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BIG DATA: TERADATA UNIFIED DATA ARCHITECTURE™ IN ACTION

BIG DATA: TERADATA UNIFIED DATA

ARCHITECTURE™ IN ACTION

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BIG DATA: TERADATA UNIFIED DATA ARCHITECTURE™ IN ACTION

TABLE OF CONTENTS

2 What’sTeradata’sPerspectiveonBigDataanditsValue?

6 WhatistheTeradataUnifiedDataArchitecture™anditsValue?

11 HowdoesTeradataUnifiedDataArchitecture™Work?

15 HowcanMyCompanygetStarted?

16 Summary

WHAT’S TERADATA’S PERSPECTIVE ON BIG DATA AND ITS VALUE?

Teradatabelievesbigdataisreal,hasvalue,andwillchangethespeedandqualityofbusinessinsights.Wealsobelieveitisanevolution,notarevolution,andthatincorporatingbigdataintoyourexistingITframeworkisfairlystraightforward,buttherearepitfallswecanhelpyoueasilyavoid.

Is Big Data Real?

Yes.FromaTeradataperspective,bigdataisaphenomenonweaddressedwithourcustomersaspartofourongoingproductinnovationprocessoverthepasttwoyears.FromaStrengths,Weaknesses,Opportunities,Threats(SWOT)perspective,wewereabletoturnbigdatafromathreatintoanopportunity.TheopportunityisforourcustomerstobeattheircompetitorsbyleveragingTeradataexpertisetostayaheadoftechnologytrends.Itmeantmakingsomeproductacquisitions(suchasAsterData),creatingnewpartnerships(Hortonworks,forinstance)anddoingaconsiderableamountofproductandservicesintegrationwork.Thebigdatatrendfitswiththemulti-yearanalyticaleco-systemevolutionofour“centralizeddatawarehouse”approachtodatawarehousing.Werecognizethatthereareavarietyofcost-effectivebest-of-breeddataandcomputationplatforms,andthatourprimaryvaluetoourcustomersistoprovideflexible,well-integratedoptionsforcaptur-ingdata,generatinginsight,andtakingaction.

Does Big Data have Value?

Yes,butsofarit’smostly“atthemargin,”meaningtheanalysisofnewdatasourcesandtypescanimprovewhatyouarealreadydoing.BasedonTeradatacustomeruses,weseethreebroadcategories—customerexpe-rience/service,operationalefficiency,andcorporatemarketing1—thatcanbemade5to20percentbetterbyexploitingbigdatatogleandeeperinsights.Gartner2surveyed720ITprofessionalsacrossindustriesinearly2013andaskedwhattopthreebusinessareastheirbigdataprojectstargeted.TheirresultsareshowninFigure1.

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Is Big Data an Evolution or a Revolution?

Whilesomevendorsgooutoftheirwaytoover-hypethistechnologyasrevolutionary,webelievebigdataisanaturalevolutionofseveraltrendsalreadyhappeningwithtraditionaldata.Specifically,threetrendswithbigdatastaythesame:

~ Same #1. The goal of using data and analytics as a competitive weapon.

IthasbeenprovenbyTomDavenportandMIT3,4thatcompanieswithadata-decisioncultureare5to6per-centmoreproductive,withassociatedimprovementsinassetutilization,ROE,andmarketvalue.Butismoredatathegoal?No.Whilesizeisn’treallyaconcernsinceTeradatawasarchitectedformassiveamountsofdataandparallelcomputation,werecognizedyearsagothatalldataisnotcreatedequal.Theplacementofdataonthemostcost-effectivelayersofastoragehier-archyiscriticalbecauseitcontributestohealthyROI.*

~ Same #2. The need for people using analytical and visualization tools to glean insights from data and accelerate the rate of generating insights.

Youwillalwaysneedskilledusersofthetools,whetherwecallthemdatascientistsorbusinessanalysts.Bottomline:thedatahaszerovalueunlessyouhavethepeople,processes,andtoolstotransformitandaccessitintimelyandrelevantways.Thegoodnewsisthatthetoolsaregettingeasiertouse,andthenumberofpre-builtanalyticmodulesthatplug-and-playtocreatenewinsightsisgrowingrapidly.Butthathasbeenthetrendforyears.†

~ Same #3. The substantial effort to drive insights into production.

Whileinsightsaregreat,theyarenotenough.Youcan’thaveascienceexperimentinthebasement,inisolationfromtherestofthecompany.This“operationaliza-tionproblem”istheAchillesheelforbigdataprojects

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Enhanced security capabilities

Regulatory compliance

Monetize information directly

Improved risk management

Cost reduction

More targeted marketing

New products/new business models

Process efficiency

Enhanced customer experience

Percentage of Respondents

Top priority Second ThirdN = 687

Source: “Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype”, by Lisa Kart, Nick Heudecker, Frank Buytendijk. Gartner, September 12, 2013

Figure 1. Top Priorities for Big Data.

For technical readers, we’ll add occasional technical footnotes that can be skipped by business readers.* Teradata’snoveltechnicalapproachiscalled“TeradataVirtualStorage”andautomatestheintelligentstorageofdatabasedonusage.Ourfastinterconnect

(highthroughputTeradataBYNET™V5onInfiniBand)ensuresthatcombiningandmovingdataacrosssystemsisalsocost-effectiveinthearchitectureswebuild.† Teradatahasbuiltourlibrariesoffunctionsintwoways.TheAsterDataacquisitionbroughtus80newanalyticandvisualizationfunctionsspecificallyforbigdata.

WehaveinvestedinpartnershipswithBIvendorslikeSAS,IBMSPSS,RevolutionAnalytics,ZementisandInformationBuilderstoensurewehavethebest-of-breedanalyticsontopofthedataplatforms.OurrecentpartnershipwithFuzzyLogixaddedanother600functions.Finally,wehavealsopartneredwithvisualizationvendorslikeTableauandTibcoSpotfire.

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becausemostpeoplegrosslyunderestimatethecostsoftakinginsightsintoproduction,whichcanbeordersofmagnitudelargerthaninitialbigdataprojectinsightgenerationcosts.*

Bottom line: thereisacertainconstancytotheproblemsbeingaddressedwithbigdata.Theage-oldquestionsofhowtoincreaseprofits,decreasecosts,improveman-ufacturingefficiency,andcurtailfraudhavebeenaroundforalongtimeandarestillbeingasked.

