Get smart: digitial business innovation

H T T P : / / W W W. A N A LY T I C S - M A G A Z I N E . O R G
JANUARY/FEBRUARY 2016DRIVING BETTER BUSINESS DECISIONS
BROUGHT TO YOU BY:
Executive Edge
Ernst & Young CAO
Chris Mazzei on data
analytics’ better half:
the human element
data lakes
ALSO INSIDE:
• Get smart: digital business innovation
• Customer lifetime value: new insights
• Corporate profile: BNSF Railway
• What ISIS fears most: stability
The premise, promise, potential for
managing big data
Deep dive into

W W W. I N F O R M S . O R G2 | A N A LY T I C S - M AGA Z I N E . O RG
INSIDE STORY
If you and your company haven’t yet tak-
en a dive into a data lake, maybe it’s time to
test the waters. In this issue’s lead feature,
Sean Martin, founder and chief technical
officer of Cambridge Semantics, explains
what the relatively new method of man-
agement of big data is all about and what’s
driving all the excitement concerning data
lakes. But dive and swim at your own risk;
Martin also details the potential risks.
Formoreaboutthepremise,promiseand
potential, as well as the rewards and risks of
the next great, “big data” and analytics inno-
vation, see “Deep dive into data lakes.”
When it comes to risk in today’s world,
nothing can match the seemingly intracta-
ble problem of international terrorism. ISIS
and other terrorist organizations have clear-
ly instilled fear and chaos with their mur-
derous and seemingly random worldwide
attacks. While the attacks are strategically
insignificant on a national level let alone a
global scale – and we can argue whether
rhetoric from political leaders from some of
those countries attacked has only served
to heightened the fear – perhaps a better
question to ask in order to best counter ter-
rorism is: What do terrorists fear most?
The answer could be distilled down to
a single word: “stability.”
Scott Mann, a retired Army lieutenant
colonel, Green Beret and longtime Special
Ops officer, was an architect and original
implementer of the Village Stability
Operations (VSO) program inAfghanistan.
In his book, “Game Changer,” and drawing
on his on-the-ground experiences from
missions in Afghanistan, Iraq, Colombia
and other conflict zones, Mann makes
the case that “going local” – establishing
stable communities on a village-by-village
basis in conflict areas – is perhaps the
best way to thwart terrorism.
Doug Samuelson, himself a seasoned
defense analyst, interviewed Mann for
the article titled, “Changing the game:
How analytics can help defeat violent
extremism around the world.”
These two articles bookend the fea-
ture section of this issue of Analytics. In
between, you’ll find offerings on digital
business innovation, estimating customer
lifetime value and a profile of BNSF Rail-
way and its operations research and ad-
vanced analytics team. In addition, regular
columnists Vijay Mehrotra, Rajib Ghosh
and Harrison Schramm provide commen-
tary on such diverse topics such as the
good and bad side of Uber, what 2016
holds for healthcare analytics and predict-
ing Navy football games, respectively. ❙
– PETER HORNER, EDITOR
peter.horner@mail.informs.org
Only thing we have to fear

DRIVING BETTER BUSINESS DECISIONS
C O N T E N T S
JANUARY/FEBRUARY 2016
Brought to you by
W W W. I N FO R M S . O RG4 | A N A LY T I C S - M AGA Z I N E . O RG
64
FEATURES
DEEP DIVE INTO DATA LAKES
By Sean Martin
The premise, the promise, the potential of method for managing
big data has drawn widespread attention.
GET SMART: DIGITAL BUSINESS INNOVATION
By Haluk Demirkan and Bulent Dal
Smart technologies, services, processes and people add up to
smart systems for every sector.
CUSTOMER LIFETIME VALUE
By Matthew Lulay
Leveraging predictive analytics adds key new insights for
estimating familiar marketing metric.
CORPORATE PROFILE: BNSF RAILWAY
By Amy Casas
Operations research and advanced analytics team helps power
rail giant’s success now and in the future.
CHANGING THE GAME
By Doug Samuelson
How analytics and village stability operations can help defeat
violent extremism around the world.
32
40
50
56
64
50
56

XLMINER®
: Data Mining Everywhere
Predictive Analytics in Excel, Your Browser, Your Own App
have in Excel, and generate the same reports, displayed
in your browser or downloaded for local use.
XLMiner SDK: Predictive Analytics in Your App.
Access all of XLMiner’s parallelized forecasting, data
mining, and text mining power in your own application
written in C++, C#, Java or Python. Use a powerful
object API to create and manipulate DataFrames, and
combine data wrangling, training a model, and scoring
new data in a single operation “pipeline”.
Find Out More, Start Your Free Trial Now.
Visit www.solver.com to learn more, register and
download Analytic Solver Platform or XLMiner SDK.
And visit www.xlminer.com to learn more and register
for a free trial subscription – or email or call us today.
XLMiner® in Excel – part of Analytic Solver® Platform –
is the most popular desktop tool for business analysts
who want to apply data mining and predictive analytics.
And soon it will be available on the Web, and in SDK
(Software Development Kit) form for your own apps.
Forecasting, Data Mining, Text Mining in Excel.
XLMiner does it all: Text processing, latent semantic
analysis, feature selection, principal components and
clustering; exponential smoothing and ARIMA for
forecasting; multiple regression, k-nearest neighbors,
and ensembles of regression trees and neural networks
for prediction; discriminant analysis, logistic regression,
naïve Bayes, k-nearest neighbors, and ensembles of
classification trees and neural nets for classification;
and association rules for affinity analysis.
XLMiner.com: Data Mining in Your Web Browser.
Use a PC, Mac, or tablet and a browser to access all
the forecasting, data mining, and text mining power of
XLMiner in the cloud. Upload files or access datasets
already online. Use the same Ribbon and dialogs you
The Leader in Analytics for Spreadsheets and the Web
Tel 775 831 0300 • info@solver.com • www.solver.com

REGISTER FOR A FREE SUBSCRIPTION:
http://analytics.informs.org
INFORMS BOARD OF DIRECTORS
President Edward H. Kaplan, Yale University
President-Elect Brian Denton, University of Michigan
Past President L. Robin Keller, University of California, Irvine
Secretary Pinar Keskinocak, Georgia Tech
Treasurer Sheldon N. Jacobson, University of Illinois
Vice President-Meetings Ronald G.Askin,Arizona State University
Vice President-Publications Jonathan F. Bard, University ofTexas atAustin
Vice President-
Sections and Societies Esma Gel,Arizona State University
Vice President-
Information Technology Marco Lübbecke,
RWTHAachen University
Vice President-Practice Activities Jonathan Owen, CAP, General Motors
Vice President-International Activities Grace Lin, Institute for Information Industry
Vice President-Membership
and Professional Recognition Susan E. Martonosi, Harvey Mudd College
Vice President-Education Jill Hardin Wilson, Northwestern University
Vice President-Marketing,
Communications and Outreach Laura Albert McLay, University of
Wisconsin-Madison
Vice President-Chapters/Fora Michael Johnson, University of
Massachusetts-Boston
INFORMS OFFICES
www.informs.org • Tel: 1-800-4INFORMS

Executive Director Melissa Moore
Meetings Director Laura Payne
Director, Public Relations Marketing Jeffery M. Cohen

Headquarters INFORMS (Maryland)
5521 Research Park Drive, Suite 200
Catonsville, MD 21228
Tel.: 443.757.3500
E-mail: informs@informs.org
ANALYTICS EDITORIAL AND ADVERTISING
Lionheart Publishing Inc., 506 Roswell Street, Suite 220, Marietta, GA 30060 USA
Tel.: 770.431.0867 • Fax: 770.432.6969
President Advertising Sales John Llewellyn
john.llewellyn@mail.informs.org
Tel.: 770.431.0867, ext. 209
Editor Peter R. Horner
peter.horner@mail.informs.org
Tel.: 770.587.3172
Assistant Editor Donna Brooks
donna.brooks@mail.informs.org
Art Director Alan Brubaker
alan.brubaker@mail.informs.org
Tel.: 770.431.0867, ext. 218
Advertising Sales Sharon Baker
sharon.baker@mail.informs.org
Tel.: 813.852.9942
Aileen Kronke
aileen@lionhrtpub.com
Tel.: 770.431.0867, ext. 212
6 | ANALYTICS-MAGAZINE.ORG
DRIVING BETTER BUSINESS DECISIONS
Analytics (ISSN 1938-1697) is published six times a year by the
Institute for Operations Research and the Management Sciences
(INFORMS), the largest membership society in the world dedicated
to the analytics profession. For a free subscription, register at
http://analytics.informs.org. Address other correspondence to the
editor, Peter Horner, peter.horner@mail.informs.org. The opinions
expressed in Analytics are those of the authors, and do not
necessarily reflect the opinions of INFORMS, its officers, Lionheart
Publishing Inc. or the editorial staff of Analytics. Analytics copyright
©2016 by the Institute for Operations Research and the Management
Sciences. All rights reserved.
14
DEPARTMENTS
2 Inside Story
8 Executive Edge
14 Analyze This!
20 Healthcare Analytics
24 INFORMS Initiatives
28 News Notes
70 Conference Preview
74 Five-Minute Analyst
78 Thinking Analytically
74

ANALYTIC SOLVER®
PLATFORM
From Solver to Full-Power Business Analytics in Excel
Solve Models in Desktop Excel or Excel Online.
From the developers of the Excel Solver, Analytic Solver
Platform makes the world’s best optimization software
accessible in Excel. Solve your existing models faster,
scale up to large size, and solve new kinds of problems.
Easily publish models from Excel to share on the Web.
Conventional and Stochastic Optimization.
Fast linear, quadratic and mixed-integer programming is
just the starting point in Analytic Solver Platform. Conic,
nonlinear, non-smooth and global optimization are just
the next step. Easily incorporate uncertainty and solve
with simulation optimization, stochastic programming,
and robust optimization – all at your fingertips.
Fast Monte Carlo Simulation and Decision Trees.
Analytic Solver Platform is also a full-power tool for
Monte Carlo simulation and decision analysis, with 50
distributions, 40 statistics, Six Sigma metrics and risk
measures, and a wide array of charts and graphs.
Plus Forecasting, Data Mining, Text Mining.
Analytic Solver Platform samples data from Excel,
PowerPivot, and SQL databases for forecasting, data
mining and text mining, from time series methods to
classification and regression trees and neural networks.
And you can use visual data exploration, cluster analysis
and mining on your Monte Carlo simulation results.
Find Out More, Download Your Free Trial Now.
Analytic Solver Platform comes with Wizards, Help, User
Guides, 90 examples, and unique Active Support that
brings live assistance to you right inside Microsoft Excel.
Visit www.solver.com to learn more, register and
download a free trial – or email or call us today.
Supports Tableau,
Power BI and
Apache Spark Big Data

