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![W W W. I N FO R M S . O RG8 | A N A LY T I C S - M AGA Z I N E . O RG
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](https://image.slidesharecdn.com/pages-160217080929/85/Get-smart-digitial-business-innovation-8-320.jpg)
![JA N UARY / F E BR UARY 2016 | 15A NA L Y T I C S
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](https://image.slidesharecdn.com/pages-160217080929/85/Get-smart-digitial-business-innovation-15-320.jpg)
![W W W. I N FO R M S . O RG16 | A N A LY T I C S - M AGA Z I N E . O RG
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](https://image.slidesharecdn.com/pages-160217080929/85/Get-smart-digitial-business-innovation-16-320.jpg)
![W W W. I N FO R M S . O RG18 | A N A LY T I C S - M AGA Z I N E . O RG
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.](https://image.slidesharecdn.com/pages-160217080929/85/Get-smart-digitial-business-innovation-18-320.jpg)
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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](https://image.slidesharecdn.com/pages-160217080929/85/Get-smart-digitial-business-innovation-30-320.jpg)
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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](https://image.slidesharecdn.com/pages-160217080929/85/Get-smart-digitial-business-innovation-42-320.jpg)
![JA N UARY / F E BR UARY 2016 | 43A NA L Y T I C S
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](https://image.slidesharecdn.com/pages-160217080929/85/Get-smart-digitial-business-innovation-43-320.jpg)
![W W W. I N FO R M S . O RG46 | A N A LY T I C S - M AGA Z I N E . O RG
DIGITAL BUSINESS INNOVATION
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](https://image.slidesharecdn.com/pages-160217080929/85/Get-smart-digitial-business-innovation-46-320.jpg)
![JA N UARY / F E BR UARY 2016 | 47A NA L Y T I C S
• 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.](https://image.slidesharecdn.com/pages-160217080929/85/Get-smart-digitial-business-innovation-47-320.jpg)
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DIGITAL BUSINESS INNOVATION
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](https://image.slidesharecdn.com/pages-160217080929/85/Get-smart-digitial-business-innovation-48-320.jpg)
![JA N UARY / F E BR UARY 2016 | 51A NA L Y T I C S
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.](https://image.slidesharecdn.com/pages-160217080929/85/Get-smart-digitial-business-innovation-51-320.jpg)
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VILLAGE STABILITY OPERATIONS
This means working with local and
tribal leaders, participating in their govern-
mental and dispute resolution processes,
and encouraging them in community de-
velopment and security initiatives without
imposing what we think would work. He
describes a number of such efforts in de-
tail in the book.
According to Mann, the current political
rhetoric, from all sides, is “ridiculous,” and
it’s reinforcing the narrative our enemies
want to spread. “What ISIS wants to do
is divide us against Muslims and exploit
gaps within our communities about trust.”
The success of extreme political
rhetoric in the United States, he hypoth-
esizes, is a response to a general vague
sense of unease about the current situa-
tion. “Americans see that it’s not working,
and they’re primed for something new,
ready for some leadership,” he states.
“I’ll say this in a very apolitical way: We
just need to understand what works and
do it. And we’re not.”
What we do need, he offers, is a reex-
amination of the authorizations Congress
gives to Special Forces to “get outside the
wire” and work with local leaders, particu-
larly to work with irregular forces along
with the regulars. In Syria, he asserts,
the authorizations were so narrow and
restrictive that they impeded what might
have been effective. “If we had had per-
sistent presence out there, we would have
government we can work with, top-down,
applying massive conventional force.
We’ve been in this conflict for 15 years;
we ought to be adapting, and we’re not.
“There’s nothing new in this ap-
proach; it’s just that nobody’s listening,”
he continues. “We had a coordinated
program called FID, foreign internal de-
fense, to apply all our instruments of
power in fragile countries. It’s run by the
embassy country team, coordinating de-
fense, law enforcement and economics.
We’ve been doing it for decades, and
we’re good at it. We did it in Colombia,
El Salvador and the Philippines. But after
9/11, we abandoned it and went back to
COIN, counterinsurgency, and it was as
much a failure in Iraq and Afghanistan as
it had been in Vietnam.”
Mann cautions, “It takes a long time.
At this point we’ll have to punch our way
back in, but then we need to conduct
long-term FID, position our talent at both
bottom-up and top-down, looking for sta-
bility inhospitable to violent extremists at
the local and national levels.
“Everything was in place after the surge
[in Iraq],” he said, “but then we needed to
implement FID. In these places where vio-
lent extremists set up shop, 80 percent of
the land is tribal, outside national control.”
Hence, he adds, “we have to learn to ex-
ploit an honor-based society to create the
opportunities we want.”](https://image.slidesharecdn.com/pages-160217080929/85/Get-smart-digitial-business-innovation-66-320.jpg)
![W W W. I N FO R M S . O RG68 | A N A LY T I C S - M AGA Z I N E . O RG
VILLAGE STABILITY OPERATIONS
• what people do when they have
a grievance (do they trust local
government and law enforcement
enough to work through those
channels?); or
• what happens to new economic
development (in unstable regions,
promising new facilities get blown
up).
Creative, sensible analytical ap-
proaches – not unduly restricted by prior
assumptions and backed and informed
by close contact with the people with
deep experience – can greatly advance
getting the policies right.
VSO, Lt. Col. Mann concludes,
“is not a silver bullet, but it’s the best
approach we’ve got.” The challenge for
analytics is to assess, in terms senior
decision-makers will readily grasp,
how right or wrong this conclusion is,
and how it might be shaped to be more
effective. ❙
Douglas A. Samuelson (samuelsondoug@yahoo.
com), D.Sc. in operations research, is president
and chief scientist of InfoLogix, Inc., a small RD
and consulting company in Annandale, Va. He is a
contributing editor of OR/MS Today and Analytics.
He is a longtime member of INFORMS.
creatively oriented analysts in the Joint Im-
provised Explosive Devices Defeat Organi-
zation (JIEDDO) came up with a clever way
to depict which tribes were where, based
on contact reports. Gen. David Petraeus
complimented these (unnamed) analysts at
the time for the useful support they had pro-
vided. Demonstrating that a hostile tribe’s
stronghold was also heavily intermingled by
a friendly tribe could convince a command-
er not to apply massive force. The problem
with wiping out much of the hostile tribe
is that it also removes the friendly source
of information – and turns the survivors
unfriendly. According to Mann, the tribe-
depicting analytical products originated at
JIEDDO remain among the more useful
tools available in theater, and they are still
being generated and used.