Sowhat’snew?Wecanaddressthesequestionsinnewer,fasterwaysthatwouldpreviouslyhavebeentooexpensiveortaketoomucheffort.Sothatleadstothefourthingswithbigdatathataredifferent:

~ Different #1. The amount of data you can economically collect is going up rapidly.

Oftencalled“dataexhaust,”thingsleftinthedatacentercorner—likedigitizedphonecallstocustomercare,orWebserverdetailedlogs,orevenexternaldataliketweets—cannowbecapturedandanalyzedtocreatenewvalue-addedinsights.Withever-decreasingstorageprices,thischangestheeconomicsbutrequiresmoresophisticationabouthowmuchdatatocapture,wheretotransformandstoreit,howlongtoretainthedata,andmore.

~ Different #2. New analytics on these new, non-relational data types (coupled with existing relational data) open up exciting new insight possibilities.

Example:youcancreatenewcustomerattritionfactorsforpeoplewhoareangryandshouting,orusingcodewordslike“ripoff”whenspeakingtoCustomerServicereps.Further,youcancombineanalyticsonvariousdatatypes.Forexample,arethoseangrycustomersalllocatedinonegeography?Subjecttonewcontracttermsandconditions?Experiencingrecentcustomerserviceissues?Discoveringrootcasesmayrequirecombinatorialanalyticsonvoice,text,geospatial,temporal,andtraditionalcustomerdata—aswellastheabilitytodothisquicklyandseamlessly.

Otherexamples:Teradatacustomersareexploitinghaildamageandweatherreportstofindagriculturefraud;grabbingTwitterfeedstodiscoverunhappycustomersinrealtimeinordertoreactquickly;andmonitoringemailinvestmentpromisesbybrokerstoclients.

~ Different #3. The process for generating insights is changing.

The“oldway”ofdoinganalyticsassumedthatnewdatawouldbestoredinthedatawarehouse.Hencemanyprocessesthatprecedethediscoverysteprequiredrigorindatamodeling,dataintegration,andETL.The“newway”separatesdiscoveryfromintegrationanduse,freeingupsometimeuntilafterinsightshavebeengeneratedandexplored.Inshort,itfreesupmoretimeforanalysisandexploration.

Example:afraudteamthinksthatperhapsafraudsterwhohasstolensomeone’sidentityspendsadifferentamountoftime—andclicksindifferentways—ontheirbankingwebsitefromthetrueownerofanaccount.Capturingwebclicksandtimedurations,andthesequenceofclicks,theycreatea“normalpattern,”thenwatchfordeviationstothatpatterninend-endexperiments.Theygraballthewebclickdatatheyrequireforexperimentationbutdonotneedtomodelthedatarigorouslyordesignproductionfraudpatternsuntiltheyarecertaintheyhavefoundaninterestingandworthwhilecaseoffraud.

~ Different #4. One kind of processing engine (e.g., just an EDW) is not going to be enough—the architecture must evolve to accommodate specialized platforms, integrated into a comprehensive system.

Justlikeinyourgarage,youwillneedaworkbenchthatincludesavarietyoftools.Inthedataanalyticsworldthismeansworkload-specificengines/platformsforstorageandanalysis,newtype-specificanalyticfunctionsandengines,andintegrationtechnologiestocopyandmovedataorinsightsintoaneasy-to-useenvironmentforyourbusinessandtechanalysts.Gartnercallsthisthe“LogicalDataWarehouse,”andwecallthistheTeradataUnifiedDataArchitecture.™TeradatacustomerslikeIntel®agreeandhaveheadedinthisdirection.5

* It’sanareawhereTeradatahastraditionallyexcelled:rock-solidarchitectureandengineering,allthetechnologiesandservicesthatyouneed(withstraightfor-wardextensionsforbigdataprojects),includingthingslikedatasynchronization,datagovernance,workloadmanagement,backupandrecovery,systemman-agement,metadatamanagement,configurationplanning—many“real-world”requirementsthatareNOTeasytoputinplaceonyourown.Yes,anarchitectureforbigdatalookscomplicatedbutthevaluefromTeradataistheintegrationofour(andpartnertechnology)componentstoreducethetimetooperationalizeinsights.Andit’sengineeredwithflexibilityinmindsonewcomponents,newpartners,canbeincludedwhilemaintainingcontrolofITandensuringROI.

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Bottom line:theeconomicsofhowtocapturedataandwheretostoreit,wheretocomputeonit,andhowtouseit,areconstantlychanging.ThediscoveryprocessisacceleratingwiththehelpofnewtoolkitstogenerateinsightswithouttheusualrigorrequiredbyanEDW.

Andasaconsequence,Teradatahasextendeditsarchi-tecturetoaccommodatetheneedsofbigdata.

Who is adopting big data, and am I late to the party?

ThesameGartnersurvey2alsotriedtogaugehowfaralongrespondents’companieswereinplanningandexecutingbigdataprojects.Weretheygatheringknowl-edgeaboutbigdata?Developingstrategy?Doingpilots?Alreadyinproduction?Figure2showstheresultsforboththosecompanieswhohavealreadyinvested,andthoseplanningtoinvest.

AquickestimateofTeradatacustomersisthatabout15percenthaveabigdataprojectunderway(mostlyusingTeradataAster®and/orHadoopwithTeradataDatabase),andanother20to30percentareconsideringprojects.Earlyadopterswhohavefinishedprojects,believetheyhaveachievedaone-totwo-yearcompetitiveadvantage.Someofthesehavefiveormorebigdataprojectsundertheirbelts.Mostleadingcompaniesareoperationalizingtheirfirstprojectsnow,whilethelaggardsarestillinlearn-ingmode.However,it’snevertoolatetocatchup.

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Planning to invest (n = 247)

Percentage of Respondents

Source: Gartner (September 2013)Source: “Survey Analysis: Big Data Adoption in 2013 Shows Substance Behind the Hype”, by Lisa Kart, Nick Heudecker, Frank Buytendijk. Gartner, September 12, 2013

Figure 2. Big Data Adoption by Investment Stage.

