Elements of the physical environmentElements of the Physical E.docx

Elements of the physical environment
Elements of the Physical Environment
The environment can basically described to be everything that
surrounds man, interacts with man and influences the way he
exists. The elements therefore can be put to four different
categories: the atmosphere which contains the air, gases,
climate and water, the hydrosphere that includes water in
oceans, seas, rivers and lakes, the lithosphere that describes
things on the earth’s surface like rocks, soil and landforms and
finally the biosphere that contains life or living things like the
flora – plants and fauna – animals. Environment means the
surroundings. Land, water, air, plants, animals, solid wastes and
other things that are surrounding us constitute our environment.
Man and environment are closely intertwined with each other, to
maintain a balance or equilibrium in nature.
The Earth's atmosphere is a thin layer of gases that surrounds
the Earth. It made up of 78% nitrogen, 21% oxygen, 0.9%
argon, 0.03% carbon dioxide, and very minimal amounts of
other gases. This thin gaseous layer insulates the Earth from
extreme temperatures; it keeps heat inside the atmosphere and it
also blocks the Earth from much of the Sun's incoming
ultraviolet radiation. It’s approximated to be about 480 km
thick, but most of the atmosphere (about 80%) is within 16 km
of the surface of the Earth. There is no exact place where the
atmosphere ends; it just gets thinner and thinner, until it merges
with outer space. The atmospheric air pressure at sea level, is
about equals 760 mm (29.92 inches) of mercury, 14.70 pounds
per square inch the air pressure decreases. At an altitude of
10,000 feet, the air pressure is 10 pound per square inch and
there is less oxygen to breathe. The atmosphere is made up of
layers namely: the thermosphere which includes the exosphere
and part of the ionosphere, the exosphere which is the outermost
layer of the Earth's atmosphere. The exosphere goes from about

640 km high to about 1,280 km, the ionosphere that starts at
about 70-80 km high and continues for hundreds of kilometers
like 640 km. It contains many ions and free electrons. The ions
are created when sunlight hits atoms and tears off some
electrons. Auroras occur in the ionosphere. Others include the
mesosphere characterized by decreasing temperatures as you
rise, the stratosphere characterized by slight temperature
increase with altitude, the tropopause which sits between the
stratosphere and the troposphere and finally the troposphere
which is the lowest region in the earth’s atmosphere.
A hydrosphere is the total amount of water on a planet. The
hydrosphere includes water that is on the surface of the planet,
underground, and in the air. A planet's hydrosphere can be
liquid, vapor, or ice. On Earth, liquid water exists on the
surface in the form of oceans, lakes and rivers. It also exists
below ground—as groundwater, in wells and aquifers. Water
vapor is most visible as clouds and fog. The frozen part of
Earth's hydrosphere is made of ice: glaciers, ice caps and
icebergs. The frozen part of the hydrosphere has its own name,
the cryosphere. Water moves through the hydrosphere in a
cycle. Water collects in clouds, then falls to Earth in the form
of rain or snow. This water collects in rivers, lakes and oceans.
Then it evaporates into the atmosphere to start the cycle all over
again. This is called the water cycle.
The lithosphere is the solid, outer part of the Earth. The
lithosphere includes the brittle upper portion of the mantle and
the crust, the outermost layers of Earth’s structure. It is
bounded by the atmosphere above and the asthenosphere
(another part of the upper mantle) below. The lithosphere is the
most rigid of Earth’s layers. Although the rocks of the
lithosphere are still considered elastic, they are not viscous. The
asthenosphere is viscous, and the lithosphere-asthenosphere
boundary (LAB) is the point where geologists and rheologists—
scientists who study the flow of matter—mark the difference in
ductility between the two layers of the upper mantle. Ductility
measures a solid material’s ability to deform or stretch under

stress. The lithosphere is far less ductile than the asthenosphere.
The elasticity and ductility of the lithosphere depends on
temperature, stress, and the curvature of the Earth itself. The
lithosphere is also the coolest of Earth’s layers. In fact, some
definitions of the lithosphere stress its ability to conduct heat
associated with the convection taking place in the plastic mantle
below the lithosphere. There are two types of lithosphere:
oceanic lithosphere and continental lithosphere. Oceanic
lithosphere is associated with oceanic crust, and is slightly
denser than continental lithosphere. Continental lithosphere,
associated with continental crust, can be much, much thicker
than its oceanic cousin, stretching more than 200 kilometers
(124 miles) below Earth’s surface.
The biosphere is made up of the parts of Earth where life exists.
The biosphere extends from the deepest root systems of trees, to
the dark environment of ocean trenches, to lush rain forests and
high mountaintops. In all the categories of the elements in terms
of spheres: atmosphere, hydrosphere and the lithosphere, the
biosphere overlaps in all of these because there is life in every
one of them. Although the biosphere measures about 20
kilometers from top to bottom, almost all life exists between
about 500 meters below the ocean’s surface to about 6
kilometers above sea level. In this, the whole human, animal,
bird and insect populations are contained.
References
Hydrosphere. National Geographic - Education. Retrieved
October 16, 2015, from the World Wide Web:
http://education.nationalgeographic.com/encyclopedia/
The 4 Main Elements of a Physical Environment. Quizlet.
Retrieved October 16, 2015, from the World Wide Web:
https://quizlet.com/5546762/the-4-main-elements-of-a-physical-
environment-flash-cards/
The Earth’s Atmosphere. EnchantedLearning.com. Retrieved

October 16, 2015, from the Web:
http://www.enchantedlearning.com/subjects/astronomy/planets/e
arth/Atmosphere.shtml
Page 1 of 1
E
lements of the physical environment
Page
1
of
1
E
lements
of the Physical Environment
T
he
environ
ment can
basically
described to be everything that surrounds man, interacts
with man and influences the way he exists.
T
he
eleme
n
ts therefore can be put to four different
climate and water, the

hydrosphere that
includes water in oceans, seas, rivers and lakes, the lithosp
here that
describes
things on
the
earth
’
s surface
like rocks, soil and landforms
and finally the biosphere
that contains
life or living
things like the flora
–
plants
and fauna
–
animals.
Environment means the surroundings. Land,
water, air, plants, animals, solid wastes and other things that are
surround
ing us constitute our
environment. Man and environment are closely intertwined with
each other, to maintain a
ba
lance or equilibrium in nature.
The Earth's atmosphere is a thin layer of gases that

surrounds the Earth. It made up
of
78% nitrogen, 21% oxygen, 0.9% argon,
0.03%
carbon dioxide, and very minimal
amounts of
other ga
ses. This thin gaseous layer insulates the Earth from extreme
temperatures; it keeps heat
inside the atmosphere and it also blocks the Earth from much of
the Sun's i
ncoming ultraviolet
radiation.
It’s
approximated to be
about
480 km
thick,
but most of the atmosphere
(about 80%) is
within
16 km
of the surface of the Earth. There is no exact place where the
atmosphere ends; it
just gets thinner and thinner, unt
il it merges with outer space.
T
he

atmospheric air pressure at
sea
level,
is about
equals 760 mm (29.92 inches) of mercury, 14.70 pounds per
square inch
the air
pressure decreases. At an altitude of 10,000 feet, the air pressu
re is 10 pound per square inch
and
there is less oxygen to breathe
.
T
he
atmosphere
is
made up of laye
rs
nam
e
ly: the thermosphere
w
hich includes the exosphere and part of the ionosphere, the
exosphere
which
is the outermost
layer of the Earth's atmosphere. The exosp
here goes from about
640 km
high to about

1,280 km
,
the
i
onosphere
that
starts at about
70
-
80 km
high and continues for hund
reds of
kilometers
like
Elements of the physical environment
Page 1 of 1
Elements of the Physical Environment
The environment can basically described to be everything that
surrounds man, interacts
with man and influences the way he exists. The elements
therefore can be put to four different
climate and water, the hydrosphere that
includes water in oceans, seas, rivers and lakes, the lithosphere
that describes things on the
earth’s surface like rocks, soil and landforms and finally the
biosphere that contains life or living
things like the flora – plants and fauna – animals. Environment
means the surroundings. Land,
water, air, plants, animals, solid wastes and other things that are
surrounding us constitute our

environment. Man and environment are closely intertwined with
each other, to maintain a
balance or equilibrium in nature.
The Earth's atmosphere is a thin layer of gases that surrounds
the Earth. It made up of
78% nitrogen, 21% oxygen, 0.9% argon, 0.03% carbon dioxide,
and very minimal amounts of
other gases. This thin gaseous layer insulates the Earth from
extreme temperatures; it keeps heat
inside the atmosphere and it also blocks the Earth from much of
the Sun's incoming ultraviolet
radiation. It’s approximated to be about 480 km thick, but most
of the atmosphere (about 80%) is
within 16 km of the surface of the Earth. There is no exact place
where the atmosphere ends; it
just gets thinner and thinner, until it merges with outer space.
The atmospheric air pressure at sea
level, is about equals 760 mm (29.92 inches) of mercury, 14.70
pounds per square inch the air
pressure decreases. At an altitude of 10,000 feet, the air
pressure is 10 pound per square inch and
there is less oxygen to breathe. The atmosphere is made up of
layers namely: the thermosphere
which includes the exosphere and part of the ionosphere, the
exosphere which is the outermost
layer of the Earth's atmosphere. The exosphere goes from about
640 km high to about 1,280 km,
the ionosphere that starts at about 70-80 km high and continues
for hundreds of kilometers like
Communications of the Association for Information Systems
Volume 34 Article 65

