White paper "From Big Data to Big Busine$$"

1. WHITE PAPER
FROM BIG DATA
TO BIG BUSINE$$
PAPER 1 : Fad or
Performance Enhancer ?

2. Chapitre 1 - p.01
LIVRE 1 - LIVRE BLANC DU BIG DATA AU BIG BUSINESS

3. PAPER 1 : Fad or performance
lever ?

4. Table of Contents - p.04
FROM BIG DATA TO BIG BUSINESS - PAPER 1
Why this paper ?
A shift in management
thinking
Big Data definition
Disruption or evolution ?
Big Data history
Form follows function
Processing weak
signals
Big Data Projects
p.06
p.07
p.07
p.08
p.08
p.08
p.09
p.10
CHAPTER
01
Big data, background
and principles
p.06
A closer look at existing, untapped
information repositories
Proliferation of raw, internal data
available
Proliferation of public, external
and purchasable data
Open Data
Monetizing data
Full-scale data cross-referencing
p.20
p.21
p.23
p.24
p.24
p.25
CHAPTER
03
The new uses created by
Big Data
p.20
p.13
p.14
p.16
p.17
p.18
Big Data’s 3Vs
Beyond the 3Vs: the 5Vs
Beyond the 5Vs: the 3Ps
Data, information,
knowledge and wisdom…
What is the difference, what is
their value ?
The accumulation of data
seemed meaningless but use has
changed all that.
CHAPTER
02
Data p.11
p.26
p.27
p.29
p.30
p.30
p.30
Big Data hardware architecture
specifications
Big Data software architecture
specifications
Big Data database specifications
No ready-made database
Many tools, each specialized in
one field
Big Data architecture, specific to
Big Data?
CHAPTER
04
Architectures and
algorithms
p.26

5. What to remember about Big
Data?
The 10 key points
p.39
p.40
CHAPTER
07
How to convert Big Data
into Big Busine$$
p.39
p.32
p.32
p.32
p.33
p.34
p.35
The return of EIM (Enterprise
Information Management)
How to get started?
Valuing data by dedicating it to
business
The new Big Data jobs
From “punishment”
to career prospect
The relationship between MDM
and Big Data
CHAPTER
05
The Big Data jobs p.31
p.36
p.36
Heads: the hope for a dynamic
sector that will boost the whole
economy
Tails: the privacy debate
CHAPTER
06
Big Data or Big Brother? p.36
p.05 - Table of Contents
PAPER 1 - FROM BIG DATA TO BIG BUSINESS

6. 1
Especially since Big Data involves new forms of
reasoning such as, forms of inductive reasoning (see
page 8). We can quite safely refer to Big Data as a new
philosophy and as whole a revolutionary approach to
marketing.
CHAPTER 01
Big Data, background
and principles
Why this paper ?
Big Data literature abounds; this is a
sure sign that Big Data’s importance
is strongly felt by the market as a
whole and across the world. Howe-
ver, even in cases where documen-
tation is of high quality, it is usually
mostly descriptive in nature and fo-
cused on exaggerated, near-apoca-
lyptic concerns associated with the
exponential growth in data and data
sources. These approaches do not
help understand the real challenges
posed by Big Data or how companies
can exploit them.
Even though, at this point, it is hard
to predict what the future holds, we
are convinced that Big Data1
will im-
pact heavily on companies and civil
society in a number of increasingly
changing ways. By rapidly “cutting
their teeth” on Big Data, currently still
in infancy, businesses will not only
master the phenomenon, but also
learn, how to make the most of it.
The aim of this white paper is to pro-
vide companies with key insights that
will help them approach Big Data,
not as a “mythology”, but as a power-
ful performance optimization tool
that can be adapted to their specific
contexts.
We hope that this will help readers
lay, or maybe even validate, the foun-
dations for a smooth, controlled in-
tegration of Big Data into their com-
pany’s ecosystem.

