Big Data Maturity as a Business: A Retail Case Study

1 ©HortonworksInc.2011– 2016.AllRightsReserved

UnderstandingBigDataMaturity-RetailandConsumerGoodsApril2017


Contents

Ã BigDatainRetail– DigitalRevolution– ExplosionofData

Ã BigDataMaturityAnalysis– HortonworksBigDataMaturityScorecard– RetailandCPGMaturityAnalysis

Ã BigDataUseCases– RetailUseCaseMaturityMap– SingleViewofCustomer

Ã BigDatainAction– RetailCaseStudy– CalltoAction

45%ofF200firmswanttobecomeandIntegrated

Digitaleco-systemprovider

47%offirmswillintroduceanewdigitalproductportfolioin18months

69%sayimprovingtheirdatastrategy willbekeytotheirrelationshipwith

thecustomer48%believesustainabilityasakeyreasontochangetheir

digitalbusinessmodelby2019

78%ofF500organizationshaveamediumtopoorBigDataandAnalytics

capabilities

63%of$10bn+firmsarewitnessingtheir core

businessmodeldisrupted

Only36%CEOshave a sharedadigitaltransformationvision

although93%oftheemployeesbelieveitistherightthingtodo

DigitalandtheFourthIndustrialRevolutionthroughthenumberslens…

4 ©HortonworksInc.2011– 2017AllRightsReserved Hortonworks Confidential. ForInternalUseOnly.

SignificanceofBigDatainRetail

Source:McKinsey,PressSearch

Improvementpotentialinretailoperatingmarginsthrough BigData

Improvement inMarketingpotentialthrough BigData

RetailcompaniestouseBeaconsinthe

next5years

EstimatedannualeconomicimpactofIOTinRetailby

2025

Consumersnowuseadeviceorin-storetechnology during shopping

Estimatedcross-channelretail

salesintheUSby2017

60% 70%80% $1.8T>$500B15-20%


Contents





6 ©HortonworksInc.2011– 2017AllRightsReserved Hortonworks Confidential. ForInternalUseOnly.

ThereareseveralchallengesthatareinhibitingcompaniesadoptBigData– AccordingtoGartnerValueis#1

Source:Gartner

Determininghowtogetvaluefrom

bigdata

Obtainingskillsandcapablitiesneeded

Riskandgovernance

issues

Fundingforbigdata-relatedinitiatives

Definingourstrategy

Integratedmultipledata

sources

Integratingbigdatatechnologywithexistinginfrastructure

Infrastructureand/or

architecture

Leadershipororganizational

issues

Understandingwhatis"Big

Data"

Greaterthan50%

30%to49%

20%to29%Less than19%

Only15%firmsareableto

calculateROIforanyDigitalInitiative

-MckinseyDigital


TailoraValue-basedPathtotheSummitofData-drivenExcellence

DataDrivenFuturisticOrganization

• Stage1:AWAREBigdataisdiscussedbutnotreflected inbusinessstrategiesorprocessesbeyondhistoricalanalysis

•

• Stage2:EXPLOREEmergingconsensusonthepotentialofbigdataandlocalizedexperiments andresults

• Stage3:OPTIMIZINGOperationalperformance isoptimizedinuptothreedimensions:customerlifecycle,productlifecycle,anfacilitylifecycle

• Stage4:TRANSFORMINGDataembracedascurrency,asbusinessvaluestreamsarecreatedthroughpredictiveanalytics

HortonworksBigDataMaturityScorecardhelpsyoustartthatjourney

StagesofH

ortonw

orks

BigDa

taM

aturity

Scorecard


Companiesneedtounderstandwheretheystandintermsofbigdatamaturitysothattheycanprogressandidentifytherequiredinitiatives.

OurBigDataScorecardhelps inassessing your company’s currentstatealongfivekeycapabilitydomains:1) Sponsorship;2) DataandAnalytics;3) Technology andInfrastructure;4) OrganizationandSkills; and5) ProcessManagement.

Within eachofthesecapabilitydomains, weidentify fourkeyfocus areasthatindicatematurity,andthenassess eachareaaccordingtotheirspecific maturitylevel

FivecapabilitydomainsofHortonworksMaturityModel

Although thepurpose of ourframeworkistoevaluateyourcompany’s maturitylevelintheseareas,webelieve it’sfarmoreimportanttounderstandhow tocapitalizeonyourexistingcapabilities, andtoinvestinthose focusareaswherewecanbestmaximizeprogress towarddefinedbusiness objectives.