TIP:ASKPROPONENTSOFBIGDATAPROJECTSTHREEQUESTIONS:1. Whatbusinessareaswillimprovebecauseofthe

collection,analysis,anduseofbigdata?

2. What’stheincrementalReturnonInvestment(ROI)fromexploitingmoredata?Howdoyouplantocalculatethis?

3. Willourenvironmentsupportthegenerationofmoreusefulinsightsandactionsfasterthanourcompetitors?

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WHAT IS TERADATA UNIFIED DATA ARCHITECTURE™ AND ITS VALUE?

Justlikehybridcarsgetsuperiorgasmileagefromcouplingtraditionalgasengineswithelectricbatteries,theTeradataUnifiedDataArchitecture™integratesthreetypesofenginestoprovidethebestfit-for-purposeanalyticalcapabilitiesforanykindofdata.

Teradatahasbeeninthebigdatamarketlongerthananyone,sowe’veleveragedourexpertisetotacklethe“what’snew”partofthebigdataphenomenonwithfiveparallelengineeringactivities.Beginningwithourcoreproduct,theTeradataIntegratedDataWarehouse,we:

1. Defined a new architecturecalledtheTeradataUnifiedDataArchitecture™thataddsadiscoveryplatformandadataplatformtocomplementtheTeradataIntegratedDataWarehouse.IntheTeradataadvocatedsolution,thediscoveryplatformisTeradataAster,whilethedataplatformcaneitherbeHadooporaTeradataIntegratedBigDataPlatformforlarge,cost-effectivestorageandprocessingofbigdata.

2. Engineered new access connectorsbetweentheplatformsandtoexternalsourcesofbigdata,andextendedourhardwareplatformportfolioandinter-connectoptionstoprovideevenfasterdatatransfers.

3. Created a library of pre-built Teradata Aster analytic modules to speed up and simplify discovery,allinaneasy-to-useTeradataAsterSQL-MapReduce®programmingparadigm.WealsocontinuetoaddmoretechnicalpartneranalyticstotheTeradataAsterandTeradataengines.

4.Extended our traditional systems management products(Viewpoint,Studio™)forsimilarusewithHadoopandTeradataAster.

5. Developed new Professional and Customer Service offerstohelpcustomersquicklydesignanddeployenterprisedataarchitectureandanalyticsprojects.

Teradata Unified Data Architecture™

We’lldividetheexplanationoftheengineeringworkintotwoparts:logicalandphysicalarchitectures.

ThelogicalviewofTeradataUnifiedDataArchitecture™inFigure3showstheflowfromlefttorightofdatato

Marketing Executives

Operational Systems

Frontline Workers

Customers Partners

Engineers

Data Scientists

Business Analysts

htaMand Stats

Data Mining

Business Intelligence

Applications

Languages

Marketing

USERS

ERP

SCM

CRM

Images

Audio and Video

enihcaMLogs

Text

Web and laicoS

SOURCES

DATA INSIGHTS ACTION

FastLoading

Filtering andProcessing

OnlineArchival

DataDiscovery

PatternDetection:

Path, Graph,Time-series

Analysis

New Modelsand

Model Factors

ReportsDashboards

Real-timeRecommendations

OperationalInsights

Rules Engines

ACCESSMANAGE

GOVERNANCE AND INTEGRATION TOOLS

MOVE

ANALYTICTOOLS

Figure 3. Teradata Unified Data Architecture™—logical view.

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insightstoactionthatarerequiredtoprocessbigdata.Datasourcesappearontheleft(bothtraditionalandnewsources),andconsumersofdataandinsightsappearwiththeirend-usertoolsontheright.

Thispictureismeanttobevendor-agnosticbecauseTeradataUnifiedDataArchitecture™isatoolkit.Thelogicalflowalsoshowsablockforthegovernanceandintegrationfunctionsneededforoperationalization,includingpoliciesformoving,managing,andaccessingdataandinsights.

Figure4showstheadvocatedphysicalimplementationofTeradataUnifiedDataArchitecture™withthreeenginecategories—dataplatform,integrateddatawarehouseanddiscoveryplatform.WhileanyoftheseplatformscouldbeusedforloadingandstoringrawdatafromawidevarietyofsourcesusingmanydifferentETLtools,

neweconomicsdrivetowardtheuseofHadoopforland-ing,transforming,andstoringbigdata(asopposedtotraditionaldata,whichcontinuestoflowintoTeradata).6Somepeoplecallthisa“datalake”.AnotheroptionifyouwantthepowerandsimplicityofTeradataistousethenewhigh-capacityTeradataIntegratedBigDataPlatform(IBDP).

TheTeradataUnifiedDataArchitecture™alsoprovidesthreeoptionsforwheretododiscovery.Again,doingtheanalysisdependsontheeconomicsofcomputation,staffskillsets,andbigdatadiscoveryandvisualizationtools,aswellasaccesstoinformationthatmayhavebeenstoredinothersystems(orinanexternalsource).NeweconomicsanddiscoveryprocesschangesdriveustoadvocatetheTeradataAsterDiscoveryPlatformasthebigdatadiscov-eryplatform.

TERADATADATABASE

MarketingExecutives

OperationalSystems

CustomersPartners

Engineers

DataScientists

BusinessAnalysts

Mathand Stats

Data Mining

Business Intelligence

Applications

Languages

Marketing

USERS

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INTEGRATED DATA WAREHOUSE

ERP

SCM

CRM

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Audio and Visual

MachineLogs

Text

Web and Social

SOURCES

DATA PLATFORM

TERADATA DATABASE

TERADATA ASTER DATABASE

HORTONWORKSHADOOP

TERADATA UNIFIED DATA ARCHITECTURESystem Conceptual View

ACCESSMOVE MANAGE

ANALYTIC TOOLS & APPS

FrontlineWorkers

Figure 4. Teradata Unified Data Architecture™—physical view.