7. A shift in management
thinking
The Big Data evolution is extremely
important to companies. More than
a simple trend, it is a revolution in
thinking, a crucial contribution to the
management arsenal that will funda-
mentally change the world of business.
Its obvious impact on marketing should
not obscure consequences of a per-
fect command of the subject on other
fields such as analysis, management,
production, supply chain management
and RD to name but a few.
This major revolution in thinking is
however ill-served by an abundant lite-
rature that tends to either censor the
new field’s too-technical vocabulary in
order to conceal its complexity, some-
times to the very detriment of compre-
hension; or, on the contrary, to go too
deeply into this complexity and its even
more technical 4 components (bu-
siness function, technology, databases
and statistics) and lose the reader. The
risk then is for readers to erroneously
think that the Big Data subject is either
too generic to constitute a real innova-
tion or too innovative for useful appli-
cation to day-to-day business.
As a result, Big Data2
“is perceived as a
problem by companies” when in fact, it
is a solution. The aim of this paper, the
first of a series of 6, is to describe the
impact and uses of Big Data in a simple
and clear manner. Instead of impoveri-
shing the narrative, we will explain the
jargon in order to make the advantages
of these new tools accessible to all.
This first paper is dedicated to the Big
Data phenomenon in general, with
each chapter detailing aspects of the
subject a little further. Subjects that will
be dealt with in subsequent chapters
are:
• Paper 2 on data, Big Data’s essential
fuel
• Paper 3 on Big Data uses
• Paper 4 on Big Data architectures
algorithms
• Paper 5 on Big Data professions
• Paper 6 on data confidentiality, user
protection and ethics
We will provide both a global (in this
paper) and detailed (in subsequent
papers) view of Big Data in order to
make it accessible to all professionals
who want their business to benefit
from this new approach and its tools.
Big Data3
definition
The term Big Data refers to a new disci-
pline that is at the crossroads of seve-
ral others: statistics, technology,
databases and business functions
(marketing, finance, HR, etc.).
A new discipline that owes its existence
to technological power has rende-
red possible things that until now re-
mained in the realm of the theoretical.
The things that we talk about here are
mainly associated with two challenges:
data volume and data complexity.
2
For more information go to Tendances Trends.be 3
Although both the singular and the plural are often used
to qualify data, Big Data is mostly referred to as a mass
noun, i.e. a singular phenomenon of mass information.
Consequently, Big Data will always be referred to as a
mass noun in the singular in this document.

8. 6
See article on the New York Times blog:
http://bits.blogs.nytimes.com/2013/02/01/the-origins-
The objective of Big Data is to tap into
exponentially increasing volumes of
data that have become near impos-
sible to process, using traditional da-
tabase management and information
management tools4
, and to handle
complex data in a timely manner.
According to the works and words of
the 451 group and Gartner; the aim of
Big Data is to achieve competitive ad-
vantage through data collection, ana-
lysis and use methods that, until now,
could not be used due to the econo-
mic, functional or technical constraints
associated with the volumes, proces-
sing velocity and variety of data invol-
ved.
Disruption or evolution ?
Big Data is sometimes presented as a
disruptive phenomenon that challen-
ges the very foundation of everything
done in the past in terms of decision
support or, on the contrary, simply
as the next evolutionary step in bu-
siness intelligence organizations and
systems. Though this may seem like a
mere semantics debate, it is not. In-
deed, depending on their opinion on
the matter, the scenarios put in place
by companies will be very different.
Big Data history
Big Data’s meaningful history clearly
highlights the field’s specific nature;
if the term Big Data was first used by
analyst firm Gartner in 2008, Big Data’s
origins can however be traced much
further back. In a way, concurrent with
the rise of information technology, the
concept-like all other innovations-sim-
ply took time to become widespread
and to refine itself.
Gil Press sets this new discipline’s
first appearance in an even more dis-
tant past5
(1944). But without going
that far, and even if somewhat tech-
nical discussions on who actually
coined the term “Big Data6
” persist,
we can however trace the earliest do-
cumentation on the famous 3Vs (Vo-
lume, Velocity and Variety) predicting
exploding amounts of data and the
creation of a new data processing
back to the beginning of 2001, accor-
ding to analysis firm Gartner.
It should also be mentioned that
Big Data is the culmination of the
Data Mining approach, popular du-
ring years 1995-2000, which itself
was born out of the association of
two relatively old schools of thought
(trends), i.e. statistics and artificial in-
telligence.
Form follows function
Regarding the discipline’s origins de-
bate, the excellent O’Reilly Big Data
glossary could help reach a consen-
sus as it focuses on the e-merchants
and other collaborative web players
who actually were part of the pheno-
menon rather than the analysts who
simply described it.
In other words, it would seem that,
much like for O’Reilly’s famous Web
2.0 in 2004, the phenomenon’s des-
Big Data, background and principles - p.08
FROM BIG DATA TO BIG BUSINESS - PAPER 1
4
Please view also complete definition in Wikipedia’s open
encyclopedia which inspired ours:
http://en.wikipedia.org/wiki/Big_data
5
http://www.forbes.com/sites/gilpress/2013/05/09/a-very-

