r:evolution 2014 - martin willcox
DESCRIPTION
Martin Willcox from Teradata spoke about Big Data at our launch event on the future of retail.TRANSCRIPT
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Big$Data$Then$And$Now$Presenta(on*to*R:Evolu(on*Conference*!*5th*March*2014*!*Mar(n*Willcox,*Director*Big*Data*CoE*(Interna(onal)*
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The$technology$industry$thrives$on$hype:$“Big$Data”$is$hot$right$now$
“Big$Data”$has$recently$overtaken$“cloud$compu>ng”$as$the$most$hyped$expression$in$IT.$
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We$are$currently$bombarded$with$“facts”$and$opinions…$
“Unprecedented*data*growth…*that*con1nues,*regardless*of*budget*constraints”*Ten*Trends*&*Technologies*To*Impact*IT*Over*The*Next*5*Years*
David*Cappucio,*Research*VP*(Gartner),*January*9th*2013*
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…on$both$sides$of$the$“Big$Data”$debate$
“Big*Data*is*bullshit…*it’s*really*just*data.”*Harper*Reed,*CTO*Obama*For*America*
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Spoiler:$both$sides$are$half$right$
Yes*it’s*a*big*deal…* …no*it’s*not*unprecedented*
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Big$Data,$circa$1986$Deployment*of*EPoS*systems*in*the*late*80s*revolu(onises*Retail*
• Enormous$(by$the$standards$of$the$day)$Teradata$system$enables$WalMart$to$capture$store*/*SKU*/*day*level*aggregated$data$across$all$its$stores$in$North$America;$
• The*rest*is*Retail*history…*
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Big Data Big Data “…really$we$got$big$by$replacing$inventory$with$informa>on…”$–$Sam*Walton,*Founder,*WalMart*
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From*transac1ons*I*to*interac1ons:*the*three*new*waves*of*Big*Data*
Analysis$of$clickstream$data$enables$Amazon$and$eBay$to$achieve$“mass$customisa>on”$of$their$webSsites.$
Analysis$of$social$/$interac>on$data$enables$Amazon,$Apple$and$LinkedIn$to$go$social$(“people$who$like$what$you$like$also$like…”)$
Increasing$instrumenta>on$is$now$leading$to$the$emergence$and$op>misa>on$of$“the$Internet$of$Things”.$
People*interac1ng*with*
things*
People*interac1ng*with*
people*
Things*interac1ng*with*
things*
*(1)**
*(2)**
*(3)**
These*trends*are*real*and*accelera1ng*–*but*are*they*about*“more”,*or*“different”?*
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“Big$Data”$are$oVen$“unstructured”$and$difficult$to$store$and$analyse$in$tradi>onal$database$technologies…$
I didn’t say Bill was ugly. I didn’t say Bill was ugly. I didn’t say Bill was ugly. I didn’t say Bill was ugly. I didn’t say Bill was ugly. I didn’t say Bill was ugly.
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…now$“new”$informa>on$management$strategies,$Analy>cs$and$suppor>ng$technologies$are$enabling$us$to$extend$Enterprise$Analy>cs$
Structured(data(
Mul,-structured(data(
Non-tradi,onal((,me-series(/(path(/(graph)(analy,cs(
Coun,ng(things(and(sta,s,cal(analy,cs(
Business$Intelligence$&$Analy>cs$
Capture,*Store,*Refine*
Explora1on*&*Discovery*
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Take$home$lesson$#1$
“Big*Data”*aren’t*just*“lots*more*data”;*“big”*oZen*means*“different”.*
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The$corollary$of$Moore’s$Law$Simple*compu(ng*devices*are*now*incredibly*inexpensive*
An*iPad2*would*have*stayed*on*the*list*of*the*world’s*most*powerful*supercomputers*through*1994.*
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13 06/03/2014 Teradata Confidential
I$CAN$SENSE$YOUR$MOVEMENT$&$UNDERSTAND$YOUR$BEHAVIOR$
Shopping cart will track the consumer’s every move
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14 06/03/2014 Teradata Confidential
I$KNOW$WHO$YOU$ARE$FACIAL$RECOGNITION$
Retailers are testing new facial recognition technology
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15 06/03/2014 Teradata Confidential
I$KNOW$YOU$AND$CAN$ENGAGE$VIA$MOBILE$
Mobile enabling you to engage in the Store to find the items you like
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16 06/03/2014 Teradata Confidential
I$KNOW$WHAT$INTERESTS$YOU$
Improving on-shelf availability with cameras
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17 06/03/2014 Teradata Confidential
Facebook-enabled coat hanger tracks the number of ”likes”
I$CAN$UNDERSTAND$IF$YOU$ARE$SOCIALLY$INFLUENCED$
Photo © C&A Brazil/DDB Brazil
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Take$home$lesson$#2$
The*(smart)*machines*are*coming,*bearing*data.**We*will*soon*be*able*to*measure*anything*–*and*everything.*
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New$sources$of$data$follow$the$same$trajectory$From*byZproduct*to*raw*material;*from*BI*to*CI*
“We*are*used*to*the*idea*of*deploying*new*
technology*to*improve*produc(vity*and*
efficiency...*But*data*are*no*longer*merely*the**byZproduct*of*process*improvement,*they*are*becoming*the*raw*
material*of*business.”$
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“Hot$right$now”$in$Retail$Big$Data$Discovery$Analy>cs$
Mass$personalisa>on$/$collabora>ve$filtering$ Social$/$sen>ment$analysis$
Marke>ng$ahribu>on$/$PPC$analy>cs$ Golden$path$/$pathStoSchurn$analy>cs$
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21 06/03/2014 Teradata Confidential
Tradi>onal$BI:$what$is$the$answer$to$the$ques>on?$Discovery$&$Explora>on:$what$are$the$interes>ng$ques>ons?$
“Capture only what’s needed”
IT delivers a platform for storing, refining, and
analyzing all data sources Business explores data for questions worth answering
Big Data Analytics Multi-structured & Iterative Analysis
IT structures the data to answer those questions
Business determines what questions to ask
Classic BI Structured & Repeatable Analysis
“Capture in case it’s needed”
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22 06/03/2014 Teradata Confidential
EDW Model V Big Data Discovery EDW Model
Highly Planned & Controlled Slow Release Schedule • 3x releases 2 years High Central funding cost Low Risk / High Success
Discovery Model
Small Iterative Projects • 40+ Discoveries / 2 years Low cost per project • $20k-$50k per project BAU funded initiatives • $Project funded • $Central discovery team /
BIU for free thinking High Risk / High Fail • Iterates to a new project
$5m
$5m
$5m
3 Releases Over 2 years
Release 1
Release 2
Release 3
40+ Projects Over 2 years
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23 06/03/2014 Teradata Confidential
EDW Model V Big Data Discovery EDW Model
• EDW projects must succeed
• Successful Discoveries productionised as part of release schedule
Discovery Model
• Many projects “fail”
• Failure is accepted as part of the process and leads to new innovations and iterative projects
• Successful projects are often productionised on the EDW for execution
$5m
$5m
$5m
3 Releases Over 2 years
Release 1
Release 2
Release 3
40+ Projects Over 2 years
Successful Project Failed Project
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Take$home$lesson$#3$
Enabling*innova1on*means*embracing*risk,*“failing*fast”*–*and*moving*on.*
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And$finally…$
Good*technology*is*necessary,*but*not*sufficient;*organisa1on*and*culture*maber*more.*@Willcoxmnk*