CON3132_Prabaharan-Innovation Through Predication- OOW Presentation Final

8/17/2019 CON3132_Prabaharan-Innovation Through Predication- OOW Presentation Final

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“Innovation through Prediction”- Hybrid Cloud Big Data Platform

John Andrewracle !nter"ri#e Architect

John$andrew%oracle$com

dict$ In'uence$



Co"yright ( )*+, racle and or it# a.liate#$ All right# re#erved$

/afe Harbor /tatement 0he following i# intended to outline our general "roduct direction$ It i#intended for information "ur"o#e# only1 and may not be incor"oratedinto any contract$ It i# not a commitment to deliver any material1 code1or functionality1 and #hould not be relied u"on in ma2ing "urcha#ingdeci#ion#$ 0he develo"ment1 relea#e1 and timing of any feature# orfunctionality de#cribed for racle3# "roduct# remain# at the #oledi#cretion of racle$




Agenda

Innovation Bu#ine## Driver#

4#e Ca#e Conte5t

6eference Architecture Conte5t

Hybrid Cloud /olution Conte5t

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:hat if ;

+$ =ou 2new what "roduct# your cu#tomer# would

be the mo#t li2ely to buy in advance>)$ =ou could ma5imi?e your "ro@t# by determining

the highe#t "rice a cu#tomer will "ay for a"roduct>

9$ =ou could o"timi?e cu#tomer #ervice to re#olveconcern# "roactively before they become i##ue#>and fnally….<$ Political "artie# had a way of determining and

in'uencing the voter# to vote for them>


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:hat doe# it mean for Bu#ine##;

Increased CustomerSatisfaction and

Revenue

Drastically ReducedBusiness Risk

IncreEfficienProduc



Core bu#ine## theme# and building bloc2# ;

Predicting andInfluencing

Model AccuracyAnytime & Anyw ere

availa!ility

"ime to #alEconomy o



oogle 0rend Analy#i# a# of ct )*+,

PredictiveAnalytic#rediction

Big Data

oundation

Model

Accuracy

CloudDe"loyment

EconomyOf scale




Hind#ight -E In#ight -E ore#ight

DescriptiveAnalytics PredictiveAnalytics PrescriptAnalyt

• Forecasting• Trends

• Relationships

• Analytics• Models• Semantics

• Reports• Alerts

• Discovery

• RDBMS / SQL• ( !"LA#• $areho%se

Bu#ine##!5"ectation

ArchitecturePattern

• #redi• &e t

• 'n l%

• Mach• Decis• "ptim




Agenda

Innovation Bu#ine## Driver#

se !ase !onte"t



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C A P D “D




Co ect ng A Pr c ng re ate Data; “Data:rangling”

Enterprise Data +on Enterprise Data




“0raining” Pricing model ; achine &earning!ustomerPreference

!onsumerPrice'nde"

,istoricalPricin#Av#

'nventory-urnover

.e#ment'D

!ustomer.e#ment

/es 0% 12 0 )M ,o *N $9< $+, A P Lo ,N $+) $9* 6 &Lo ,*N $+F $9, P46 L

/es % 1 0 + - ,

o G,N $,9 $<* 6B &Lo <N $<, $), C HLo *N $ $9F / I L

Have an algorithm determine what i# diOerent and theim"ortance of the diOerence# in the green and gray

metric# to @guring out "reference




Advanced Analytic# Algorithm#More t6an 7ust linear regression to 6elp predict t6e future and discoveryrelations6ips

&ogi#tic 6egre##ion

Deci#ion 0ree#a ve Baye#

/u""ort Lector achine#

e#ression&inear 6egre##ion

/u""ort Lector achine#

!lassi8cation

ulti-&ayer eural etwor2#

Anomaly Detection

ne-Cla## /L

Attri9ute 'mportanceinimum De#cri"tio

Princi"al Com"onen

!lusterin#Hierarchical 2- ean#

Hierarchical -Clu#ter

!5"ectation- a5imi?ation )eature E"traction

onnegative atri5

/ingular Lalue Decom"o#

Collaborative ilte-e"t Minin#

0o2eni?ation

0heme !5traction

Algorithm# wor2# acro## data #et# 6elational and on-6elationalQ




Current /tate Con#traint# and a"#1 Analytical !6allen#es

• i##"eci@cation and u#ing a #am"le to e#timate the model

• 6e#ource memoryQ con#traint# of analytical #cri"t# 6 #cri"t#Q

• Analy?e data without hel" from I0

2 Data Mana#ement !6allen#es

• Com"le5ity and Co#t i##ue# re#ulted u#ing #maller data #et# for

analytic#• /maller the data #et#1 le## accurate analytical outcome#

• Data latency i##ue# increa#ed a# the re#ult of data e5i#t# inmulti"le "lace#

3 Deployment !6allen#es

• 4 front lar e Ca !5 to build and de lo the Platform




Agenda

Innovation Bu#ine## Driver#4#e Ca#e Conte5t

eference Arc6itecture !onte"t


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f "redictive analytic#Q R uni@ed data