SponsorshipData&Analytics

PracticesTechnologyandInfrastructure

OrganizationandSkills

ProcessManagement

OverallFindingsforRetailandConsumerGoodsSector

1.51.2

2.01.71.61.71.71.51.5

2.01.71.71.61.5

1.91.81.92.11.6

1.9

2.92.6

3.12.92.92.82.92.82.83.1

2.82.82.92.6

3.03.03.03.13.02.9

CrossFunctio

nalP

ractice

s

InhouseorO

utsourced

OperationsSecurityGov

PlanningandBudgetin

g

Functio

nality

InvestmentFocus

2.9

DataProcessin

g

DataStorage

DataCo

llection

HostingStrategy

Busin

essCase

Advocacy

Funding

DataAnalysis

LeadershipM

odel

Visio

nStrategy

ProgramM

easurement

AnalyticDe

vSkills

Integration

AnalyticTools

1.7

• Overall,firmsarestillintheExplorationstage–– Firmslackenterprisevision aroundBigDatawithlittleexecutivesponsorship

– FirmsareprimarilyusingstructureddataandareoutsourcingBigDataprojects

– Theyarestartingtoadoptanalyticaltoolsforprojectspecific objectives

• Inthenext2-3years,firmsareexpectedtobeinOptimizing phase– Withenterprise-wide vision andalignmentwithsponsorship andfunding

– Firmswillhavedatalakewithunstructureddataandintegrated,analyticaltools ontopofit

– Willleveragemixofin-house andoutsourced resources

Current In2-3YearsBigDataMaturityScores(Average)KeyTakeaways

Inthenextfewslides,weanalyzetheBigDatascorecardresults alongthe5capability domains


CapabilityDomain:Sponsorship

29%

6%

65%

35%35%24%

6%

4321

6%12%

53%

29%41%

24%24%12%

4321

6%

24%

41%

29%

41%

29%24%

6%

4321

12%

47%35%

6%

35%35%24%

6%

321 4

• VisionandStrategy:Currently,mostofthefirmsareinearlystagesofestablishingenterprise-widevisiononBigDataandin2-3yearsmostofthemwillhaveone• Funding:BigdataprojectsareprimarilydrivenbyITprojectsandbudget.ButBigDataprogramswillbepartofcyclicalbudgetingprocessinthenearfuture• Advocacy: Thereissomelevelofexecutivesponsorshipwhichwillonlyincreasewithbetteralignmentamongthe leadership• BusinessCase: Mostofthefirmsalthoughdon’t havebusinesscaserightnow,theyplantohaveoneinthenext2-3years

VisionandStrategy Funding

BusinessCaseAdvocacy

Current In2-3Years%offirmsataMaturityLevel

KeyTakeaways


CapabilityDomain:DataandAnalyticsPractices

29%35%35%

41%

24%29%

6%

4321

6%12%

35%47%

35%29%

35%

0%

4%3%2%1%

6%6%18%

71%

18%35%35%

12%

1 432

6%12%18%

65%

29%35%29%

6%

4321

DataCollection DataStorage

DataAnalysisDataProcessing


• DataCollection:Althoughmostofthefirmsstillusestructureddata,theyexpecttomakebigstridesandwilldeployautomatedmechanismstocollectboth structuredandunstructureddatain2-3years• DataStorage: Moststilldiscardmajorityofthedatabutareplanningtohave“datalake”tokeeptheirdata• DataProcessing: Currently,processingismanualbutfirmsexpecttohaveenterprise-widemetadatastandardsinthenearfuture• DataAnalysis:Mostofthefirmsfocusmainlyonbusinessmetricsreportingthat isgoingchangetomoreadvancedandpredictiveanalyticsinthenext2-3years

KeyTakeaways


CapabilityDomain:TechnologyandInfrastructure

6%

24%

59%

12%

29%29%29%

12%

4321

0%

24%24%

53%

24%

41%29%

6%

4%3%2%1%

6%

24%

35%35% 35%35%29%

0%

1 432

0%6%

41%53%

18%

53%

18%12%

4321

HostingStrategy Functionality

IntegrationTools


• HostingStrategy:Mostofthefirmscurrentlystoredataon-premisebutexpecttodeployhybridhostinginfrastructuregoingforward• Functionality: MajorityofthefirmscurrentlydeployEDWdatawarehousesandareintheprocessofcomplementingitwithHadoop-basedclusters• Tools: Firmsarestartingtoadoptanalyticaltoolsforprojectspecificobjectivesandwillincreasinglyhavecentraladministrationofthesetools• Integration: Currently,thereislittleintegrationbetweenthetoolsbutwiththeHadoopdeploymenttherewillbebetterintegrationandcross-functionalanalysis