5-2014
Tutorial: Big Data Analytics: Concepts,
Technologies, and Applications
Hugh J. Watson
University of Georgia, [email protected]
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Recommended Citation
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mailto:[email protected]>
Tutorial: Big Data Analytics: Concepts, Technologies, and
Applications
Hugh J. Watson
Department of MIS, University of Georgia
[email protected]
We have entered the big data era. Organizations are capturing,
storing, and analyzing data that has high volume,
velocity, and variety and comes from a variety of new sources,
including social media, machines, log files, video,
text, image, RFID, and GPS. These sources have strained the
capabilities of traditional relational database
management systems and spawned a host of new technologies,
approaches, and platforms. The potential value of
big data analytics is great and is clearly established by a
growing number of studies. The keys to success with big
data analytics include a clear business need, strong committed
sponsorship, alignment between the business and
IT strategies, a fact-based decision-making culture, a strong
data infrastructure, the right analytical tools, and people
skilled in the use of analytics. Because of the paradigm shift in
the kinds of data being analyzed and how this data is
used, big data can be considered to be a new, fourth generation
of decision support data management. Though the
business value from big data is great, especially for online

companies like Google and Facebook, how it is being
used is raising significant privacy concerns.
Keywords: big data, analytics, benefits, architecture, platforms,
privacy
Volume 34, Article 65, pp. 1247-1268, April 2014
Applications
Applications
1248
I. INTRODUCTION
Big data and analytics are hot topics in both the popular and
business press. Articles in publications like the New
York Times, the Wall Street Journal, and Financial Times, as
well as books like Super Crunchers [Ayers, 2007],
Competing on Analytics [Davenport and Harris, 2007], and
Analytics at Work [Davenport, Harris and Morison, 2010]
have spread the word about the potential value of big data and
analytics.
Today, many organizations are collecting, storing, and
analyzing massive amounts of data. This data is commonly
referred to as “big data” because of its volume, the velocity

with which it arrives, and the variety of forms it takes. Big
data is creating a new generation of decision support data
management. Businesses are recognizing the potential
value of this data and are putting the technologies, people, and
processes in place to capitalize on the opportunities.
A key to deriving value from big data is the use of analytics.
Collecting and storing big data creates little value; it is
only data infrastructure at this point. It must be analyzed and
the results used by decision makers and organizational
processes in order to generate value.
Big data and analytics are intertwined, but analytics is not new.
Many analytic techniques, such as regression
analysis, simulation, and machine learning, have been available
for many years. Even the value in analyzing
unstructured data such as e-mail and documents has been well
understood. What is new is the coming together of
advances in computer technology and software, new sources of
data (e.g., social media), and business opportunity.
This confluence has created the current interest and
opportunities in big data analytics. It is even spawning a new
area of practice and study called “data science” that
encompasses the techniques, tools, technologies, and
processes for making sense out of big data.
Big data is creating new jobs and changing existing ones.
Gartner [2012] predicts that by 2015 the need to support
big data will create 4.4 million IT jobs globally, with 1.9
million of them in the U.S. For every IT job created, an
additional three jobs will be generated outside of IT. Big data is
also creating a high demand for people who can
analyze and use big data. A 2011 study by the McKinsey Global
Institute predicts that by 2018 the U.S. alone will
face a shortage of 140,000 to 190,000 people with deep
analytical skills as well as 1.5 million managers and
analysts to analyze big data and make decisions [Manyika,

Chui, Brown, Bughin, Dobbs, Roxburgh, and Byers,
2011]. Because companies are seeking people with big data
skills, many universities are offering new courses,
certificates, and degree programs to provide students with the
needed skills. Vendors such as IBM are helping
educate faculty and students through their university support
programs.
At a high level, the requirements for organizational success
with big data analytics are the same as those for
business intelligence (BI) in general [Williams, 2004]. At a
deeper level, however, there are many nuances that are
important and need to be considered by organizations that are
getting into big data analytics. For example,
organizational culture, data architecture, analytical tools, and
personnel issues must be considered. Of particular
interest to information technology (IT) professionals are the
new technologies, platforms, and approaches that are
being used to store and analyze big data. They aren’t your
mother’s BI architecture [Watson, 2012].
Governments and companies are able to integrate personal data
from numerous sources and learn much of what
you do, where you go, who your friends are, and what your
preferences are. Although this leads to better service
(and profits for companies), it also raises privacy concerns
[Clemons, Wilson, Barnett, Jin and Matt, 2014]. There
are few legal restrictions on what big data companies such as
Facebook and Google can do with the data they
collect.
In this tutorial, we first consider the nature and sources of big
data. Next, we look at the history of analytics, the
various kinds of analytics, and how they are used with big data.
Starbucks, Chevron, U.S. Xpress, and Target are
used to illustrate various uses of big data analytics. Current

research is documenting the benefits of big data and
provides a compelling argument for its use. The requirements
for being successful with big data are discussed and
illustrated, including establishing a clear business need; having
strong, committed sponsorship; alignment between
the business and analytics strategies; a fact-based decision-
making culture; a strong data infrastructure; the right
analytical tools; and users, analysts, and data scientists skilled
in the use of big data analytics. Special attention is
given to the technologies, platforms, and approaches for storing
and analyzing big data. Privacy concerns about the
use of big data are also explored.
1249
II. WHAT IS BIG DATA
From an evolutionary perspective, big data is not new. A major
reason for creating data warehouses in the 1990s
was to store large amounts of data. Back then, a terabyte was
considered big data.
1
Teradata, a leading data
warehousing vendor, used to recognize customers when their
data warehouses reached a terabyte. Today,
Teradata has more than 35 customers, such as Wal-Mart and
Verizon, with data warehouses over a petabyte in
size. eBay captures a terabyte of data per minute and maintains
over 40 petabytes, the most of any company in the

world.
So what is big data? One perspective is that big data is more
and different kinds of data than is easily handled by
traditional relational database management systems (RDBMSs).
Some people consider 10 terabytes to be big data,
but any numerical definition is likely to change over time as
organizations collect, store, and analyze more data.
Another useful perspective is to characterize big data as having
high volume, high velocity, and high variety—the
three Vs [Russom, 2011]:
—the amount or quantity of data
—the rate at which data is created
—the different types of data
In short, “big data” means there is more of it, it comes more
quickly, and comes in more forms.
Both of these perspectives are reflected in the following
definition [Mills, Lucas, Irakliotis, Rappa, Carlson, and
Perlowitz, 2012; Sicular, 2013]:
Big data is a term that is used to describe data that is high
volume, high velocity, and/or high variety;
requires new technologies and techniques to capture, store, and
analyze it; and is used to enhance decision
making, provide insight and discovery, and support and
optimize processes.
It is important to understand that what is thought to be big data
today won’t seem so big in the future [Franks, 2012].

Many data sources are currently untapped—or at least
underutilized. For example, every customer e-mail,
customer-service chat, and social media comment may be
captured, stored, and analyzed to better understand
customers’ sentiments. Web browsing data may capture every
mouse movement in order to better understand
customers’ shopping behaviors. Radio frequency identification
(RFID) tags may be placed on every single piece of
merchandise in order to assess the condition and location of
every item. Figure 1 shows the projected growth of big
data.
Figure 1: The Exponential Growth of Big Data (Source:
Palfreyman, 2013)
1
As a frame of reference, a terabyte can hold 1,000 copies of the
Encyclopedia Britannica. Ten terabytes can hold the printed
collection of the
Library of Congress. A petabyte can hold approximately 20
million four-door filing cabinets full of text. It would take
about 500 million floppy disks
to store the same amount of data. See www.whatsabyte.com.
1250
III. BIG DATA SOURCES

Big data has many sources. For example, every mouse click on a
web site can be captured in Web log files and
analyzed in order to better understand shoppers’ buying
behaviors and to influence their shopping by dynamically
recommending products. Social media sources such as Facebook
and Twitter generate tremendous amounts of
comments and tweets. This data can be captured and analyzed to
understand, for example, what people think about
new product introductions. Machines, such as smart meters,
generate data. These meters continuously stream data
about electricity, water, or gas consumption that can be shared
with customers and combined with pricing plans to
motivate customers to move some of their energy consumption,
such as for washing clothes, to non-peak hours.
There is a tremendous amount of geospatial (e.g., GPS) data,
such as that created by cell phones, that can be used
by applications like Four Square to help you know the locations
of friends and to receive offers from nearby stores
and restaurants. Image, voice, and audio data can be analyzed
for applications such as facial recognition systems in
security systems.
IV. BIG DATA ANALYTICS
By itself, stored data does not generate business value, and this
is true of traditional databases, data warehouses,
and the new technologies such as Hadoop for storing big data.
Once the data is appropriately stored, however, it
can be analyzed, which can create tremendous value. A variety
of analysis technologies, approaches, and products
have emerged that are especially applicable to big data, such as
in-memory analytics, in-database analytics, and
appliances (all discussed later).
What Is Analytics?

It is helpful to recognize that the term analytics is not used
consistently; it is used in at least three different yet
related ways [Watson, 2013a]. A starting point for
understanding analytics is to explore its roots. Decision support
systems (DSS) in the 1970s were the first systems to support
decision making [Power, 2007]. DSS came to be used
as a description for an application and an academic discipline.
Over time, additional decision support applications
such as executive information systems, online analytical
processing (OLAP), and dashboards/scorecards became
popular. Then in the 1990s, Howard Dresner, an analyst at
Gartner, popularized the term business intelligence. A
typical definition is that “BI is a broad category of applications,
technologies, and processes for gathering, storing,
accessing, and analyzing data to help business users make better
decisions” [Watson, 2009a, p. 491]. With this
definition, BI can be viewed as an umbrella term for all
applications that support decision making, and this is how it is
interpreted in industry and, increasingly, in academia. BI
evolved from DSS, and one could argue that analytics
evolved from BI (at least in terms of terminology). Thus,
analytics is an umbrella term for data analysis applications.
BI can also be viewed as “getting data in” (to a data mart or
warehouse) and “getting data out” (analyzing the data
that is stored). A second interpretation of analytics is that it is
the “getting data out” part of BI. The third interpretation
is that analytics is the use of “rocket science” algorithms (e.g.,
machine learning, neural networks) to analyze data.
These different takes on analytics do not normally cause much
confusion, because the context usually makes the
meaning clear. The progression from DSS to BI to analytics is
shown in Figure 2.
Figure 2: From DSS to BI to Analytics.

