BIG DATA : ce qu'elles sont, les enjeux, les opportunités pour le Business. Mise en bouche en perspective de DATACRUNCH

1. BIG DATA
What ?
Issues
Business Opportunies
How ?

2. Definition
big data consists of datasets that grow so large that they become
awkward to work with using on-hand database management
tools. Difficulties include capture, storage, search, sharing,
analytics, and visualizing. « WIKIPEDIA »
« The increasing volume and detail of information captured by enterprises,
the rise of multimedia, social media, and the Internet of Things will fuel
exponential growth in data for the foreseeable future ».
McKinsey Global Institute, http://bit.ly/tE7fiZ

3. Data Deluge : Evolution 2010-2015
e
bas
Data
X 11
3, 4 milliards 4 50 000
d’abonnés pétaoctects
3G en 2015
contre 500 250 000
millions en pétaoctects
s
2010 F ile
Ericson
ail
s 30 000 X 10
M
pétaoctects
X6
1 pétaoctet (Po)= 1 000 To= 1 000 000 000 000 000 d'octets International Communication Union
1 zettaoctect (Zo) = 10puissance 21 octets.

4. Non Structured Data
Données non structurées
Données structurées

5. Available DATA
files
mail
… Mobile
Social Network Apps….
Comportemental DATA RFM
Socio-psycho-demographic
Text DATA
DATA
Life Instant Non structured

6. Contextual DATA

7. DATA Road Map
----->
-2015----
-----------
2013-------------
2012

8. BIG DATA : Theorical & practical
Landscape
Human Societi
es
IT system?
Customers, Co
nsumers ?
Privacy ?
Knowledges, H
abilities ?
Management, W
ork
Organisation ?

9. BIG DATA : A Thermodinamic
Evolution
Species (Human societies..)
produce energy &
modify environment (human competition, natural resources..)
The Red Queen Energy Information
effect : Run Maximization memorization
faster to stay in
place!

10. IT ecosystem
Industries
Data Created &
duplicated

11. IT system, today
Competitivity
Réduce Cost (not Only)
4 1
Environment
Environment impact 3
adaptation
5 2
Information 6….
Memorization
Resources
Moore Law

12. Like that, the future is a BIG DATA
CRUNCH
In a thermodynamic Schéma, BIG DATA Is
Increase STORAGE + Inscrease TREATMENT +
Increase DATA PRODUCTION +…
Energy Dissipation
critical threshold
??

13. IT System Tomorrow
Entropy Management
DATA sens
Reduce Energie consumption

14. A BIG DATA Process
DATA Management Multi canal (today) Cross Canal
(Tomorrow)
Life Instants
Comportemental
web mail
Emission DATA
…
voice
Capture
….
Network &
real Time
Storage
Silo
traitment
Predictive Actions
Analysis making
Reuse
Contextual information PUSH

15. Consumers, customers Voice
Professionnal
Consumer

16. Privacy by Design
Visibility
Security
Transparency
Prevention

17. BIG DATA = A Co-adaptativ
Environment
E Hyperstructure
X
T Technologic Model
I
N
C
T
Specialization
I
O
N
E
V U C
Decentralized
O S E collective
L E N intelligence
U R T Memetic
T
R Evolutiv
I Environment
I
O
N c

18. Data Hominem = BIG DATA
Knowledges
DATA Specialists who know collect, analyze
and reuse efficiency the data in a business
way

19. Few Ways BIG DATA
Capture Voice = Life Instant &
Comportemental DATA
Understanding = Few to one,
One to One.
Interaction = ATAWAD « any
time, any where, any device »

20. Industrialize singularity
Several years, Customers have good technology and
consumption habits since they use different Medias to
behave and stay informed: internet, mobile, touch pad,
interactive interface, so on. The ways of consumption
could be defined as a set of situations (probably
circumstances) experienced by the customers. We
observe an increase of the used medias combinations
during the purchase process. So it becomes very
difficult to understand real customers needing in only
using statistical indicators. And however it’s the goal of
everyone in the company. So, how can we measure the
customer experience especially to understanding their
new purchases habits? Customer reality would be
elusive? Our process should be as complex as their
behavior? No, smarter but not especially complex.
Sometimes, expanded uses methods can be a good
alternative. Finally, understanding user experience
within customer centricity seems the best way to
industrialize singularity.
ME…

21. Business in Progress
Transaction
space between
financial and
retail actors

22. BIG DATA, an opportunity for
the Retail

23. Find DATA Connexity : Ex.
WallMart Lab
with Social Genome wihtout Social Genome

24. DATA Matching : Ex. DATALIFT
R&D Project
In order to see the Web of data emerge, it is necessary to provide methods and
tools all along the semantic lifting process. The main objective of
DATALIFT is to bootstrap semantic lifting of raw data on the Web.
Interconnexion des données avec d'autres jeux
de données
Publication sur le web de données
Conversion des données en RDF en rapport
avec la ou les ontologies selectionnées
Sélection des ontologies pouvant décrire les
données

25. BIG DATA Interaction : a
diagram of Customer evolution
Bring a lot of information on current changes and
emerging phenomena.
Communication on the efficiency and
performance will focus on the essentials
iteration.
The dashboard of the future can not be
compared, but it will tell us that is most important
in analysis and decision making.

26. Build a BIG DATA vision
1. Capture Voice
Life Instants 2. Understanding
Comportemental
DATA F
O
N …
C
T
I Scoring
O
N Predictiv
N Analysis
I Expert
T rules
Y
GRANULARITY
3. Interaction
Diagram of Dashboard
evolution 360°
Alerts …

27. HOW ?
DATACRUNCH, 13 APRIL 2012, LILLE, France
RESEAU & ABLE
www.reseaunable.net