BY CHRIS MAZZEI
For years, companies have spent millions of dol-
lars on data analytics, but many have not seen a
breakthrough return on this investment. The problem?
Despite massive spending on technology to produce
analytics, these companies have spent relatively little
on their ability to consume analytics – what we call the
“human element of analytics.”
Business executives acknowledge that this discon-
nect is at the heart of the data analytics’ conundrum.
The latest EY/Forbes Insight study, “Analytics: Don’t
Forget The Human Element” [1], highlights many of
the obstacles to making analytics more actionable,
and emphasizes what leaders are doing most effec-
tively to achieve analytics excellence.
The study surveyed 564 senior leaders and found
that a majority of respondents do not have an effec-
tive business strategy for competing in a digital, ana-
lytics-enabled world. However, there is a segment of
executives, the top 10 percent of survey participants,
that is achieving a higher level of maturity and seeing
competitive advantage.
The top 10 percent of participants identified in the
survey typically meet two criteria:
• They use data analytics in their decision-making
“all of the time” or “most of the time.”
Data analytics’ better half
Despite massive spending
on technology to produce
analytics, companies have
spent relatively little on
their ability to consume
analytics – what we call
the “human element of
analytics.”
Why investing in the human element of analytics pays off.
EXECUTIVE EDGE

Your Analytics App – Everywhere
Use Solver, Risk Solver, XLMiner in Excel Online, Google Sheets
Or Turn YOUR Excel Model into a Web or Mobile App in Seconds
Your Excel Model Can Be a Web/Mobile App.
The magic begins in Excel with Frontline Solvers V2016:
Our Create App button converts your Excel optimization
or simulation model to a RASON model, embedded in a
Web page, that accesses our cloud servers via a simple
REST API. You’re ready to run analytics in a browser or
mobile device! Or if you prefer, run your RASON model
on your desktop or server, with our Solver SDK®. Either
way, you’re light-years ahead of other software tools.
Find Out More, Sign Up for a Free Trial Now.
Visit www.solver.com/apps to learn more, and visit
rason.com to sign up for a free trial of RASON and our
REST API. Or email or call us today.
The easiest way to build an analytic model – in Excel – is
now the easiest way to deploy your analytic application
to Web browsers and mobile devices – thanks to the
magic of Frontline Solvers® and our RASON® server.
Use our Analytics Tools in your Web Browser.
Solve linear, integer and nonlinear optimization models
with Frontline’s free Solver, and run Monte Carlo
simulation models with our free Risk Solver® tool, in
Excel Online and Google Sheets. Use our free XLMiner®
Analysis ToolPak tool for statistical analysis, matching
the familiar Analysis ToolPak in desktop Excel.
Build Your Own Apps with RASON Software.
RASON – RESTful Analytic Solver® Object Notation – is a
new modeling language for optimization and simulation
that’s embedded in JSON (JavaScript Object Notation).
With support for linear, nonlinear and stochastic
optimization, array and vector-matrix operations, and
dimensional tables linked to external databases, the
RASON language gives you all the power you need.

EXECUTIVE EDGE
be asking, “What is my analytics strate-
gy?” They should be asking, “What is my
business strategy to compete in a digital,
analytics-enabled world?” A slight majority
(54 percent) of executives with leading an-
alytics organizations report that analytics
is central to their overall business strategy,
versus approximately 1 in 10 of respon-
dents in the remaining 46 percent of enter-
prises who are “lagging” or “learning.”
2. Leadership and culture: Excellence
in big data and analytics requires strong
leadership. Close to two-thirds (64 percent)
of executives in the top 10 percent of en-
terprises indicate they “have a dedicated
C-level executive – a chief analytics officer
(CAO) – overseeing their data and analytics
programs and engagements.” In contrast,
only two in five (40 percent) of the lagging
organizations have a designated CAO.
However, it must be noted that effec-
tive analytics leaders are a rare breed. In
many ways, they need to be a renaissance
professional, with in-depth knowledge of
the business, analytics and statistics, while
also being an innovator, a network builder
and a leader of teams.
In addition to the analytics leadership
role, there are five challenges that the
CEO and C-suite executives must address
to build an analytics-enabled culture:
• Delegate an influential executive to
lead the enterprise-wide analytics
program.
• They report a “significant” shift
in their company’s ability to meet
competitive challenges.
THE HUMAN FACE OF ANALYTICS
Investing in new technology and tools,
data quality and advanced analytics skill
sets is common to many companies. Af-
ter all, these elements are critical for the
“production” of analytics.
But it is only half of the equation. What
is often missing is the behavioral alignment
required to move from insights to action
to value. This includes key components
such as culture, organizational processes,
skills of the business “users” and individual
employees’ incentives. These are the ca-
pabilities required to “consume” analytics
throughout the organization.
Finding ways to embed analytics into
business processes at the point where
decisions are made is essential to driving
true value in analytics. It is also where
organizations find the biggest challenge.
THE ORGANIZATIONAL LEVEL
Successwithanalyticsrequiresanorga-
nizational commitment to make productive
use of data that is integral to the business
strategy. Companies demonstrate this or-
ganizational alignment in three ways:
1. Strategy: Analytics is central to the
business strategy of leading enterprises,
but that does not mean executives should

JA N UARY / F E BR UARY 2016 | 11A NA L Y T I C S
connect people and analytics within their
organizations. More than half (56 percent)
of these top companies have already
aligned enterprise, department and lines-
of-business data and analytics groups,
compared with just 13 percent of the rest
of the organizations.
THE INDIVIDUAL LEVEL
Strong leadership and the right or-
ganizational and business processes in-
crease the likelihood that a company will
successfully be able to leverage analyt-
ics. But to achieve a positive impact, an-
alytics must be used at the point where
decisions are made – by individuals.
There are three factors to this:
1. Decision bias: Companies need
to provide the training to help individuals
recognize decision biases – the psycho-
logical assumptions that often lead to poor
• Use analytics to challenge existing
mental models in the leadership
team.
• Be clear on the critical business
objectives and quantifiable measures
for success.
• Navigate the inevitable conflicts
between established institutions or
executives that analytics creates.
• Foster collaboration within the
C-suite to set an example for the rest
of the organization.
• Tolerate failure as part of using
analytics to learn and innovate.
3. Organization and processes:
Aligning analytics delivery and business
requirements is crucial to enabling an
organization to consume analytics. The
survey found that the top 10 percent of
organizations had processes in place to
Figure 1: Leading enterprises have aligned their organizations around data and analytics.

EXECUTIVE EDGE
CONCLUSION
All companies will need to have ana-
lytics as a core competency in order for
business decisions to be informed by
data. End users of the analytics, whether
they are doctors, marketing profession-
als, factory workers, customer service
representatives or financial profession-
als, will enhance their decision-making
with the help of analytics. But this cannot
happen without recognizing that the con-
sumption of analytics is as important as
the production.
Now is the time to ask if your invest-
ment in producing data-driven insights
is delivering a competitive advantage. If
not, ask yourself if your organization is
effectively consuming analytics. And as
you look forward to what analytics will
deliver for your organization in 2016, do
not forget the human element. ❙
Chris Mazzei is the global chief analytics officer
(CAO) and global Analytics Center of Excellence
(COE) leader at Ernst Young, LLP, where he
is responsible for the overall development and
go-to market strategy for EY’s various analytics
businesses, as well as working with clients to
transform core services through the use of analytics.
decision-making. By being more aware
of this subconscious thinking, employees
can better interpret and act on the insights
from analytics.
2. Capabilities: For analytics to cre-
ate value, individuals within an organiza-
tion must be able to understand and use
the data and insights. First and foremost,
this comes down to training. In the survey,
we found that the top 10 percent of firms
are more likely than their peers to conduct
on-site seminars or workshops, enroll em-
ployees in off-site education programs or
coaching, and provide mentoring by data
and analytics professionals or leaders. But
this kind of education is about more than
what an individual knows; it also establishes
an analytics mindset within the organization.
As a result, everyone becomes more com-
fortable with analytics, which removes the
fear factor when switching from judgment-
based to analytics-based decision-making.
3. Incentives: Incentives, rewards
and measurement need to be aligned with
the actions suggested from the analytics-
based insights. According to the survey,
the top 10 percent understand the impor-
tance of motivation, with 40 percent of
them having aligned incentives to desired
change from analytics, compared with 23
percent of their peers. More than two-fifths
(42 percent) of the top 10 percent also of-
fer greater opportunities for promotion and
advancement to individuals.
REFERENCES
1. http://www.forbes.com/forbesinsights/ey_
data_analytics_2015/index.html
2. Figure 1 was taken from the EY/Forbes Insight
study, “Analytics: Don’t Forget The Human
Element.”

b 30-credit-hour curriculum
with admission offered
in Fall, Spring and
Summer semesters
b Gain skills in demand by
industrial, research and
commercial firms
b Concentrations and graduate
certificates available:
Logistics and Supply Chains,
Energy Systems, Lean Six
Sigma and Systems Analytics
Now Accepting Applications
DistanceEd.uncc.edu
704-687-1281
Master of Science in
EnginEEring
ManagEMEnt
Delivered 100% Online
A Technical Alternative to the MBA
Fast track option available – Finish in 12 months!

