.
Joi n th e An alyti cs Sect ion of INF OR MS
For more information, visit: http://www.informs.org/Community/Analytics/Membership
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W W W . I N F O R M S . O R G
Sizing up the potential impact of prescriptive analytics driven by proliferation of images and video.
The human brain simultane-
ously processes millions of
images, movement, sound
and other esoteric informa-
tion from multiple sources. The brain is
exceptionally efcient and effective in its
capacity to prescribe and direct a courseof action and eclipses any computing
power available today. Smartphones now
record and share images, audios and vid-
eos at an incredibly increasing rate, forc-
ing our brains to process more.
Technology is catching up to the
brain. Googles image recognition in
Self-taught Software is working to
replicate the brains capacity to learn
through experience. In parallel, pre-
scriptive analytics is becoming far more
intelligent and capable than predictiveanalytics. Like the brain, prescriptive
analytics learns and adapts as it pro-
cesses images, videos, audios, text
and numbers to prescribe a course of
action.
THE FUTURE IS NOW
Google is working on simulating the
human brains ability to compute, evalu-
ate and choose a course of action using
massive neural networks.
The image and video analytics science
has scaled with advances in machine vi-sion, multi-lingual speech recognition and
rules-based decision engines. Intense in-
terest exists in prescriptive analytics driv-
en by real-time streams of rich image and
video content. Consumers with mobile
devices drive an explosion of location-
tracked image and video data. Lowering
costs have democratized cloud-based
high-performance computing. Andrew
McAfee and Erik Brynjolfsson in Har-
vard Business Review in October 2012
called this Big Data: The ManagementRevolution.
Image analytics is seen as a po-
tential solution to social, political, eco-
nomic and industry issues. Thirty years
of Intels Gordon E. Moores law and
Images & videos: really big data
BY FRITZ VENTER (LEFT) AND ANDREW STEIN
T
T H E N E X T B I G T H I N G
14 | A N A L Y T I C S - M A G A Z I N E . O R G A N A L Y T I C S | N O V E M B E R / D E C E M B E R 2 012
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15 | A N A L Y T I C S - M A G A Z I N E . O R G
Harvard Business Schools Clayton
Christensens disruptive innovation
have created the current experience-
driven generation that is fully aware
of technologys potential to solve is-
sues plaguing these global domains.
On the consumption side, mobile
consumption of video is growing dra-
matically. Bandwidth is no longer a con-
cern. Prescriptive analytics is poised to
deliver relevant video to viewers be-
yond Netix algorithm for DVDs to rent
based on viewing interests.
IMAGE ANALYTICS: TECHNOLOGY
PROCESS
Image analyticsis the automatic al-
gorithmic extraction and logical analy-
sis of information found in image data
using digital image processing tech-
niques. The use of bar codes and QR
codes are simple examples, but in-
teresting examples are as complex
as facial recognition and position and
movement analysis.
Today, images and image sequenc-
es (videos) make up about 80 percent
I MAG E ANA L Y T I C S
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Decision Sciences Institute
advancing the science and practice of decision making
The Decision Sciences Institute is a nonprofit
professional organization of researchers, managers,educators, and students interested in decision-
making techniques and processes in private and
public organizations. The Institute is an international
organization with over 3,500 members in 32 coun-
tries. The annual meetings and regional conferences
attract over 3,500 participants a year.
The Decision Sciences Institute
Is Committed to . . .
Research.A focus on the integration of research
in the art and science of managerial decision making
across traditional functional academic disciplines; an
international forum for presentation and discussion
of research.
Teaching.A forum for presentation and discussion
of innovative teaching; recognition of teaching
excellence and curriculum innovation.
Practice.An exchange of ideas between leading
professional practitioners and educators.
Benefits Members Receive:
Annual meeting. Members receive discounted fees to
attend, and the meeting draws over 1,500 professionals
together to share current thoughts on theoretical and
applied issues.
Decision Sciencesis a highly respected journal among
scholars and is subscribed to by over 1,000 libraries.
It seeks and publishes high quality, theoretical and
empirical articles.
Decision Sciences Journal of Innovative Education is
a high quality, peer-reviewed scholarly journal whose
mission is to publish significant research.
