For what matters Supercharged Analytics 2011 Case Study: Canadian Imperial Bank … ·...

Supe

rcha

rged

Ana

lytics

2011

Case

Stud

y:

Cana

dian I

mper

ial B

ank o

f Com

merce

For

wha

t m

atte

rs.

Banking, Financial Services and InsuranceSAS Global Forum 2011

For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

203

0-20

11

•C

anad

ian

Im

peri

al B

ank

of C

omm

erce

(C

IBC

) is

a le

adin

gC

anad

ian

-bas

ed f

inan

cial

inst

itu

tion

•w

e of

fer

a fu

ll ra

nge

of

prod

uct

s an

d se

rvic

es t

o n

earl

y 11

mill

ion

indi

vidu

als

and

smal

l bu

sin

esse

s, c

orpo

rate

an

din

stit

uti

onal

clie

nts

•A

t ye

ar-e

nd

(Oct

ober

31

, 20

10

):•

Mar

ket

capi

taliz

atio

n w

as $

30

.7 b

illio

n•

Tier

1 c

apit

al r

atio

was

13

.9%

•em

ploy

ed m

ore

than

42

,00

0 e

mpl

oyee

s w

orld

wid

e•

clos

e to

1,1

00

bra

nch

es in

Can

ada

and

nea

rly

4,0

00

AB

Ms

•C

onst

itu

ent

of t

he

Dow

Jon

es W

orld

In

dex

(DJS

I)fo

r n

ine

con

secu

tive

yea

rs (

one

of 2

5 b

anks

wor

ldw

ide)

Abo

ut

CIB

C


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

303

0-20

11

Toda

y’s

Dis

cuss

ion

•O

verv

iew

–R

isk

Man

agem

ent

An

alyt

ics

•Th

e O

ppor

tun

ity

•M

akin

g It

Hap

pen


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

403

0-20

11

Ove

rvie

w -

Ris

k M

anag

emen

t A

nal

ytic

s•

Key

acc

oun

tabi

litie

s of

Ris

k M

anag

emen

t A

nal

ytic

s in

clu

de:

•ac

quir

ing

rele

van

t, a

ccu

rate

, com

plet

e an

d ti

mel

y ri

sk d

ata

•m

anag

ing

defa

ult

eve

nt

his

tory

an

d w

rite

-off

s /

reco

veri

es

•de

velo

pin

g an

d m

onit

orin

g pe

rfor

man

ce o

f ri

sk m

odel

s•

mod

elin

g m

ult

iple

bu

sin

ess

scen

ario

s•

esta

blis

hin

g B

asel

II

/ B

asel

III

Fra

mew

ork

para

met

ers

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easu

rin

g pe

rfor

man

ce o

f po

rtfo

lios

of a

sset

s

•su

ppor

tin

g bu

sin

ess

prog

ram

s w

ith

ris

k da

ta in

sigh

ts

•re

gula

tory

an

d m

anag

emen

t re

port

ing

•m

onit

orin

g co

mpl

ian

ce t

o en

terp

rise

ris

k po

licie

s an

d st

anda

rds


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

503

0-20

11

•Ti

mel

ines

s•

to d

evel

op a

nd

depl

oy a

nal

ytic

an

d B

I ap

plic

atio

ns

•Ef

fici

ent

and

Effe

ctiv

e M

anag

emen

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Ris

k D

ata

•m

ovem

ent

of la

rge

volu

mes

of

data

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dun

dan

cy (

sam

e ve

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of

data

acr

oss

proj

ects

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curi

ty a

cces

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d co

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esou

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inte

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lopm

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and

test

ing

of p

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odel

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iter

ativ

e

Key

Ch

alle

nge

s


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

603

0-20

11

Ris

k M

anag

emen

t A

nal

ytic

“R

oad

Map

”

Iden

tify

req

uir

ed c

han

ges

in b

usi

nes

s pr

oces

ses,

su

ppor

ted

by t

ools

an

d ro

bust

, sca

labl

e in

fras

tru

ctu

re

•sh

orte

n t

he

load

tim

es f

or la

rge

volu

mes

of

sou

rce

data

•im

prov

e pr

oces

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g ca

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lity

of D

BM

S /

data

base

ser

ver

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pdat

e SA

S to

ols

to le

vera

ge la

test

fu

nct

ion

alit

y

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tim

ize

the

dist

ribu

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of

wor

k be

twee

n t

he

SAS

App

licat

ion

ser

ver

and

the

DB

MS

/ da

taba

se s

erve

r

•re

duce

ope

rati

ng

cost

s


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

703

0-20

11

Key

Ele

men

ts o

f th

e So

luti

on

•In

-Dat

abas

e P

roce

ssin

g

•D

ata

War

ehou

se A

pplia

nce

•C

han

gin

g th

e M

inds

et a

nd

Bu

sin

ess

Pro

cess

es


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

803

0-20

11

Wh

at is

In

-Dat

abas

e P

roce

ssin

g?