Time to roll. I’ve got to get to the other side of town,
quickly, for a meeting. I pull the phone out of my pocket,
click a single icon and the dot starts to flash: That’s me!
They’re looking for me! Soon thereafter a detailed map
appears with my location clearly indicated: They found
me! With another click, a message goes out across the
network, and within seconds information about my ride
– the driver’s name, cell phone number, car make and
model, license plate and estimated time of arrival – ap-
pears on my screen: They are coming to get me! While
I wait, I watch the driver’s progress on my map, and
if I need to clarify the pick-up details, I just hit another
button to call the driver to sort things out. Within min-
utes, I’m picked up in a clean and comfortable vehicle,
driven to my destination via a smart GPS-identified op-
timal route, and released as soon as I arrive (payment
is handled automatically via credit card).
Uber: good, bad side of
automated free markets
BY VIJAY MEHROTRA
I’m impressed and inspired
by the way that
several sophisticated
technologies have been
seamlessly stitched
together by Uber.
At the same time,
there is so much about
Uber that I intensely
dislike.
ANALYZE THIS

long hours, high pressure, lack of work/life
balance and utmost secrecy. None of this
is unique to Uber, but there’s something
about this particular San Francisco-based
company that embodies the way that the
tech industry and culture seems to have
swallowed much of San Francisco almost
overnight, with many of the diverse and
creative people that inspired me to move
here in the first place now priced out of an
overheated real estate market that seems
to be dominated by youngsters flush with
tech dollars – all of whom seem to be con-
stantly riding around in Uber cars.
But Uber’s reach extends far beyond
its San Francisco Bay Area home base, as
the company is constantly expanding. Its
basic approach is to thumb its nose at local
laws until eventually managing to get them
changed in an Uber-friendly direction. As
Tracey Lien wrote in a recent Los Ange-
les Times article, “It [Uber] punches itself
into markets and spends big on advance
teams, lawyers and lobbyists to fight op-
position and gain a foothold in markets
around the world” [4]. Uber’s ambitions are
vast, and its hiring of former Obama cam-
paign strategist David Plouffe reflects the
business importance of its constant com-
bative campaigning.
Meanwhile, Uber drivers – the people
who not only do the actual transporting
of passengers but also are required to
invest their own capital to purchase and
That’s Uber in action. Feels like magic,
especially compared to the faith-based and
stressful exercise of calling a dispatcher or
trying to hail a cab (especially here in San
Francisco, where there has always been
a terrible shortage of traditional taxis [1]),
then wondering whether the driver is giv-
ing me the runaround in order to jack up
my fare, and finally fumbling around in my
wallet looking for cash and hoping the driv-
er has the requisite change.
Beyond the convenience, I’m im-
pressed and inspired by the way that sev-
eral sophisticated technologies have been
seamlessly stitched together by Uber.
Among other things, the Uber experience
depends on smartphone hardware and
software, 21st century telecommunica-
tions infrastructure, increasingly sophisti-
cated GPS systems, payment processing
platforms and good, old-fashioned e-mail.
The Uber platform – elegantly designed,
smartly integrated – indeed makes the
user feel empowered, lending some emo-
tional truth to the company’s “everyone’s
private driver” tagline [2].
So I am both joyful and amazed every
time my Uber car pulls up. At the same
time, there is so much about Uber that I
intensely dislike.
For starters, the company’s founder
and CEO Travis Kalanick has a well-
chronicled reputation for arrogance and
misogyny [3]. The company is known for its

including food delivery, in-home services,
package shipment, elder care, overnight
lodging, shopping and administrative work.
From my perspective, these companies
are market makers seeking to optimize the
market dynamics in their own favor and
service delivery networks seeking to oper-
ate cost effectively on a large-scale basis
to capture customers, generate profits and
crush potential competitors.
Generating an expanding and relent-
less stream of proprietary operational
data, these young firms provide analytics
professionals with tremendous opportu-
nities to put our talents to use. Indeed,
in addition to the army of data scientists
that it employs, Uber’s recent wholesale
hiring of 40+ researchers from Carnegie
Mellon’s famed Robotics Institute [9] is a
vivid illustration of the value of special-
ized technical skills in this growing slice
of the business world.
But be aware: This so-called “gig econ-
omy” in which smart software platforms
efficiently match workers with tasks rep-
resents a major disruption at many differ-
ent companies. As tech heavyweight Tim
O’Reilly wrote prior to his recent “What’s
the Future of Work?” Conference [10],
“every industry and every organization
will have to transform itself in the next few
years” as a result of the increasing num-
ber of jobs that can be defined, transmitted
and/or delivered via integrated platforms
operate the individually owned vehicles
that collectively comprise Uber’s fleet –
are seeking to be treated as employees
in California [5] (rather than independent
contractors) and have been granted the
right to unionize in Seattle [6]. Recently,
Uber’s unilateral decisions to decrease
its prices while also increasing its share
of total revenues have led to sharp drops
in income for its drivers. Its practices for
screening the drivers in its network have
also been under scrutiny [7].
Uber’s growth has been phenome-
nal. Though the company is less than six
years old, it is now possible to hail a ride
in more than 150 cities around the United
States and 68 countries around the world
[8]. Nor are the company’s ambitions lim-
ited to moving passengers. To date, Uber
has experimented with a variety of new
pilot projects that leverage its platform
and driver network to provide drugstore
items (UberESSENTIALS), restaurant
meals (UberEATS), urgent package de-
liveries (UberRUSH) and even flu shots
(UberHEALTH). The company, it appears,
wants to be the Amazon.com of in-person
service delivery. Not yet six years old and
still privately held, Uber was recently val-
ued at somewhere north of $50 billion.
Along with Uber, a number of other
companies are developing specialized
software platforms for matching buyers
to sellers in many different industries,
ANALYZE THIS

BUSINESS
ANALYTICS
Top Rated College
by Forbes
Princeton Review
BECOME MORE AT THE
Beacom School of Business
Online MBA
Ranked Top
25by Princeton
Review
IN
THE
WORLD
Best Value
MBA Ranked Top
10AFFORDABILITY
ACCREDITATION
by Best Master’s
Degree
Online
MBA
Get started at
www.usd.edu/onlinemba
cde@usd.edu • 800-233-7937
MBA – General
MBA – Business Analytics
MBA – Health Services Administration

ANALYZE THIS
like Uber’s. We now have an estimated
53.7 million freelance workers in the Unit-
ed States [11].
Analytics professionals will continue
to play a big role in this revolution, so it
is important for us to consider not just its
technical challenges but also its social
consequences. Marina Gorbis, executive
director of the not-for-profit think tank The
Institute for the Future, calls these plat-
forms “new operating systems” for getting
work done that are “based on always-on In-
ternet, mobile devices, social media, sen-
sors and geolocation technologies.” She
also warns that these economic platforms
“could also be riddled with catastrophic
bugs, pushing large swaths of the popula-
tion to labor at subsistence levels, with no
benefits and little predictability over their
earning streams” [12].
Personally, I’m still haunted by Jaron
Lanier’s ominous warnings about Siren
Servers [13]. Like Lanier, I don’t believe
that highly automated and unfettered
free markets for all kinds of services are
inherently optimal. As freelance business
writer Erik Sherman recently pointed out,
there is “a systemic imbalance in favor
of the company that can ignore or avoid
regular conditions of doing business”
[14], which sounds a lot like Uber when
it enters a new market. I talk frequently
with my MBA students and alums about
the potential downside of concentrating
NOTES REFERENCES
1. My friend Brad Newsham, a former San Francisco taxi driver,
provides a nice description of this situation at http://www.
bradnewsham.com/articles/why_so_hard.shtml
2. https://vimeo.com/58800109
3. See for example http://www.modernluxury.com/san-francisco/
story/the-smartest-bro-the-room
4. http://www.latimes.com/business/la-fi-0822-uber-revenue-
20150822-story.html.
5. http://recode.net/2015/06/17/uber-drivers-are-employees-not-
contractors-california-labor-commission/
6. http://www.nytimes.com/2015/12/15/technology/seattle-
clears-the-way-for-uber-drivers-to-form-a-union.html
7. http://www.fastcompany.com/3050172/tech-forecast/the-truth-
about-ubers-background-checks
8. https://www.uber.com/cities
9. http://www.nytimes.com/2015/09/13/magazine/uber-would-
like-to-buy-your-robotics-department.html
10. http://conferences.oreilly.com/nextcon/economy-us-2015
11. “Freelancing in America: 2015,” accessible online at https://
www.upwork.com/i/freelancinginamerica2015/
12. https://medium.com/the-wtf-economy/designing-a-
new-operating-system-for-work-28d1dc3e0f64?imm_
mid=0dde51cmp=em-na-na-na-newsltr_
econ_20151218#.vtbs6vot4
13. http://www.analytics-magazine.org/july-august-2014/1069-
analyze-this-dark-side-of-the-digital-world
14. http://www.forbes.com/sites/eriksherman/2015/12/10/
the-gig-economy-depends-on-unequal-treatment-of-
businesses
15. Even before Uber’s ascent, the San Francisco taxi driver
community had been hit by “friendly fire” from City Hall.
To learn more, see http://ww2.kqed.org/news/wp-content/
uploads/sites/10/2013/01/NewshamArticle.pdf
16. For some recent highlights, see https://www.
popularresistance.org/anti-uber-protests-around-the-world/
too much power in too few online pro-
curement and delivery channels.
Yet there’s also no real case for de-
fending the traditional taxi industry either,
certainly not here in San Francisco [15]
and probably not in many other places.
As Uber’s relentless expansion into new
markets continues, expect to see more
battles with local taxi companies and driv-
ers [16] – and more passengers getting on
the Uber app.
Sorry, gotta go. My Uber just pulled up. ❙
Vijay Mehrotra (vmehrotra@usfca.edu) is a
professor in the Department of Business Analytics
and Information Systems at the University of San
Francisco’s School of Management and a longtime
member of INFORMS.

THE NATION’S FIRST
Associate in Applied Science (A.A.S.)
degree in Business Analytics
on campus or online.
Flexibility
• Open-door enrollment
• Courses are offered in the fall and spring
• Courses can be taken online or on campus
• Competitively priced tuition
Credential options
• Enroll in one or several:
• AAS degree
• Certificates: Business Intelligence,
Business Analyst, Finance Analytics,
Marketing Analytics, and Logistics
Analytics
Gain skills in:
• Data gathering
• Collating
• Cleaning
• Statistical Modeling
• Visualization
• Analysis
• Reporting
• Decision making
• Presentation
Use data and
analysis tools:
• Advanced Excel
• Tableau
• Analytics Programming
• SAS Enterprise Guide
• SAS Enterprise Miner
• SPSS Modeler
• MicroStrategy
Accelerated Executive Program
Our accelerated learning options allow students to complete
certificate credentials in two semesters part time or one semester
full time. Accelerated options are available for the Business
Intelligence and the Business Analyst certificates.
Why Study Business Analytics?
The Business Analytics curriculum is designed to provide students
with the knowledge and the skills necessary for employment and
growth in analytical professions. Business Analysts process and
analyze essential information about business operations and also
assimilate data for forecasting purposes. Students will complete
course work in business analytics, including general theory, best
practices, data mining, data warehousing, predictive modeling,
project operations management, statistical analysis, and software
packages. Related skills include business communication, critical
thinking and decision making.The curriculum is hands-on, with
an emphasis on application of theoretical and practical concepts.
Students will engage with the latest tools and technology utilized
in today’s analytics fields.
Questions?
Tanya Scott
Director, Business Analytics
919-866-7106
tescott1@waketech.edu
Funded in full by a $2.9 million Dept. of Labor Trade Adjustment Assistance Community College Career Training (DOLTAACCCT) grant.
businessanalytics.waketech.edu