Different Kinds of Analytics
It is useful to distinguish between three kinds of analytics
because the differences have implications for the
technologies and architectures used for big data analytics. Some
types of analytics are better performed on some
platforms than on others.
Descriptive analytics, such as reporting/OLAP,
dashboards/scorecards, and data visualization, have been widely
used for some time, and are the core applications of traditional
BI. Descriptive analytics are backward looking (like a
car’s rear view mirror) and reveal what has occurred. One trend,
however, is to include the findings from predictive
analytics, such as forecasts of future sales, on
dashboards/scorecards.
1251
Predictive analytics suggest what will occur in the future (like
looking through a car’s windshield). The methods and
algorithms for predictive analytics such as regression analysis,
machine learning, and neural networks have existed
for some time. Recently, however, software products such as
SAS Enterprise Miner have made them much easier
to understand and use. They have also been integrated into
specific applications, such as for campaign
management. Marketing is the target for many predictive
analytics applications; here the goal is to better understand

customers and their needs and preferences.
Some people also refer to exploratory or discovery analytics,
although these are just other names for predictive
analytics. When these terms are used, they normally refer to
finding relationships in big data that were not previously
known. The ability to analyze new data sources—that is, big
data—creates additional opportunities for insights and
is especially important for firms with massive amounts of
customer data.
Golden path analysis is a new and interesting predictive or
discovery analytics technique. It involves the analysis of
large quantities of behavioral data (i.e., data associated with the
activities or actions of people) to identify patterns of
events or activities that foretell customer actions such as not
renewing a cell phone contract, closing a checking
account, or abandoning an electronic shopping cart. When a
company can predict a behavior, it can intercede,
perhaps with an offer, and possibly change the anticipated
behavior.
Whereas predictive analytics tells you what will happen,
prescriptive analytics suggests what to do (like a car’s GPS
instructions). Prescriptive analytics can identify optimal
solutions, often for the allocation of scarce resources. It, too,
has been researched in academia for a long time but is now
finding wider use in practice. For example, the use of
mathematical programming for revenue management is
increasingly common for organizations that have
“perishable” goods such as rental cars, hotel rooms, and airline
seats. For example, Harrah’s Entertainment, a
leader in the use of analytics, has been using revenue
management for hotel room pricing for many years.
Organizations typically move from descriptive to predictive to

prescriptive analytics. Another way of describing this
progression is: What happened? Why did it happen? What will
happen? How can we make it happen? This
progression is normally seen in various BI and analytics
maturity models [Eckerson, 2004].
V. EXAMPLES OF BIG DATA ANALYTICS
Let us consider several examples of companies that are using
big data analytics. The examples illustrate the use of
different sources of big data and the different kinds of analytics
that can be performed.
Introducing a New Coffee Product at Starbucks
Starbucks was introducing a new coffee product but was
concerned that customers would find its taste too strong.
The morning that the coffee was rolled out, Starbucks monitored
blogs, Twitter, and niche coffee forum discussion
groups to assess customers’ reactions. By mid-morning,
Starbucks discovered that although people liked the taste
of the coffee, they thought that it was too expensive. Starbucks
lowered the price, and by the end of the day all of
the negative comments had disappeared.
Compare this fast response with a more traditional approach of
waiting for the sales reports to come in and noticing
that sales are disappointing. A next step might be to run a focus
group to discover why. Perhaps in several weeks
Starbucks would have discovered the reason and responded by
lowering the price.
Drilling for Oil at Chevron
Each drilling miss in the Gulf of Mexico costs Chevron upwards
of $100 million. To improve its chances of finding oil,

Chevron analyzes 50 terabytes of seismic data. Even with this,
the odds of finding oil have been around 1 in 5. In
the summer of 2010, because of BP’s Gulf oil spill, the federal
government suspended all deep water drilling
permits. The geologists at Chevron took this time to seize the
opportunity offered by advances in computing power
and storage capacity to refine their already advanced computer
models. With these enhancements, Chevron has
improved the odds of drilling a successful well to nearly 1 in 3,
resulting in tremendous cost savings.
Monitoring Trucks at U.S. Xpress
U.S. Xpress is a transportation company. Its cabs continuously
stream more than 900 pieces of data related to the
condition of the trucks and their locations [Watson and
Leonard, 2011]. This data is stored in the cloud and analyzed
in various ways, with information delivered to various users,
from drivers to senior executives, on iPads and other
tablet computers. For example, when a sensor shows that a truck
is low on fuel, the driver is directed to a filling
station where the price is low. If a truck appears to need
maintenance, drivers are sent to a specific service depot.
Routes and destinations are changed to ensure that orders are
delivered on time.
1252
Trucks experience necessary and unavoidable idle time. An
example of the former is when a truck is stuck in traffic
and nothing can be done about it (unless it is routed around a

traffic delay). An example of the latter is when a driver
stops for lunch in the winter and keeps the truck running in
order to keep the cab warm. By monitoring its trucks,
U.S. Xpress can tell which is which, and has saved millions in
fuel costs and reduced emissions into the
environment by incenting its drivers to reduce avoidable idle
time.
Targeting Customers at Target
Target received considerable negative attention in publications
such as the New York Times [Duhigg, 2012] and
Forbes [Hill, 2012] for mining data to identify women who are
pregnant. The negative press began when a father
complained to a Target store manager in Minneapolis that his
daughter had received pregnancy-related coupons.
He felt that the coupons were inappropriate and promoted teen
pregnancy. Little did he know that his daughter was
pregnant. He later apologized to the store manager and said that
there had obviously been some activities going on
in his household of which he was unaware.
How did Target identify pregnant women? To build its
predictive models, Target focused on women who had signed
up for the baby registry—an excellent indicator that they were
pregnant. They then compared the women’s
purchasing behavior with the purchasing behavior of all Target
customers. Twenty-five variables were found useful
for identifying this market segment—pregnant women—and
when their babies were due. The variables included
buying large quantities of unscented lotions; supplements such
as calcium, magnesium, and zinc; scent-free soaps;
extra large bags of cotton balls; hand sanitizers; and
washcloths. Using these variables, pregnancy predictive
models were built and used to score the likelihood that a woman
was pregnant and when she was likely to deliver.

For example, pregnant women tend to buy hand sanitizers and
washcloths as they get close to their delivery date.
Target used these predictions to identify which women should
receive specific coupons.
The story continues, however, with another public-relations
nightmare. . Soon afterward, Target received
unfavorable press for predicting engagements. Target was
sending out invitations to join its bridal registry before
sons and daughters told their parents they were engaged.
In response to the negative press, Target no longer sends out
only pregnancy-related coupons, but mixes in others,
such as for lawnmowers. Target is also much more guarded in
what information it shares about its data mining
activities. While Target’s data mining is legal, it strikes many
people as creepy, if not inappropriate.
VI. THE BENEFITS OF BIG DATA ANALYTICS
As has been discussed, collecting and storing big data does not
create business value. Value is created only when
the data is analyzed and acted on. As the Starbucks, Chevron,
and U.S. Xpress examples show, the benefits from
big data analytics can be varied, substantial, and the basis for
competitive advantage. Because of its potential
benefits, some people add a fourth V to the characteristics of
big data: high value. This value is realized, however,
only when an organization has a carefully thought out and
executed big data strategy.
Research shows the benefits of using data and analytics in
decision making. One study of 179 large publicly traded
firms found that companies that have adopted data-driven
decision making have output and productivity that is 5%
to 6% higher than that of other firms. The relationship extends

to other performance measures such as asset
utilization, return on equity, and market value [Brynjolfsson,
Hitt and Kim, 2011]. In 2010, the MIT Sloan
Management Review, in collaboration with the IBM Institute for
Business Value, surveyed a global sample of nearly
3,000 executives [LaValle, Lesser, Shockley, Hopkins, and
Kruschwitz, 2010]. Among the findings were that top-
performing organizations use analytics five times more than do
lower performers, and that 37% of the respondents
believe that analytics creates a competitive advantage. A
follow-up study in 2011 found that the percentage of
respondents who reported that the use of analytics was creating
a competitive advantage rose to 58%, which is a
57% increase. Although these studies do not focus exclusively
on big data, they do show the positive relationships
between data-driven decision making, organizational
performance, and competitive position.
There are also potential benefits from governments’ use of big
data. A TechAmerica report [Miller, Lucas, Irakliotis,
Ruppa, Carlson and Perlowitz, 2012] describes the following
scenario of a world that is benefiting from big data
analytics:
Imagine a world with an expanding population but a reduced
strain on services and infrastructure;
dramatically improved health care outcomes with greater
efficiency and less investment; intensified threats
to public safety and national borders, but greater levels of
security; more frequent and intense weather
events, but greater accuracy in prediction and management.
Imagine a world with more cars, but less
congestion; more insurance claims but less fraud; fewer natural
resources, but more abundant and less
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habits.html?pagewanted=1&_r=2&hp&
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expensive energy. The impact of big data has the potential to be
as profound as the development of the
Internet itself.
This scenario may be optimistic, but it suggests uses of big data
analytics that are being aggressively pursued.
VII. THE REQUIREMENTS FOR BEING SUCCESSFUL WITH
BIG DATA ANALYTICS
The requirements for success with big data analytics, such as
executive support and sponsorship, are largely the
same as with most projects, including analytics and BI in
general [Williams, 2004; Watson, 2013]. The differences
are in the details, and some of the details, such as the storage
and analysis platforms, are very important.
A Clear Business Need
It is common knowledge that projects should be business rather
than technology driven. They should address a
business need such as solving a problem or seizing an
opportunity. While the media attention given to big data has
created an awareness of its potential, it has also led some
executives to push big data projects without clearly
defined goals. Following are opportunities for big data analytics

in different industries [Franks, 2012].
– pricing, client risk
analysis, fraud detection, faster claims processing
– analysis of patterns of
services across social networks, profitability of
customers’ social networks, churn minimization
–
tracking shelf availability, assessing the impact of
promotional displays, assess the effectiveness of promotional
campaigns, inventory management, pricing,
advanced clickstream analysis
– real-time fleet
management, RFID for asset tracking
– analysis of smart grid data to
determine variable pricing models, smart meters to
forecast energy demand, customized rate plans for customers
– game play analysis to provide
feedback to game producers, opportunities for in-
game offers
– identifying people linked
to known trouble groups, determining the location
of individuals and groups
In many organizations, the initial business case for big data
analytics focuses on customer-centric objectives and
uses existing and newly accessible internal sources of data. Big
data analytics can be especially helpful for
companies that seek to understand customers better, develop

meaningful relationships with customers, and improve
operations that enhance the customer experience [Schroeck,
Schockley, Smart, Romero-Morales and Tufano,
2012]. Whatever the focus, successful big data initiatives
should start with a specific or narrowly defined set of
objectives rather than a “build it and they will come” approach
[Miller, et al., 2012].
Strong, Committed Sponsorship
Without solid sponsorship, it is difficult to succeed with any IT
project, and this includes big data analytics projects. If
the project is departmental, sponsorship can reside at the
departmental level. However, projects that are more
strategic and enterprisewide should have senior management
support.
An IBM study [Schroeck et al., 2012] found that in the early
stages of big data adoption, the CIO is often the
sponsor, but as the technology infrastructure is put in place and
business opportunities are identified, sponsorship
tends to shift to a function-specific executive such as a CMO or
CFO or even the CEO.
Alignment between the Business and Analytics Strategy
It is important to make sure that big data analytics projects
support the business strategy. This is why most projects
should be driven by business people rather than by IT. In
analytics-based organizations, the alignment is especially
close; in fact, it may be impossible to separate the business and
analytics strategies. Without analytics as an
enabler, the business strategy cannot succeed.
Large online retailers such as Amazon.com and Overstock.com
are great examples of analytics-based organizations