% #cale Q"redictive model#




Architecture Li#ion

Creating an 4ni@ed Data K Advanced Analytic

Platformfor the !ra of Big Data and the Cloud

.imple

• Any data #i?e• Any data

variety• Any "latform

nify

• !nter"ri#e AllQData

• Analyticalodel#

• 4#er Interaction

.ecure

• Control Acce##• Protect• Integrity

•

•

•




Architecture 0heme#

Architect%re FitA#-i# Data Di#coveryAt-#ource Data Analytic#

At #cale and Performance

Financial Fit6educed S "er odelCa"!5 to "!5 0ran#ition

&ower 0C

"peratioAutomated Infr4ni@ed an

/im"li@ed /u""

T




- TAnalytic#

Data'n#estion

.ervice

3 rd PartyData !loud

.ervice

c#v

5l#

U*i# Data

Discovery.ervice

!lient En#ine

V

W

*i# Data.ervice

X

EnterpriseData

:are6ouse

Y

*i# Data .;<

Tl




TAnalytic#

*i# Data!loud

.ervice

DataPreparation

!loud.ervice


.ervice

c#v

5l#

!lient En#ine

U X

*i# DataDiscovery

!loud

.ervice

=

W

>Data9ase

!loud.ervice

* i # d a

t a . ; <

TA l i #




TAnalytic#

*i# Data!loud

.ervice

*i# DataPreparation

!loud.ervice

*i# DataDiscovery

!loud.ervice


.ervice

c#v

5l#

EnterpriseData

:are6ouse

!lient En#ine

U X V

Y

W




Analytic 6eference ArchitectureBuilt it once and use it multiple times

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6eference Architecture Data low ;

• Im"ort and Inge#t• Clean#e and ormali?e• 6e"air and /tandardi?e• Cla##ify and !5tract• Augment and !nrich• Li#uali?ation

Orc6estration

'nte#ration

<inea#eand

Preparation

Data .ources Data .tora#e andMana#ement

Data'n#estion

Sales &Inventory

!ontinuous

Data$are ouse

Daily pdates

Com%etitorsData

On?demand

Arc iveMainframe

• Public /ource# reeQ• 9 rd Party /ource# PayQ

utsideData

Structured

nstructured

Structured

Structured

racle

0eradata

IB

Data !onsumpt

Discovery <a9*usiness sers

Analytical <aDevelopment

Analytical

Models

DataDiscove

ry

ni8ed

.ecu

reAccess

• /earch• Li#uali?e• 0ran#form• /hare /ub#e

t

• Data ovement

• Data Acce##

Data eservoir$.emi and

nstructured&

Enter%riseData

Data :are6ouse$.tructured&

,adoop$,D).&+o.;<

Metadata

Analytic

En#ine

DataMinin#AnalyticEn#ine'n?

memoryProcessi

n#

.par@




Agenda

Innovation Bu#ine## Driver#4#e Ca#e Conte5t


,y9rid !loud .olution !onte"t78A

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racle Analytic# a# a /ervice PlatformPrivate1 Public and Hybrid Cloud de"loyment o"tion#

rap6 Analytics

li? i




reali?ation




4ni@ed Analytical odel !5ecution

!lient En#ine

racle Databa#e

4#er table#

In-#ta

O E pac@a#es )unctions

B

Oracle Advanced Analytics for ,adoop Oracle D* Advanc

*i# .;< .ervicesData Minin#




Zic2 /tart =our Hybrid Cloud Big Data /trategy+$ uiding /ucce## actor#

• Integrate bu#ine## and technology vi#ion Identify #toc2holder#

that will carry over to im"lementationQ$ ocu# on the ne5t +)month#• Identify your target architecture

[ Avoid tunneling on one u#e ca#e• Zee" in mind Big Data i# not a cure-all

• Hadoo" i# a com"lementary to your e5i#ting !D:$ It could

very well be your “/y#tem of truth” but mo#t li2ely not your“/y#tem of record”)$ Jum" #tart your Innovation with

• racle "roven reference architecture• racle "roven Analytical "latform• racle "roven hybrid cloud de"loyment




:hy racle Hybrid Cloud Analytical Platform

Enterprise? rade !loud !apa9ilities

Discover and

Predict B )ast

overn

.ecureData

.implify

Access to AllData

Performance Integration Availability !la#ticity an

CON3132_Prabaharan-Innovation Through Predication- OOW Presentation Final

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