It’s hard to believe that 2015 and half of the sec-
ond decade of the new century is over. Many industries
have changed or were disrupted during this time. Many
more will share the same fate as we move through the
decade. We have seen many changes in healthcare
too, albeit at a slower pace than other industries such
as mobile or transportation. Nonetheless, changes in
2015 caused the otherwise conservative and closed
healthcare industry to change direction. Healthcare has
become data- and analytics-driven in almost all parts of
the value chain. As a direct consequence of the Afford-
able Care Act (ACA) traditional business models have
changed. In the coming years those changes are ex-
pected to continue. In this article I focus on four trends
that will drive healthcare analytics in 2016 and beyond.
NO. 1: CONSUMERISM IN HEALTHCARE
HAS BEGUN
Since the implementation of ACA in 2010, pundits
predicted that consumers would have bigger voices
in the healthcare industry. We didn’t see much prog-
ress in the initial years of ACA. That is changing. More
and more Americans are now buying high-deductible
health plans. Enrollment in such plans doubled since
Four mega trends to
watch in 2016
BY RAJIB GHOSH
Changes in 2015 caused
the otherwise conservative
and closed healthcare
industry to change
direction.
HEALTHCARE ANALYTICS

Center for Medicare and Medicaid (CMS)
and some private health plans have
pushed delivery organizations to accept
more risks for population health manage-
ment. Provider organizations, feeling this
price pressure from public and private
plans, are trying to consolidate in many
markets to retain pricing power. This trend
became quite pervasive in 2015. Com-
bining hospitals with physician groups is
growing. Kaiser is leading the way as their
CEO, Bernard Tyson, said in a recent in-
terview that their model is the best way to
deliver care for patients and populations.
To steer power away from payer organi-
zations, providers are also offering their
own plans and trying to adopt KP-like in-
tegrated delivery network (IDN) models.
To counter that strategy in 2015, we have
seen a mega merger trend among payers
as well. Anthem Blue Cross and Cigna,
Humana and Aetna, United Healthcare
and Catamaran are just a few examples.
The business drivers for most mergers are
cost containment and defending pricing
power. Mega mergers create opportunities
to combine large data sets with analytics
to have a bigger impact on delivering bet-
ter population health management.
NO. 3: PREDICTIVE ANALYTICS IN
HEALTHCARE FINALLY ARRIVED
Some 40 percent of healthcare ex-
ecutives reported more than 50 percent
2010 to about a quarter of all American
workers with health plan benefits in 2014.
This forces consumers to pay more for
healthcare as out-of-pocket expenses.
Data from Commonwealth Fund shows
that out-of-pocket household expenses for
healthcare, including premiums and de-
ductible, doubled to 9.6 percent of house-
hold income between 2003 and 2013.
This is driving consumer demand for the
ability to compare gross and net prices for
healthcare services.
In theory, price transparency may al-
low consumers to make better decisions for
their healthcare, and price competitiveness
should drive costs down like other indus-
tries. Care delivery organizations should
scrutinize their costs, rethink their deliv-
ery workflow and manage their revenue
cycle well to keep costs down and attract
more clients. Whether that will happen or
not remains to be seen. At the same time
consumers are increasingly gravitating to-
ward wearables to make self-care easier.
A recent IDC report shows that worldwide
wearable shipment has grown 163 percent
since 2014. Both areas have made positive
impact on the need for better data analytics.
NO. 2: PROVIDERS ARE TAKING
MORE RISK FOR OUTCOMES AND
CONSOLIDATING
Results from the initial accountable
care organizations were quite mixed. The

HEALTHCARE ANALYTICS
to manage the health of a population.
Pharmaceutical companies may follow
suit and become a partner in care with
healthcare organizations. Government
payers, i.e., Medicare and Medicaid, are
fast moving toward capitated payment
and value-based-purchasing models
where outcome will be measured and
rewarded. To be successful in this new
model, data and analytics will become as
important as providers, and soon a data
analyst will figure in the care teams with-
in provider organizations alongside with
physicians, nurses and case managers.
2016 marks the beginning of the sec-
ond half of this decade, and it is expected
to be transformative for the healthcare in-
dustry overall. It is also the year for the
presidential election. If politics do not
get in the way of this fast moving train of
“transformation,” we should buckle up for
more disruptive changes. ❙
Rajib Ghosh (rghosh@hotmail.com) 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 field of
IT-enabled sustainable healthcare delivery in the
United States as well as emerging nations. Follow
Ghosh on twitter @ghosh_r.
data volume increase in 2014 according
to a report by Manatt, Phelps and Phillips,
a prominent U.S. law and consulting firm.
As the data sets become bigger, health
systems and payers take advantage
of predictive analytics. In 2014, 47 per-
cent of the managed care organizations
(MCO) possessed predictive analytics
tools. By 2016 the number is expected
to rise to 80 percent. That’s a significant
jump. Healthcare organizations are also
adopting the insight that social determi-
nants of health contribute to the wellbe-
ing of a patient more than the medical
issues. In 2016, both social determinants
of health along with usual suspects such
as drug use and emergency room ad-
missions data will drive predictive model
for identifying cost risks of population
cohorts.
NO. 4: CAPITATED PAYMENT WILL
DRIVE STAKEHOLDERS TOWARDS
ANALYTICS DRIVEN POPULATION
HEALTH MANAGEMENT
One delivery organization can’t un-
dertake population health management
unless it is an integrated delivery net-
work. Apatient seldom visits just one care
delivery organization during a disease
life cycle. Access issues and the insur-
ance exchange marketplace will support
patient mobility in 2016. As a result, we
can expect non-competing healthcare
organizations to partner with each other

CPLEX Optimization Studio®
.
Still the best optimizer and
modeler for the ﬁnance industry.
Now you can get it direct
CPLEX Optimization Studio is well established as
the leading, complete optimization software. For
years it has proven effective in the ﬁnance industry
for developing and deploying business models and
optimizing business decisions.
Now there’s a new way to get CPLEX – direct
from the optimization industry experts.
Find out more at optimizationdirect.com
The IBM logo and the IBM Member Business Partner mark are trademarks of
International Business Machines Corporation, registered in many jurisdictions
worldwide.
*IBM ILOG CPLEX Optimization Studio is trademark of International Business Machines
Corporation and used with permission.

CAP NEWS: INFORMS TO LAUNCH
ASSOCIATE PROGRAM
INFORMS will launch an Associate Certified Ana-
lytics Professional (aCAP) program in 2016. Aimed at
young professionals and career changers, the aCAP
program allows individuals to apply for and take the
CAP® exam and hold the aCAP designation until
they’ve earned the requisite work experience to apply
for the CAP credential.
If you’ve already earned CAP certification, you
may be interested in serving as a CAP ambassa-
dor. INFORMS will soon provide CAP holders with
information regarding the ambassador program and
how you can help INFORMS increase the value and
visibility of CAP certification.
For those interested in taking the CAP exam,
INFORMS offers online, computer-based testing so you
can test on your schedule,
as well as paper-and-pencil
exams at selected sites. To
access any exam, you must
first apply and be approved for
the CAP examination. Eligible
veterans can use their GI Bill
to reimburse the exam fee.
aCAP, pro bono
Data Science Bowl
The aCAP program allows
individuals to apply
for and take the CAP
exam and hold the aCAP
designation until they’ve
earned the requisite work
experience to apply for the
CAP credential.
INFORMS INITIATIVES

INFORMS SUPPORTS DATA
SCIENCE BOWL
INFORMS is once again a partner in
the National Data Science Bowl, an online,
three-month-long (ending March 14, 2016)
competitive event sponsored by Booz Al-
len Hamilton and Kaggle. Held in conjunc-
tion with the National Heart, Lung and
Blood Institute (part of the National Insti-
tutes of Health), this year’s challenge is to
develop an algorithm to empower doctors
to more easily diagnose dangerous heart
For more information, visit:
www.certifiedanalytics.org.
Who is a CAP? INFORMS recently
queried its applicant pool (includes both
CAP holders and those who have ap-
plied for certification) and came up with
a snapshot illustrated by the following
graphs:
conditions and help advance the science
of heart disease treatment.
Declining cardiac function is a key
indicator of heart disease. Doctors de-
termine cardiac function by measuring
end-systolic and end-diastolic volumes
(i.e., the size of one chamber of the heart
at the beginning and middle of each
heartbeat), which are then used to de-
rive the ejection fraction (EF). EF is the
percentage of blood ejected from the left
ventricle with each heartbeat. Both the

This year’s Data Science Bowl chal-
lenges individuals and teams to create
an algorithm to automatically measure
end-systolic and end-diastolic volumes
in cardiac MRIs after examining MRI
images from more than 1,000 patients.
The data set was compiled by the Na-
tional Institutes of Health and Chil-
dren’s National Medical Center and is
an order of magnitude larger than any
cardiac MRI data set released previ-
ously. With it comes the opportunity for
the data science community to take ac-
tion to transform how to diagnose heart
disease.
The competition offers an award of
$200,000 to the winner. For more infor-
mation, visit www.datasciencebowl.com/
and watch the tutorial video (https://you-
tu.be/dFu_5T0ODrM)
volumes and the ejection fraction are
predictive of heart disease.
While a number of technologies can
measure volumes or EF, magnetic reso-
nance imaging (MRI) is considered the
gold standard test to accurately assess
the heart’s squeezing ability.
The challenge with using MRI to mea-
sure cardiac volumes and derive ejection
fraction, however, is that the process is
manual and slow. A skilled cardiologist
must analyze MRI scans to determine EF.
The process can take up to 20 minutes to
complete – time the cardiologist could be
spending with his or her patients. Making
this measurement process more efficient
will enhance doctors’ ability to diagnose
heart conditions early, and carries broad
implications for advancing the science of
heart disease treatment.
INFORMS INITIATIVES

solve challenges and create new op-
portunities for success with the scientific
process of transforming data into insight.
The initiative matches INFORMS’ ana-
lytics professional volunteers with non-
profit organizations that would benefit
from advanced analytics and operations
research training and techniques. By fo-
cusing on current analytics issues as they
relate to non-profit organizations, the Pro
Bono Analytics team will be able to take
the necessary steps in assisting to solve
the most complex of issues. ❙
INFORMS TO LAUNCH ‘PRO BONO
ANALYTICS’ PROGRAM)
INFORMS, the leading professional
association in analytics and operations
research, recently announced it is launch-
ing a new initiative – “Pro Bono Analytics”
– in an effort to connect analytics experts
with non-profit organizations seeking to
improve how they achieve greater results
by leveraging data and information.
With the Pro Bono Analytics initia-
tive, non-profit organizations have the
opportunity to work with analytics pro-
fessionals on a volunteer basis to help

