Martin Willcox from Teradata spoke about Big Data at our launch event on the future of retail.

1. Big$Data$Then$And$Now$
Presenta(on*to*R:Evolu(on*Conference*!*5th*March*2014*!*Mar(n*Willcox,*Director*Big*Data*CoE*
(Interna(onal)*

2. 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.$

3. 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*

4. …on$both$sides$of$the$“Big$Data”$debate$
“Big*Data*is*bullshit…*it’s*really*just*
data.”*
Harper*Reed,*CTO*Obama*For*America*

5. Spoiler:$both$sides$are$half$right$
Yes*it’s*a*big*deal…* …no*it’s*not*unprecedented*

6. 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…*

7. Big DataBig Data“…really$we$got$big$by$replacing$inventory$with$
informa>on…”$–$Sam*Walton,*Founder,*WalMart*

8. 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”?*

9. “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.

10. …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*

11. Take$home$lesson$#1$
“Big*Data”*aren’t*just*“lots*more*data”;*“big”*
oZen*means*“different”.*

12. 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.*

13. 13 06/03/2014 Teradata Confidential
I$CAN$SENSE$YOUR$MOVEMENT$&$UNDERSTAND$YOUR$BEHAVIOR$
Shopping cart will track the
consumer’s every move

14. 14 06/03/2014 Teradata Confidential
I$KNOW$WHO$YOU$ARE$FACIAL$RECOGNITION$
Retailers are testing
new facial recognition
technology

15. 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

16. 16 06/03/2014 Teradata Confidential
I$KNOW$WHAT$INTERESTS$YOU$
Improving on-shelf
availability with
cameras

17. 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

18. Take$home$lesson$#2$
The*(smart)*machines*are*coming,*bearing*data.**We*will*soon*
be*able*to*measure*anything*–*and*everything.*

19. 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.”$

20. “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$

21. 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”

22. 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

23. 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

24. Take$home$lesson$#3$
Enabling*innova1on*means*embracing*risk,*“failing*fast”*–*and*
moving*on.*

25. And$finally…$
Good*technology*is*necessary,*but*not*sufficient;*
organisa1on*and*culture*maber*more.*
@Willcoxmnk*