As in any other problem of measure-
ment and analysis, it is vital to start with a
clear objective and good metrics of whether
that objective is being met. “We need a point
of departure of what relative stability looks
like [in the locale of interest], then we need
to shape goals by what is possible,” Mann
says. “We need a definition of relative stabil-
ity that is agreed upon by all levels of power,
and we shouldn’t go to war without it.”
Metrics of such stability might include,
for example, observations of:
• whether locals tell the police where
the criminals are or tell the criminals
where the police are;
REFERENCES
1. D. Scott Mann, “Game Changers: Going Local
to Defeat Violent Extremists,” Tribal Analysis
Publishing, Leesburg, Va., 2015.
2. Telephone interview with Lt. Col. Scott Mann,
Dec. 17, 2015.](https://image.slidesharecdn.com/pages-160217080929/85/Get-smart-digitial-business-innovation-68-320.jpg)
Uploaded byDr. Bülent Dal
Get smart: digitial business innovation
The document discusses the importance of incorporating the human element into data analytics, highlighting that many companies invest heavily in technology but neglect the ability to effectively utilize analytics. It features insights from a study showing that top-performing executives leverage data analytics regularly in decision-making, contrasting with lagging organizations. Additionally, it emphasizes the need for strong leadership and a culture that integrates analytics into business strategy for effective application and competitive advantage.
![[Report] What Do We Do With All This Big Data? Fostering Insight and Trust i...](https://cdn.slidesharecdn.com/ss_thumbnails/reportwhatdowedowithallthisbigdataaltimetergroup-150120123936-conversion-gate02-thumbnail.jpg?width=640&height=640&fit=bounds)
Get smart: digitial business innovation
- 1. H T TP : / / 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
- 2. 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
- 4. DRIVING BETTER BUSINESSDECISIONS 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
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- 6. REGISTER FOR AFREE 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
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- 8. W W W.I N FO R M S . O RG8 | A N A LY T I C S - M AGA Z I N E . O RG 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
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- 10. W W W.I N FO R M S . O RG10 | A N A LY T I C S - M AGA Z I N E . O RG 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
- 11. 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.
- 12. W W W.I N FO R M S . O RG12 | A N A LY T I C S - M AGA Z I N E . O RG 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.”
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- 14. W W W.I N FO R M S . O RG14 | A N A LY T I C S - M AGA Z I N E . O RG 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
- 15. JA N UARY/ F E BR UARY 2016 | 15A NA L Y T I C S 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
- 16. W W W.I N FO R M S . O RG16 | A N A LY T I C S - M AGA Z I N E . O RG 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
- 17. BUSINESS ANALYTICS Top Rated College byForbes 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
- 18. W W W.I N FO R M S . O RG18 | A N A LY T I C S - M AGA Z I N E . O RG 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.
- 19. THE NATION’S FIRST Associatein 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
- 20. W W W.I N FO R M S . O RG20 | A N A LY T I C S - M AGA Z I N E . O RG 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
- 21. JA N UARY/ F E BR UARY 2016 | 21A NA L Y T I C S 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
- 22. W W W.I N FO R M S . O RG22 | A N A LY T I C S - M AGA Z I N E . O RG 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
- 23. CPLEX Optimization Studio® . Stillthe 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.
- 24. W W W.I N FO R M S . O RG24 | A N A LY T I C S - M AGA Z I N E . O RG 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
- 25. JA N UARY/ F E BR UARY 2016 | 25A NA L Y T I C S 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
- 26. W W W.I N FO R M S . O RG26 | A N A LY T I C S - M AGA Z I N E . O RG 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
- 27. JA N UARY/ F E BR UARY 2016 | 27A NA L Y T I C S 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
- 28. W W W.I N FO R M S . O RG28 | A N A LY T I C S - M AGA Z I N E . O RG 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
- 29. JA N UARY/ F E BR UARY 2016 | 29A NA L Y T I C S 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,
- 30. W W W.I N FO R M S . O RG30 | A N A LY T I C S - M AGA Z I N E . O RG 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
- 31. JA N UARY/ F E BR UARY 2016 | 31A NA L Y T I C S 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
- 32. W W W.I N FO R M S . O RG32 | A N A LY T I C S - M AGA Z I N E . O RG 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.
- 33. JA N UARY/ F E BR UARY 2016 | 33A NA L Y T I C S 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.
- 34. W W W.I N FO R M S . O RG34 | A N A LY T I C S - M AGA Z I N E . O RG 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.
- 35. JA N UARY/ F E BR UARY 2016 | 35A NA L Y T I C S 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
- 36. W W W.I N FO R M S . O RG36 | A N A LY T I C S - M AGA Z I N E . O RG 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
- 37. JA N UARY/ F E BR UARY 2016 | 37A NA L Y T I C S 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
- 38. W W W.I N FO R M S . O RG38 | A N A LY T I C S - M AGA Z I N E . O RG 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
- 39. JA N UARY/ F E BR UARY 2016 | 39A NA L Y T I C S 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
- 40. W W W.I N FO R M S . O RG40 | A N A LY T I C S - M AGA Z I N E . O RG 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:
- 41. JA N UARY/ F E BR UARY 2016 | 41A NA L Y T I C S 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.
- 42. W W W.I N FO R M S . O RG42 | A N A LY T I C S - M AGA Z I N E . O RG 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
- 43. JA N UARY/ F E BR UARY 2016 | 43A NA L Y T I C S 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
- 44. W W W.I N FO R M S . O RG44 | A N A LY T I C S - M AGA Z I N E . O RG DIGITAL BUSINESS INNOVATION 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/
- 45. EVERY BUSINESS… EVERYORGANIZATION… 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
- 46. W W W.I N FO R M S . O RG46 | A N A LY T I C S - M AGA Z I N E . O RG DIGITAL BUSINESS INNOVATION 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
- 47. JA N UARY/ F E BR UARY 2016 | 47A NA L Y T I C S • 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.
- 48. W W W.I N FO R M S . O RG48 | A N A LY T I C S - M AGA Z I N E . O RG DIGITAL BUSINESS INNOVATION 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
- 49. HAW II 2016 INTERNATIONAL AbstractSubmission 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
- 50. W W W.I N FO R M S . O RG50 | A N A LY T I C S - M AGA Z I N E . O RG 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
- 51. JA N UARY/ F E BR UARY 2016 | 51A NA L Y T I C S 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.
- 52. W W W.I N FO R M S . O RG52 | A N A LY T I C S - M AGA Z I N E . O RG 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,
- 53. JA N UARY/ F E BR UARY 2016 | 53A NA L Y T I C S 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.
