R:evolution 2014 - Martin Willcox

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

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R:evolution 2014 - Martin Willcox

  1. 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. 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. 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. 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. 5. Spoiler:$both$sides$are$half$right$ Yes*it’s*a*big*deal…* …no*it’s*not*unprecedented*
  6. 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. 7. Big DataBig Data“…really$we$got$big$by$replacing$inventory$with$ informa>on…”$–$Sam*Walton,*Founder,*WalMart*
  8. 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. 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. 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. 11. Take$home$lesson$#1$ “Big*Data”*aren’t*just*“lots*more*data”;*“big”* oZen*means*“different”.*
  12. 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. 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. 14 06/03/2014 Teradata Confidential I$KNOW$WHO$YOU$ARE$FACIAL$RECOGNITION$ Retailers are testing new facial recognition technology
  15. 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. 16 06/03/2014 Teradata Confidential I$KNOW$WHAT$INTERESTS$YOU$ Improving on-shelf availability with cameras
  17. 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. 18. Take$home$lesson$#2$ The*(smart)*machines*are*coming,*bearing*data.**We*will*soon* be*able*to*measure*anything*–*and*everything.*
  19. 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. 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. 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. 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. 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 $5m 3 Releases Over 2 years Release 1 Release 2 Release 3 40+ Projects Over 2 years Successful Project Failed Project
  24. 24. Take$home$lesson$#3$ Enabling*innova1on*means*embracing*risk,*“failing*fast”*–*and* moving*on.*
  25. 25. And$finally…$ Good*technology*is*necessary,*but*not*sufficient;* organisa1on*and*culture*maber*more.* @Willcoxmnk*

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