[Watson, Hofer and Wixom, 2009b]. The most visible examples
of analytics at work are the product
recommendations that appear when customers use their web
sites. Recommendations are the result of
recommendation engines that consider the search terms entered,
previous mouse-clicks, market-basket analysis of
other shoppers’ purchases, the availability and profitability of
various products, and what the shopper has
considered or purchased in the past. Less visible but equally
important BI applications include reporting,
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telco.html
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Data-to-Boost-On-Shelf-Availability80766
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2013-report-1977336.pdf
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gaming/
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products/investigation/articles/6396543-How-Big-Data-is-
helping-law-enforcement/
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dashboards/scorecards, demand forecasting, pricing, product
return analysis, market segmentation analysis,
campaign management, and search engine optimization.

In a conversation with Patrick Byrne, the CEO of
Overstock.com, the question was asked: “How would you
describe
your company?” Byrne responded, “A BI (analytics) company.”
[Byrne, 2009]. He also explained that at one time his
company was in trouble, implemented BI, and was turned
around, in large part due to BI. His answer reflects how
important analytics has been to the success of Overstock.com
and the need for close alignment between the
analytics and IT strategies.
A Fact-Based Decision-Making Culture
To benefit from big data analytics, decisions must be based on
“the facts” (generated by analytics) and there should
be constant experimentation to see what works best. Changing
the organizational culture associated for how
decisions are made can be more challenging than solving
technical issues. This was seen in Moneyball, which tells
the story of the Oakland Athletics and general manager Billy
Beane’s use of analytics to make personnel and other
baseball decisions [Lewis, 2003; Miller, 2011]. Beane had to
overcome the authority and influence of dissenters who
had years of baseball experience in order to implement his new
analytical approach. Now, every major sporting
team relies on analytics for all kinds of decisions, such as when
to try a two-point conversion in football.
Harrah’s became a leader in the gaming industry through its use
of analytics and its Total Rewards loyalty program
[Watson and Volonino, 2000]. Prior to the introduction of
analytics, decisions were typically based on “Harrahisms”—
practices that managers believed worked well. After analytics
became the norm, decisions had to be based on the
facts. Today it is said that three things will get you fired at
Harrah’s: stealing, sexual harassment, and failing to make

decisions based on the facts.
To create a fact-based decision-making culture, senior
management can do several things. First, recognize that
some people can’t or won’t adjust and will have to be replaced.
First American Corporation, a regional bank
headquartered in Nashville, Tennessee, was in financial trouble
and brought in a new management team [Cooper,
Watson, Wixom and Goodhue, 2000]. After considering several
strategies, management decided on a customer
intimacy strategy where the bank would use analytics to
understand its customers especially well and design
products and services that would meet customers’ needs and
preferences as well as increase bank profits. The
strategy was highly successful, but some people were not able
to adjust to the change. Prior to the analytics-based
approach to running the bank, the marketing department
consisted of 12 people. Afterwards, there were still 12, but
none of the original people were still in marketing. They had
either gone to other positions or left the bank. As the
CEO explained: “Their idea of marketing was giving balloons
and suckers along the teller line and running focus
groups, but marking has become very analytical.”
Senior management can do other things to change the culture. A
survey by the Economist Intelligence Unit [2012]
found the top strategies in promoting a data-driven culture are
top-down guidance and/or mandates from executives,
promotion of data-sharing practices, increased availability of
training in data analytics, and communication of the
benefits of data-driven decision making. Other management
strategies not included in the survey are stressing that
outdated methods must be discontinued, asking to see what
analytics went into decisions, and linking incentives and
compensation to desired behaviors.

A Strong Data Infrastructure
Data is critically important to BI and analytics. When a strong
data infrastructure is in place, applications can often
be developed in days. Without a strong data infrastructure,
applications may never be completed. IT understands
the importance of the data infrastructure, but the business units
sometimes assume it is a given and don’t fully
appreciate what is required to create and maintain it.
Technology Advances
At the turn of the century, companies were struggling to store
big data. Fortunately, improvements in storage and
CPU capabilities, all at a lower cost, saved the day.
Of particular significance is the emergence of the scale-out
architecture, in which hundreds or thousands of low-cost
commodity servers are placed in parallel. Each server has
multiple CPUs and large, shared memory caches. Data is
spread across the servers, and processing takes place in parallel.
This approach allows vast quantities of data to be
stored and analyzed quickly. If more storage and processing
power are needed, additional servers are added to this
massively parallel processing (MPP) architecture. Big data
platforms rely on a scale-out architecture.
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In-memory and solid-state disks are other important
technological advances. Each improves response times by

storing data in memory rather than on hard disk drives. The
major bottleneck to performance (i.e., response time) is
the time it takes to access and return data from disk drives;
these new technologies greatly reduce this impediment.
The need to store and analyze big data has spawned a variety of
technologies, approaches, and platforms. Many of
them are complementary. Special attention will be given to
Hadoop/MapReduce because of the considerable
attention that it is receiving and its potential importance.
Data Warehouses
For many organizations, data warehouses provide the single
version (or source) of the truth for decision support
data. The data is extracted from source systems (e.g.,
operational systems, ERPs), transformed (e.g., consistent
formats), integrated (e.g., around a common key, such as a
customer ID), and loaded into the data warehouse. The
data can be thought of as “squeaky clean” because of the care
taken to ensure its accuracy. Users and applications
access the data from the warehouse to support decision making.
Data warehouses are primarily designed for the storage and
analysis of structured data—that is, data easily stored
in the rows and columns of relational databases. They employ
an MPP architecture to provide massive, scalable
storage capacity and powerful analytical capabilities. The data
is used for queries, reporting, online analytical
processing (OLAP), dashboards/scorecards, data visualization,
and regulatory and compliance requirements. It is
the workhorse for descriptive analytics but also supports
predictive and prescriptive analytics.
The four major BI companies—IBM, Oracle, SAP, and
Microsoft—offer data warehousing products, as does

Teradata, an especially important player in the upper end of the
market. IBM and Teradata have customers with
many of the largest data warehouses in the world.
Data Mart Appliances
Appliances provide an integrated “stack” of hardware, software,
and storage in a single "box.” They are built from the
ground up for speed to process queries very quickly. Appliances
can be used in a variety of ways. For example, they
can be a stand-alone system that meets a smaller organization’s
or a department’s needs. Or, they can be tied to a
data warehouse and used to offload data and specific
applications. Some companies use appliances as
“sandboxes” to develop and test new applications before moving
them to the warehouse. Some appliances are
designed to handle unstructured data by incorporating
Hadoop/MapReduce. Others are fine-tuned for specific
applications such as retail data and call data records in
telecommunication firms.
Appliances deliver high performance in a variety of ways. They
use an MPP architecture and are highly scalable.
Some, such as Vertica, have databases that are columnar rather
than row based. Others, such as Teradata, use
solid-state disks to store data. And still others, such as Tradata
Aster, integrate Hadoop/MapReduce into their
architecture.
Many people say that Teradata’s data warehouse products were
the first appliances because they were the first
purpose-built systems designed specifically for storing and
processing large amounts of data, but the company
never described its products as appliances. The first company to
use the appliance term was Netezza (now an IBM
company, and recently renamed IBM PureData for Analytics) in

the early 2000s. Today, all of the major BI vendors
as well as other companies such as HP (e.g., Vertica) and EMC
(e.g., Greenplum) offer appliances.
Analytical Sandboxes
Advanced analytics can be very computationally intensive and
create performance problems for descriptive analytics
such as reports and dashboards when competing for computing
resources. Query managers (part of the RDBMS)
can help by prioritizing the order in which queries are
processed, but do not provide a complete solution. Another
approach is to create an analytic sandbox where modelers can
“play” with advanced analytics without affecting other
users.
Sandboxes can be real or virtual. With a real sandbox, a
separate platform such as an appliance is used. Data for
the sandbox is sourced from the data warehouse and possibly
augmented by other data that the modelers add. In a
virtual sandbox, a partition of the data warehouse is loaded with
the data the modelers need. The data warehouse
and sandbox reside in the same database software but operate as
separate systems. A virtual sandbox requires
data warehousing software that supports its creation and use.
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In-Memory Analytics
An impediment to fast response times for queries is the time

required to find data on disk and to read it to memory.
This time can be reduced anywhere from 10 to 1,000 times by
in-memory analytics where the data is stored in
random access memory (RAM) rather than on a physical disk
[Read, 2013]. There is no need to page data in and
out of disk storage with this technology.
In-memory technology comes in two forms: either on the
platform or with the BI tool. When implemented on the
platform, the server stores the data in-memory, and data is
accessed by the BI tools. SAP’s Hana is an example of
in-memory analytics on the platform. Some desktop BI tools
such as QlikView provide in-memory analytics by
maintaining data on the desktop computer’s memory. Up to a
terabyte of data can be stored on computers with 64-
bit operating systems. In-memory BI tools also move data
between disk (e.g., in the data warehouse) and the local
desktop memory so that the most frequently used data (so called
“hot data") is available in memory.
Some applications are especially well suited for in-memory
analytics. For example, with OLAP (often incorporated in
reports and dashboards/scorecards), users want to “slice and
dice” data to look at the business from different
perspectives, such as comparing this and last year’s sales in
different locations. When all of the data is in memory,
this analysis can be done very quickly providing analysis at the
speed of thought.
Not all applications require or are well suited for in-memory
analytics. Some applications, such as a market basket
analysis that is run weekly or applications that require more
data than can be provided by current in-memory
technologies, are not good candidates. While the cost and
reliability of in-memory technology continues to improve,
it is still relatively expensive and prone to failure.