INFORMS ANNOUNCES 2016 EDELMAN
AWARD FINALISTS
INFORMS has named six organizations repre-
senting applications of real-world operations research
and advanced analytics for the 2016 Franz Edelman
Award competition. The winner will be announced at
the INFORMS Conference on Business Analytics
Operations Research in Orlando, Fla., in April follow-
ing a daylong series of presentations before a panel
of judges.
Edelman, queues,
STEM survey
The Edelman finalists
were chosen after a
rigorous review by
verifiers, all of whom have
led successful analytics
projects.
NEWS NOTES

University of Chicago and University of
Maryland.
Now in its 45th year, the Franz Edel-
man Award is the world’s most prestigious
recognition for excellence in developing
and applying advanced analytical meth-
ods to help organizations solve complex
problems or create new opportunities that
result in highly impactful outcomes for the
economy and society.
ART, SCIENCE AND PSYCHOLOGY OF
MANAGING LONG QUEUES
As a world-renown expert in queue-
ing theory, MIT professor Richard Larson,
aka “Dr. Queue,” knows all about waiting
in lines. So it’s no surprise that when the
Washington Post’s Wonkblog reporterAna
Swanson needed an expert source for her
story on the art and science of managing
long queues, she called on Dr. Queue.
According to Larson, people can ex-
pect to spend one to two years of their
lives waiting in line, most of it stuck in
traffic. But those five-minute waits in the
The finalists include:
• 360i for “360i’s Digital Nervous
System”
• BNY Mellon for “Transition State
and End State Optimization Used in
the BNY Mellon U.S. Tri-Party Repo
Infrastructure Reform Program”
• Chilean Professional Soccer
Association (ANFP) for “Operations
Research Transforms Scheduling of
Chilean Soccer Leagues and South
American World Cup Qualifiers”
• The New York City Police
Department (NYPD) for “Domain
Awareness System (DAS)”
• UPS for “UPS On Road Integrated
Optimization and Navigation (Orion)
Project”
• US Army Communications
Electronics Command (CECOM)
for “Bayesian Networks for US Army
Electronics Equipment Diagnostic
Applications: CECOM Equipment
Diagnostic Analysis Tool, Virtual
Logistics Assistance Representative”
The finalists were chosen after a rig-
orous review by verifiers, all of whom
have led successful analytics proj-
ects. The verifiers come from organi-
zations such as Verizon Wireless, HP,
Turner Broadcasting, Carnegie Mel-
lon University, PriceWaterhouseCoo-
per, SAITECH, Princeton Consultants,

To read the complete article “What re-
ally drives you crazy about waiting in line
(it actually isn’t the wait at all),” click here.
STEM MAJORS WITH THE BEST
VALUE
Not surprisingly, WorldWideLearn.
com’s updated list of the “STEM Majors
With the Best Value for 2015” is loaded
with majors common among members of
the analytics community. The list includes
information technology (No. 1), computer
programming (No. 3), computer and infor-
mation science (No. 5), engineering (No.
7), data modeling (No. 9), computer sys-
tems analysis (No. 11), mathematics (No.
18), management science (No. 21), infor-
matics (No. 22), petroleum engineering
(No. 23) and physics (No. 25).
WorldWideLearn.com analyzed 122
majors belonging to the STEM disci-
plines. To be included in the rankings,
each major had to meet at least one of
the following criteria:
• Be on the 2012 STEM-Designated
Degree Program List from the
Department of Homeland Security
checkout line at the supermarket, stuck be-
hind someone talking on their smartphone
while fumbling with a pile of coupons and
dollar bills to give to the checker, can be
just as annoying.
As Swanson notes in the article, wait-
ing in line not only irritates the customer,
it’s bad for business. “A long and unpleas-
ant wait can damage a customer’s view of
a brand, cause people to leave a line or not
enter it in the first place (what researchers
respectively call ‘reneging’ and ‘balking’),
or discourage them from coming back to
the store entirely,” she writes.
Businesses, of course, realize this and
come up with various ways to solve the
problem, starting with good, old-fashioned
distraction such as magazines in the doc-
tor’s waiting room and near the supermar-
ket checkout lines. Larson, a past president
of INFORMS, considers Disney the “undis-
puted master” of designing queues that are
entertaining and that create anticipation for
the ride. “In my book, [Disney is] number
one in the psychology and in the physics of
queues,” Larson tells the Post.
Writes Swanson: “The design is so
successful that parents with young chil-
dren can happily stand in line for an hour
for a four-minute ride – a pretty remark-
able feat, [Larson] points out. And of
course, the capacity of the line and the
ride are carefully calculated to balance
customer satisfaction with profits.”
NEWS NOTES

both Python and R, both of which
are used heavily in the data science
community, but faculty members are
not adapting their courses to teach
these new languages.
• With few exceptions, there seems
to be misalignment between the use
of modeling languages in academia
and the use of modeling languages
in practice.
The survey of 72 self-selected par-
ticipants, all of whom were onsite at the
INFORMS Annual Meeting, was com-
prised of college professors (44 percent),
students (32 percent) and practitioners
(24 percent). The non-scientific “snap-
shot” survey was designed to compare
the responses of these three groups about
solvers, programming languages, model-
ing languages and software development
based on the participants’ last two years of
experience. ❙
• Be matched by the National Center
for Education Statistics to a job on
the Bureau of Labor Statistics’ list of
STEM occupations
Ranking criteria including education-
al availability, educational affordability,
earnings and employment opportunity.
GAPS BETWEEN TEACHING,
PRACTICE OF ADVANCED ANALYTICS
Students of advanced analytics who
aspire to leave academia and succeed
quickly in business and government are-
nas should assess their approaches and
tools in the classroom and their research,
according to an informal Princeton Con-
sultants survey conducted at the 2015
INFORMS Annual Meeting in Philadel-
phia. The survey revealed notable gaps
between what students learn, what profes-
sors teach and what practitioners need.
Irv Lustig of Princeton Consultants, a
longtime INFORMS member and a for-
mer employee of CPLEX, ILOG and IBM,
reported the following findings:
• Students must learn more about
building applications with modern
technologies so they have the skills
needed by the practice community.
• Professors are, for the most part, not
teaching the programming languages
used by students or in practice.
Students and practitioners are using

The ascendency of
he data lake concept occu-
pies a central place of prom-
inence in contemporary big
data initiatives. The past two
years have unveiled numerous headlines,
vendor solutions (including repackaging
of former solutions) and enterprise use
cases for the utility of this centralized ap-
proach for accumulating, analyzing and
actuating big data.
The fervor for this method of manag-
ing big data is based on a simple prem-
ise that promises value for organizations
regardless of size or vertical industry.
Data lakes provide a singular repository
for storing all data – unstructured, semi-
structured and structured – in their native
formats, granting access and insight to all
without lengthy IT preparation.
Moreover, the data lake movement
is largely spurred by adoption rates for
Hadoop. As Hadoop’s presence increases,
its function as an integration hub for all data
delivers more credence and traction to the
notion of data lakes. The data lake concept
may be relatively new, but the association
data lakes
BY SEAN MARTIN
T
DEALING WITH BIG DATA
The premise, the promise, the potential of new method
for managing big data.

a world in which organizations are con-
fronted with new and differing technolo-
gies, tools and platforms daily, data lakes
offer something of an oasis: a one-stop
hub for all aspects of big data, from initial
ingestion to analytics-based action, that
makes big data more manageable and
demonstrable of its value.
DATA LAKE DRIVERS
Big data is the principal driver of data
lakes. Organizations realize the business
value that collecting large quantities of
data engenders; they understand that
exploiting this opportunity will give them
an advantage over competitors who do
of Hadoop and big data is nearly as
ubiquitous as big data itself.
The combination of these two fac-
tors, Hadoop’s deployment as a data
lake and the storage and access benefits
this method produces, is largely respon-
sible for the widespread attention data
lakes have garnered. A recent post from
Gartner reveals that data lake interest is
“becoming quite widespread.” Forbes in-
dicates that “one phrase in particular has
become popular for the massing of data
into Hadoop, the ‘Data Lake.’”
Most of all, the intrigue behind the data
lake phenomenon pertains to the poten-
tial of these centralized repositories. In
Big data is the principal driver of data lakes.

DATA LAKES
Organizations can encompass data from
different sources (with varying schema
and structure, or lack thereof) that utilize
multiple technologies (cloud, social, mo-
bile, etc.). Additionally, they can do so to
suit the needs of individual business units
and across vertical industries, if need be.
Nonetheless, the driver that is likely
to make data lakes mainstream is the
perception of open source technologies.
Hadoop’s salience is directly related to
the burgeoning familiarity, acceptance,
and penetration of open source technolo-
gies. Granted, adoption rates for Hadoop
reflect many of the foregoing drivers for
data lakes. However, its ubiquity is also
linked to a greater ease to attain upper-
level management support for the data
lake concept, since many executives al-
ready associate big data with Hadoop.
The notion of dark data, and the re-
alization that elucidating such data im-
proves big data’s ROI, also contributes to
the ascendency of data lakes. Positioning
an organization’s entire data assets into
a single place provides the first step in at-
taining insight, and then value, from them
comprehensively. With the majority of the
world’s newly generated data involving
unstructured and semi-structured forms,
data lakes are poised as the optimal en-
vironment to parse and utilize such data
in accordance with structured data for a
holistic overview of data assets.
not. The most immediate advantages of
this architecture involve costs for stor-
age and physical infrastructure. Data
lakes enable organizations to store mas-
sive amounts of data at reduced costs
that were not previously available. Ad-
ditionally, this architecture is extremely
scalable and suited for daily ingestion of
petabytes.
Alternative methods of storing such
data present greater upfront costs than
open source Hadoop does. Data lakes
also enable organizations to simplify
their infrastructure; their comprehensive
nature decreases the needs for silos and
data marts. Consequently, there is less
physical infrastructure, which translates
to cost benefits associated with manag-
ing and maintaining a single repository
instead of multiple ones.
Another driver for data lakes is the in-
creased availability and accessibility they
deliver. This advantage is best measured
in temporal terms. Data lakes dispel the
lengthy data preparation processes that
typify the involvement of IT departments
with other options for managing big data.
Instead, users across the enterprise can
access data from the same place with a
degree of immediacy that is vital to the
speed at which big data is absorbed.
That accessibility correlates to an
availability of data that is unparalleled
with traditional database life cycles.