Decision Line, the Institutes news publication, is
published five times annually and includes feature
columns, as well as information on members, regions,
annual meetings, and placement activities.
Job Placement Servicesare offered throughout the
year, with position and applicant listings available via
the Internet. Also at the conference, special facilities
aid in position search activities.
www.DecisionSciences.org
Decision Sciences Institute43rd ANNUAL MEETING
NOVEMBER 17-20, 2012
Join us.
Log on to our website and complete
the membership application to begin
enjoying your benefits.
Regular membership: $160/year
Student membership: $25/year
Figure 1: Fast-growing consumption of mobile video.
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of all corporate and public unstructured
big data. As growth of unstructured data
increases, analytical systems must as-
similate and interpret images and videos
as well as they interpret structured data
such as text and numbers.
An image is a set of signals sensed
by the human eye and processed by
the visual cortex in the brain creat-
ing a vivid experience of a scene that
is instantly associated with concepts
and objects previously perceived and
recorded in ones memory. To a com-
puter, images are either a raster image
or a vector image. Simply put, raster
images are a sequence of pixels with
discreet numerical values for color; vec-
tor images are a set of color-annotated
polygons. To perform analytics on im-
ages or videos, the geometric encod-
ing must be transformed into constructs
depicting physical features, objects and
movement represented by the image
or video. These constructs can then be
logically analyzed by a computer.
The process of transforming big data
(including image data) into higher-level
constructs that can be analyzed is or-
ganized in progressive steps that each
adds value to the original information in
a value chain (see Figure 2) a conceptdeveloped by Harvard professor Michael
Porter. Prescriptive analytics leverages
the emergence of big data and computa-
tional and scientic advances in the elds
of statistics, mathematics, operations
research, business rules and machine
learning.
Prescriptive analytics is essentially
this chain of transformations whereby
structured and unstructured big data is
processed through intermediate repre-
sentations to create a set of prescrip-
tions (suggested future actions). These
actions are essentially changes (over a
future time frame) to variables that in-
fluence metrics of interest to an enter-
prise, government or another institution.
These variables influence target metrics
over a specified time frame. The struc-
ture of the relationship between a met-
ric and the variables that influence it is
a called a predictive model. A predictive
model represents detected patterns,
time series and relationships among
sets of variables and metrics. Predictive
models of key metrics can project future
time series of metrics from forecasted
influencing variables.
The first step in the prescriptive
analytics process transforms the ini-
tial unstructured and structured data
sources into analytically prepared data.
Although there are paral lels with stan-dard data-warehousing/ETL, this step
is different from that approachin that it
contends with the complexities of pre-
processing of unstructured data, as well
as structured data including databases,
narrative text files, images, videos and
sound.
For more details on the image analyt-
ics technology process,click here.
DEFENSE AND SECURITY DRIVING
DEMAND
The need to analyze data and pro-
actively prescribe actions is pervasive
in nearly every vibrant growth industry,
government and institutional sector.
This has created a vacuum, or demand,
I M A G E A N A L Y T I C S
Figure 2: Value chain of transformations.
The need to analyze
data and proactively
prescribe actions is
pervasive in nearly
every vibrant growth
industry, government and
institutional sector.
http://www.informs.org/http://www.analytics-magazine.org/http://tdwi.org/blogs/philip-russom/2011/07/analytic-data-prep-is-not-etl-for-data-warehousing.aspxhttp://tdwi.org/blogs/philip-russom/2011/07/analytic-data-prep-is-not-etl-for-data-warehousing.aspxhttp://analytics-magazine.org/november-december-2011/694-images-a-videos-really-big-data#sidebarhttp://analytics-magazine.org/november-december-2011/694-images-a-videos-really-big-data#sidebarhttp://analytics-magazine.org/november-december-2011/694-images-a-videos-really-big-data#sidebarhttp://tdwi.org/blogs/philip-russom/2011/07/analytic-data-prep-is-not-etl-for-data-warehousing.aspxhttp://www.informs.org/http://www.analytics-magazine.org/http://meetings.informs.org/analytics20138/11/2019 Analytics Novemberdecember 2012
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1 7 | A N A L Y T I C S - M A G A Z I N E . O R G
for prescriptive analytics systems. De-
fense and security, as well as health-
care, are particularly good examples
of industries that are driving demand
for such systems.