- 54. W W W.I N FO R M S . O RG54 | A N A LY T I C S - M AGA Z I N E . O RG CUSTOMER LIFETIME VALUE the number of circulation units increases, improving advertising frequency. Another application of CLV is prioritiz- ing the customer service experience. Once a current subscriber base is scored, mean- ing their lifetime value has been estimated, customer service teams can leverage that data to improve the customer experience and make it more efficient. One example of this in the newspaper industry is having dedicated customer service representatives for high-value subscribers, where calls from these customers are prioritized to minimize wait times and provide the highest level of service possible. CLV data can also be used in the customer service department to cre- ate customized retention (stop save) script- ing based on estimated subscriber value, where representatives are given the ability to offer more aggressive save offers to keep high-value subscribers on the books. The three application examples above are just a small sample of the mul- titude of positive impacts CLV can have on businesses in a variety of industries. CONCLUSION We’ve seen that predictive CLV has several advantages over historical CLV, APPLICATIONS Predictive CLV has a variety of useful applications, varying from acquisition op- timization to upgrade campaign targeting to customer service prioritization. Con- sider the simplified example in Figure 3. While the telemarketing source deliv- ers a cost per order one-tenth that of direct mail, the types of customers acquired from the telemarking source are much less valu- able to the firm, and thus, in the aggregate, are less profitable, even when considering the much lower cost to acquire. In this ex- ample, CLV helps marketers prioritize their acquisition efforts to acquire subscribers with the most lifetime value to the firm. Additionally, CLV can help inform up- grade campaigns. Since the value of cus- tomers is known with CLV, steps can be taken to increase engagement and in- crease the value of existing subscribers. In the newspaper industry, for example, pub- lishers use CLV to target lower value Sun- day-only subscribers with paid upgrade offers to weekend subscriptions. This pro- motes a higher level of engagement with the product from the subscriber, which may lead to better retention in the long term and also provides value to advertisers, as Figure 3: Simplified example of CLV in telemarketing vs. direct mail.
- 55. JA N UARY/ F E BR UARY 2016 | 55A NA L Y T I C S toolset to help inform strategic, data-driv- en and profit-maximizing decisions. ❙ As a director for Mather Economics, Matthew Lulay helps media companies optimize pricing through data-driven analysis. In addition, he has authored several white papers on environmental economics, including a model forecasting the employment effects of increased funding along the Gulf Coast region following the Deepwater Horizon oil spill. He has extensive experience collecting, formatting and analyzing data from various state, national and international sources including the Bureau of Labor Statistics, the Federal Reserve and the World Bank. Lulay has a degree in economics from the University of Minnesota and a master’s degree in economics from Florida Atlantic University, where his research focused on macroeconomics and U.S. tax policy. the most important of which being that it is a dynamic, forward-looking tool, allow- ing users to calculate the risk-adjusted value of a customer’s lifetime. Leveraging some of the econometric tools at our dis- posal, including survival analysis, allows us to add the predictive portion to CLV by estimating retention probabilities for individual customers. Once calculated, CLV provides the analytical foundation for many applications, including those in acquisition, retention and customer ser- vice. As such, predictive CLV can serve as a valuable asset in a firm’s analytical INFORMS offers advertising opportunities in 11 of our scholarly journals covering the latest OR/MS research, industry developments, methods, and applications. INFORMS also organizes professional conferences and application-oriented meetings that offer numerous sponsorship and exhibit opportunities. See what INFORMS can do for you. INFORMS Advertising Helps You Reach • Consultants to Help You Solve Business Problems • Individual Purchasers of OR/MS Related Products Services • Institutional Product Services Purchasing Decision Makers • Analytics Professionals Executives • Career Opportunities Click to view rates: www.informs.org/advertising • National International Meeting Attendees • OR/MS Job Seekers • Specialized Software Developers • Students Seeking Summer Employment ADVERTISE WITH INFORMS HAW II 2016 INTERNATIONAL Nashville 2016 meetings.informs.org/philadelphia2015 INFORMS ANNUAL MEETING INFORMSAnnualMeeting–PhiladelphiaNovember1–4,2015
- 56. W W W.I N FO R M S . O RG56 | A N A LY T I C S - M AGA Z I N E . O RG CORPORATE PROFILE Operations research and advanced analytics team helps power rail giant’s success now and in the future. hen people think of rail- roads, what often comes to mind are thoughts of the American West with steam- ing locomotives chugging along endless miles of track interrupted only by the oc- casional train station. While railroads are not settling the Wild West anymore, they are continuing to break into new frontiers of trade, efficiency and innovation that find them pioneering and on the cutting edge of research and technology. Such is the case for today’s BNSF Railway, which is the product of nearly 400 different rail- road lines that merged or were acquired during more than 160 years. The Fort Worth, Texas-based BNSF has more than 47,000 employees spread across its 32,500 miles of rail network operating in 28 states and three Canadian provinces. As one of the larg- est freight railroads in North America, BNSF serves more than 10,000 custom- ers by transporting goods and commod- ities that make people’s lives better. The coal hauled by BNSF powers one out of BNSF Railway BY AMY CASAS W
- 57. JA N UARY/ F E BR UARY 2016 | 57A NA L Y T I C S In fact, BNSF is able to move a ton of freight almost 500 miles on a single gallon of diesel fuel thanks to the tech- nological advancements achieved in operating today’s locomotives. Unlike other forms of freight trans- portation, BNSF trains operate on an infrastructure built and financed mostly by the railroad. BNSF has created one of the most advanced rail networks in the world by better utilizing exist- ing rail capacity and making record investments in new infrastructure and equipment directly connected to its op- erations. Since 2000 BNSF will have reinvested more than $50 billion by the end of this year to improve the safety every 10 homes in the nation. BNSF also transports enough grain to supply 900 million people with a year’s supply of bread, enough asphalt in a year to lay a single lane road four times around the equator, and last year it moved more than 5 million containers of con- sumer products that were sold by big box retailers and specialty shops alike all across America. According to the most recent Com- modity Flow Survey, railroads carry more than 40 percent of freight volume in the United States – more than any other mode of transportation – and pro- vide the most fuel- and cost-efficient means for moving freight over land. BNSF moves a ton of freight almost 500 miles on a single gallon of diesel fuel thanks to technological advancements.