Therefore, warehousing is incongruent
with the current self-service movement
within data management, which seeks to
empower the business and give it more
control over its data.
COMPARATIVE DISADVANTAGES
Data lakes rectify the cost concerns
for storage and the rapidity of access as-
sociated with warehousing time-sensitive
big data. However, these benefits be-
come disadvantageous without critical
aspects of data management that require
COMPARATIVE ADVANTAGES
A comparison between data lakes
and traditional repository methods for
big data illustrate a number of pivotal ad-
vantages and disadvantages – for both.
Data lakes are arguably displacing data
warehousing as the de facto means of
storing data and facilitating analytics.
Multiple facets of data warehouses ren-
der them unsuitable for the quantities
and varieties of big data that are required
to truly profit from this technology. The
most readily apparent are storage costs,
which are exorbitant com-
pared to those for Hadoop.
The increase in sources
and types of big data mere-
ly exacerbates the stor-
age issue, and makes the
warehouse approach par-
ticularly unwieldy.
This fact is compound-
ed by the time consump-
tion of warehousing and
the traditional BI it was
designed to support. The
business is constantly wait-
ing for IT to model, prepare
and transform data before
any analysis and report-
ing is performed, which
decreases the value of the
velocity at which big data
is ingested and consumed.
Translated into 9 languages
Used in courses at more
than 30 universities
Predictive Analytics
The Power To Predict Who
Will Click, Buy, Lie, or Die
READ THE POPULAR BOOK - NOW REVISED AND UPDATED - AND IN PAPERBACK
*Free audiobook with purchase of paperback or e-book
More info: www.thepredictionbook.com

DATA LAKES
needed to extract value for or even ob-
tain clean access to the data residing in
the data lake. As inflexible and cumber-
some as they are, data warehouses can
draw on an army of DBAs, armed with a
host of mature data wrangling technolo-
gies and will generally produce reliable
reports on a regular schedule. In many
cases data lakes can rapidly resemble a
“Wild West” for data.
MAXIMIZING DATA LAKE UTILITY
The data lake concept fulfills its prom-
ise via smart data lakes that leverage se-
mantic models and graphs to eliminate
the aforementioned points of disorder
while adding additional advantages such
as delivering drastically improved busi-
ness end-user self-service capability.
Semantic models (based on ontologies)
provide concise descriptions of data and
are visually represented in a semantic
graph. These ontologies clarify data and
enhance context by denoting just what
the data mean, regardless of source,
structure, type or schema. The visual
representation of data in a graph illus-
trates their relationships to one another,
providing further context and the founda-
tion for application and analytics usage.
These definitions and relationships are
digestible for the business and other end
users, which expedites their access to
and deployment of big data.
more than just depositing data into Ha-
doop or NoSQL stores; failing to imple-
ment them frequently results in these
points of chaos:
Lack of context and meaning: Large
data volumes, disparate data types and
big data sources are collected in data
lakes without any sort of context or readily
discernible meaning. Without those con-
ventional, lengthy preparation processes
facilitated by IT, end users (or data scien-
tists) are left to implement them as best
they can, oftentimes without formal train-
ing in this critical prerequisite. The result
is an obfuscation of data’s meaning and
makes data discovery extremely difficult.
Inconsistent data: The jumbled data
in data lakes lack semantic and metada-
ta consistency, creating further ambiguity
about data’s meaning, purpose and rela-
tion to other data. Subsequently, there are
considerable deleterious effects for …
Data governance: The unrestrained
approach of unmanaged data lakes con-
siderably worsens some of the hallmarks
of data governance including role-based
access to data, security concerns, and
transparent data lineage and traceability.
Another serious problem that imple-
menters of early data lakes struggle to
address is the scarcity of the data scien-
tist and big data manipulation or even big
data programming skills that are usually

the data is in one place, restrictions and
permissions to their use are as enforce-
able as if the data were siloed accord-
ing to governance mandates, providing
internal security for disparate use cases
of the same repository.
Provenance and regulatory com-
pliance: Provenance issues are ad-
dressed due to the inherent consistency
of semantic models and the ease with
which it is possible to augment data sets
with metadata capturing the originating
context and full data lineage; the ensuing
Utility solutions:
Role-based access: Semantic tech-
nologies also maintain the necessary
governance and security policies for
long-term sustainability of data lakes.
Organizations can implement role-based
access to data in accordance with gov-
ernance protocols by specifying who can
and cannot view data elements as ex-
pressed by triples. Such access is one of
the primary means of engendering order
and structure to data lakes based on en-
terprise-wide policies. Thus, even though
• Find the seasoned professionals you need – over 800 analytics professionals expected
• Provide your recruitment materials in a casual setting
• Arrange discreet on-site meetings in private booths
• Enjoy discounted combination pricing with the fall Annual Meeting Career Fair
• Enhance your visibility with an ad in Analytics or OR/MS Today
Questions? careers@informs.org or call (800) 4-INFORMs
RESERVE YOUR SPACE NOW FOR THE INDUSTRYʼS PREMIER CAREER FAIR!
INFORMS Conference on
Business Analytics Operations Research
April 10–12, 2016 Hyatt Regency Grand Cypress, Orlando, Florida
www.meetings.informs.org/analytics2016
Are You Looking For an Analytics
Professional to Make Sense of Your Data?
CAREER
CENTER

DATA LAKES
– according to highly specific needs of
end users. Depending on the discernible
attributes and context of data elements.
In life science organizations for ex-
ample, clinicians and data scientists
have found significant value in quickly
juxtaposing the data from multiple clini-
cal trials results through ad hoc queries
that navigate across multiple data sets.
In financial services, identifying the
potential for misuse of material nonpub-
lic information can be extremely ardu-
ous. Links and relationships need to be
examined by compliance officers to un-
derstanding what, how, why and when
information is shared and whether it is
compliant or not. Similarly difficult is tying
together information that builds a com-
prehensive picture of counterparty risks.
2016 PREDICTIONS
Analytic expansion: Of all the ways
that semantically enhanced data lakes
will influence the data landscape in 2016,
their impact on analytics will be the most
profound. The numerous aforementioned
possibilities of such data lakes coalesce
into the fact that by deploying them, it is
possible to place an organization’s entire
data assets on an RDF graph, elucidat-
ing the relationships between elements in
such a way that effectively overcomes the
dark data phenomenon. Innately under-
standing the context and meaning of data
traceability and lineage is critical for de-
termining regulatory compliance. This
method allows organizations to analyze
any variety of data sources and appli-
cations—emails, online account activ-
ity, trades, etc.—to see just where and
how data was used, and if it was done or
should be done in accordance to regu-
lations. The degree of meta-tagging and
metadata consistency that such models
provide also improves regulatory com-
pliance by enabling semantic models
to be mapped to compliance protocols
in conjunction with relevant metadata
attributes.
Data discovery: The combination
of open data standards-based semantic
models and their graphic representation
also enhances the data discovery pro-
cess, as end users can query the rela-
tionships and meaning of data associated
with data sets to see which are appropri-
ate for specific use cases. The applica-
tion of the semantic standards ensure
that the data is both immediately avail-
able for reuse and that it is self-describ-
ing through the use of standards-based
tags that tie them to the associated busi-
ness concept. This application of seman-
tic technologies may provide the greatest
utility to organizations via the sort of ce-
leritous integration of complex unstruc-
tured, semi-structured and structured
data sets – of any magnitude and type

enterprise-wide ubiquity of data steward-
ship will prove vital to organizations.
Automating IT and data science:
Additionally, the alignment of smart data
lakes with the self-service movement
will result in automation of some of the
more mundane, but highly necessary
aspects of data science and the work of
IT departments. Facets of integration,
data discovery and data preparation that
consume so much time of those working
in these two departments are either ex-
pedited or unnecessary with smart data
lakes, enabling these professionals to
concentrate on more substantial ways to
improve data-driven processes and drive
more quickly to value.
Finally, the preeminence of smart data
lakes themselves will be another trend
that should foment in the new year. The
interest in this method for managing big
data deployments will continue to multi-
ply as organizations realize that they can
facilitate all of its benefits, while negating
its detriments, through the utilization of
user-friendly semantic technologies that
belong in front offices as much as, if not
more so, than in back ones. ❙
Sean Martin is the founder and chief technical
officer of Cambridge Semantics, a provider of smart
data solutions driven by semantic web technology.
Prior to Cambridge Semantics, he spent 15 years
with IBM Corporation where he was a founder and
the technology visionary for the IBM Advanced
Internet Technology group.
prior to analysis profoundly affects the
type, degree and nature of analytics per-
formed, which considerably refines their
results and use.
Semantics at scale: The ultimate ex-
pression of what is actually an expansion
of analytical prowess is the concept of se-
mantics at scale, in which the organization
utilizing a smart data lake graph is opti-
mized for analytics with in-memory, mas-
sively parallel processing of semantically
tagged data. Such an engine, when com-
bined with a smart data lake’s RDF graph
and ontological models of business mean-
ing, incorporates all relevant enterprise
data for comprehensive results at a speed
which semantic technology advancements
have only recently been able to produce.
Democratization of stewardship:
The expedience of access and avail-
ability of data provided by data lakes is
aligned with the self-service movement
and the notion of the democratization
of big data that in turn supports it. Data
lakes will contribute to the solidification
of these trends by facilitating the democ-
racy of data stewardship. Semantic mod-
els and semantic graphs will help end
users discern data and their relations to
other data elements, which will enable
a more pervasive form of governance
than that conventionally reinforced by
only a few dedicated data stewards. With
increasing regulatory mandates, this

PRODUCT OR SERVICE?
Smart technologies, services, processes and people
add up to smart systems for every sector.
compete in the market-
place and maintain rel-
evancy, companies need
to constantly innovate.
Just as important, today’s economic en-
vironment demands that innovation also
consider how to design and transform
delivery processes to improve produc-
tivity and performance. While there is
a desire to be more global, integrated
and customer-centric, actually getting
new products and services to market
are rare, and what we call frequent and
radical innovations – new services and
products that dramatically change the
marketplace – are even rarer. For the
past decade, many organizations have
focused on traditional product innovation
Digital business innovation
BY HALUK DEMIRKAN (left) AND BULENT DAL
To
Get smart:

ARE THESE PRODUCTS OR SERVICES?
IS THIS A PRODUCT OR SERVICE?
“An automobile is actually art, entertainment
and mobile sculpture, which, coincidently,
also happens to provide transportation.”
– Robert Lutz, chairman, GM
The Kindle’s real breakthrough springs
from a feature that its predecessors never
offered: wireless connectivity. As a result,
says Amazon founder Jeff Bezos: “This isn’t
a device, it’s a service.”
IS THIS A PRODUCT OR SERVICE?
to address the challenges of globaliza-
tion and economic transformation. Most
of these companies are still clinging to
what we call the invention model, cen-
tered on structured, bricks-and-mortar
product development processes and
platforms. If everybody is doing innova-
tion, what are you doing differently?
TODAY, WHEN A CUSTOMER BUYS A
DRILL, DOES HE/SHE WANT A DRILL
OR A HOLE?
According to research, people don’t
want to buy a quarter-inch drill. They want
a quarter-inch hole. Another example can
be cars. Robert Lutz, chairman of GM,
once said, “An automobile is actually
art, entertainment and mobile sculpture,
which, coincidently, also happens to
provide transportation.” Other examples
are service platforms such as Uber, the
world’s largest taxi company but owns no
taxis; Airbnb, the largest accommodation
provider but owns no real estate; Skype,
one of the largest phone companies but
owns no telco infrastructure; Alibaba, the
world’s most valuable retailer but has no
inventory; Facebook, the world’s most
popular media owner but creates no
content; and Netflix, the largest movie
house but owns no cinemas.

