Post on 14-Apr-2017
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UnderstandingBigDataMaturity-RetailandConsumerGoodsApril2017
2 ©HortonworksInc.2011– 2016.AllRightsReserved
Contents
à BigDatainRetail– DigitalRevolution– ExplosionofData
à BigDataMaturityAnalysis– HortonworksBigDataMaturityScorecard– RetailandCPGMaturityAnalysis
à BigDataUseCases– RetailUseCaseMaturityMap– SingleViewofCustomer
à 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%
5 ©HortonworksInc.2011– 2016.AllRightsReserved
Contents
à BigDatainRetail– DigitalRevolution– ExplosionofData
à BigDataMaturityAnalysis– HortonworksBigDataMaturityScorecard– RetailandCPGMaturityAnalysis
à BigDataUseCases– RetailUseCaseMaturityMap– SingleViewofCustomer
à BigDatainAction– RetailCaseStudy– CalltoAction
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
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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
8 ©HortonworksInc.2011– 2016.AllRightsReserved
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.
9 ©HortonworksInc.2011– 2016.AllRightsReserved
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
10 ©HortonworksInc.2011– 2016.AllRightsReserved
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
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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
Current In2-3Years%offirmsataMaturityLevel
• DataCollection:Althoughmostofthefirmsstillusestructureddata,theyexpecttomakebigstridesandwilldeployautomatedmechanismstocollectboth structuredandunstructureddatain2-3years• DataStorage: Moststilldiscardmajorityofthedatabutareplanningtohave“datalake”tokeeptheirdata• DataProcessing: Currently,processingismanualbutfirmsexpecttohaveenterprise-widemetadatastandardsinthenearfuture• DataAnalysis:Mostofthefirmsfocusmainlyonbusinessmetricsreportingthat isgoingchangetomoreadvancedandpredictiveanalyticsinthenext2-3years
KeyTakeaways
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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
Current In2-3Years%offirmsataMaturityLevel
• HostingStrategy:Mostofthefirmscurrentlystoredataon-premisebutexpecttodeployhybridhostinginfrastructuregoingforward• Functionality: MajorityofthefirmscurrentlydeployEDWdatawarehousesandareintheprocessofcomplementingitwithHadoop-basedclusters• Tools: Firmsarestartingtoadoptanalyticaltoolsforprojectspecificobjectivesandwillincreasinglyhavecentraladministrationofthesetools• Integration: Currently,thereislittleintegrationbetweenthetoolsbutwiththeHadoopdeploymenttherewillbebetterintegrationandcross-functionalanalysis
KeyTakeaways
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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
Current In2-3Years%offirmsataMaturityLevel
• AnalyticalandDevelopmentSkills:Currently,theBigDataskillsaremostlylocatedwithintheITorganizationbutfirmsareinvestingalottogainadvancedanalyticalskillsacrosstheorganization• In-houseorOutsourced: Formajorityofthefirm,significantworkisbeingoutsourced butfirmswilldeploymixofin-houseandoutsourced skill-setinthenearfuture• Leadership:MajorityfirmsarealsoexpectedtohaveacentralizedanalyticsgrouptohelpdrivetheBigDataprograms• Cross-functionalPractices:Withcentralizedgroup,firmswillhaveincreasingcapabilityforcross-functionalcollaborationandanalysis
KeyTakeaways
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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
Current In2-3Years%offirmsataMaturityLevel
• PlanningandBudgeting:AlthoughthereislittleformalplanningandbudgetingforBigDataprogramscurrently,thefirmsexpecttohaveonein2-3years• Operations,SecurityandGovernance: Firmsvaryinthisdimension– majoritywillhaveenterprise-widepolicyandprotocolinthenearfuture• ProgramMeasurement:MajorityofthefirmsexpecttomonitortheoutcomesfromBigDataprogramsalongwiththeformalplanning• InvestmentFocus:Althoughinvestmentiscurrentlymadeonadhocbasis,itisexpectedtochangetofindnewsourcesofrevenueandbusinessmodelsmovingforward
KeyTakeaways
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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
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Contents
à BigDatainRetail– DigitalRevolution– ExplosionofData
à BigDataMaturityAnalysis– HortonworksBigDataMaturityScorecard– RetailandCPGMaturityAnalysis
à BigDataUseCases– RetailUseCaseMaturityMap– SingleViewofCustomer
à BigDatainAction– RetailCaseStudy– CalltoAction
17 ©HortonworksInc.2011– 2016.AllRightsReserved
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
à BigDatainRetail– DigitalRevolution– ExplosionofData
à BigDataMaturityAnalysis– HortonworksBigDataMaturityScorecard– RetailandCPGMaturityAnalysis
à BigDataUseCases– RetailUseCaseMaturityMap– SingleViewofCustomer
à BigDatainAction– RetailCaseStudy– CalltoAction
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
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CalltoAction
TakeBigDataScorecardSurveyonHortonworkswebsite
CollaboratewithHortonworkstocreatevalue-basedBigDataroadmap