KeyTakeaways


CapabilityDomain:OrganizationandSkills

6%12%

29%

53%41%

29%

12%18%

4321

0%12%

29%

59%

18%

47%35%

0%

4%3%2%1%

6%6%

41%47%

24%

41%29%

6%

1 432

6%6%

35%

53%

29%41%

18%12%

4321

AnalyticalandDevelopmentSkills In-houseorOutsourced

Cross-functionalPracticesLeadershipModel


• AnalyticalandDevelopmentSkills:Currently,theBigDataskillsaremostlylocatedwithintheITorganizationbutfirmsareinvestingalottogainadvancedanalyticalskillsacrosstheorganization• In-houseorOutsourced: Formajorityofthefirm,significantworkisbeingoutsourced butfirmswilldeploymixofin-houseandoutsourced skill-setinthenearfuture• Leadership:MajorityfirmsarealsoexpectedtohaveacentralizedanalyticsgrouptohelpdrivetheBigDataprograms• Cross-functionalPractices:Withcentralizedgroup,firmswillhaveincreasingcapabilityforcross-functionalcollaborationandanalysis

KeyTakeaways


CapabilityDomain:ProcessManagement

0%

35%29%

35%41%

35%

18%

6%

4321

6%12%

29%

53%

35%24%

41%

0%

4%3%2%1%

35%

0%0%

24%

76%

18%35%

12%

4321

0%

18%18%

65%

29%35%29%

6%

1 432

PlanningandBudgeting Operations,SecurityandGovernance

InvestmentFocusProgramMeasurement


• PlanningandBudgeting:AlthoughthereislittleformalplanningandbudgetingforBigDataprogramscurrently,thefirmsexpecttohaveonein2-3years• Operations,SecurityandGovernance: Firmsvaryinthisdimension– majoritywillhaveenterprise-widepolicyandprotocolinthenearfuture• ProgramMeasurement:MajorityofthefirmsexpecttomonitortheoutcomesfromBigDataprogramsalongwiththeformalplanning• InvestmentFocus:Althoughinvestmentiscurrentlymadeonadhocbasis,itisexpectedtochangetofindnewsourcesofrevenueandbusinessmodelsmovingforward

KeyTakeaways


SponsorshipData&Analytics

PracticesTechnologyandInfrastructure

OrganizationandSkills

ProcessManagement

CaseStudy:AleadingEuropeanRetailer

1.01.0

3.03.0

2.02.0

3.03.03.03.03.0

2.0

3.0

1.0

3.0

2.0

3.03.03.03.0 3.03.0

4.04.04.04.04.04.04.04.04.04.04.0

3.0

4.04.04.04.04.04.0

2.5

3.9

InvestmentFocus

ProgramM

easurement

OperationsSecurityGov

PlanningandBudgetin

g

CrossFunctio

nalP

ractice

s

Busin

essCase

Advocacy

Funding

Visio

nStrategy

LeadershipM

odel

InhouseorO

utsourced

AnalyticDe

vSkills

Integration

AnalyticTools

Functio

nality

HostingStrategy

DataAnalysis

DataProcessin

g

DataStorage

DataCo

llection

• RetailerisalreadyinOptimizingstageandwillattainthehighestmaturity,Transforming,inBigDatain2-3years– Hasanenterprise-widevisionandstrategy– onwhichrestofthekeyelementsofBigDatadepend

– Hasstartedtoingestunstructureddataandrarelydiscarddata

– HasadoptedHadooptoaccomplishdifferentworkloadswithintegrationofanalyticaltools acrosstheorganization

– IsinvestinginBigDataskillsforadvancedanalyticsandleveragesbothinhouse andoutsourced resources

– HasalreadyincludedBigDataprogramsinitsbudgetingandplanningcycle

Current In2-3YearsBigDataMaturityScores

KeyTakeaways


Contents






Transformation

--- Maturity Stages àOptimizationExplorationAwareness

---

Mat

urit

y St

ages

à

Marketing

Merchandising

IT Ops

Digital

Store Operations

Purchasing & Logistics

2

6

7

4

10

11

13

1a

1b

12

8

3

95

15

14

16

17

PeerCompetitive Scale

Standardamongpeergroup

Commonamongpeergroup

Strategicamongpeergroup

NewInnovations

RetailIndustry– UseCaseMaturityRoadmap

No UseCaseName

1a SingleViewofCustomer1b SingleViewofCustomer2 Basket Analysis3 SocialListening4 EnrichedBasketAnalysis5 ClickstreamAnalysis6 Recommendation Engine7 Price Optimization