DIGITAL BUSINESS INNOVATION
organizational systems and their exter-
nal, resource-network and market sys-
tems. If that is the case, we need to look
at things differently. The convergence
of information communication technolo-
gies (ICT) and service thinking changed
the nature of businesses, services and
products by delivering them through digi-
tal solutions. This revolution created an
emerging field called “digital business in-
novation,” “digitization” or “digital service
innovation.”
TAKING THE PATH TO SERVICE
TRANSFORMATION, ORIENTATION
AND DIGITAL BUSINESS
INNOVATION
Influenced by the emerging field of
service science and systems (e.g., ser-
vice-oriented technologies and manage-
ment), several companies have gained
attention in the past few years by devel-
oping more flexible business processes
that co-create value with customers [2].
For example, Rolls Royce leveraged its
expertise in aircraft engine manufactur-
ing to implement a service-oriented pow-
er-by-the-hour offering for customers.
This new business model better met cus-
tomer needs and gave Rolls Royce more
information about the way their custom-
ers use resources to create value. Apple
and Google became the world’s largest
software platforms without writing apps.
Amazon became the world’s largest
Today …
• Customers want to “hire” a product to
do a job.
• Commoditization of products results
in price and margin pressures.
• Customers are demanding services
and solutions.
• Services can provide platforms for
profitability.
• Loyalty and customer satisfaction are
often driven by services.
• Service offerings can differentiate
firms in highly competitive industries.
• The “ICT-enabled services-based-
economy” is growing exponentially.
As a result, flexibility and agility to re-
spond to changing business needs and
to harness resources across global value
chain partners are creating many chal-
lenges and issues for companies. Many
organizations attempt to overcome these
challenges and issues through improved
efficiency, quality and speed of their op-
erations, through mergers and networks
[1]. But unanticipated consequences
result in unnecessary costs, lack of re-
sponsiveness to customers, and missed
opportunities for innovation. However,
they often find that traditional innova-
tion methods are inadequate and cre-
ate negative externalities because they
have insufficient scope in relation to the
complexity and dynamics of their internal

transportation, telecommunication, lo-
gistics, supply chain, etc., will increase
rapidly. We will increasingly utilize intel-
ligent robotics, additive manufacturing
(e.g., 3-D printers), self-driving cars and
augmented reality. This will result in more
data generation and collection storage,
as well as increase the need for analy-
sis and cognitive business (e.g., IBM
Watson, Apple Siri, Microsoft Cortana,
Google Now, Amazon Echo and Face-
book AI). Digital innovations have great
potential to reduce costs, increase effi-
ciency and improve outcomes.
DIGITAL BUSINESS REVOLUTION
WITH CONVERGENCE OF ICTS AND
SERVICES
In today’s globally competitive busi-
ness environment, innovation is not
a strategic option; it is a fundamental
prerequisite for a company’s survival,
organizational renewal and national
economic wealth. Firms are now estab-
lishing market leadership and growing
their revenues by mastering digital ser-
vice innovations. For example, the tra-
ditional advertising agencies now have
to be able to blend digital products and
virtual computing service provider with
its cloud platform.
Service thinking has transformed tra-
ditional products and services by adopt-
ing manufacturing concepts such as
division of labor and knowledge, stan-
dardization and coordination of produc-
tion and delivery to enable new forms of
value creation and consumption. Indus-
tries such as retail, hospitality, restau-
rant, telecommunications, healthcare,
transportation, finance and education are
undergoing this type of transformation.
ICT has enabled traditional manufactur-
ers to become providers of services [3].
At the same time, ICT is moving off
the desktop and out of offices and homes
and into buildings, infrastructure and ob-
jects. Our ability to collect and analyze
a flood of data from mobile solutions,
sensors, cameras, etc. is getting much
more efficient and effective. Cisco pre-
dicts that the Internet of Things (IoT) is
expected to generate $14 trillion rev-
enue in the next decade by connecting
more than 200 billion devices [4]. Internet
speed may double by next year. Smarter
cities, retail, manufacturing, healthcare,
Request a no-obligation INFORMS Member Benefits Packet
For more information, visit: http://www.informs.org/Membership

often changes the roles of providers,
co-producers and customers of ser-
vices and alters their patterns of in-
teraction. Different organizations have
different perspectives on the opportu-
nities created by the ICTs, but all are
looking to improve efficiency and out-
comes. One of the biggest challenges
is deciding where and how to start this
journey, and how to stay on course.
Culture change/mindset. Under-
stand the service (value co-creation).
Service, which can be defined as the
application of competence, knowledge
and capability to create benefit (or
value) for another, derives from the in-
teractions of entities known as service
systems. They are: intangible, inability
to inventory, perishable, inseparabil-
ity (produced and consumed simulta-
neously), value co-creation process,
collaboration with many stakeholders
(e.g., B2B2C), high involvement of
people in delivery (or service system),
and very complex. Simultaneity of pro-
duction and consumption of services
occur in complex service environments
due to interaction of people, processes,
technology and shared information.
Customer experience. Think about
how to measure and improve customer
experience. The customer experience
embodies what it’s like to be a digital
service customer of your organization,
services with creative strategy. Amazon
is as much a retailer and supply chain
leader as it is a digital service innovator.
Similarly, the Netflix business model is
heavily reliant on continuously building
and enhancing digital products and ser-
vices to compete in the entertainment
industry. Ford is realizing that its future
competitors are likely to be Facebook
and Google and not BMW and Toyota.
Apple is more than a computer manu-
facturer with iTunes, apps, cell phones,
tablets, etc.
Another good example can be smart
retail platforms (e.g., Obase Detailer, In-
tel’s AIM suite) that collect and analyze
data from transactional systems, data
warehouses, customer relationship man-
agement systems and location-based
analytics.
SO, ARE YOU LOOKING TO BE AN
INNOVATIVE SERVICE PROVIDER
WITH DIGITAL BUSINESS? HOW TO
START SUCH A JOURNEY, AND HOW
TO STAY THE COURSE.
Digital innovation is a new way of
thinking and doing things. A key char-
acteristic of digital innovation is that it
Subscribe to Analytics
It’s fast, it’s easy and it’s FREE!
Just visit: http://analytics.informs.org/

EVERY BUSINESS… EVERY ORGANIZATION… AND EVERY
ANALYTICS PROFESSIONAL...
Experiences the ups and downs, and the twists and turns of analytics. Making analytics work in real
organizations can be a dynamic (dare we say wild?) ride for even the most seasoned practitioners.
Analytics 2016 will help you conquer the challenge.
SUBMIT AN ABSTRACT OR POSTER!
ABSTRACT SUBMISSIONS OPEN
Check the site for current
abstract submission information
meetings.informs.org/analytics2016

segments, it may be best to subdivide
into focused units rather than try to apply
one model.
Platform economics. Driving digi-
tal business innovation with analytics,
smart services, social, cloud, Internet
of Everything (IoE), service-orientation
and cognition for value co-creation and
outcome [7]:
• To achieve economies of scale with
digital business models requires the
development and reuse of service-
based digitized platforms across the
enterprise [8].
• Review the business processes,
applications, data networks and
connections, databases, servers,
etc., to identify which applications
need to remain in their present
form, and which can be removed
to the new framework. Also identify
which IT platforms servers, PCs,
workstations, operating systems and
software need to be upgraded or
replaced.
• Global platform but customizable
locally. This means an enterprise
with a federated business
architecture with a global content
repository, expanded taxonomies,
modular design and global and local
innovation.
• Enabled with IoT, smart services
gather and share information directly
whether buying digital or physical prod-
ucts. Amazon’s customer experience
includes the website and the digitized
business processes touching the cus-
tomer, like the shopping cart and pay-
ment options, as well as messaging,
such as delivery alerts and email ac-
knowledgments with design thinking. The
experience also includes Amazon’s well-
developed customer-created content:
customer product ratings and reviews, as
well as sophisticated tools like search, a
detailed history of purchases and tailored
recommendations [5].
Strategy re-mapping. Redefine
your market space for future growth by
assessing your current market space.
This would include: current markets/
current offerings (market penetration
strategy), new markets/current offer-
ings (market development strategy),
current markets/new products-services
(product/service development strat-
egy) and maximum opportunity strat-
egy (new markets/new products and
services).
Concept/idea. A “new value proposi-
tion” targeted at a particular market. One
way to reduce that risk is to make chang-
es to your company’s mix of products or
services [6]. Focus narrowly, search for
commonalities across products and ser-
vices or create a portfolio of offerings. If
your business currently serves multiple