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Onceinsightsarecreated,theyneedtobeusedtohavevalue.Again,therearenumerouschoicesofhowtocon-nectthedataandinsightgenerationenginesshownheretoaction-takingsystems,likeaCRMsystem,acontactcenter,awebsiterenderingengineinreal-time,depen-dentdatamartsdrivingotherapps,orevenanexecutivedashboardingsystem.TheadvocatedsolutionhereistheTeradataIntegratedDataWarehouse,wellknownforitsabilitytoscaletothousandsofBIanalysts(andmil-lionsofconsumers),runningavarietyofapplicationsaswellasBItoolsfortheendconsumersofbigdata(andtraditionaldata).Withthisunderstandingoftheadvo-catedpiecesofTeradataUnifiedDataArchitecture™let’sdrillaleveldeeperintothecomponents.

Teradata Aster

In2011,TeradataacquiredAsterDatawithitspatentedSQL-MapReduce®-baseddiscoveryplatform,useful

todatascientistsandbusinessanalystswhowanttogetfasterinsightsoncombinationsoftraditional(i.e.,relational)andnon-traditionaldata(essentiallyanytypeofdata),alsoknownas“multi-structureddata.”AtypicaluseoftheTeradataAsterDiscoveryPlatformistopullinformationfromavarietyofsources(e.g.,rawweblogsfromwebpagerenderingsystems,machinedatafromfactoryfloorsystems,andtraditionaldatafromTeradata)todoinvestigativeanalytics,tofindnewpatternsofcustomerbehavior,ornewaberra-tionsinmanufacturingdatathatmightindicatequalitycontrolissues.

TeradataAsterisbasedonapatentedSQL-MapReduce®implementationwhichmakesiteasyforBIanalystswhoknowSQLtowriteextensionsthatusefourclassesofbuilt-inanalyticalfunctions(seeFigure5)toveryquicklycapture,prepare,analyze,andvisualizebigdatasets.

TeradataAccess

HadoopAccess

RDBMSAccess

DataAdaptors

DataTransformers

Statistical

PatternMatching

Time Series

GraphAlgorithms

Geospatial

FlowVisualizer

HierarchyVisualizer

AffinityVisualizer

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BITOOLS

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Module

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ModuleAnalyticsModule

VisualizationModule

Figure 5. Teradata Aster analytics and access modules.

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Integrated Data Warehouse Data Platform Discovery

Series 6000 2000 6000 1000 HADOOP ASTER

Purpose

Stage1-5Balanced

ActiveDataWarehouse

Stage1-3+ScalableData

Warehouse

CostEffective,SingleNode

Teradata

OnePlatform,ManyUses

ApplianceforHadoop

AsterBigAnalyticsAppliance

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StrategicandOperationalIntelligence,

HighConcurrencyActive,10+

ApplicationsRealTime

Update

StrategicIntelligence,

DSS,FastScan,Low

ConcurrencyActive,<10

Applications

Test/Development,

SmallDataWarehouse

ArchiveReporting,

DeepAnalytics,

DataRefinery,DataLabs

Storing,CapturingandRefiningData,Hortonworks

HDP1.1

BigDataAnalyticswith

EmbeddedSQL

MapReduceforNewData

TypesandSources

Software Teradata Database Horton Works Aster Database

Figure 6. Platform Offers: Four Teradata engine options, plus Hadoop and Teradata Aster.

Hadoop

In2013,TeradataannouncedtheTeradataPortfolioforHadoop,providinganotherdatacapture/storage/refiningplatform(leveragingourpartnersHortonworksandCloudera).Theinitialfocusisonwide,cheapcaptureof“dataexhaust”forpossiblestand-aloneuse,butmorelikelyasasourceorETLsystemtotheTeradataAsterDiscoveryPlatformorTeradataIntegratedDataWarehouse,augmentingthevolumeandtypesofdatathatcanbeincludedinanalysis.

Hardware Platforms

Fromahardwareperspective,addingTeradataAsterandHadoopaugmentedthenumberofoptionsfromwhichtochoose,asshowninFigure6.Customerscanevenbuyworkload-specificappliancesthatareconfiguredwithcombinationsofTeradataAsterandHadoopnodes.

Teradataalsoaddedafabric-basedhighthroughputcomputinginterconnectcalledTeradataBYNET™V5onInfiniBand,forveryhigh-speeddatamovementandfaulttoleranceamongthevariousplatforms.Thisenablesfastinterchangeofdatatothemostoptimizedplatformforspecificstorage,processing,oranalysisrequirements.

Data Connectors

ConnectingthesethreesystemsrequiredthatTeradatabuildbulkdataconnectorsaswellasspecializedconnec-torsforon-the-flydataaccess,e.g.,TeradataSQL-H™andTeradataAsterSQL-H™foraccessingdatainHadoop,andanAster-TeradataConnector.

Pre-built Analytic Models

Ourgoalistomakeinsightgenerationfast,despitetheincreasingamountsandtypesofdata.Todothat,we

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SAMPLE TERADATA ASTER SQL-MAPREDUCE® FUNCTIONS BY CATEGORY

A N A L Y T I C S M O D U L E

DATA ACQUISITION ANDPREPARATION MODULE

VISUALIZATION MODULE

PARTNERS MODULE

Sessionization, IPGEO Mapping,XML Relation, XML and ISON Parsers

PATH AND PATTERNTeradata Aster nPath™, Path Generator,

Path Summarizer, Attribution

STATISTICALHistogram, Linear Regression, Sampling,

F-measure, Naive Bayes Text Predict, GLM

TIME SERIESDiscrete Wavelet Transformation,

Symbolic Aggregation Approximation (SAX)

RELATIONSHIP ANALYSISSingle Source Shortest Path,Collaborative Filtering, nTree

TEXTText Classifier, Text Parser, Chinese Text

Segmentation, Text Tagging, Sentiment Extraction

Teradata Aster nPath™ Visualizer, cFilter Visualizer

Attensity Sentiment Categorization, Attensity Triples Finder, Zementis PMML, SAS and R In-Database Scoring

Figure 7. Teradata Aster pre-built analytics functions.

haverapidlygrownthenumberofTeradataAsterpre-builtfunctionstomorethan80(seeFigure7;formoredetailsoneachfunction,seetheTeradataAsterDiscoveryPortfoliowhitepapers7listedintheSuggestedRead-ingsection).Thesecanbeusedinisolation,orinvariouscombinations,toenable“multi-genreanalytics”andlookatdatathroughdifferentperspectivestoimproveaccu-racyofmodelsandalgorithmsforchurn,fraud,customersegmentation,andmore.