In-database Analytics
A change is taking place as to where analytics is performed. In
the past, data was moved to a server (think of a
sandbox) and the analysis was performed there. The trend is to
make analytics part of the database software so that
data does not have to be moved. With this approach, the
analytic capabilities are part of the database software.
SAS, a leading provider of advanced analytics software, has
partnered with Oracle and Teradata to integrate SAS
analytics into their products.
There are several advantages to in-database analytics. First, it
eliminates the need for a separate server. It also
makes all of the warehouse data, not just the typical subset,
available for analysis. This improves model accuracy.
When the final model is created, it can be used easily with
warehouse data. For example, a propensity-to-buy
predictive model might be created, and then customers can be
scored to assess whom to target in a marketing
campaign.
Columnar Databases
Historically, RDBMS have stored records in rows, as shown in
Figure 3. This is very efficient for entering, updating,
and deleting records. It is less efficient, however, for analytics
where only a few columns are needed (think of the
typical WHERE clause in a SQL query) and the table has
perhaps hundreds of columns.
In response, Sybase IQ (now a SAP company) in the mid 1990s
offered the first columnar database that reversed
the rows and columns, as shown in Figure 3. This approach
provides greater processing speed for queries and

opportunities for data compression. A columnar database is an
RDBMS, but with the rows and columns reversed.
Columnar databases are used by Vertica and ParAccel in their
appliances and by Teradata in its data warehouse
and appliances.
Figure 3: Row and Column-Oriented Databases.
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Streaming and Complex Event Processing (CEP) Engines
We are entering the “Internet of Things” where devices such as
cars and utility meters automatically send out data
across the Internet [Chui, Loffler, and Roberts, 2010]. For
businesses, there is value in receiving this data,
processing it in real time, and taking immediate action. High-
profile applications include automatic stock trading,
credit card fraud detection, supply chain management, and
equipment monitoring.
Streaming and complex event processing (CEP) engines such as
Tibco StreamBase and BusinessEvents provide
continuous intelligence by ingesting large amounts of real-time
data; accessing historical data from high
performance databases, appliances, and data warehouses;
making calculations and correlations; detecting patterns
and anomalies; applying business rules to incoming data
streams; supplying information to users; and automating

decision making. With streaming event processing, there is only
a single data source; with CEP there are multiple
sources [Eckerson, 2011].
Credit card monitoring provides a good example of a streaming
application. A common fraudulent sequence of
events is a $5 charge for gas at a convenience store (to see
whether the credit card is good), followed by the
purchase of thousands of dollars of electronic equipment at a
big-box store. When this stream is detected, store
personnel are alerted to possible fraud.
Cloud-based Services
The cloud is now in the mainstream of computing. With the
cloud, computing resources are virtualized and offered
as a service over the Internet. The potential benefits of the
cloud include access to specialized resources, quick
deployment, easily expanded capacity, the ability to discontinue
a cloud service when it is no longer needed, cost
savings, and good backup and recovery. These same benefits
make the cloud attractive for big data and analytics.
Cloud-based services come in a variety of forms. Public clouds
are offered by third-party providers; private clouds
are implemented within a company’s firewall. Concerns about
data security is a primary reason that private clouds
are sometimes preferred over public clouds. We will discuss
public clouds—although the same approaches and
technologies are used with private clouds.
Cloud services are available as software-as-a-service (SaaS),
platform-as-a-service (PaaS), or infrastructure-as-a-
service (IaaS), depending on what software is provided. Cloud
services are all similar in that a company’s data is
loaded to the cloud, stored, and analyzed, and the results are

downloaded to users and applications.
With SaaS, the vendor provides the hardware, application
software, operating system, and storage. The user
uploads data and uses the application software to either develop
an application (e.g., reports) or simply process the
data using the software (e.g., credit scoring). Many BI and
analytics vendors offer cloud services versions of their
software, including Cognos, Business Objects, MicroStrategy,
and SAS. SaaS is a particularly attractive option for
firms that lack the financial or human resources to implement
and maintain the software and applications in house.
PaaS differs from SaaS in that the vendor does not provide the
software for building or running specific applications;
this is up to the company. Only the basic platform is provided.
The benefits of this approach include not having to
maintain the computing infrastructure for applications that are
developed; access to a dependable, highly scalable
infrastructure; greater agility in developing new applications;
and possible cost savings. Examples of PaaS include
Oracle Cloud Computing, Microsoft Windows Azure, and
Google App Engine.
With IaaS, the vendor provides raw computing power and
storage; neither operating system nor application software
are included. Customers upload an image that includes the
application and operating system. Because the customer
provides the operating system, different ones can be used with
different applications. IaaS vendors’ offerings include
Amazon EC2 (part of the Amazon Web Services offerings),
Rackspace, and Google Compute Engine.
Consider several interesting examples of cloud-based services
that involve big data. Amazon introduced Amazon
RedShift in the spring of 2013 as an offering within Amazon

Web Services (AWS). RedShift manages the work of
setting up and operating a data warehouse in the cloud. Once
the data warehouse is in place, data can be accessed
through SQL queries and analytic applications. What is
particularly interesting is the cost—storing a terabyte of data
is only $1,000 per year [Imhoff 2013]. Also in 2013, Jaspersoft,
a pioneering open source BI vendor, offered BI
Professional for AWS [Kavanagh, 2013]. This cloud-based
service is easily set up, costs less than $1 per hour,
includes connectivity to RedShift, and can be started, stopped,
and restarted.
The second example involves Zynga, a pioneer in online social
gaming with games like FarmVille and Mafia Wars
and, more recently, Candy Crush Saga. Zynga provides an
unusual but effective example of a cloud-based business
1258
strategy [Babcock, 2011]. Most companies use the cloud as an
extension of their data center, shifting work to the
cloud when in-house data storage capacities are exceeded.
Zynga, because of the nature of the online gaming
industry, turns this strategy around.
When a new game is introduced, there is considerable
uncertainty as to how many players it will attract. It is
important to have sufficient capacity in case the game quickly
becomes popular. If Zynga isn’t prepared for the
demand, players will drift away. Because of this, Zynga
launches games on Amazon’s EC2 infrastructure-as-a-

service, and only after the demand is well understood is the
game brought in-house to Zynga’s own Z Cloud. At one
time, Zynga’s mix of Amazon EC2 and Z Cloud was 80/20, but
this has flipped to 20/80 as Zynga has learned how to
reap efficiencies from a cloud that is custom-designed to meet
its specific needs [Babcock, 2013].
Online gaming is highly dependent on big data and analytics
[Rudin, 2010]. Zynga collects a tremendous quantity of
data. Upwards of 60 million people play every day, and every
mouse click is recorded. Like most big data, however,
only a small portion of the data merits long-term storage and
analysis. Alerts are sent within minutes when there are
problems such as a game going down for a group of players.
Reports are generated that show metrics such as the
number of players, how many signed up today, how often they
play, how many haven’t played in 30 days, and the
amount of revenue generated (nearly all from the purchase of
virtual goods) for every game. Game designers make
changes to the games weekly and analysts work with them to
investigate how to make the games more engaging
and profitable.
Other gaming companies use big data analytics too. For
example, EA found that 80% of the players who bought
grenades (a virtual good) blew themselves up in the popular
game Battlefield 3 [Cifio and Meley, 2011]. This was not
fun for the players or good for the sale of virtual goods. In
response, EA sent training e-mails to users about how to
use the grenades and feedback to game developers about this
problem with the game.
Non-Relational (NoSQL) Databases
Relational databases have been a computing mainstay since the
1970s. Data is stored in rows and columns and can

be accessed through SQL queries. By way of contrast, non-
relational—NoSQL—databases are relatively new
(1998), can store data of any structure, and do not rely on SQL
to retrieve data (though some do support SQL and
are perhaps better called “not only SQL databases”). Data such
as XML, text, audio, video, image, and application-
specific document files are often stored and retrieved “as is”
through key-value pairs that use keys to provide links to
where files are stored on disk. There are specialized non-SQL
databases that are designed for specific kinds of data
such as documents and graphs and use their own storage and
retrieval methods. Non-relational databases such as
Apache Cassandra, MongoDB, and Apache Couchbase tend to
be open-source; store data in a scale-out,
distributed architecture; and run on low-cost, commodity
servers. Hadoop/MapReduce, which is discussed next, is
one example of a non-relational database. Because non-
relational databases are often relatively new and open
source, they are not as well supported as established RDBMS.
They also are weaker on security, which can limit
their usefulness for some applications such as financial ones.
Hadoop/MapReduce
Of all the platforms and approaches to storing and analyzing big
data, none is receiving more attention than
Hadoop/MapReduce. Its origins trace back to the early 2000s,
when companies such as Google, Yahoo!, and
Facebook needed the ability to store and analyze massive
amounts of data from the Internet. Because no
commercial solutions were available, these and other companies
had to develop their own.
Important to the development of Hadoop/MapReduce were Doug
Cutting and Mike Cafarella, who were working on
an open-source Web search engine project called Nutch when

