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Data and AnalyticsAccelerating Outcomes by being Data Driven1
What Does Centricity Mean?2
Companies become data drivenby being data and analytic centric3
Different Points of View Data / Analytic Centric4 Application / Process Centric– Identify subject area– Define known data– Model data for relationships– Define access and output– Collect and store data– Model for performance– Access and cast for output– Collect data– Optimize performance for often runanalytics based on business value– Transform and Store
Data Centricity Drives AnalyticsWhich portal banner ads aregenerating the most profit?How many products or offersdid I sell on promotion lastweek?Which ads are beingclicked on the most?Which are beingtimed-out and shouldbe cancelled?ConsumerHow much of Product A didI sell online in the past 30minutes?StoreTransaction SalesLaborE-Commerce(Web Logs)CategoriesDaily Sales(Day/ Store Product)Advertising andPromotionProducts and OffersPartners and AffiliatesFrom which categories are mymost profitable customersbuying?5Which offers should Idiscontinue based on thelowest sales in the category?Which offersshould Ipromotebased on lift,sales, andmargin?Which partnersor affiliatesare meetingperformanceobjectives?
Is not about Volume, Velocity and Variety anymore .It is about how you integrate the data and analytics7
Up-front data modelling and data integration aredifficult, time-consuming and expensive; maybe we justshouldn’t bother with them any more?8 2014 Teradata
but there are no free lunches in InformationManagement – merely more and different optionsTightly CoupledLoosely CoupledNon CoupledData migrates based on usageData is leveraged dynamicallyExplicit, or implicit, there is always, always, always (at least one) schema;“pay me now, or pay me later (and over and over)”9
Characteristics of ��sText,Multi-media10’sHighLowLittle to NoneHigh, maWell DefinedKnownChangingModeledOn WriteAs neededOn ReadUsageReports, dualValue10ERP, SCM, POS
INSURANCE11
CLAIMSPOLICYAgreementFeaturesPolicy PremiumCoverage MetricsAgreement GUpsell/Cross SellMessagingCampaign History12CUSTOMERNameAddressAgeMiles / yrTicketsCarQuotationParty and AgentApplication DetailRiskUNDERWRITING
CLAIMSTELEMATICSAUDIO AND VIDEO REPORTSMILES DRIVENELECTRONIC COMMERCELOCATIONSTIME LEROMETERAgreementFeaturesPolicy PremiumCoverage MetricsAgreement Status2855Upsell/Cross SellMessagingCampaign HistoryEXTERNAL INTERACTIONSNameAddressAgeMiles / yrTicketsCarQuotationParty and AgentApplication DetailRiskDIGITAL ADVERTISINGIVR RoutingSOCIAL MEDIACUSTOMER CARE –AUDIO RECORDINGSCUSTOMER PORTALINTERACTIONSCLICKSTREAM INTERACTIONSUNDERWRITINGRATINGS & REVIEWSMARKETING13CUSTOMER
CLAIMSTELEMATICSTIME VERAGEPOLICIESCUSTOMERRISK PROFILEUNDERWRITINGMARKETING14
What is thechange in riskprofiles by agegroup over thepast 6 months?What is thetypical path topurchase for apolicy withincreaseddeductions?What can textbased serviceforms tell usaboutpotentiallylarger safetyissues?How manycustomers thatcalledCustomerServiceexpressed afrustrated toneof voice?SQL ANALYTICSPATH / TIME SERIESANALYTICSTEXTANALYTICSRICH MEDIAANALYTICS15Whichcustomers arehighly influentialon social mediaand regularlypost about ourclaims service?GRAPHANALYTICS