- 58. W W W.I N FO R M S . O RG58 | A N A LY T I C S - M AGA Z I N E . O RG CORPORATE PROFILE Every day Dewan and the Operations Research and Advanced Analytics team help the company bridge the gap be- tween practice and academia. Over the years the team has grown to play an inte- gral part of the decision-making process at BNSF in projects such as creating the most efficient and safest train routes, the availability of train crews, building trains at rail terminals, equipment ordering, train dispatching, fueling and inspecting trains, and so much more. “Considering the complexity of rail- road operations and the speed with which business, technology and gov- ernment regulations can change, it is important for us to be able to use scien- tific thinking to help us solve problems,” says Rollin Bredenberg, BNSF’s vice president for capacity planning and op- erations research. The team always looks to spend qual- ity time with its internal clients and when- ever possible even shadow them so they can gain additional insights that help them frame and solve problems. They often put together cross-functional teams to devel- op solutions that their internal clients can understand and find useful. When certain tools aren’t being used, they reach out to their stakeholders to learn why. This helps ensure that the tools are still relevant and when necessary improve upon the models and insights. and reliability of its rail network and ac- commodate expected growth. DRIVING SOLUTIONS WITH O.R. ANALYTICS Behind the scenes helping equip BNSF leaders with the research necessary to make decisions on everything from capital investments to improving train efficiency is the company’s Operations Research and Advanced Analytics team. The team is led by Pooja Dewan, BNSF’s general director of decision support systems, who provides analytical consulting and decision support throughout the company. Dewan has led the team for the past 12 years and has been with BNSF for 17 years. Through the years the team has grown to include more than 20 full-time team members with advanced degrees in a variety of fields including operations research, industrial engineering and ap- plied math and statistics. “We use advanced analytics to help drive and enhance the decisions made by BNSF’s leaders,” Dewan says. “With a network of thousands of miles of tracks and tens of thousands of train move- ments to move millions of containers and railcars, we have vast amounts of data. Our team works to make sense of all that data. That analysis then gives business leaders insight into how we can increase efficiency and grow our business.”
- 59. JA N UARY/ F E BR UARY 2016 | 59A NA L Y T I C S with similar destinations are grouped at freight yards for movement on trains. Each group of railcars, known in the rail indus- try as a block, moves together from one rail yard to the next, where the cars are re-sorted into new groups with railcars more closely matching their destination. The objective of grouping together railcars with similar destinations is to minimize the miles and amount of sorting that has to happen before the shipment reaches its final destination, all while taking into con- sideration each rail yard’s capacity. Working closely with their BNSF col- leagues, the Operations Research andAd- vanced Analytics team developed a series of tools to help improve the flow of mer- chandise trains across BNSF’s network by assigning the right railcar groups to the “I believe our recipe for success has been our abil- ity to hire technically strong team members, who help us deliver quality solutions that solve the right prob- lem,” Dewan says. “To be effective we have to take these technically strong in- dividuals and educate them about the railroad industry and help each person cul- tivate strong relationships within the organization.” DESIGNING A TRAIN TRANSPORTATION PLAN In 2014, BNSF handled more than 10 million shipments. Roughly 19 per- cent of those shipments were moved in what are known as “merchandise” trains. Those are trains made up of rail cars that contain a variety of mixed freight. They include lumber, paper, machinery and various industrial parts, as well as tank cars containing various types of chemi- cals used in manufacturing. The merchandise train business is complex. Those rail shipments move be- tween 2,300 railway stations and consist of small groups of railcars ranging from 1 to 40 railcars, which are added together to make full-length trains. To facilitate efficient movement of these shipments, railcars INFORMS member Pooja Dewan (seated, third from left) leads BNSF’s Operations Research and Advanced Analytics team.
- 60. W W W.I N FO R M S . O RG6 0 | A N A LY T I C S - M AGA Z I N E . O RG CORPORATE PROFILE the consideration of a large number of factors in a very limited amount of time. The train crew plan- ner is tasked with assign- ing crews located across a vast geography in the most efficient and cost-effective manner possible so trains are not delayed or can- celed. The task is made more complicated by the large number of rules the crew planners must observe to maintain safe rail operations. Partnering with operations leaders, the BNSF’s Operations Research and Advanced Analytics team developed an application to assist in the crew planning process. Launched in 2013, the Crew Decision Assist (CDA) application uses a formal optimization algorithm that sug- gests, in real time, effective crew plans to dispatchers. The implementation of this application has helped reduce the overall cost of positioning crews to operate trains within a territory. The CDA application is currently being used in approximately 60 percent of BNSF’s train crew districts with plans to expand it even more. Timing is everything, especially when customers are relying on the railroad to get their shipments where they need to be on time. It is especially critical for BNSF’s right trains. Those tools allow service de- sign planners to determine the most effi- cient frequency and timing of trains, which has reduced the amount of time it takes for each railcar to reach its final destination. The team’s efforts to develop these tools have been recognized with the Daniel H. Wagner Prize for Excellence in Operations Research Practice and being named a semifinalist for the Franz Edelman Award from INFORMS. CREW PLANNING TIME PREDICTION TOOLS In the railroad industry, like many in- dustries with large workforces, labor costs are the businesses’ biggest expense. For decades, experienced crew planners have, for the most part, manually assigned train crews. Planning train crew assign- ments is a complex process that involves One of the largest freight railroads in North America, BNSF’s 32,500-mile rail network spans 28 states and three Canadian provinces.