Google published papers on the Google File System
(2003) and MapReduce (2004). Impressed with Google’s work,
Cutting and Cafarella incorporated the concepts into
Nutch. Wanting greater opportunities to further his work,
Cutting went to work for Yahoo!, which had its own big data
projects under way. With Yahoo!’s support, Cutting created
Hadoop (named after Cutting’s son’s stuffed elephant)
as an open-source Apache Software Foundation project [Harris,
2013].
Apache Hadoop is a software framework for processing large
amounts of data across potentially massively parallel
clusters of servers. To illustrate, Yahoo has over 42,000 servers
in its Hadoop installation. Hadoop is open source
and can be downloaded at www.apache.org. The key component
of Hadoop is the Hadoop Distributed File System
(HDFS), which manages the data spread across the various
servers. It is because of HDFS that so many servers
can be managed in parallel. HDFS is file based and does not
need a data model to store and process data. It can
store data of any structure, but is not a RDBMS. HDFS can
manage the storage and access of any type of data
(e.g., Web logs, XML files) as long as the data can be put in a
file and copied into HDFS.
http://www.apache.org/
1259
The Hadoop infrastructure typically runs MapReduce programs
(using a programming or scripting language such as
Java, Python, C, R, or Perl) in parallel. MapReduce takes large

datasets, extracts and transforms useful data,
distributes the data to the various servers where processing
occurs, and assembles the results into a smaller,
easier-to-analyze file. It does not perform analytics per se;
rather, it provides the framework that controls the
programs (often written in Java) that perform the analytics.
Currently, jobs can be run only in batch, which limits the
use of Hadoop/MapReduce for near real-time applications.
Although Hadoop and MapReduce are discussed and
typically used together, they can be used separately. That is,
Hadoop can be used without MapReduce and vice
versa.
Figure 4 illustrates how processing occurs with
Hadoop/MapReduce [van Groningen, 2009]. This is a simple
processing task that could also be done with SQL and a
RDBMS, but provides a good example of
Hadoop/MapReduce processing. At the left is a data file with
records containing Deer, Bear, River, and Car. The
objective is to count the number of times each word occurs. The
first step is to split the records and distribute them
across the clusters of servers (there are only three in this simple
example). These splits are then processed by
multiple map programs such as Java and R running on the
servers. The objective in this example is to group the
data by a split based on the words. The MapReduce system then
merges the shuffle/sort results for input to the
reduce program, which then summarizes the number of times
each word occurs. This output can then be input to a
data warehouse where it may be combined with other data for
analysis or accessed directly by various BI tools (e.g.,
Tableau, MicroStrategy).
Figure 4: Hadoop/MapReduce Processing Flow.

Many related Apache projects are part of the Hadoop ecosystem.
For example, Pig is a high-level parallel
processing programming language that is used to write
MapReduce programs to run within the Hadoop framework.
HBase is a distributed columnar database that gives Hadoop a
data storage option for large tables. Hive is used for
SQL-like queries and data summarization. Mahout is a library of
data mining algorithms for clustering, classification,
and filtering. Collectively, these and other Apache projects
provide an ever-growing set of capabilities for processing
and analyzing big data. The particular projects (i.e., parts of the
Hadoop ecosystem) that are implemented depend
on the intended applications and form a BI or analytics stack.
Although you can download Apache Hadoop and other parts of
the ecosystem for free, one consultant said: “It’s like
downloading a bag full of razor blades.” The various projects
are independent and often have competing
functionality, separate release schedules, and aren’t well
integrated. Because of this, companies like Cloudera,
Hortonworks (a spinoff from Yahoo!), and MapR have written
and offer software that integrates the various parts;
provide additional capabilities and administrative tools; and
offer consulting services, training, and support.
Companies use Hadoop/MapReduce in three major ways
[Eckerson, 2011]. One is for companies to employ Hadoop
as an online archive because of its expandable storage capacity
and low cost. A second way is as a source system
for a data warehouse. In this situation, Hadoop/MapReduce
processes data of any structure and then passes the

1260
output file to a data warehouse where it can be analyzed along
with other data. With this usage,
Hadoop/MapReduce complements a data warehouse. A third use
is for either the MapReduce programs or the data
analysis tools that work with the output file to be used to
analyze the data. This latter use is growing as vendors see
the opportunity to capitalize on companies’ desire to analyze
semi- and unstructured data.
Hadoop is considered to be fault tolerant. Data is always
replicated on three separate servers, and if a node fails, is
unavailable, or simply slow, another node takes over the
processing of the data. When a server recovers or is
added, the system automatically recognizes and adds it. The one
weakness is the NameNode that keeps the
directory tree of all files in the file system and tracks where
across the cluster the file data is kept. If it fails, it brings
the Hadoop cluster down [Russom, 2013].
Over time, Hadoop/MapReduce will become easier to use as
various open-source and commercial vendors
introduce enhancements and complementary products. For
example, prominent BI tool vendors like MicroStrategy
and Tableau have extended their products to work directly with
Hadoop/MapReduce, and new vendors like
Datameer have emerged. Datameer’s software is designed
specifically to work with Hadoop and supports data
integration, analytics, and data visualization. Its built-in
analytical capabilities include web analytics, predictive
scoring, behavioral analytics, clustering, decision trees, column
dependency analysis, and a recommendation

engine.
Which Platform Is Best
The phrase “courses for horses” means that some horses
perform better on certain kinds of tracks (e.g., short/long,
dry/wet) than on others. The same is true for big data analytics.
Some kinds of work are better done on some
platforms than on others. For example, reporting and
dashboards/scorecards normally rely on warehouse data
because of the “squeaky clean” data stored there.
There is no formula for choosing the right platforms; however,
the most important considerations include the volume,
velocity, and variety of data; the applications that will use the
platform; who the users are; and whether the required
processing is batch or real time. Some work may require the
integrated use of multiple platforms. The final choices
ultimately come down to where the required work can be done
at the lowest cost.
Integrating the Various Platforms
An increasing number of organizations are using multiple
platforms to realize the value from big data. Which
platforms are added depends on the platforms, the applications
that use the platforms, and the organization’s
maturity in working with the various platforms. For example, a
company might add an appliance to off-load some
computationally intensive applications (e.g., predictive and
prescriptive analytics) from a data warehouse. Or it might
turn to SaaS for particular applications (e.g., data visualization
and analytics).
In many organizations, there is an evolutionary path with
greater integration of the platforms over time. For example,

Hadoop/MapReduce may initially be implemented in isolation
to RDBMS-based systems. Because of its newness,
firms often want to test the technology and its potential value. It
this goes well, Hadoop/MapReduce is put to work on
the tasks that it does best. It usually becomes apparent,
however, that there is great value in analyzing all of the
data together. For example, it is useful to be able to look at not
only at sales figures but also at what customers are
saying about products, and this is likely done best by using
Hadoop/MapReduce as a source system for the data
warehouse.
There should be fast, seamless interaction and collaboration
among the different platforms. For example, a report
run on one platform should mirror a report run on another; that
is, the data needs to be synchronized. An analysis
run on a specialized platform should be able to access
information from the data warehouse (which is a source
system for a specialized platform like Hadoop/MapReduce). If
needed, the results of an analysis run on a
specialized platform should be stored in the data warehouse (the
specialized platform is a source system for the
warehouse). Vendors recognize the importance of integrating
the component parts and are including software
solutions for doing this. For example, Teradata is currently
emphasizing its Unified Data Architecture that ties its
family of products together. However, this integration does not
include non-Teradata platforms.
The use of SQL and SQL-like query languages is an important
trend for integrating the various platforms and may
ultimately be the foundation for creating single logical systems.
SQL is a powerful query language that is well known
by many IT and BI professionals. Many companies who
implement Hadoop/MapReduce quickly turn to Hive and its
HiveQL language because people who know SQL quickly learn

it. Platform as well as BI tool vendors are quickly
evolving their products to be able to combine data of any
structure and to access and analyze it using some variant
of SQL.
1261
Figure 5 shows how the various platforms might be integrated
and used. The line through the middle of the figure
divides what Eckerson [2011] calls the top-down and bottom-up
architectures. The top is the traditional BI
architecture; the bottom is the new big data architecture. Casual
users—that is, business users—use BI tools to
access reports, dashboards/scorecards, and data visualizations
based primarily on structured data in the data marts
and warehouse (and the new, streaming/CEP engines). Power
users—analysts, data scientists—access a wide
variety of data sources, including big data, on a variety of
platforms, in a variety of ways, including SQL and
analytical workbenches.
Notice that Hadoop and the data warehouse co-exist; one is not
a replacement for the other [Russom, 2013]. Each
is best for certain kinds of data and processing tasks, and they
complement one another.
Machine
Data
Web Data
Hadoop Cluster

Power User
BI
Server
Casual User
Operational
System
Operational
System
Free-standing
Analytical
sandbox
Logical or
Physical
Data Mart
Data Warehouse
Virtual Sandboxes
Top-down BI
Bottom-up BI
External
Data
Audio/video
Data

Streaming/
CEP Engine
Visual
discovery
tools
ODS
In
te
ra
ctive
d
a
sh
b
o
a
rd
s
Source: Eckerson 2011
Figure 5: An integrated Analytics Architecture

The Right Analytical Tools
Although traditional BI vendors claim their tools support data
mining/predictive analytics, this is not always true.
Slicing and dicing data and data visualization are not data
mining. Data mining requires tools that incorporate
algorithms and processes that are designed specifically to find
hidden relationships in data.
SAS and SPSS (now an IBM company) have long been leaders
in this space, with products like SAS Enterprise
Miner and IBM SPSS Modeler. Each product provides a
“workbench” where the analysis process is designed using
a drag-and-drop visual interface and execution of the process is
automated by the workbench. The most popular tool
for data mining is R, a programming language and software
environment for statistical computing and graphics. It is
also at the core of many open-source data mining products.
Excel has long been denigrated by BI vendors but continues to
be extremely popular with users and business
analysts alike, including for use with big data [Healy, 2012].
Excel can handle a million rows of data, can source data
from virtually any database or BI product, and has powerful
native or third-party plug-in analytical features. The plug-
ins include PowerPivot (for advanced pivot table capabilities)
and Data Mining (which requires SQL server) from
Microsoft, and Analyse-IT (for statistics), Excellent Analytics
(for importing Web analytics data from Google
Analytics), and Unistat (for statistics).
1262

People Skilled in the Use of Analytics
The final requirement for being successful with big data
analytics is having people with the necessary skills. It is
useful to consider a continuum of analytics users, anchored at
one end by end users, with analysts in the middle and
data scientists at the other end, as shown in Figure 6 [Watson,
2013a]. Each group requires different skills when it
comes to working with big data, including a mixture of
business, data, and analytics expertise.
Business users access big data-related information through
reports, OLAP, dashboards/scorecards, and data
visualization tools. They are information consumers rather than
information creators. Two things stand out about big
data analytics in comparison to analytics in general. The first is
the ability to analyze and display more kinds of
information than ever before. For example, think of the fleet
managers at U.S. Xpress who monitor the conditions of
their trucks on iPads and route them in for servicing when
sensor data indicates that it is necessary. The second is
the ability to provide additional context for decision making by
combining information from multiple sources. To
illustrate, rather than just providing sales data on a new
product, it is possible to analyze what consumers are saying
about it, as shown earlier in the Starbucks’ example.
Business users should have extensive business domain
knowledge and understand the potential of big data
analytics. They need to understand what data is available and be
able to access it, manipulate it in simple ways, and
use it to create business value. They don’t need to be experts on
the details of the algorithms and models used to
analyze big data.

