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BIG DATA : ce qu'elles sont, les enjeux, les opportunités pour le Business. Mise en bouche en perspective de DATACRUNCH
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BIG DATAWhat ?
Issues
Business Opportunies
How ?
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
Data Deluge : Evolution 2010-2015
X 11
X 6
X 10
Database
Files
Mails
4 50 000 pétaoctects
250 000 pétaoctects
30 000 pétaoctects
International Communication Union
3, 4 milliards d’abonnés 3G en 2015 contre 500 millions en 2010
Ericson
1 pétaoctet (Po)= 1 000 To= 1 000 000 000 000 000 d'octets
1 zettaoctect (Zo) = 10puissance 21 octets.
Non Structured Data
Données structurées
Données non structurées
Comportemental DATA
Socio-psycho-demographicDATA
Text DATA
RFM
Available DATA
Life Instant Non structured
mailfiles
Social Network
Mobile
Apps….
…
Contextual DATA
DATA Road Map
20122013----------------
---------2015--------->
BIG DATA : Theorical & practical Landscape
Human SocietiesIT system?
Customers, Consumers ? Privacy ?Knowledges, Habilities ?
Management, Work Organisation ?
BIG DATA : A Thermodinamic Evolution
The Red Queen effect : Run faster to stay in place!
Species (Human societies..) produce energy &
modify environment (human competition, natural resources..)
Energy Maximization
Informationmemorization
IT ecosystem
Data Created & duplicated
Industries
IT system, today
Réduce Cost (not Only)
Environment adaptation
Information Memorization
Environment impact
Resources
1
2
3
4
5
6….
Competitivity
Moore Law
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
IT System Tomorrow
Entropy ManagementDATA sensReduce Energie consumption
A BIG DATA Process
Capture
Emission
Storage
Reuse
traitment
DATA Management Multi canal (today) Cross Canal (Tomorrow)
web mail
…
….
Netw
ork &
real Time
Contextual information PUSH
Actions making
voice
Predictive Analysis
Silo
Life InstantsComportemental
DATA
Consumers, customers Voice
Professionnal Consumer
Privacy by Design
Visibility
Transparency
Security
Prevention
BIG DATA = A Co-adaptativ Environment
Specialization
Technologic Model
Hyperstructure
U C
S E
E N
R T
R
I
c
EXTINCTION
EVOLUTION
Decentralized collective intelligence
Memetic Evolutiv Environment
Data Hominem = BIG DATA Knowledges
DATA Specialists who know collect, analyze and reuse efficiency the data in a business way
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 »
Industrialize singularitySeveral 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…
Business in Progress
Transaction space between financial and retail actors
BIG DATA, an opportunity for the Retail
Find DATA Connexity : Ex. WallMart Lab
with Social Genome wihtout Social Genome
DATA Matching : Ex. DATALIFT R&D Project
Sélection des ontologies pouvant décrire les données
Conversion des données en RDF en rapport avec la ou les ontologies selectionnées
Publication sur le web de données
Interconnexion des données avec d'autres jeux de données
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.
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.
Build a BIG DATA vision
Life InstantsComportemental
DATA
1. Capture Voice
2. Understanding
F
O
N
C
T
I
O
N
N
I
T
Y
Expert rules
GRANULARITY
PredictivAnalysis
Scoring
…
3. Interaction
Diagram of evolution
Dashboard 360°
Alerts …
RESEAU & ABLE
www.reseaunable.net
HOW ?
DATACRUNCH, 13 APRIL 2012, LILLE, France