- 61. JA N UARY/ F E BR UARY 2016 | 61A NA L Y T I C S the use of this tool to other parts of the business to assist with planning for train crew operations and inspections, as well as equipment and track maintenance. PROJECTS CURRENTLY UNDERWAY In today’s data rich environment ev- erything creates an electronic trail, and the Operations Research and Advanced Analytics team can use that information to generate better inputs for the models they develop. Whether it’s GPS tools that help track the exact location of BNSF’s assets or sensors and gauges that intermodal business, which moves con- tainers and trailers carrying the consumer products people use every day. Working with BNSF’s intermodal group, the Opera- tions Research and Advanced Analytics team is developing a model that more ac- curately predicts train arrival times. The Train Travel Time Prediction tool will help BNSF further enhance the infor- mation that it provides its customers. The development of this tool will enable BNSF to provide customers with information that more accurately tracks their shipments in real time. There are also plans to expand • POST employers to you. • SEARCH • PERSONALIZED job alerts notify you of relevant job opportunities right to your in-box. • ASK the experts advice, resume critique and writing, career assessment test services and more! Job Seekers: Find Your Next Career Move INFORMS Career Center contains the largest source of O.R. and analytics jobs in the world. It’s where careercenter.informs.org JOB SEEKER BENEFITS www.informs.org | 800.446.3676 CAREER CENTER ® powered by
- 62. W W W.I N FO R M S . O RG62 | A N A LY T I C S - M AGA Z I N E . O RG team has helped BNSF save millions of dollars with their work. As comput- ing powers continue to advance and the data that is generated and gathered increases exponentially, the team will continue to play an increasingly valu- able role in developing decision tools that help BNSF leaders manage and operate trains in the most effective and efficient way possible. “To continuously improve the way your rail operations work, you have to evolve the way you make opera- tional decisions,” says David Free- man, BNSF’s senior vice president for transportation. “The recommendations and programs that our Operations Re- search and Advanced Analytics team has developed have resulted in solu- tions that have positively impacted our bottom line and the way we run trains today and into the future.” ❙ Amy Casas (Amy.Casas@BNSF.com) is the director of corporate communications for BNSF Railway. During her career in communications she has led the creation and successful implementation of comprehensive communications plans, lead media relations efforts and provided strategic communications and public relations counsel to leadership. monitor and assist in determining when would be the best time to schedule rail- cars and locomotives for inspections and maintenance, these tools analyze data that enable the team to delve deeper and create effective solutions. With thousands of sensors across its network, BNSF produces large amounts of data daily. These data sets are so large and complex that traditional data processing applications are inadequate. The Operations Research and Advanced Analytics team is currently looking into how to best process and synthesize all that information more effectively for use throughout the organization. Another tool currently on the horizon is Movement Planner. When complete this tool will improve the movement of trains across BNSF’s entire network in- stead of just certain segments. Having this tool will enable BNSF to better dis- patch trains and improve the utilization of its tracks. WHAT THE FUTURE HOLDS Over the years the Operations Research and Advanced Analytics CORPORATE PROFILE Join the Analytics Section of INFORMS For more information, visit: http://www.informs.org/Community/Analytics/Membership
- 63. CERTIFIED ANALYTICS PROFESSIONAL AnalyzeWhat CAP Can Do For You www.certifiedanalytics.org ® ®
- 64. W W W.I N FO R M S . O RG64 | A N A LY T I C S - M AGA Z I N E . O RG How analytics can help defeat violent extremism. thoughtful, deeply experi- ence-based analysis by a retired U.S. Army Special Forces officer offers a new perspective on the political debate rag- ing over how to defeat terrorism: “They’re all wrong.” What we need, he says, is a change in focus from conventional force, applied in support of national governments, to village stability operations (VSO), em- phasizing developing local leadership and building from there. Lt. Col. (ret.) Scott Mann should know. In his 22 years of service, he led VSOs in Iraq, Afghanistan, Colombia, Peru and Ec- uador. Leading U.S. experts labeled his ac- complishments, and those by his Special Forces colleagues, as the “game changer” in Afghanistan in 2010-2012. In his new book, “Game Changers: Going Local to Defeat Violent Extremists,” he outlines the VSO approach in four steps: 1. Get yourself surrounded. Move in with the locals, get to know their concerns first-hand and on a day-to- day basis, let them get to know you. 2. Meet them where they are. As he explains in his book, “For VSO to work, we must embrace local realities. That means working with what is already there, and not with what we want to be there. In Nangahar Province (Afghanistan), it took two successive teams of Special Forces to identify all of the local grievances standing in the way of village autonomy and connection to the Afghan government. Only when these Green Berets started to help locals address their own local Changing the game BY DOUGLAS A. SAMUELSON A VILLAGE STABILITY OPERATIONS
- 65. JA N UARY/ F E BR UARY 2016 | 65A NA L Y T I C S took about two years for the “master narrative of local clans standing up for themselves, supported by their government, against an oppressive and unwanted group of violent extremists” to take hold. The narrative spread to other communities in the area. “This expansion was possible largely because of a compelling narrative and well-told stories,” he adds. NEED TO ADAPT “We’re losing now,” Lt. Col. Mann told Analytics magazine in a recent interview, “because we keep trying to find a national problems did they persuade them to stand up for themselves.” 3. Connect through extreme collaboration. “It takes more than a village. It takes a network to empower that village,” he writes. “Let’s not forget our own organizational complexity, tensions and self-induced feuding as our second enemy in defeating violent extremism.” 4. Tell a story that sticks. “The side that tells the most compelling story, and backs it up with meaningful action, is the side that wins,” he asserts. In Nangahar, he adds, it Lt. Col. (ret.) Scott Mann (right), shown with an unidentified Afghan National Army fighter, has led village stability operations in Iraq, Afghanistan, Colombia, Peru and Ecuador.
- 66. W W W.I N FO R M S . O RG66 | A N A LY T I C S - M AGA Z I N E . O RG VILLAGE STABILITY OPERATIONS This means working with local and tribal leaders, participating in their govern- mental and dispute resolution processes, and encouraging them in community de- velopment and security initiatives without imposing what we think would work. He describes a number of such efforts in de- tail in the book. According to Mann, the current political rhetoric, from all sides, is “ridiculous,” and it’s reinforcing the narrative our enemies want to spread. “What ISIS wants to do is divide us against Muslims and exploit gaps within our communities about trust.” The success of extreme political rhetoric in the United States, he hypoth- esizes, is a response to a general vague sense of unease about the current situa- tion. “Americans see that it’s not working, and they’re primed for something new, ready for some leadership,” he states. “I’ll say this in a very apolitical way: We just need to understand what works and do it. And we’re not.” What we do need, he offers, is a reex- amination of the authorizations Congress gives to Special Forces to “get outside the wire” and work with local leaders, particu- larly to work with irregular forces along with the regulars. In Syria, he asserts, the authorizations were so narrow and restrictive that they impeded what might have been effective. “If we had had per- sistent presence out there, we would have government we can work with, top-down, applying massive conventional force. We’ve been in this conflict for 15 years; we ought to be adapting, and we’re not. “There’s nothing new in this ap- proach; it’s just that nobody’s listening,” he continues. “We had a coordinated program called FID, foreign internal de- fense, to apply all our instruments of power in fragile countries. It’s run by the embassy country team, coordinating de- fense, law enforcement and economics. We’ve been doing it for decades, and we’re good at it. We did it in Colombia, El Salvador and the Philippines. But after 9/11, we abandoned it and went back to COIN, counterinsurgency, and it was as much a failure in Iraq and Afghanistan as it had been in Vietnam.” Mann cautions, “It takes a long time. At this point we’ll have to punch our way back in, but then we need to conduct long-term FID, position our talent at both bottom-up and top-down, looking for sta- bility inhospitable to violent extremists at the local and national levels. “Everything was in place after the surge [in Iraq],” he said, “but then we needed to implement FID. In these places where vio- lent extremists set up shop, 80 percent of the land is tribal, outside national control.” Hence, he adds, “we have to learn to ex- ploit an honor-based society to create the opportunities we want.”