Inaddition,wehaveworkedwithpartnerslikeSAS®,IBMSPSS®, RevolutionAnalytics,Zementis,and FuzzyLogix™,8 leadingtextvendorslikeAttensity,andgeo-spatialvendorslikeESRI,toincreasethenumberofTeradata-basedanalyticmodulesthatoftenusein-memory(orin-database)capabilitiestospeedinsightdevelopmentonTeradata.ThetraditionalBIapproachofusingaTeradataDataLabonstructureddatahasbeenextendedtoworkinconjunctionwiththeTeradataAsterDiscoveryPlatformtoprovidearichcollectionofoppor-tunitiesforinsightgenerationandintegration.

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Management

Asyouaddmorecomponentstotheenterprisedataarchitecture—andinparticular,asyoumovefromdiscov-erytooperationalization—adangeristhatmanymorepeoplemightberequiredtomonitorandmanagethesystem.InourtraditionalTeradatasystems,wedrivelowertotalcostofownership(TCO)byautomating,andinmanycaseseliminating,manysystemsmanage-mentfunctions,buildinginself-monitoringandtuningcapabilities,andmakingDBAfunctionseasierthanthecompetition’s.WeholdtruetothatcorporategoalwiththeTeradataUnifiedDataArchitecture,™bringingmanyoftherichTeradatamanagementtoolsandworld-classsupportinfrastructuretotheTeradataAsterDiscoveryPlatformandtheTeradataPortfolioforHadoop.

KeytechnologiesincludeTeradataViewpoint,TeradataVitalInfrastructure,andTeradataStudio™tonameafew.TheTeradataUnityportfolioformanagingmultipleTeradatasystemsisbeingenhancedtosupportTeradataAsterandHadoopenvironmentswherethetechnologiesfit,cost-effectivelytakingmanagementoftheTeradataUnifiedDataArchitecture™tothenextlevel.Teradataalsohascomprehensivedatagovernanceoffers,extendingmetadataandmasterdataservicestoleveragethevalueandreliabilityoftraditionaldatamanagementacrossallthreeplatforms.Thisgivesorganizationsatrustedfoun-dationforadata-drivenculture.Bottomline:TeradatarecognizesthathandlingbigdatarequiresmanyofthesameproductionqualitytoolsthattraditionalTeradatahasprovidedforyears,andthisisabigpartofourTCOvaluetoourcustomers.

Training and Services

OurfinalpieceoftheTeradataUnifiedDataArchitecture™isourtrainingcurriculumalongwithProfessionalandCustomerServices.WehavehiredexpertswithnewskillsetsinHadoop,aswellastrainingaconsiderablenumberofourProfessionalServicespeopletobedatascientists.Inengagements,theysitside-by-sidewithourcustomers,trainingthemontheTeradataUnifiedDataArchitecture™technologiesandprovidingleave-behindtemplatesforprojectsanddatadiscoveryinsightmethodsthatcanbeeasilyreplicated.OurIndustryConsultantscollectthelatestandgreatestcustomercasestudiesandcanhelpevaluateprojectplansforbigdataeffortsusingourIndustryFrameworks.And“takingaction”onnewinsights—whichiswherethevaluelies—oftendependson

discussionsanddatasharingwithothergroups,orintegrationprojects.Forexample,Teradatacustomersareleveragingbigdatainsightsbyconnectingtoanyofahostofautomatedfront-endsystemsliketheContactCenter,thewebgroup,thefactoryfloor,ormobiledashboards.Planning,designing,deploying,andmaintainingtheenvironment—alltheseactivitieshavecorrespondingProfessionalServiceoffersthathavebeenextendedtocoverbigdataneeds.ThiscomprehensiveviewofbigdataandhowitisoperationalizedinyourbusinesssetsTeradataapartfromothertechnologyvendors—andgivesyou“onethroattochoke”ifyouhaveproblemsand“onehandtoshake”withsuccess.

HOW DOES TERADATA UNIFIED DATA ARCHITECTURE™ WORK?

TheprimaryvalueofTeradataUnifiedDataArchitecture™

istoconvertdata—bigandsmall,andallcombinations—intouseful,actionableinsights.Thisinvolvesanalyticalapproachesdesignedtouncoverpreviouslyunknownpatterns,ortheidentificationofkeyeventsthattriggercustomerbehaviorslikedecisionstobuyproductsorcancelcontracts.Inthissection,we’llbringtheTeradataUnifiedDataArchitecture™tolifebyshowing“ADayintheLife”oftwocustomerprojectsfromvariousindustries,highlightingvariousimplementationdetails.

SCENARIO 1: RETAILMULTI-CHANNELINSIGHTSDRIVEBETTERMARKETINGANDCATEGORYMANAGEMENT

SeverallargeretailersuseTeradata’sUnifiedDataArchitecture™tohelptheirmarketingandcategorymanagersbetterunderstandconsumerbehaviorontheirwebsites,onmobiledevices,andinthestores.Inthiscompositecaseillustratingseveralcustomers’bestuses,theseretailersalreadyhadTeradatasystemsthatcontaineddetailsaboutmillionsofcustomers,sales,andproducts.Theirbigdatainterestwasinaddingwebandmobiledata,specificallytryingtounderstandtheimpactofmarketingcampaigns,andcouponingeffortstodrivelargermarketbasketsorimproveoverallmargins.

Data

Oneretailerbegantheirproofofconcept(POC)byloadingverydetailedwebserverlogrecordsintoaHortonworksHadoopcluster.Thisrawdatatransferwasfast;theyfoundtheycouldloadtwomonthsofweblog

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inonehour(about60timesfasterthantheirtraditionalapproach).Oneday’sworthofdataincludedabout30millionwebclickentries,eachinvolvinguptohundredsofattributesthatneededtobeparsed.

Sessionization

Hadoopwasfurtherusedtosessionizethedatafrommultiplelogsourcestocreateintegratedcustomervisits.Sincematerializingeachpagehitmightcausemul-tiplelogrecordstobewrittentoseveralservers,thereareoftenfrom5to30recordsthatneedtobesplicedtogethertoshowwhat’shappenedonanyparticularpage.Oncethisisdone,thenthetemporalsequenceofpagescanbereconstructedforanyparticularcus-tomer.Splicingthistogetherwithcustomerstoredataonmarketbasketsprovidedamoreusefuldatasetofeachcustomer’sbehavior.