There are two major categories of analysts. BI analysts are part
of a BI or analytics department and work throughout
the organization. Business analysts, on the other hand, work in a
business unit such as marketing and perform their
analytics work there. Both categories represent information
producers rather than information consumers. BI
analysts typically understand the organization’s data and
available tools better than the business analysts do. For
example, they might implement an enterprise-wide scorecarding
system. MIS graduates are good fits for this
position. The business analysts, however, understand their
business unit’s use of analytics better. For example, a
supply-chain analyst might work to optimize supply-chain
processes, from sourcing raw materials to distributing
products to the point of sale. Many organizations have both
kinds of analysts who work both separately and
collaboratively on projects.
Figure 6: A Continuum of Big Data Analytics Users.
Data scientists are at the far right of the continuum. These are
the highly trained, skilled, and experienced
professionals who discover new insights in big data. The term
data scientist is relatively new and is often attributed
to D.J. Patil and Jeff Hammererbacher, who are in charge of
data and analytics at LinkedIn and Facebook,
respectively [Davenport and Patil, 2012]. Data scientist has
been called “the sexiest job of the 21
st
century”

[Davenport and Patil, 2012].
The job of data scientists is to discover patterns and
relationships that no one else has seen or wondered about, and
turn these discoveries into actionable information that creates
value for the organization. To do this requires a rich
mixture of skills. Data scientists need to understand the
different types of big data and how they can be stored (e.g.,
RDBMS, Hadoop), write code (e.g., Java, Python, R), access
data (e.g., SQL, Hive), analyze it (e.g., regression
analysis, social networks), and communicate findings to
management in business terms (e.g., briefings, reports).
Data scientists are typically very curious, like to solve difficult
problems, and have advanced degrees (often PhD’s)
in analytical fields such as statistics, management
science/operations research, computer science, and
mathematics. Because of these requirements, data scientists are
in short supply and command high salaries.
Fortunately, organizations don’t need many of them.
1263
Many data scientists don’t have degrees in business. To be
effective, however, they need to understand the specific
industry and organization in which they work. To make up for
this potential deficiency, it is common to have data
scientists work closely with people in the organization who
have business domain knowledge.
Big data analytics users make up a continuum. The reason is
that there are users all along the continuum. For

example, some end users become power users in their
departments and perform analyst roles such as developing
reports for other users. Other analysts further develop their
skills and are able to perform some of the tasks
associated with data scientists, especially when provided
analytical workbenches like SAS Enterprise Miner and IBM
SPSS Modeler.
Appendix A describes how universities, companies, and vendors
are responding to meet the demand for people
skilled in the use of big data analytics.
VIII. BIG DATA AND PRIVACY
The collection, storage, and mining of big data will only
increase. A big data issue that is gaining attention and will
become more important is individual privacy: What data should
the government and organizations be allowed to
collect and what safeguards should be in place about how it is
used? The Target story provides a glimpse into the
uses that make some people uneasy. Other people see no
problem with this use of big data, because it results in
better customer service and appealing offers.
In the summer of 2013, there were numerous news stories about
Edward Snowden, a former CIA employee and
NSA consultant, who revealed secrets about how the U.S.
government was tracking private individuals’ phone calls
and using this information. Many people, especially in the
government, viewed Snowden as a traitor who
jeopardized the country’s national security in the fight against
terrorism; others saw him as a whistle-blower who
informed the public about a practice that threatens civil
liberties.
Clemons et al. [2014] identify three different ways to

characterize privacy and online invasions of privacy. The first
way is an uninvited intrusion into a user’s personal space. This
includes online marketing, spam advertising, pop-
ups, and sponsored sites around the edges of a Web page. In
their study, most people see this is as the most
salient type of privacy invasion, even though its potential
consequences are the least harmful. The most serious
threats are fraudulent e-commerce transactions and identity
theft. Although these are concerns, people are not
worried that these kinds of activities would be perpetrated by
big data companies like Google and Facebook. The
third kind of invasion is personal profiling for commercial
advantage. This occurs when companies like Google,
Facebook, and Yahoo! combine hundreds or thousands of pieces
of data from different sources (i.e., data blending)
to understand who you are, where you live, where you go, who
your friends are, what you buy, and the like. This
blended information may be used simply to make offers that are
likely to appeal to you, or for less benign purposes
such as knowing whether you engage in risky hobbies and
should be charged higher insurance rates [Clemons et
al., 2014].
Research shows that most people have very little understanding
and concern about how organizations are using big
data [Clemons et al., 2014]. However, as individuals understand
the potential uses better, their concerns increase
quickly. This suggests that as companies increasingly use big
data analytics on customer data, the public is likely to
become more concerned.
Although some laws limit the activities of telecommunications
companies (e.g., they can’t listen to phone calls), few
regulations and laws are applicable for the new digital age and
are largely non-existent for Internet firms. These
companies privacy policies largely serve their commercial

interests rather than protecting individuals’ privacy. We
need laws about individual privacy that people think that are
consistent, reasonable, transparent, and easy to
understand [Clemons et al., 2014].
IX. CONCLUSION
From a historical perspective, big data can be viewed as the
latest generation in the evolution of decision support
data management [Watson and Marjanovic 2013c]. The need for
data to support computer-based decision making
has existed at least since the early 1970s with DSS. This period
can be thought of as the first generation of decision
support data management. It was very application-centric, with
data organized to support a single decision or a set
of related decisions. By the 1990s, there was a need to support a
wide variety of BI and analytic applications (e.g.,
reporting, executive information systems) with data. Having
separate databases (i.e., independent data marts) for
each application was costly, resulted in data inconsistencies
across applications, and failed to support enterprise-
wide applications. The outcome was the emergence of enterprise
data warehouses, the second generation, which
represented a data-centric approach to data management. The
third generation was real-time data warehousing.
1264
Technology had improved by 2000 so that it was possible to
capture data in real time and trickle-feed it into the data
warehouse. The significance of this evolution is that it changed

the paradigm for what kinds of decisions could be
supported. With real-time data, operational decisions and
processes could be supported. Big data is the fourth
generation of decision support data management. The ability to
capture, store, and analyze high-volume, high-
velocity, and high-variety data is allowing decisions to be
supported in new ways. It is also creating new data
management challenges.
For many years, companies developed data warehouses as the
focal point for data to support decision making. This
is changing as new data sources, platforms, and cloud-based
services emerge. As a result, data is becoming more
federated; that is, data is stored and accessed from multiple
places. Adding to this trend are business units such as
finance and marketing that have the business needs, resources,
and political clout to acquire their own platforms,
services, and tools. In many organizations, IT is losing some
control over data management. This is not bad if it
leads to more agility and better organizational performance. The
downside, however, includes data silos that don’t
share data, data inconsistencies, inefficiencies in storing data,
and duplication of resources. Organizations are
accepting that data federation is going to exist, at least in the
short to medium term, and are instituting greater
controls over their data management practices. Some are putting
more emphasis on data governance (e.g., data
stewards, metadata management, and master data management).
They are also creating BI or analytics centers of
excellence to provide strategic direction for the use of data and
analytics, prioritize projects, provide shareable
resources, establish guidelines and standards, participate in tool
selection, troubleshoot problems, and more.
Organizations are gaining unprecedented insights into customers
and operations because of the ability to analyze

new data sources and large volumes of highly detailed data
[Russom, 2011]. This data is bringing more context and
insight to organizational decision making. Success with big data
is not guaranteed, however, as there are specific
requirements that must be met. Organizations should start with
specific, narrowly defined objectives, often related to
better understanding and connecting with customers and
improving operations. There must be strong, committed
sponsorship. Depending on the project(s), the sponsorship can
be departmental or at the senior executive level. The
CIO is typically responsible for developing and maintaining the
big data infrastructure. For some companies (e.g.,
Google), alignment between the business and IT strategies is
second nature because big data is what the business
is all about. For others, careful consideration needs to be given
to organization structure issues; governance; the
skills, experiences, and perspectives of organizational
personnel; how business needs are turned into successful
projects; and more. There should be a fact-based decision-
making culture where the business is run by the numbers
and there is constant experimentation to see what works best.
The creation and maintenance of this culture depends
on senior management. Big data has spawned a variety of new
data management technologies, platforms, and
approaches. These must be blended with traditional platforms
such as data warehouses in a way that meets
organizational needs cost effectively. The analysis of big data
requires traditional tools like SQL, analytical
workbenches like SAS Enterprise Miner, and data analysis and
visualization languages like R. All of this is for
naught, however, unless there are business users, analysts, and
data scientists who can work with and use big data.
As organizations make greater use of big data, it is likely that
there will be increased concerns and legislation about
individual privacy issues.