- 67. JA N UARY/ F E BR UARY 2016 | 67A NA L Y T I C S It is hard to document the benefits and problems of VSO succinctly and convinc- ingly, especially to senior leaders caught up in a perpetual cycle of PowerPoint briefings. There is some movement at the policy-making levels of DoD away from operations research back toward wargaming, based on the recognition that more flexibility is needed in depicting and evaluating possible courses of ac- tion. However, according to experienced wargamers, stability operations actions in wargames tend to be portrayed in ways that require numerous assump- tions about feasibility and effects – bet- ter than ignoring such actions entirely as O.R. models too often do, but not con- vincing to anyone who has strongly held differing views. Nevertheless, creative analytics can generate substantial benefits. Nearly six years ago, a small team of O.R.-trained but teams building community presence and maintaining fire bases. But it might take more than a decade; Co- lombia took 40 years.” Many of the same principles apply to what is known within the Unit- ed States as “community policing” – building com- munity relationships and networks of people who will help rather than relying mostly on the use of massive force when crises erupt. “I’d say pretty much the same approach would work in Ferguson, Mo., and Balti- more, too,” Mann says. This also means that American nation- al leaders need to have a much better un- derstanding of the problems. “We Green Berets took nine years to arrive at the VSO program in Afghanistan,” Mann says. “Jef- fersonian democracy, targeted aid and tar- geted killings all failed. We need to step back and reframe.”And that in turn, he em- phasizes, means “the days of outsourcing local leadership are over. You have to dive in. You don’t just get handed some talking points right before walking into some place to give a speech.” ANALYTICS CONTRIBUTIONS Analytics has much to contribute to the reframing Lt. Col. Mann advocates. One of the keys to VSO is getting to know locals and their concerns first-hand and on a day-to-day basis, and to let them get to know you.
- 68. W W W.I N FO R M S . O RG68 | A N A LY T I C S - M AGA Z I N E . O RG VILLAGE STABILITY OPERATIONS • what people do when they have a grievance (do they trust local government and law enforcement enough to work through those channels?); or • what happens to new economic development (in unstable regions, promising new facilities get blown up). Creative, sensible analytical ap- proaches – not unduly restricted by prior assumptions and backed and informed by close contact with the people with deep experience – can greatly advance getting the policies right. VSO, Lt. Col. Mann concludes, “is not a silver bullet, but it’s the best approach we’ve got.” The challenge for analytics is to assess, in terms senior decision-makers will readily grasp, how right or wrong this conclusion is, and how it might be shaped to be more effective. ❙ Douglas A. Samuelson (samuelsondoug@yahoo. com), D.Sc. in operations research, is president and chief scientist of InfoLogix, Inc., a small RD and consulting company in Annandale, Va. He is a contributing editor of OR/MS Today and Analytics. He is a longtime member of INFORMS. creatively oriented analysts in the Joint Im- provised Explosive Devices Defeat Organi- zation (JIEDDO) came up with a clever way to depict which tribes were where, based on contact reports. Gen. David Petraeus complimented these (unnamed) analysts at the time for the useful support they had pro- vided. Demonstrating that a hostile tribe’s stronghold was also heavily intermingled by a friendly tribe could convince a command- er not to apply massive force. The problem with wiping out much of the hostile tribe is that it also removes the friendly source of information – and turns the survivors unfriendly. According to Mann, the tribe- depicting analytical products originated at JIEDDO remain among the more useful tools available in theater, and they are still being generated and used. As in any other problem of measure- ment and analysis, it is vital to start with a clear objective and good metrics of whether that objective is being met. “We need a point of departure of what relative stability looks like [in the locale of interest], then we need to shape goals by what is possible,” Mann says. “We need a definition of relative stabil- ity that is agreed upon by all levels of power, and we shouldn’t go to war without it.” Metrics of such stability might include, for example, observations of: • whether locals tell the police where the criminals are or tell the criminals where the police are; REFERENCES 1. D. Scott Mann, “Game Changers: Going Local to Defeat Violent Extremists,” Tribal Analysis Publishing, Leesburg, Va., 2015. 2. Telephone interview with Lt. Col. Scott Mann, Dec. 17, 2015.
- 69. INFORMS MEMBERSHIP ISCHANGING FROM CALENDAR YEAR TO ANNIVERSARY DATE INFORMS has switched from a calendar year membership (January through December) to an anniversary membership (one year from your official join date). Renew today to preserve your member benefits. Learn more about what this change means for you. Visit www.informs.org/Membership/Anniversary-Date-Memberships-FAQs Online renewals available at renew.informs.org 443.757.3500 or 1.800.446.3676 informs@informs.org !
- 70. W W W.I N FO R M S . O RG70 | A N A LY T I C S - M AGA Z I N E . O RG Do the twists and turns of making smart business decisions by maximizing the value of your data have you feeling like you’re on an analytical rollercoaster? If so, the 2016 INFORMS Conference on Business Analytics and Operations Research, set for the Hyatt Regency Grand Cypress in Orlando, Fla., on April 10-12, is the one analytics conference you should attend. This conference consistently delivers proven applications and processes that will improve your business’s bottom line. Nearly 1,000 leading analytics professionals and industry experts are expected to come together to share ideas, network and learn about a wide range of problem-solving techniques and methods. With presentations that revolve around real-world solutions, attendees will hear the full story behind suc- cessful analytics projects. This, in turn, will help you gain insight and drive your business planning. This year’s world-class line-up of speakers and strategic thought leaders, including keynote speakers Paul Ballew of Ford and Samuel Eldersveld of Uber (see 2016 Conference on Business Analytics and O.R. set for Orlando The conference consistently delivers proven applications and processes that will improve your business’s bottom line. CONFERENCE PREVIEW
- 71. JA N UARY/ F E BR UARY 2016 | 71A NA L Y T I C S PaulBallew,globalchiefdataandanalytics officer, Ford Analytics, and Samuel K. Eldersveld, director of operations research, Uber Technologies, will deliver the keynote talks at the 2016 INFORMS Conference on Business Analytics Operations Research. The conference will be held April 10-12 at the Hyatt Regency Grand Cypress in Orlando, Fla. Ballew leads Ford’s global data and analytics teams including development of new capabilities supporting connectivity and smart mobility. Prior to joining Ford, he was chief data, insight analytics officer at Dun Bradstreet where he was responsible for the company’s global data and analytic activities along with the company’s strategic consulting practice. Ballew’s impressive career includes stints as Nationwide’s senior vice president for customer insight and analytics (where he directed customer analytics, market research and information and data management functions), as General Motors Corporation’s executive director for global market and industry analysis and as a partner for J.D. Power and Associates. Ballew’s keynote presentation is scheduled for 8-9 a.m. on April 11. Eldersveld, Ph.D., joined Uber Technologies Inc. in 2015 to work with the data sciences teams in San Francisco. He received his Ph.D. in operations research from Stanford University in 1992 and worked as a mathematician for manufacturing and planning at Boeing from 1991-2000 in the Phantom Works division. He joined Expedia as a startup and spent time at Starbucks before moving to Amazon as a principal research scientist in Seattle. He was also affiliated with the University of Washington Foster School of Business as a non-tenure-track auxiliary faculty member and lecturer in the department of Information Systems and Operations Management from 2005-2008. Elersveld’s keynote presentation, “O.R. in Today’s Dynamic Business Environment,” is scheduled for 10:30-11:20 a.m. on April 12. ❙ sidebar), will present talks on a wide range of problem-solving techniques and methods. In- vited tracks on Analytics Lead- ership Soft Skills, Decisions Risk Analysis, Fraud Detec- tion Life Sciences, Internet of Things, Marketing Analytics, Revenue Management Pric- ing, Sports Entertainment and Supply Chain Analytics will be of great interest to anyone looking to make analytics work in real organizations. About 30 hand- picked, member-contributed talks, in-depth technology work- shops, poster presentations, panels and several opportuni- ties for networking will round out the program. The Edelman Awards Gala, an Oscar-like dinner and awards celebration and a tra- ditional conference highlight, will be held April 11. Consid- ered the “Super Bowl of O.R.,” the Edelman recognizes the world’s best applications of analytics, operations research and management science. The Edelman Gala is included in the conference registration fee. For more on the Edelman finalists, click here. Ford’s Ballew, Uber’s Eldersveld to deliver keynotes Paul Ballew of Ford. Samuel Eldersveld of Uber.