9. criptive term appeared after the phe-
nomenon itself, as is often the case in
the digital sphere.
Cloud Computing, for example, stem-
med from major websites’ (Amazon,
Google, e-Bay, Microsoft, etc.) excess
hosting capacity, a result of the origi-
nal architectures they had set up to
support visitor traffic as well as meet
hosting “elasticity” needs. Similarly,
Big Data materialized from the over-
dose of data generated by Internet
users’ activities on sites like Amazon,
Yahoo! and much later Google. The
main players have not changed which
led to unforeseen innovative marke-
ting applications7
.
A mechanism has been made avai-
lable to influential companies. They
can now invest in technologies in
order to find ways to satisfy user
needs in an extremely short period
of time. The Internet bubble made
experimenting in real-time and with
unlimited resources possible…the ul-
timate researcher’s dream!
Logic has been overthrown: data co-
mes before use, business initiative
before research, function, in a sense,
precedes form…and the uses created
by these technological advances are
numerous, as described in the chap-
ter on uses (see Chapter 3: page 5).
Before thinking about whether Big
Data is a disruption or an evolution,
let us present the new developments
triggered by the phenomenon in the
following 4 categories: data, uses,
work methods and tools.
The fact that Big Data helps take advantage
of weak signals does not mean that weak
signal detection is a new subject. Philippe
Cahen, author of “Uncertainty Marketing”,
explains how weak signals impact
marketing and why they are so important.
What is a weak signal? Philippe Cahen
gives the following definition in his book
“Everything there is to know about
uncertainty marketing”:
“A weak signal is a lateral thought-
provoking piece of information […]. A
weak signal is not a small fact that gives
information about the future. It would be
too easy, somewhat naïve, to think that
information about
the future would be so freely available. It
is how you mentally approach it that helps
you decipher the future. Intuition plays a
major role in detecting and interpreting
weak signals.”
“The future is a leap into the unknown, an
unknown that can be either reassuringly
friendly or so unbearable that all we want
to do is run away. Actually living life the way
it was planned is rare. Usually, it is quite
the opposite.”
Big Data fuels the thinking process and
actions associated with weak signals; it
helps derive hypotheses that can then
be tested by cross-referencing data and
behaviors.
Source: “Marketing Uncertain” by Philippe
Cahen, Kawa publishing , 2012
7
Lise Gasnier on Solucom Insight
Processing weak signals
(P. Cahen)
p.9 - Big Data, background and principles
PAPER 1 - FROM BIG DATA TO BIG BUSINESS

10. Big Data projects
Big Data projects typically have four
components.
First, it involves Big Data technologies,
hardware and software. Second,
it requires a specific methodology
approachthatwillbebrieflymentioned
in this document and further detailed
in following publications.
The third component is a legal one
since a perfect command of the legal
framework associated with handled
data and intended uses is important.
Last but not least, every Big Data
project includes a social component
that must be taken into considera-
tion. What is the ability of our so-
cieties and each group of people or
individuals to accept the circulation
and use of their personal data? To
avoid exposing one’s project and the
whole area of application to risks, it
will be up to companies to self-re-
gulate and to legislators to adapt
to these new technology-driven
contexts and possibilities.
Big Data, background and principle- p.10
FROM BIG DATA TO BIG BUSINESS - PAPER 1