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