- 72. W W W.I N FO R M S . O RG72 | A N A LY T I C S - M AGA Z I N E . O RG to connect in a casual atmosphere. The career fair is included the registration for this conference. The conference site, the Hyatt Re- gency Grand Cypress, is a world-class resort that offers an impressive collection of on-site activities, in addition to being just one mile from Walt Disney World and close to everything else Orlando has to offer. Registration rates for this conference start at $1,065 for INFORMS members. Special rates are available for students, retiredattendees,conferencenewcomers and selected speakers. A team discount is also available. ❙ For information regarding conference registration or submitting a presentation, visit meetings.informs.org/ analytics2016. Special programs within the confer- ence are designed for future analytics leaders. The Early Career Connection provides early-career professionals with new perspectives into some of the most critical problems facing industry today, enabling them to broaden their research agendas. The INFORMS Professional Colloquium is designed to help practice- oriented master’s and Ph.D. students transition into successful careers. Par- ticipants in both programs can register for the full conference at a discounted rate but must be nominated and select- ed to attend. The Analytics Career Fair is INFORMS’ premier, professional career event that allows top analytics employers and seasoned professionals the ability CONFERENCE PREVIEW The Hyatt Regency Grand Cypress in Orlando, Fla., will host the conference.
- 73. Sponsored by: DataScienceBowl.com Ingenuity meetspassion. Big data meets the human heart. Data science is more than a skill – it’s a way of life. It rewards grit as much as talent. Failure, curiosity, and small successes lead to discovery. This year’s Data Science Bowl will empower data scientists from across the world to improve both the ability to diagnose and the capacity to care for those with heart disease, enabling people to live longer, healthier lives. At stake is a $200,000 prize to those able to observe the right patterns, ask the right questions, and create an algorithm to cut the time and cost of the traditional diagnostic process. THE UNIVERSITY OF MICHIGAN Department of Industrial and Operations Engineering Faculty Positions The Department of Industrial and Operations Engineering at the University of Michigan invites applications and nominations for faculty positions beginning September, 2016. We seek outstanding candidates for faculty positions in all areas of industrial engineering -- methodological and applied -- with a particular focus on (1) Manufacturing Systems, (2) Transportation Systems, (3) Big Data/Analytics, (4) Cognitive Ergonomics and (5) Engineering Education. Individuals at all ranks are encouraged to apply. Candidates must have a Ph.D. and must demonstrate a strong commitment to high-quality research and evidence of teaching potential. Candidates for Associate or Full Professor should have a commensurate record of research publications and are expected to provide organizational and research leadership, develop sources of external funding, build relationships with industry, and interact with faculty colleagues. Candidates should provide (i) a current C.V., (ii) a list of references, and one page summary statements describing: (iii) career teaching plans; (iv) research plans, and (v) course (teaching) evaluations for candidates with prior teaching experience. Candidates should have their references send recommendations to us directly at IOEFacultySe- arch@umich.edu. The deadline for ensuring full consideration of an application is October 25, 2015, but the positions will remain open and applications may still be considered, at the discretion of the hiring committee, until appointments are made. We seek candidates who will provide inspiration and leadership in research and actively contribute to teaching. We are especially interested in candidates who can contribute, through their research, teaching and/or service, to the diversity and excellence of the academic community. The University of Michigan is responsive to the needs of dual career families. Please submit your application to the following: Web: http://ioe.engin.umich.edu/people/fac/fac_search/ If you have any questions regarding the web application submittal process or other inquiries, please contact, Gwendolyn Brown at gjbrown@umich.edu or (734) 763-1332. The University of Michigan is a non-discriminatory, affirmative action employer. QUEEN’S SCHOOL OF BUSINESS (Queen’s University, Kingston, Ontario CANADA) invites applications for two tenure-stream positions in Management Science/Operations Management. Candidates at all levels of experience will be considered. Entry-level candidates must have a PhD or be near completion. Applicants with research interests in all areas of operations management and management science will be welcomed, but we are particularly interested in at least one new faculty member with research interests in applications of online, real time, optimization and analytics. The successful candidate will exhibit potential for outstanding scholarly research and excellent teaching in support of the School’s public and private programs and will be expected to make contributions in service to the School, to the University, or the broader community. The MSOM group is a strong research group with particular expertise in revenue management and pricing, sustainability, energy markets, supply chain management and the interface of operations and marketing. For more information about our faculty see http://business.queensu.ca/faculty_and_research/index.php . The University invites applications from all qualified individuals. Queen’s is committed to employment equity and diversity in the workplace. All qualified candidates are encouraged to apply; however, in accordance with Canadian Immigration requirements, Canadian citizens and Permanent Residents of Canada will be given priority. The University will provide support in its recruitment processes to applicants with disabilities, including accommodation that takes into account an applicant’s accessibility needs - klewis@business.queensu.ca . The effective date of the appointment will be July 1st, 2016, but is flexible. Please submit a cover letter, current CV, references and a research sample, electronically to: omrecruiting@business.queensu.ca
- 74. W W W.I N FO R M S . O RG74 | A N A LY T I C S - M AGA Z I N E . O RG My longtime followers will know that as a graduate of the U.S. Naval Academy, I have a soft spot for Navy football. I’ve written about it before (Analytics magazine, November/December 2013 issue). I also have been very excited about the re-launch of the INFORMS Section on O.R. in Sports (SpORts) led by Walt DeGrange and Scott Nestler. With the epic season that Navy just had combined with new directions in INFORMS, I decided to write about predicting football games. Now, there are people who make a living at this, and what I propose here is just to spark discussion and pique interest. Taking the game history to date, we can use the (observed) expected points scored and variance to compute a probability of win by im- posing normality. (I say “impose” normality because I have no basis on which to assume it.) Letting X de- note the differential score (to date), N denote Navy and O denote opponent, we have: Predicting Navy football games BY HARRISON SCHRAMM This turned out to be more difficult than expected. Instead of constructing a rigorous hypothesis test, we compare differential methods with metrics. FIVE-MINUTE ANALYST