The defense industry has pushed the
envelope for image processing, and it is
reected in the storage that is being pro-
cured by government. GovWin Consult-
ing reports that Defense agencies are
the largest spenders on a per-agency ba-
sis at the federal level for electronic data
storage. The Army, Navy and Air Force,
along with the Department of Defense,
account for 58.4 percent of all federal
spending for storage. GovWin indicates
that the drivers for this spend are big
data and full motion video.
The proliferation of captured data of
interest to defense and security comes
from four clear sources.
1. Predator drones gathering
intelligence via video and image
reconnaissance at reduced
risk as they seek out hostile
scenarios.
2. In-place surveillance cameras
increasingly prevalent in public
places, managed by federal, state
and local governments.
3. Stationary commercial and
institutional surveillance mounted
I M A G E A N A L Y T I C S
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Learn from the BEST about High-Impact ANALYTICS & O.R. APPLICATION
Predator drones gather intelligence via video and image reconnaissance.
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in public places of business, the
workplace, hospitals and schools.
4. Consumer-created image and video
shared on YouTube, Facebook,
Twitter, blogs and other online social
media sharing/publishing sites.
While the demand drives proliferation,
it also presents a conict between safety
and privacy. People value surveillance
as a resource when a child is taken or
a loved one goes missing. On the other
hand, people see it as an invasion of pri-
vacy during everyday activities. Likewise,
people value sharing their personal pho-
tos with family and friends, but they areconcerned that their images and videos
may be anonymously processed and an-
alyzed to identify criminal activity. Where
is the ethical line of too much drawn?
And, do younger generations have the
same privacy-loss perspective?
Major cities around the world, from
London to Las Vegas, have cameras in-
stalled so densely that its nearly impos-
sible to move about the city without being
recorded. Keeping up with the installa-
tion statistics is almost impossible. The
availability of easy-to-deploy, consumer-
installed cameras is ubiquitous. This rate
of adoption for security video capture
makes an accurate assessment of how
much video is being recorded difcult.
We just know it is BIG.
Is all this surveillance coupled with the
potential of video/image analytics help-
ing? Research published in the Journal
of Experimental Social Psychology sug-
gests that increased surveillance only
increases our propensity to be Good Sa-
maritans, not reduce crime. Eric Jaffe
calls this the reverse the bystander ef-
fect in his recent article. In the end, sur-
veillance and image analytics does giveprovide data that can help ofcials pur-
sue criminal activity and pursue justice,
albeit ex post facto.
How does cost drive the demand for
video and image analytics? People expect
the nations defense and security effort
to be cost-effective. This means that the
country will move to a smaller but more
educated ghting force and at the same
time increase the use of remote sensing,
observation and monitoring tools. Simply
put, this means more image and video
capture or surveillance everywhere.
HEALTHCARE A PERFECT DOMAIN
The complexity of healthcare makes it
a perfect domain to explore the potential
for prescriptive analytics and imaging.
Healthcare has been a pioneer in captur-
ing rich imaging information and built da-
tabases to develop a variety of statistical
medical norms. The next step is to usethis image analytics to provide real-time
insight to healthcare providers during di-
agnosis and treatment.
The advances in medical science
come fast, and physicians have a dif-
ficult time keeping up with new proce-
dures, treatments and pharmacology
while they care for patients. Whether a
routine office visit, serious dise ase or an
emergency, prescriptive analytics inte-
grated in medical workflow promises to
improve the standard of care and speed
of diagnosis, treatment and recovery.
Its happening now. InScience Busi-
ness,Alan Kotok wrote about University
of Michigan researchers who adapted
computed tomography image analytics to
I M A G E A N A L Y T I C S
Parametric response mapping lung images.
Research suggests that
increased surveillance only
increases our propensity
to be Good Samaritans,
not reduce crime. Eric Jaffe
calls this the reverse the
bystander effect.
Help Promote Analyt ics
Its fast and its easy! Visit:http://analytics.informs.org/button.html
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diagnose chronic obstructive pulmonary
disease (COPD).
Advan ced medical decision-support
systems (MDDS) link massive knowl-
edge bases to multiple clinical data-
bases. These in turn are linked to a
pa