Analytics

ThesesequenceswerethenaccessedusingHCatalogbyAsterforfurtheranalysisbasedonpathmatching(usingthepre-builtTeradataAsternPath™function).TheirengineersbuiltatoolsouserscouldeasilywriteSQL-MapReducequeriestodopathmatchingatahighersemanticlevel.Forexample:“findallcustomerswholookedatproductsinthecategory‘LawnFurniture’andclickedthreetimesinthatcategorybutneverpurchasedanything,”thendrilldownintowhatthosecustomerswerelookingat,specifically,togetdeeperinsightsaboutproductinterest.Theycouldalsocross-correlatepur-chasersversusnon-purchasersfromreferralsites(websiteswithadsdrivingcustomerstothewebsite),sothey

couldseewhichadplacementsdrovethemoreprofitablecustomerstotheirwebsite.Theycouldalsocomparethewebpagesthatpurchasersandnon-purchaserswerebrowsing,toseeagainthedifferences—allprovidinglevelsofinsightthecategorymanagerscouldnevergetbefore.Finally,byaccessingtheTeradatadatawarehouse,theycouldtiethiscustomeranalysistogetherwithpreviousstorevisits,purchases,lifetimevalue,responsetopromotionsofvarioustypes,andothercustomerscoresalreadyintheirexistingdatabasetobettercalibratefuturemarketingcampaigns.

AnotherTeradatacustomerfocusedonomni-channelbehaviors,specificallyemailcampaignsthatdrove(ordidnotdrive)conversionsusingcouponsinthestores.Theydrilledintofindingwhichpeoplebought/searched/clippedcouponsforitemA,andthenranAster’saffinityandclusteringanalyticstorevealdeeperinsightsaboutwhatcombinationsofitemsareattractingorrepellingcoupon-orientedshoppers.Clusteringcustomergroupsbybehaviorthenprovidedthecluestheyneeded,foraparticularshopper,tomatch“herhistoricalbaskets”and“othercouponshopperslikeher”tocomeupwithbetterpersonalizedrecommendationsandcouponsforfutureemails(seeFigure8).

YetanothercompanyalsousedtheTeradataUnifiedDataArchitecturetoanalyzewhichemailsdrovepeopleto“un-subscribe”altogetherfromemails.Forexample,arethereparticularwordsorphrasesthatseemtocausethis(discoveredusingtextanalytics),oraretime-of-day,day-of-week(temporal),ornumberofrecentemailsdrivingun-subscriptionevents?

Product A: BOUGHT

Product B: LOOKED

Product C: BOUGHT

Product B

COUPON for Product E

A Customer’s“Historical Basket” + =Shopping Patterns

of Similar ShoppersPersonalized

Recommendations

Figure 8. Matching Customer Behavior with Clusters to Drive Recommendations.

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Value to Users?

Forthefirsttime,categorymanagerscouldeasily“see”exactlyhowcustomerswerebehavingonthewebsite,andwhatwerethe“hot”and“cold”partsofthewebsite.Notjustthe“score”ofacustomersegment,buttheactualbrowsingbehaviorofdiscreteindividualsandmicro-segments.Theycouldseedwelltimesonvariouspages,andunderstandbetterwhichpagescontributedtothepathwaytopurchases,asopposedtopathwaysthatendedindropouts(non-purchases),whichhelpeddriverethinkingofunproductivewebpages.Theycouldseewhichcouponoffersworkedanddidn’t.

Action

Thefasterinsightsusingdetailedwebbehaviorswereusedbythisretailertochangewebdesignlayoutsandcontent.Theyalsohelpproductcategorymanag-ersderivebetterinsightsaboutcustomersandbetterpersonalizedoffers.Theycouldalsorecalibratetheirspendingonsearchenginekeywordsonothersites,andfocusonunproductiveemailtextandbademailtiming.

The Business Value?

Theseareexpectedtoincreasemarketbasketsizeandimproveconversionratesofexistingcustomersbyfivetotenpercentanddriveanothereighttotenpercentmorebrowsersintothesalesfunnelthroughbettertargeting.Thebottomlineimpactistensofmillionsofdollars.

Project Time

Theseprojectswerecompletedtypicallyinthreetosixweekswithonetotwofull-timeanalysts,usuallyoneTeradatadatascientistandoneTeradataclientBIexpert,plusthesponsorandalittlehelpfromanETLengineer.

SCENARIO 2: BANKINGIMPROVINGTHECUSTOMERSUPPORTEXPERIENCE

Alllargebanksandcreditcardcompaniesreceivemil-lionsofcustomersupportrequestsperyear.Likemostconsumercompanies,theyhavetriedtoshuntinquiriestoself-servicemodeontheirwebsitetoreducethevolumeofphonecallscomingintothecustomercontactcenter.Thegoalofthesebigdataprojectswastoseeiftheycouldfigureoutwhypeoplewerecalling,especiallywhentheyhadbeenonthewebsitejustpriortocall-ing.Theyhadneverhadtheabilityto“see”thecustomer

datathiswaybecauseitwasonvarioussystems—webdataandanalyticsononesystem(withoutsourcingofsomeanalytics),contactcenterdataonanother,interac-tivevoiceresponse(IVR)dataonyetanother,callcenteragentnotesinyetanotherplace,andcustomertransac-tioninformationononemoresystem.

Data*

Fourtosixmonthsofdatawereloadedfromfivetosevensourcesystems.500Mto1.5Buncompressedweblogrecordsfrom10-30Mcustomersessionsonthewebtook40-70minutestoloadintoTeradataAsterDatabase.800Mto1.3billionIVRandcallrecordsfrom5-15Mcontactcenterinteractionstookanother40-60minutes,and50-75millioncallcentertextnotesandcomplaintdatabaserecordstooktypicallylessthan30minutes.

Sessionization

Usingtimestamps,asshowninFigure9,theIVRandcallrecordsandmemoshadtobematcheduptocreateacompleterecordforeachcall,thenmergedwithwebses-sionsandsorted.Theythenlookedforwebsessionsthatwereimmediatelyfollowedbycallstocustomersupport.