• Significant amount of data are
collected with IoE and smart
service. New models, methods and
algorithms are needed to analyze
this data effectively and efficiently.
• The next generation of things
should have cognitive capabilities.
They should be able to learn by
driving innovative thinking and new
knowledge generation to enhance
existing services. This involves
incorporating user community
feedback and modifying, adding,
deleting and synthesizing content
and software services as indicated,
thus capturing industry trends and
needed software service categories
for adding, updating or deleting
skills, knowledge and experience
categories and content.
• Data collected is useful, relevant
and actionable. In the 21st century,
everybody and everything become
data creators and data consumers.
• After use, every “thing” should have
a plan for disabling, destroying and
disposing plans for itself if there are
no needs for them. Apply correct
privacy and security procedures.
Companies need to get value
from product complexity without
confusing customers or making it
too difficult for employees to get
things done [9].
with each other through onsite
and virtual cloud solutions, making
it possible to collect, record and
analyze new data streams faster and
more accurately. The ability to collect
and analyze a flood of data from
mobile solutions, sensors, cameras,
etc., with smart automation is getting
much more efficient and effective.
These IT-enabled solutions should
have integration capability that helps
implement the new configurations
of operational competencies by
developing the required patterns of
interactions with each other.
• Every “thing” should be able to
reconfigure itself – the ability to
rearrange existing resources and
services into new configurations of
operational competencies that better
match the environment.
• Every “thing” should be able to sense
the environment, identify needs and
spot new opportunities. It requires
tracking and monitoring service
providers’ and receivers’ activities, as
well as technology performance to
understand usage trends, navigation
trends, etc.
• Every “thing” must have coordination
capability – the ability to manage
dependencies among resources
and tasks to create new ways of
performing a set of activities.

people – supported by a new kind of or-
ganization. In other words, companies
need to retune their talent engines to
support a new generation of innovation
[10]. Organizations need to find new or
improved ways of generating, prioritiz-
ing and managing digital innovation from
idea generation through the end of the
development lifecycle when the innova-
tion becomes a new service platform or
a complementary value-added service.
These new ways of managing innova-
tion need to consider the differences be-
tween incremental and radical innovation
and recognize the leverage that can be
gained from co-creation of value with the
customer and customer experience. ❙
Haluk Demirkan (haluk@uw.edu) is a professor
of Service Innovation and Business Analytics
at the Milgard School of Business, University of
Washington-Tacoma. He has a Ph.D. in information
systems and operations management from the
University of Florida. He is a longtime member of
INFORMS.
Bulent Dal (bulent.dal@obase.com) is a co-founder
and general manager of Obase Analytical Solutions
(http://www.obase.com/index.php/en/obase),
Istanbul, Turkey. His expertise is in scientific retail
analytical solutions. He has a Ph.D. in computer
sciences engineering from Istanbul University.
ACKNOWLEDGEMENT:
Part of this article is excerpted with permission of the
publisher, HBR Turkey from Demirkan, H. and Dal,
B. “Digital Innovation and Strategic Transformation,”
Harvard Business Review (Turkish Edition; published
in Turkish), April 2015.
REFERENCES
For references, click here.
DIGITAL BUSINESS INNOVATION:
THE TIME IS NOW.
There is a big move toward digitiza-
tion of business: incorporating more of
customers’ experience; executing more
processes and working together with
partners in the value chain; increasing
the number of “digital natives” (young
current and future customers and em-
ployees who expect a brilliant digital ex-
perience in all of their interactions); and
embracing the dawning of the age of the
customer voice, in which customers have
a much stronger impact on enterprises
via ratings of their services and via online
comments through Twitter and other so-
cial media. Before the Internet, business
operated primarily in a physical world of
“place”: It was a world that was tangible,
product-based and oriented toward cus-
tomer transactions. Today, many indus-
tries – all moving at different rates – are
shifting toward a digital world of “space”:
more intangible, more service-based and
oriented toward customer experience.
Technology allows customers to pro-
duce service entirely on their own (“self-
service”), employees to provide services
from anywhere in the world (remote, out-
sourced services), and companies to
integrate technology into a total mix of
service offerings (smart services).
To be truly successful, such a move
will require a new kind of talent –T-shaped

HAW II
2016 INTERNATIONAL
Abstract Submission Registration is Now Open
2016 INTERNATIONAL CONFERENCE
HAWAII
June 12–15, 2016
Hilton Waikoloa Village
SUBMIT AN ABSTRACT:
http://meetings.informs.org/2016international/abstracts/
Hawaii 2016 delivers an impressive lineup of keynote and plenary
speakers interspersed with invited tracks emerging topics ranging
from Global Supply Chains to Social Networks affording you the
opportunity to network and collaborate with colleagues across the
globe and from both academia and industry.
REGISTER at meetings.informs.org/2016international

ustomer lifetime value (CLV)
is not a new tool for mar-
keters. Its application has
been used for decades to
understand a customer’s financial value.
It comes in many shapes and sizes, vary-
ing from historical CLV, which calculates
a CLV based only on what a customer
has previously spent with a business, to
predictive CLV, which leverages both ob-
served historical behavior and predicted
retention to estimate a discounted stream
of future (lifetime) revenue.
Historical CLV has several drawbacks,
the most important of which being that,
since it is the sum of past revenue or profit
for a particular customer or group, it only
provides insight into what has already oc-
curred, and, thus, sheds little insight into
the value of new subscribers. Predictive
CLV, however, with its ability to incorpo-
rate expected retention, allows marketers
to obtain several key insights, including
what types of subscribers will be the most
profitable over a specific time period,
where acquisition dollars earn the high-
est return on investment and what cus-
tomer attributes are drivers of retention.
These types of actionable insights can
help marketers make more well-informed,
Leveraging predictive
analytics to estimate
customer lifetime value
BY MATTHEW LULAY
C
MARKETING METRIC

minus costs. In the newspaper industry,
revenue for a particular subscriber in-
cludes the subscription rate and the sub-
scriber’s share of the market’s advertising
revenue, which comes in the form of pre-
print advertising inserted into each day’s
paper, as well as digital advertising reve-
nue via impressions on the market’s web-
site. The subscription rate can vary based
on a variety of factors, including the num-
ber of delivery days (e.g., Sunday only
vs. seven-day), the period length (e.g.,
13-week vs. 52-week), acquisition source
(e.g., direct mail vs. telemarketing) and
data-driven decisions that promote effi-
ciency, savings and revenue growth. This
article explores the basic tenets of pre-
dictive CLV, illustrated by examples from
the newspaper industry.
MAJOR COMPONENTS OF CLV
The calculation at the bottom of the
page shows the three major components
of predictive CLV: profitability, predicted
retention and discounting.
Profitability: Profitability is the sim-
plest component of the CLV metric, as it is
a straightforward calculation of revenues
CLV = [(Revenues – Costs)*(Predicted Retention Probability)] Net Present Value (NPV)
In the newspaper industry, revenue for a particular subscriber includes the subscription rate and the
subscriber’s share of the market’s advertising revenue.

CUSTOMER LIFETIME VALUE
is a method of estimating the probability
of an event occurring at a particular time
interval. Examples include the probability
of survival for a heart transplant patient,
the probability of transmission failure on
new cars or the probability of divorce after
marriage. The probability of these events
can be estimated over time using survival
analysis. With the application to the news-
paper industry, we use survival analysis to
calculate the probability of subscriber re-
tention at different intervals of time. More
specifically, we leverage historical transac-
tion information to fit a parametric survival
model with a log-logistic distribution.
We use a parametric model because
we understand the underlying distribution
of our dependent variable, which is re-
tention probability. The distribution of that
variable is log-logistic in nature, where
the rate of decline in the probability of re-
tention increases in the early stages and
decreases later. This creates a curve that
is downward sloping with a slope that de-
creases in severity over time. An example
of this is shown in Figure 1, where we esti-
mate survival probability for subscribers in
different income groups, revealing that the
most affluent subscribers in this particular
market had a retention probability approxi-
mately three times higher than those with
in the lowest income level after 365 days.
Figure1showsonlytheexpectedreten-
tion probabilities for subscribers grouped
payment method (e.g., check vs. credit
card). Pre-print advertising value is depen-
dent upon the subscriber’s demographic
profile, which is normally measured at the
zip code or zip+4 level. Costs at the sub-
scriber level for newspapers include print
and ink, delivery and acquisition.
Predicted retention: Once revenues
and costs are calculated and we arrive at
a profit level, the next component of predic-
tive CLV is estimating retention probability,
which provides us with the risk-adjusted
profit. By “risk-adjusted,” we simply mean
profit that has been adjusted to account
for the risk of subscriber churn – the prob-
ability that a particular customer will retain
over a certain time period. In the newspa-
per industry, while all subscribers come up
for renewal at different points throughout
the year based on the term length of the
subscription, not all subscribers exhibit the
same propensity to renew. In fact, subscrib-
ers with different characteristics can retain
at drastically different rates. While an av-
erage newspaper may experience overall
annual retention of 75 percent, pockets of
subscribers within the market may be re-
taining at 90+ percent, while others retain
at less than 40 percent. Mather Economics
uses an econometric method called “surviv-
al analysis” to estimate the retention prob-
abilities among different subscriber groups.
Survival analysis, originally devel-
oped for application in the biosciences,

for valuing future dollars in present value
terms. The selection of a discount rate is
an important decision, as values are highly
sensitive to this rate, especially in estima-
tions in which predictions are made over
longer periods of time. A variety of factors
are taken into account when choosing a
discount rate, including the length of time
of the estimation, costs of capital, rate of
return on private investment, interest rates
on government and corporate bonds and
output growth. With this in mind, govern-
ment agencies in the United States tend
to leverage discount rates of 2 percent to
3 percent on intra-generational projects. At
Mather Economics, we normally estimate
CLV as the risk-adjusted present value of
five years of expected earnings for an indi-
vidual subscriber and use a discount rate
of two percent.
by one variable. But when we combine all
of the information we have on a particular
subscriber, we can estimate a unique sur-
vival curve for every single subscriber in a
database. In Figure 2, predicted retention
is plotted for a new subscriber by day from
the point of acquisition to a point two years
out from acquisition. The area under the
curve gives us the second component of
predictive CLV – estimated retention (ex-
pected lifetime).
Discounting: Predictive CLV attempts
to capture the present value of a cus-
tomer’s stream of lifetime revenue. Since
we’re trying to capture the present value
of future revenue, we need to incorporate
a discount rate to account for the positive
time value, or positive time preference, of
money,whichessentiallystatesthatmoney
today is worth more than the same amount
at some point in the future. This concept is
why interest rates tend to be positive and
why the need for a discount rate exists
Figure 1: Estimate survival probability for subscribers in
different income groups.
Figure 2: Day-to-day prediction retention of a new
subscriber over a two-year period.

Get smart: digitial business innovation

Get smart: digitial business innovation

Recommended

Recommended

More Related Content

Similar to Get smart: digitial business innovation

Similar to Get smart: digitial business innovation (20)

Recently uploaded

Recently uploaded (20)

Get smart: digitial business innovation