8 Beacon/SensorMonitoringandIngest

9 StoreCommunications10 EmailManagement11 EDWEnhancement12 InventoryOptimization13 PathtoPurchase14 SupplyChainTelemetry15 CustomerServiceAnalysis16 PreventativeMaintenance17 MachineLearning/AI

Discussedinsubsequentslides

18

UseCase:SingleViewofCustomer

• Abilitytoidentify#uniquecustomerswhichdirectlyimpactsboththetop-lineROImeasurementandbottom–lineoptimization• Increasedcustomerloyalty,LFLsales,averagebasketsize,redemptionpropensityonpromotionalactivity,listingfees• Dynamicrealtimetargetedpricingwhichresultsinbettermarginsfromyourmostloyalcustomers

BusinessValue

• Bettercustomerexperienceleadingtoincreasedloyaltyandcustomeradvocacy• IncreasedMarketingEffectivenessleadingtohigherROIonevery£spent• Crosssellingandpredictivepromotionalpropensitymeansgreaternumberofmanufacturerpartnerships

WhyDoIt?

• Currently,Retailers,CPGfirmsandothermanufacturerscreateshopperprofilesbasedonhistoricaldata,SKUleveldataandBasketdata– Theyhoweverstruggletomarrythatdatawiththebehavioraldatafrommultipleotherchannels(mobile,SocialMedia,etc.)tomapout theDNAofthecustomerandfailtopredictfuturisticbuyingpatternsofcustomersacrosscategoriesandproducts

• SingleViewofthecustomernotonlyallowsorganizationstheabilitytocreatetargetedcampaignsbasedonshoppingpatternsbutalsoopensupnewavenuesofrevenuestreamsthroughadvancedmarketingeffortssuchascrossdevicemarketing,beaconsensing,proximitymktg.etc.

IdeaSummary

TheSingleViewofCustomercombineshistoricalsalesdatafromstructuredsystemswithnew,unstructuredandsemi-structureddatafromsocialmedia,sentimentanalysis,webactivity,andblogposts. SingleViewofthecustomerhelpscreatetheDNAof theconsumerthatcanbeusedtotarget,re-target,personalizemessagingtohelpaddressissuesaroundloyalty,churn,cross-selling,increasingthetoplineetc.

Inno

vate–Grow

&Ena

ble

19

Contents





Ø QuickFacts

QuickFacts

• Fordirectmarketing,thelackofvisibilityintoacustomer’screditandfinancialsituationrestrictedretailer'sabilitytopre-screen“right”customerstosendthemailers

• MismatchbetweenInventoryMerchandisingAdPlannerandWarehouseInventoryledtoincompletesales

• Generationofvariousbusinessreportstookdaystocompleteandevenafterthat,notalltheinformationwasavailabletotheBusinessstakeholders

SituationAnalysis

InnovationStrategy• RetailerbuiltanEnterpriseAnalyticsplatformbasedonHortonworksDataPlatform,breaking-downsilosandincreasinghistoricaldepthofdataavailableforanalysis

• Drovetargetedmarketingstrategywithinsightdrivencustomersegmentationanalysis,leveragingnewdatasources,includingtheavailablehistory

• Implementednear-realtimesimulationofnewCreditStrategywithrespecttoapprovalordeclineofapplicationprocessbycollectingexhaustivesetofvariablesneededforcreditpolicycodingforallcustomers

BusinessImpact• ReducedSpendonDirectMailersbyoptimizingmailingbyCustomerSegment:$3Minfirst10monthsof2016($4.5to$5.0Mexpectedrunratesavings)

• ReducedadseffectivenessanalysisinProductPerformancereport:300ximprovementinturnaround

• Reducedassociatetimeincodingforred-flagsandlookupsfordeclinerules:500xtimereduction inimplementingcreditpolicy

$3MMarketingdollarssavedto-datefromtrimmingthedirect

mailers

Upto500xTimeimprovementin

implementingcreditpolicy

Upto45xTimeimprovementingeneratingInventory

MerchandisingAdPlanner


CalltoAction

TakeBigDataScorecardSurveyonHortonworkswebsite

CollaboratewithHortonworkstocreatevalue-basedBigDataroadmap

Big Data Maturity as a Business: A Retail Case Study

Technology

Transcript of Big Data Maturity as a Business: A Retail Case Study