Page-Hit Aster nPath™: Find customers of interest

E.g., Three pages on the Web,THEN

A call to Customer Care

Call/Memo

Page-Hit

Page-Hit

Page-Hit

Call/Memo

Customer B

Customer T

Figure 9. Creating an integrated view of web activities followed by care center activities.

Analytics

UsingTeradataAster,theyfoundthatabout2percentofthemillionsofwebsessionsdrovesubsequentcallstocustomercare,providingabaseofseveralhundredthousandcasesthattheycouldthenfurtherinvestigate.

*Teradatahasdonemultiplebigdataprojectsforthisusecase;numbersbelowareacompositeorrangeoffiguresrepresentativeof“typicalimplementations.”

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Figure 10. Backmapping from high-incidence calls to the care center to pages on the Web site.

TheycreatedTeradataAsternPath™visualslikeFigure10toshowthetopwebpagespeoplehadclickedonrightbeforecalling,andmappedthosetotheCSR’s“reasonforcall”codes.

Value to Users

Thecustomersawnewinsightsaboutpaths,specificallywhichwebsitepagesandIVRsequencesseemtocauseconfusionanddropoutsresultinginexcessivecallstocustomercare.

Action

ThefindingsledtotheredesignofseveralwebsitepagesandIVRbuttonpushsequencessoconsumerscanresolveissuesontheirowninself-servicemode.

Business Value?

Transfersbetweenchannelscostfromfivetofifteenmilliondollarsannually,andrepeatcallsforunresolvedissuescostalmostthesameamount.Thebigdataprojectprovidedincreasedlevelsofself-servicewhiledecreas-ingcostofoperations,bettercustomerexperienceandsatisfaction,andprovidedearlyidentificationofvaluablecustomerswithcareissueswhomightdefect.Aroughprojectionofsavingsiscosttakeoutof10percentorroughly$1.5M-$3Mfromthisbigdataproject.

Project Time

Theseprojectswerecompletedtypicallyinfivetoeightweekswiththreetofivefulltimeanalystsandahandfulofpart-timeemployeestohelpwithdatasourcing.

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HOW CAN MY COMPANY GET STARTED?

Thisisanevolutionnotarevolution,sothesamebestpracticesapplytotheprojectmanagementofbigdataprojects.It’saboutROI,appropriateleadershipandmen-toring,staffingandtraining,aswellaslinkagesbetweenbusinessandITtofocusonkeyaspectsoftheproject.

Thekeyareatofocusonistheeconomicsofopportunitygainedorlost.Thinklikeaneconomist.Whatbusinessproblemscouldberesolvedwithmore/new/differentdata?Whatistheeconomicvalueofincrementaldata?Whatisthecost?Ormoreimportantly,whatistheeco-nomicvalueofnewinsights?Askyourbusinesspeoplequestionslike“whatwouldyoudoifyouknew,”“whatimmediateactionscouldwetakeifweknew,”and“howmuchvaluewouldthatprovide?”Ifyouaremanagingacallcenter,whatcouldyoudobetterifyougotanauto-matichourlylistofthepeoplewhoaresoangryincallsthatthiscouldbeTHEkeyfactorinradicallyimprovingcustomerretention?ROIisaboutvalueversuscosts.Thecostsareusuallyeasytoobtain—costsofstorage,soft-ware,tools,consultingservices,andongoinglicenses.It’sthevaluethat’sdifficulttocompute—andsomethingthathastocomefromthebusinessgroups,notIT.

SurveyingTeradatacustomers,it’sinterestingtoseewholaunchedthebigdataprojectorganizationally.Thecham-pionatourfirstbigdatacustomercamefromthebusinessstrategygroup,notthelineofbusiness,andnottheITgroup.That’srare.Often,initiativescomefromITpeoplesupportingmarketinggroups—whichisunderstandablesincemanyTeradataimplementationsaremarketing-sup-portsystems.Inallthesuccessfulcasesthough,theyhadabusinesssponsor,pickedaproblemthatwasbigenoughtomaketheresultsinteresting/useful/immediatelyofvalue,butnotsolargeastooverwhelmthegroupdoingthework.Byandlarge,we’vebeendelightedwithhowquicklythePOCshavegone(oftenonlyfourtosixweeks)andit’sanopportunityforourpeopletohelptrainyours.Thepoweroftechnologyiseasilyproven—itmaywellbethatthebottleneckforbigdataprojectsusingTeradataUnifiedDataArchitecture™willbethelimitsofyouranalysts’imagi-nations,notthetechnology.

A quick set of questions to get started:

1. Whatisyourcurrentpainpointoropportunitythatwillgiveyouacompetitiveedgeusingbigdata?

2. Whounderstandsandhasthepowertodriveaproject?

3. Whatisthelikelybenefitinquantifiabletermsforyou?ROI?DoyouneedhelpwithaBusinessValueDiscovery?

4. DoesTeradataUnifiedDataArchitecture™fittheproblem?

5. DoesitmakesensetoproceedtoaPOConasampledataset,withTeradata’shelp,soyoucangetyourfeetwet,getsomeearlyexperiencewiththetools,andseewhatthetechnologycandoforyou?

6. IfyoualreadyhaveTeradata,canyouplananArchitectureAssessmentsoyourITgroupislinkedinandyouachievebuy-inforoperationalization?

7. Isthetimingright?Isyourcultureready?

Three things to watch for, and avoid, based on our experience:

First,the“shinynewtoy”phenomenon.Whileit’slaud-ableforpeopletowanttolearnnewtechnologies,it’simportanttomakesuretheyaredoingsoinabusinesscontext.Therearealwaysshinynewthingscompetingforyourattention.Eightypercentofthem(and90percentoftheassociatedvendors)flameout,somakesurethatyouplaceyourbetscarefully.Foranentertainingper-spectiveonthisbyDanGraham,oneofTeradata’smostinsightfulcurmudgeons,see‘ThrowingYourHatintheRing’or‘BigElephantEatsDataWarehouse’blogposts.9

