Pierre Delort President of the French National Association of CIOs Association Nationale des DSI.

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Big Data

© P. DELORT

Mines ParisTech, Centre de Recherche en Informatique.

Pierre Delort

President of the French National Association of CIOs

Association Nationale des DSI.

OECD

ICCP Technology Foresight Forum

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Big Data

© P. DELORT

What you have typed

Oscar nomination

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The 45 queries and their topics

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Big Data

© P. DELORT

US, Region atlantic center, season 2007-08

Google Flu Trends (black)

CDC (red)

Results

Etats-Unis – Propagation du Virus

Estimation de la grippe

Google Flu Trends

CDC’s Sentinel

Induction—“the glory of

science and the scandal of philosophy

CD Broad

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NGS & Moore’s law

Ion proton sequencer

Bottleneck = Data Analysis

Cost per Mégabase

10 years ; cost divided by 10 000

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Big Data

© P. DELORT

Important (X 3 to X 10) increase of the need of :

Sboner A, et al.: The real cost of sequencing:

higher than you think! Genome Biology 2011, 12:125.

• (bio) computer scientists ;

• (bio) statisticians.

NGS ; impact on skills

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2000 (before NGS)

2010 (NGS)

2020 (Est.)

%

o

f

seq

u

en

ci

n

g

co

st

Genome's sequencing cost

Sampling & Experiment design

Sequencing

Data Management

Data reduction & synthesis

Downstream analysis

If you are looking for a career where your

services will be in high demand, you should

find something where you provide a scarce,

complementary service to something that is

getting ubiquitous and cheap. So what’s

getting ubiquitous and cheap? Data. And

what is complementary to data? Analysis. –

Prof. Hal Varian, UC Berkeley, Chief

Economist at Google, 2008.

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The last ten years

This leads to three technologies I belive will drive the future of Big Data computing :

• In-Memory ;

• SSD ;

• MPP.

Sharp decrease of Solid State Device’s cost

Innovation in

DB & software

Increase of

computing power

64 bits adressable memory

(DRAM)

X 4,2 10

9 DRAM/10

2 Flash

NAND

/10

3 /50 à /10

x

X 100

+

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Big Data

© P. DELORT

Three technologies

• In-Memory ; very fast (I/O), saves on Opex (DB tuning, energy…) but

non-persistent, requires reformatting of the software, expensive and (today) with

limited scalability ;

• SSD (Flash/NAND) ; fast (I/O) no reformating required, persistent, scalable,

extensible, cheap ; saves both on Capex and Opex (energy) ;

• MPP (MapReduce) ; very scalable (n 10

5 _{), fit for « low density data »}

(key/value), but requires new programming skills and Opex (energy).

Source : Objective Analysis (Echelle Log.)

DRAM

Flash

Disk

Tape

CPU Cache

Bandwidth (MB/S)

Cost

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Big Data

© P. DELORT

Inspiration

EXPÉRIMENTATION (2

nd

_Paradigm)

Publication

Protection

Valorisation

Conversion

Selection

Diffusion

(BIG) DATA MINING (4

th

_Paradigm)

Research

Firm / Innovation

Data

Hypothesis

Limiting factor

Validation

Publication

Protection

Valorisation

Exploration

Explanation

Idea generation

Impacts on Research & Innovation

Induction—“the glory of

science and the scandal

of philosophy

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Public Data; a right for citizens as well as scientists ?

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Big Data

© P. DELORT

Will the CIO :

1.be in charge of the (rich, up to date, reliable…) data whatever their origin (in Business) ;

• internal ;

• external ;

• public ;

• private ; available for acquisition or ;

• depending of the action of a regulator.

**1.and act as a Chief Data Officer, in charge of « Data Informed Decisions* »?**

*Kolker E., 2012

CDO, a new rôle for the (business) CIO ?

“I keep saying that the

sexy job in the next 10

years will be

statisticians,” said Hal

Varian, chief economist

at Google. “And I’m

not kidding.”

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Wisdom

Knowledge

Information

Data

pdt@andsi.fr

- pierre.delort@cri.mines-paristech.fr

Where is the wisdom we have lost in knowledge ?

Where is the knowledge we have lost in information ?

T.S. Eliot

Lee Scott, Wal-Mart

CEO to the question

« What happened to

K-Mart ? His answer,

in a word, was

“coupons”. K-Mart

overdid it with

coupons, which

became too much a

hunk of its overhead,

while also narrowing

his customer-base

toward coupon

clippers. By contrast,

Wal-Mart minimized

that kind of thing,

focusing instead on

promising “everyday

low price”.

In the intention

Economy, Doc Searle,

2012.