To take advantage of new big data opportunities, organizations need an agile approach embracing risk and failure, with many small iterative projects complementing large periodic enterprise data warehouse releases. More than technology, an organization's

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*