•w

ith

con

ven

tion

al S

AS

–D

BM

S im

plem

enta

tion

, mos

t of

th

e pr

oces

sin

g is

don

e by

th

e SA

S pr

oced

ure

•w

ith

in-d

atab

ase

proc

essi

ng,

en

able

d* S

AS

fun

ctio

ns

push

mor

e of

th

e pr

oces

sin

g to

th

e D

BM

S

* u

sin

g SA

S/A

CC

ESS

9.1

.3 f

or N

etez

za, t

her

e ar

e cu

rren

tly

60

su

ppor

ted

SA

S fu

nct

ion

s


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

903

0-20

11

AN

EX

AM

PLE

USI

NG

SA

S/A

CC

ESS:

Pro

c SQ

L;C

reat

e ta

ble

Res

ult

s as

Se

lect

coa

lesc

eco

ales

ce(t

able

_A.b

alan

ce,

tabl

e_B

,bal

ance

, ta

ble_

C.b

alan

ce)

as O

S_ba

lan

ceFr

om T

able

_A, T

able

_B, T

able

_CW

her

e Ta

ble_

A.c

ust

omer

=

Tabl

e_B

.cu

stom

eran

d Ta

ble_

B.c

ust

omer

=

Tabl

e_C

.cu

stom

er;

Qu

it;

Exam

ple:

Acc

eler

atin

g D

ata

Expl

orat

ion


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

1003

0-20

11

•A

Dat

a W

areh

ouse

App

lian

ce (

DW

A)

is p

urp

ose-

built

, in

tegr

atin

g da

taba

se, s

erve

r, a

nd

stor

age

in o

ne

com

pact

sy

stem

•O

ptim

ized

for

pro

cess

ing

“in

bu

lk”

vs. t

ran

sact

ion

pro

cess

ing

•A

rch

itec

ture

pri

nci

ples

incl

ude

:•

proc

essi

ng

clos

e to

th

e da

ta s

ourc

e

•ba

lan

ced,

mas

sive

ly p

aral

lel a

rch

itec

ture

•pl

atfo

rm is

en

gin

eere

d fo

r ad

van

ced

anal

ytic

s•

appl

ian

ce s

impl

icit

y vs

. tra

diti

onal

sep

arat

e co

mpo

nen

ts•

extr

eme

scal

abili

ty o

f in

tern

al s

tora

ge c

apac

ity

•A

dver

tise

d pe

rfor

man

ce g

ain

s of

10

x t

o 1

00

x ba

selin

e

Wh

at I

s A

Dat

a W

areh

ouse

App

lian

ce ?


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

1103

0-20

11

Leve

ragi

ng

Pow

er o

f SA

S an

d th

e D

WA

To le

vera

ge t

he

anal

ytic

pow

er o

f SA

S an

d th

e in

-dat

abas

e pr

oces

sin

g ca

pabi

litie

s of

th

e h

igh

per

form

ance

Net

ezza

DW

A:

•u

se t

he

SQL

pass

-th

rou

gh f

acili

ty•

subm

it R

DB

MS-

spec

ific

SQ

L st

atem

ents

th

at a

re s

ent

dire

ctly

to

the

DB

MS

for

exec

uti

on

•u

se t

he

SAS/

AC

CES

S LI

BN

AM

E st

atem

ent

and

PR

OC

SQ

L•

subm

it S

AS

stat

emen

ts t

hat

SA

S/A

CC

ESS

tran

slat

esin

to t

he

SQL

of t

he

RD

BM

S an

d th

e pa

sses

to

DB

MS

for

proc

essi

ng

•SA

S/A

CC

ESS

9.1

.3 f

or N

etez

zain

clu

des

60

fu

nct

ion

s


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

1203

0-20

11

The

Roa

d to

Im

plem

enta

tion

•O

n-s

ite

Pro

of o

f C

once

pt w

ith

DW

A

•B

usi

nes

s C

ase

for

the

inve

stm

ent

•Im

plem

enta

tion

pla

nn

ing

•“G

o-Li

ve”

into

Pro

duct

ion

•O

ptim

ize


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

1303

0-20

11

Impl

emen

tati

on P

lan

The

key

elem

ents

of

the

impl

emen

tati

on p

lan

incl

ude

:

•in

stal

l an

d te

st t

he

DEV

/UA

T N

etez

zaD

WA

•n

ew o

pera

tion

s su

ppor

t m

odel

wit

h t

ech

nol

ogy

grou

ps

and

the

ven

dor

•in

stal

l an

d te

st t

he

PR

OD

Net

ezza

DW

A w

ith

SA

S 9

.1.3

•m

igra

te S

QL

data

base

s to

new

Net

ezza

data

base

s

•bu

ild a

new

SA

S A

pplic

atio

n S

erve

r (3

2 b

it -

> 6

4 b

it O

S)

•u

pgra

de a

nal

ytic

app

licat

ion

s to

th

e SA

S 9

.2 c

ompo

nen

ts

•op

tim

ize

appl

icat

ion

s


For

wha

t m

atte

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©C

IBC

201

1 A

ll R

ight

s R

eser

ved

1403

0-20

11

Wh

at D

id W

e Le

arn

?