What you do with data is your businessHow you get to data is our business16
Meeting the Challenge Extend Teradata Capabilities– JSON, Data Labs, Virtual Storage, Platform Family Acquire Aster– SQL-MR– Built in Analytic function with SQL access Embrace Hadoop– Understand value and cooperate– When to use which, not which to use Create Unified Data Architecture– Ingest, Management, and Access– Operations and “Cloud-First” philosophy18
SourcesAcquisitionData EnginesAnalyticsAccessUsersREAL TIMEAudioand VideoNo SQLDATA rsTextVIRTUALQUERYOPERATIONALINGESTEMERGINGWeb andSocialOperationalSystemsCustomersPartnersIN ntDataScientistsSCMERP19 2016 TeradataPlatform ServicesDATADEVELOPMENTOPERATIONSCloud DeploymentPUBLICPRIVATEHYBRIDEngineers
SourcesAcquisitionData EnginesAnalyticsAccessUsersREAL TIMEAudioand VideoNo SQLDATA rsTextWeb opTeradataDatabaseQueryGridCustomersPartnersIN MEMORYR, kersCONVENTIONALLanguagesSAS, ronmentDataScientistsSCMERP20 2016 TeradataPlatform ServicesDATADEVELOPMENTOPERATIONSCloud DeploymentPUBLICPRIVATEHYBRIDEngineers
SourcesAcquisitionData EnginesAnalyticsAccessUsersREAL TIMEAudioand VideoNo SQLDATA rsTextWeb nterEMERGINGCustomersPartnersIN MEMORYR, MPUTECLUSTERCRMTeradataDatabaseLanguagesSAS, ronmentDataScientistsSCMERP21 2016 TeradataPlatform ServicesDATADEVELOPMENTOPERATIONSCloud DeploymentPUBLICPRIVATEHYBRIDEngineers
SourcesAcquisitionData EnginesAnalyticsAccessUsersREAL TIMEAudioand VideoNo SQLDATA rsTextWeb nterEMERGINGCustomersPartnersIN MEMORYR, MPUTECLUSTERCRMTeradataDatabaseLanguagesSAS, ronmentDataScientistsSCMERP22 2016 TeradataPlatform ServicesDATADEVELOPMENTOPERATIONSCloud DeploymentPUBLICPRIVATEHYBRIDEngineers
SourcesAcquisitionData EnginesAnalyticsAccessUsersREAL TIMEAudioand VideoNo SQLDATA rsTextWeb nterEMERGINGCustomersPartnersIN MEMORYR, MPUTECLUSTERCRMTeradataDatabaseLanguagesSAS, ronmentDataScientistsSCMERP23 2016 TeradataPlatform ServicesDATADEVELOPMENTOPERATIONSCloud DeploymentPUBLICPRIVATEHYBRIDEngineers
SourcesAcquisitionData EnginesAnalyticsAccessUsersREAL TIMEAudioand VideoNo SQLDATA rsTextWeb nterEMERGINGCustomersPartnersIN MEMORYR, MPUTECLUSTERCRMTeradataDatabaseLanguagesSAS, ronmentDataScientistsSCMERP24 2016 TeradataPlatform ServicesDATADEVELOPMENTOPERATIONSCloud DeploymentPUBLICPRIVATEHYBRIDEngineers
SourcesAcquisitionData EnginesAnalyticsAccessUsersREAL TIMEAudioand VideoNo SQLDATA rsTextWeb nterEMERGINGCustomersPartnersIN MEMORYR, MPUTECLUSTERCRMTeradataDatabaseLanguagesSAS, ronmentDataScientistsSCMERP25 2016 TeradataPlatform ServicesDATADEVELOPMENTOPERATIONSCloud DeploymentPUBLICPRIVATEHYBRIDEngineers
SourcesAcquisitionData EnginesAnalyticsAccessUsersREAL TIMEAudioand VideoNo SQLDATA rsTextWeb opTeradataDatabaseQueryGridCustomersPartnersIN MEMORYR, kersCONVENTIONALLanguagesSAS, ronmentDataScientistsSCMERP26 2016 TeradataPlatform ServicesDATADEVELOPMENTOPERATIONSCloud DeploymentPUBLICPRIVATEHYBRIDEngineers
Scalability, Agility, Efficiency - It’s in our D & A!27
ANALYTICS TEXT ANALYTICS RICH MEDIA ANALYTICS GRAPH ANALYTICS SQL ANALYTICS What is the change in risk profiles by age group over the past 6 months? . Analytics R, Spark, Giraph SAS, SPSS, KXEN DATA WAREHOUSE Teradata Database IN MEMORY Hadoop Teradata Database DATA LAKE No SQL COMPUTE CLUSTER OPERATIONALVIRTUAL INGEST Listener