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Editor’s Note: The following reference list contains hyperlinks
to World Wide Web pages. Readers who have the
ability to access the Web directly from their word processor or
are reading the paper on the Web, can gain direct
access to these linked references. Readers are warned, however,
that:
1. These links existed as of the date of publication but are not
guaranteed to be working thereafter.
2. The contents of Web pages may change over time. Where
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APPENDIX A: MEETING THE DEMAND FOR PEOPLE
SKILLED IN BIG DATA ANALYTICS
As the McKinsey Global Institute and Gartner studies cited
earlier show, there is a growing demand for people who

can work with analytics and big data. Universities, companies,
and vendors are responding in a variety of ways.
Many universities are adding a required course in analytics
(including big data analytics) to their undergraduate and
graduate programs. From a historical perspective, this is
interesting for faculty in business schools because most
business schools used to require a course in management
science or quantitative methods but phased it out when
the marketplace did not require it. Obviously, businesses now
want graduates with analytical skills.
Another significant change is the rapid emergence of
undergraduate degree programs, certificates, MBA
concentrations, and graduate degree programs in analytics. To
illustrate the growth in analytics offerings, a survey in
the fall of 2012 found 59 universities offering business
intelligence/business analytics degrees, with 22 at the
undergraduate level. Only two years earlier, a similar survey
found only 12 schools that offered BI/BA degrees
[Watson, Wixom and Ariyachandra, 2013b].
Graduate analytics offerings are located across campus,
including in business, engineering, and statistics. The
instructional delivery varies from on-campus to online. One of

the first and best known programs is the Master of
Science in Analytics at North Carolina State University. SAS
has been an important contributor to the program,
which is offered through the Institute for Advanced Analytics
and has its own facility on campus. Deloitte Consulting
partnered with the Kelly School of Business at Indiana
University to offer a certificate in business analytics for
Deloitte’s professionals. Last year, Northwestern University
initiated an online Master of Science in Predictive
Analytics offered through its School of Continuing Studies. Still
unknown, however, is how many students will
choose to study big data analytics, because it is an intellectually
challenging field of study.
Companies are responding in several ways. One approach is to
create educational opportunities for people who are
already on board (e.g., business and BI analysts) and have the
interest and aptitude to further develop their skills
through in-house programs, conferences, and college courses. In
the past, there have been dire forecasts of
shortages of people with specific skills (even COBOL
programmers), and people acquired the needed skills and took
advantage of the opportunities [Healy, 2012].
For companies that want to hire data scientists, Davenport and

Patil [2012] have useful ideas for how to proceed,
including scanning the membership rolls of user groups
dedicated to data science tools like R; looking for them on
LinkedIn; engaging them at conferences like Strata,
Structure:Data, and Hadoop World; and hosting a competition
on Kaggle or TopCoder, the analytics and coding sites.
../../../../../../hwatson/Desktop/www.teradataunivesitynetwork.co
m
http://aisel.aisnet.org/cais/vol25/iss1/39
http://teradatauniversitynetwork.com/
1268
Leading BI and analytics vendors like IBM, Oracle, SAP,

Microsoft, SAS, and Teradata sponsor university alliance
programs that make software, case studies, research reports, and
more available either free or at minimal cost to
universities. In analytics classes, it is important for students to
have hands-on experiences with the software they will
encounter in the workplace, and the alliance programs are
especially good for meeting this need. For example,
Oracle makes its database as well as reporting and analysis
software (e.g., Hyperion) available. SAS offers its data
mining/predictive analytics software (e.g., SAS Enterprise
Miner). IBM provides Cognos (reporting and analysis) and
SPSS Model (data mining/predictive analytics).
For the past 13 years, the author has been involved with the
Teradata University Network
(www.teradatauniversitynetwork.com), a free portal for faculty
and students with interests in analytics, business
intelligence, data warehousing, and database. Through the
portal, faculty and students can access software (e.g.,
Teradata, MicroStrategy, Tableau, and SAS), large data sets
(offered through the University of Arkansas), articles,
web seminars, cases, assignments, course syllabi, and more.
Plans are in place to add Teradata Aster, a big data
platform, to the portal.

ABOUT THE AUTHOR
Dr. Hugh J. Watson is a professor of MIS and a holds a C.
Herman and Mary Virginia Terry Chair of Business
Administration in the Terry College of Business at the
University of Georgia. Hugh is a leading scholar and authority
on business intelligence and analytics, having authored 24
books and over 150 scholarly journal articles. He helped
develop the conceptual foundation for decision support systems
in the 1970’s, researched the development and
implementation of executive information systems in the 1980s,
and for the past 20 years has specialized in data
warehousing, BI, and analytics. He is a fellow of the
Association for Information Systems and TDWI and is the
senior
editor of the Business Intelligence Journal and is a member of
the CAIS Advisory Board. Hugh is the founding
Director of the Teradata University Network, a free portal for
faculty who teach and research BI/DSS, analytics, data
warehousing, and database management.
Copyright © 2014 by the Association for Information Systems.
Permission to make digital or hard copies of all or part
of this work for personal or classroom use is granted without
fee provided that copies are not made or distributed for

profit or commercial advantage and that copies bear this notice
and full citation on the first page. Copyright for
components of this work owned by others than the Association
for Information Systems must be honored.
Abstracting with credit is permitted. To copy otherwise, to
republish, to post on servers, or to redistribute to lists
requires prior specific permission and/or fee. Request
permission to publish from: AIS Administrative Office, P.O.
Box 2712 Atlanta, GA, 30301-2712, Attn: Reprints; or via e-
mail from [email protected]
http://www.teradatauniversitynetwork.com/
mailto:[email protected]
ISSN: 1529-3181
EDITOR-IN-CHIEF

Matti Rossi
Aalto University
AIS PUBLICATIONS COMMITTEE
Virpi Tuunainen
Vice President Publications
Aalto University
Matti Rossi
Editor, CAIS
Aalto University
Suprateek Sarker
Editor, JAIS
University of Virginia
Robert Zmud
AIS Region 1 Representative
University of Oklahoma
Phillip Ein-Dor
Tel-Aviv University

Bernard Tan
National University of Singapore
CAIS ADVISORY BOARD
Gordon Davis
University of Minnesota
Ken Kraemer
University of California at
Irvine
M. Lynne Markus
Bentley University
Richard Mason
Southern Methodist University
Jay Nunamaker
University of Arizona
Henk Sol
University of Groningen

Ralph Sprague
University of Hawaii
Hugh J. Watson
University of Georgia
CAIS SENIOR EDITORS
Steve Alter
University of San Francisco
Michel Avital
Copenhagen Business School
CAIS EDITORIAL BOARD
Monica Adya
Marquette University
Dinesh Batra
Florida International University
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Indranil Bose
Indian Institute of Management

Calcutta
Tilo Böhmann
University of Hamburg
Thomas Case
Georgia Southern University
Tom Eikebrokk
University of Agder
Harvey Enns
University of Dayton
Andrew Gemino
Simon Fraser University
Matt Germonprez
University of Nebraska at Omaha
Mary Granger
George Washington University
Douglas Havelka
Miami University

Shuk Ying (Susanna) Ho
Australian National University
Jonny Holmström
Umeå University
Tom Horan
Claremont Graduate University
Damien Joseph
Nanyang Technological University
K.D. Joshi
Washington State University
Michel Kalika
University of Paris Dauphine
Karlheinz Kautz
Julie Kendall
Rutgers University

Nelson King
American University of Beirut
Hope Koch
Baylor University
Nancy Lankton
Marshall University
Claudia Loebbecke
University of Cologne
Paul Benjamin Lowry
City University of Hong Kong
Don McCubbrey
University of Denver
Fred Niederman
St. Louis University
Shan Ling Pan
Katia Passerini

New Jersey Institute of
Technology
Jan Recker
Queensland University of
Technology
Jackie Rees
Purdue University
Jeremy Rose
Aarhus University
Saonee Sarker
Washington State University
Raj Sharman
State University of New York at
Buffalo
Thompson Teo
Heikki Topi
Bentley University

Arvind Tripathi
University of Auckland Business
School
Frank Ulbrich
Newcastle Business School
Chelley Vician
University of St. Thomas
Padmal Vitharana
Syracuse University
Fons Wijnhoven
University of Twente
Vance Wilson
Worcester Polytechnic Institute
Yajiong Xue
East Carolina University
Ping Zhang
Syracuse University

DEPARTMENTS
Debate
Karlheinz Kautz
History of Information Systems
Editor: Ping Zhang
Papers in French
Editor: Michel Kalika
Information Systems and Healthcare
Editor: Vance Wilson
Information Technology and Systems
Editors: Dinesh Batra and Andrew Gemino
ADMINISTRATIVE
James P. Tinsley
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Copyediting by
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Communications of the Association for Information Systems5-
2014Tutorial: Big Data Analytics: Concepts, Technologies, and
ApplicationsHugh J. WatsonRecommended Citation
ISM 5150 Final Exam
1. Identify TWO (2) topics that we have covered in the course
and for EACH topic: (approximately 3 pages for both topics;
Total 10 points; 5 points for each topic)
TOPICS >>> IT PTOJECT MANAGEMENT and
KNOWLEDGE MANAGEMENT
1) Briefly explain the topic and your
understanding/interpretation of it (1 point)
2) Describe how you related to this topic in the course, how it
enabled you to stay engaged in class, with the professor, and
with your classmates (2 points)
3) Explain how this topic will enable you to be a better and
more effective professional (and student, if applicable) and how

you immediately plan to use this skill and knowledge for your
development (2 points)
2. Use the knowledge and skills you gained while working on
the WileyPlus SAP ERP simulation assignments, the EBeer
SCM simulation exercises, and the course material to answer the
following:(approximately 1 pageTotal 5 points)
1) What are the advantages and disadvantages of implementing
and using ERP systems? (2 points)
2) What are the challenges that organizations face when
managing supply chain dynamics and the related business
processes? Explain how you propose to address these issues by
the use of enterprise systems.(3 points)
3. Based on the CAIS article “Tutorial: Big Data Analytics”
Watson (2014) that we discussed in class: (approximately 1
page Total 5 points)
1) Explain briefly the main business objectives of big data
analytics and the main challenges and risks involved. (2 points)
2) Describe briefly how you would apply big data analytics to a
private or government organization to gain the objectives
discussed above. Use your experience of SAP Lumira exercises
to explain how you would use data visualization techniques.(3
points)

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