•U

sin

g SA

S pa

ss-t

hro

ugh

SQ

L an

d SA

S/A

CC

ESS

prov

ides

si

gnif

ican

t pe

rfor

man

ce g

ain

s u

sin

g th

e N

etez

zaD

WA


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

1503

0-20

11

Usi

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12

yea

rs o

f m

onth

ly a

ccou

nt

leve

l cre

dit

card

dat

a (~

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0 m

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Nu

mbe

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t•

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atte

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©C

IBC

201

1 A

ll R

ight

s R

eser

ved

1603

0-20

11

Usi

ng

12

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rs o

f m

onth

ly a

ccou

nt

leve

l cre

dit

card

dat

a (~

58

0 m

illio

n r

ows)

Join

tw

o la

rge

tabl

es o

f cr

edit

car

d da

ta, o

n t

wo

com

mon

fie

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for

:•

Nu

mbe

r of

acc

oun

ts a

nd

aver

age

uti

lizat

ion

am

oun

t•

Tota

l an

d av

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bala

nce

at

mon

th e

nd

and

stat

emen

t da

te•

Nu

mbe

r an

d pe

rcen

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rren

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redi

t B

ure

au s

core

an

d cu

rren

t ac

cou

nt

Beh

avio

ur

Scor

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min

imu

m, m

axim

um

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d av

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Per

cen

tage

of

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ts w

ith

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fast

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x fa

ster

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form

ance

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mpl

e


For

wha

t m

atte

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©C

IBC

201

1 A

ll R

ight

s R

eser

ved

1703

0-20

11

Wh

at D

id W

e Le

arn

?

•U

sin

g SA

S pa

ss-t

hro

ugh

SQ

L an

d SA

S/A

CC

ESS

prov

ides

si

gnif

ican

t pe

rfor

man

ce g

ain

s u

sin

g th

e N

etez

zaD

WA

•B

reak

-th

rou

gh p

erfo

rman

ce r

equ

ires

ch

angi

ng

how

we

thin

k ab

out

how

to

expl

ore

the

data

-m

ove

from

tra

nsa

ctio

ns

to b

ulk


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

1803

0-20

11

Ch

angi

ng

the

Min

d Se

t

•th

e N

etez

zaD

WA

is n

ot a

tra

nsa

ctio

n s

yste

m –

it d

eals

in b

ulk

•th

ink

abou

t w

hat

can

be

proc

esse

d “i

n b

ulk

”

•th

e N

etez

zaD

WA

can

pro

cess

mill

ion

s of

row

s pe

r se

con

d

•fo

r ex

ampl

e,•

bulk

load

a b

ig s

et o

f da

ta in

to o

ne

or m

ore

stag

ing

tabl

es•

proc

ess

the

data

usi

ng

SQL

in b

ulk

ope

rati

ons

•lo

ad t

he

resu

lts

into

you

r ta

rget

tab

les

•th

is is

a d

iffe

ren

t m

inds

et t

han

“ro

w a

t a

tim

e pr

oces

sin

g”


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

1903

0-20

11

Tran

sact

ion

s vs

. Bu

lk

CU

RR

ENT

STO

RED

PR

OC

EDU

RE

USI

NG

SA

S •

Rea

d a

tabl

e of

1 m

illio

n r

ecor

ds o

n D

BM

S (O

racl

e)•

For

each

rec

ord,

app

ly 4

bu

sin

ess

rule

s in

SA

S•

This

en

tire

pro

cess

tak

es h

ours

of

tim

e

USI

NG

TH

E N

ETEZ

ZA D

WA

•R

ead

a ta

ble

of 1

mill

ion

rec

ords

on

DB

MS

(Ora

cle)

into

DW

A•

Pro

cess

all

1 m

illio

n r

ecor

ds t

hro

ugh

th

e fi

rst

busi

nes

s ru

le

on D

WA

•Th

en p

roce

ss a

ll 1

mill

ion

rec

ords

th

rou

gh t

he

nex

t ru

le, e

tc.

•Th

is e

nti

re p

roce

ss t

akes

mer

e se

con

ds

ILLU

STR

ATI

VE


For

wha

t m

atte

rs.

©C

IBC

201

1 A

ll R

ight

s R

eser

ved

2003

0-20

11

oTh

e fu

ture

for

“su

perc

har

ged”

anal

ytic

s in

clu

des

in-d

atab

ase

proc

essi

ng

usi

ng

scal

able

, hig

h p

erfo

rman

ce

data

war

ehou

se a

pplia

nce

s an

d “m

ade-

to-f

it”

anal

ytic

to

ols

oTh

e st

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