- 75. JA N UARY/ F E BR UARY 2016 | 75A NA L Y T I C S ASSESSING THE PREDICTION This turned out to be more difficult than expected. Instead of constructing a rigorous hypothesis test, we instead will compare our differential methods with some other candidate metrics: 1. The “Null” method, which assumes a probability of .5 for each game 2. The “Plebe” method, which chooses Navy as winner every game 3. The “Streak” method, which takes the previous game as a predictor for success in the next game We use a “Complementary Brier Score.” We chose this somewhat arbitrari- ly; making arbitrary choices is a benefit of authorship. The Brier Score is defined as: Where p is the predicted probabil- ity and O is the outcome (1 = win, 0 = loss). In this application, the Brier Score is the same as Mean Square Error. We use the complementary score, i.e., 1-B, to rescale and let better scores correlate with better outcomes. While this is trivial mathematically, it has the desirable prop- erty of giving better predictions a higher score (see Figure 1). Now, we show in Figure 2 our pre- dicted game outcomes (in terms of
- 76. W W W.I N FO R M S . O RG76 | A N A LY T I C S - M AGA Z I N E . O RG Figure 1: Differential scores from Navy football vs. opponents; lines above the x-axis indicate wins, while lines below indicate inverse wins, sometimes known as losses. Figure 2: Results of differential score prediction. This method had two “missed classifications” – it picked Notre Dame as a win and Memphis as a loss. Figure 3: Comparison of prediction methods as measured by Complementary Brier Score. The “Plebe” model and our proposed differential scoring performed approximately equal by the end of the season, while the naïve and streak models clearly performed worse. FIVE-MINUTE ANALYST probability of Navy win) vs. the ac- tual data. Note that care was taken to ensure that only the game history to date was used in the prediction. If we are going to predict the outcome of a sporting event, we should see how well our predic- tions performed. To this end, we compare it with three other poten- tial scoring methods: the “plebe” method which always picks a Navy win; the “Streak” method, which uses that previous games’ outcome as the next game’s prediction; and the “naïve” method, which simply gives Navy a 50 percent chance of winning each game. The results of these predictions are presented in Figure 3. Conclusion: While I don’t plan to give up my daytime operations research job and go into sports analysis, this was a fun and inter- esting case study. I’d be curious to think about what other methods could be applied with a minimum of data and computing power. ❙ Harrison Schramm (harrison.schramm@ gmail.com), CAP, is an operations research professional in the Washington, D.C., area. He is a member of INFORMS and a Certified Analytics Professional.
- 77. Join Online Today!http://join.informs.org • INFORMS Allows You to Network With Your Professional Peers and Others Who Share Your Interests • INFORMS Connect, the New Member-only, Online Community Lets You Network with your colleagues • INFORMS Provides Unsurpassed Networking Opportunities Available in INFORMS Communities and at Meetings • INFORMS Offers Certification for Analytics Professionals • INFORMS Helps You Take Leadership Roles to Help Build your Professional Profile • INFORMS Career Center Provides You with the Industry's Leading Job Board Is the largest association for analytics in the center of your professional network? It should be. To-Do-List Begin to eat healthy Go to the gym EXPAND YOUR NETWORK! JOIN INFORMS
- 78. W W W.I N FO R M S . O RG78 | A N A LY T I C S - M AGA Z I N E . O RG It’s your birthday. Four of your closest friends are with you to help you celebrate. Unfortunately, as a joke, one of your friends has cut the cake into 15 dif- ferent pieces each of varying sizes. The weights of the slices are as follows (in grams): 18, 48, 19, 59, 46, 72, 67, 57, 49, 80, 50, 69, 10, 48 and 83. Question: How can you pass out all of the slic- es of cake so that everyone gets an equal amount among the five of you? Send your answer to puzzlor@gmail.com by March 15, 2016. The winner, chosen randomly from correct answers, will receive a $25 Amazon Gift Card. Past questions and answers can be found at puzzlor.com. ❙ John Toczek is the assistant vice president of predictive modeling at Ace Group in the Decision Analytics and Predictive Modeling department. He earned his BSc. in chemical engineering at Drexel University (1996) and his MSc. in operations research from Virginia Commonwealth University (2005). He is a member of INFORMS. Crazy cake BY JOHN TOCZEK THINKING ANALYTICALLY Figure 1: Cut the cake: Make sure everyone gets an equal share.
- 79. For further informationplease visit www.gams.com/ads/ggig.htm www.gams.com OPTIMIZATION GENERAL ALGEBRAIC MODELING SYSTEM Complex economic models offer a wide range of op- tions for simulation runs and return a vast amount of data, which can be explored and exploited in var- ious ways. The GAMS Graphical Interface Generator (GGIG) enables interaction with such economic mod- els through a Java-based user interface. Originally developed for the Common Agricultural Policy Re- gionalised Impact (CAPRI) modeling system, GGIG generates a basic graphical user interface (GUI) for GAMS projects based on XML files. These files define the controls for simulation runs and subsequent pro- cessing of results. GGIG is currently used in a number of economic and agricultural modeling projects around the world, including the Policy Evaluation Model of the OECD and the Global Trade Analysis Project (GTAP). High-Level Modeling The General Algebraic Modeling System (GAMS) is a high-level modeling system for mathematical programming problems. GAMS is tailored for complex, large-scale modeling applications, and allows you to build large maintainable models that can be adapted quickly to new situations. Models are fully portable from one computer platform to another. State-of-the-Art Solvers GAMS incorporates all major commercial and academic state-of-the-art solution technologies for a broad range of problem types. GAMS Integrated Developer Environment for editing, debugging, solving models, and viewing data. GGIG - A GAMS Graphical Interface Generator GGIG's strengths: • Handling of complex economic applications through an efficient and portable user interface • Visualization of results in various ways • Support of distributed teams through SVN