Second,the“scienceprojectinthebasement”phenom-enon.Incaseswherebigdataprojectshaverunintoproblems,theyusuallyoccurneartheend,whentheystruggletotaketheresultsintoproductizationwithouttheappropriateplanningandconsiderationofmanyoftheoperationalizationaspectsofrealprojects.It’softenimportanttoinsulatetherisktakersinyourcompanywhowanttobechangeagentssotheycanworkquicklyinahigh-energyenvironment.However,it’sequallyimportanttomakesurethatyouhaveagoodgame-planforfoldingtheirinsightsbackintotheotherinsightsyou’vecreatedwithTeradata,andthatyoucantakeaction.Wecallthisthe“InsightIntegration”stepandthe“TakingAction”step.Usually,the“TakingAction”aspectsofdataandinsightsdependonTeradataanditscon-nectionstofront-linesystemslikewebsites,dashboards,andmobileapps.Thisisimportant,becauseit’sveryunlikelythatHadoop(givenitsbatchorientation)oreven

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TeradataAsterisgoingtobeabletohandlethevolumeofrequestsfromoperational(asopposedtostrategic)BIworkloads,anareathatTeradatacalls“ActiveDataWarehousing™.”(YoucanfindmuchmoreinformationonTeradata.comaboutTeradata’sprowessinmixedwork-loadsaswellascasestudiesofcustomersconnectinghundredsofthousands,evenmillionsofcustomersonwebormobiledevicesdirectlytothesamesystemthathandlestheirtraditionalBIworkloads.)

Finally,monitorforthe“cultureclash.”There’softenacultureclashbetweenthepeoplewhodidthe“newproject”andthosewhohavetorunthebusiness—andthat’stheareawhereyouneedtospendsomeextraenergy.Itreallyisaboutpeople,process,andtechnology.Whilethebigdatatechnologyisprovenandthearchi-tectureisrobust,peopleandprocessissuesareoftenthestumblingblockstoputtingTeradataUnifiedDataArchitecture™intoaction.

SUMMARY

Bigdataisanevolution,notarevolution—oftechnology,analyticalcapabilities,andskillsforpeopleresponsibleforusingdatatocreateinsightsanddiscoveriesthatleadtoacompetitiveedge.OurexpertisewithTeradataUnifiedDataArchitecture™helpscompaniesnavigatetheoptions,understandtheeconomics,andlearnfrombestpracticestoputbigdataprogramsinplace.FormoreinformationvisitusatTeradata.com.

Suggested Readings

1. LisaArthur,Big Data Marketing: Engage Your Customers More Effectively and Drive Value,Wiley,2013.

2. LisaKart,NickHeudecker,FrankBuytendijk.“SurveyAnalysis:BigDataAdoptionin2013ShowsSubstanceBehindtheHype,”GartnerReportGG0255160,September12,2013.

3. ThomasH.DavenportandJeanneG.Harris,Competing on Analytics, The New Science of Winning,HarvardBusinessSchoolPublishing,2007.

4.ErikBrynjolfsson,LorinM.Hitt,HeekyungHellenKim—MITSloan,“StrengthinNumbers:HowDoesData-DrivenDecisionmakingAffectFirmPerformance?”,SocialScienceResearchNetwork,April2011.http://papers.ssrn.com/sol3/papers.cfm?abstract_id=1819486

5. Intel,“UsingaMultipleDataWarehouseStrategytoImproveBIAnalytics”,IT@IntelWhitePaper,March2013.http://www.intel.com/content/dam/www/public/us/en/documents/white-papers/using-a-multiple-data-warehouse-strategy-to-improve-bi-analytics.pdf

6. RichardWinter.“BigData—WhatDoesItReallyCost,”June2013.http://www.asterdata.com/big-data-cost

7. TodrillintomoreinformationabouttheTeradataAsterDiscoveryPlatform,read:

~ http://www.asterdata.com/resources/whitepapers.php

~ http://www.asterdata.com/resources/analyst.php

8.TeradataWebsite.“TeradataandFuzzyLogixDeliverGame-ChangingIn-DatabaseAnalytics,”TeradataBrochure,July2013,AvailableonTeradata.com.

9. DanGrahamBlogPost,http://blogs.teradata.com/data-points/throwing-your-hat-in-the-ring

10.TeradataWebsite.“TeradataUnifiedDataArchitecture™”,TDWIE-Book,May2013.Providesan11pagecompilationofinterviewsofnotedanalystsMarkMadsenandDavidLoshin,aswellasinterviewswithTeradata’sImadBiroutyandSteveWooledge.AvailableonTeradata.com.

11.TeradataWebsite.“TeradataUnifiedDataArchitecture™:AVisionaryFramework”,EB6705,April2013.Providesa6pagequickoverviewoftheTeradataUnifiedDataArchitecture™andthreecustomerusecases—twointelecommandoneinbanking.AvailableonTeradata.com.

12.TeradataWebsite.“TurningDataintoProfitwithTeradataUnifiedDataArchitecture.™”Isa2pagesummaryfromQ1-2013.http://www.teradata.com/white-papers/Turning-data-into-profit-with-Teradata-Unified-Data-Architecture

TeradataStudio,SQL-H,TeradataAsternPath,ActiveDataWarehousing,andtheTeradataUnifiedDataArchitecturearetrademarks,andTeradata,Aster,BYNET,SQL-MapReduce,andtheTeradatalogoareregisteredtrademarksofTeradataCorporationand/oritsaffiliatesintheU.S.andworldwide.NetApp,theNetApplogo,andGofurther,fasteraretrade-

marksorregisteredtrademarksofNetApp,Inc.intheUnitedStatesand/orothercountries.IntelisaregisteredtrademarkofIntelCorporationoritssubsidiariesintheUnitedStatesandothercountries.SASandallotherSASInstituteInc.productorservicenamesareregisteredtrademarksortrademarksofSASInstituteInc.,intheUSAandothercountries.

®indicatesUSAregistration.SPSSisaregisteredtrademarkofIBMSPSS.AttensityisatrademarkofAttensityCorporation.FuzzyLogixisatrademarkofFuzzyLogix,LLC.Teradatacontinuallyimprovesproductsasnewtechnologiesandcomponentsbecomeavailable.Teradata,therefore,reservestherighttochangespecificationswithoutpriornotice.Allfeatures,functions,andoperationsdescribedhereinmaynotbemarketedinallpartsoftheworld.ConsultyourTeradatarepresentativeorTeradata.comformoreinformation.

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