1

Welcome 2

TheValueDensityofData 3

TheRiseoftheEventLake 5

LogtrustEventLakeCapabilities 6

ExampleofLogtrustIoTEventLake 7

ResearchfromGartner:100DataandAnalyticsPredictionsThrough2020 8

AboutLogtrust 17

IncreasingtheValueDensityofDatawithLogtrustEventLakeTM

Issue1

2

Welcome

OneofthebestcharacterizationsofadatalakecamefromMartinFowler1:“Thedatalakestoresrawdata,inwhateverformthedatasourceprovides.Thereisnoassumptionsabouttheschemaofdata,eachdatasourcecanusewhateverschemaitlikes.”ADataLakedesignprincipleisto“StoreandForget,”waitingforothertoolsto“RequestandProcess.”Thedownstreamorganizationalimpactofthe“StoreandForget”designprincipleshouldnotbedismissed;thatistosaythatunlessyouhavetoolsandengineersworkinginextracting,normalizing,andprocessingtheevents-of-interestforeachrequestor(linesofbusiness),itisfairtosaythatthelikelihoodforaDataLaketobecomeaDataSwampishigh.

AtLogtrust,theconceptofanEventLakecamefromacombinationofthreetypesofcustomerusecases:

1. Inthefinancialsector,ontheonehandwehavecustomerswithHadoopwithvariousdataextractorsandontheotherhandaSIEMSOC-driventoolwithcomputecapacitybottlenecksleavingcriticalassetsatrisk.Theyneededafederationlayercapableofingestingtrillionsofeventspersecondandproducingevents-of-interestinreal-timetobeconsumedbytheSIEMtool,theBusinessIntelligencetool,andotherMachineLearningtoolsthattheyweretesting.

2. IntheTelecommunicationsoperatorspacewheremulti-playmedia(TV,moviestreams,internet,etc.)quality-of-experience(QoE)isparamountforavoidingsubscriberschurn,wehavesuccessfullydeployedatransversalreal-timeeventprocessinglayerthatfederatesover2.5millionset-topboxesandback-endequipmenttoproduceend-to-endreal-timeQoESLAs,notonlycapturingpacketsinreal-time,butalsofederatingmultipleequipmentCMDBstoenricheventsandproduceactionableinsights.

3. AttheintersectionbetweenSmartBuildingIoTandITOperations,wehelpedITteamsperformingreal-timeeventingestionandprocessingwithasolutionthathandleseventstreamscomingfromIoTandacceleratestime-to-actionableinsightssothatfurtheractionscanbetriggeredbasedontheseevents.

InthisGartnernewsletterwewillbeapplyingthelogisticsindustryconceptof“valuedensity”toexplainwhyLogtrustEventLakehassuccessfullyincreasedvaluedensityofFastandBigDatabyreducingthe“Time-to-Value”perGBingestedforallofourcustomers.

Eric Tran-LeGlobalChiefMarketingOfficerLogtrust

1http://www.martinfowler.com/bliki/DataLake.html

3

TheValueDensityofData

What is Density?

Densityisdefinedasaquantityperaunitofspace.Inthelogisticsindustrythenumeratoristhenumberofstopsthatsuppliers,dealers,orvehicleswouldperformandthedenominatoristhenumberofroutes,ports,railheads,orshipmentscontainers.Sayyouaretransportingoil,thedensestway—orbestway—totransportwouldbethroughapipeline.Nowifyoutransportthisovertheocean,theshipmentswillbelessdenseandthefreightcostwillbehigher.

Ifyouweretothinkabout“#ofBusinessRelevantDataperGB,”the#ofBusinessRelevantDataasanumeratorcouldbecalculatedasthe#ofevents-of-interestdetectedasdatacome,andthedenominatorcouldbethe#oflayersfromdatacollection,dataingestion,datanormalization,dataprocessingat-rest,dataprocessingin-flyanddataanalyticsthatadatamanagementteammustperformtogainactionableinsights,makedecisions,andtriggerrelevantactions.

Whatiskeytounderstandisthattherelationship between the age of data and its arrival time has a significant impact on the value density of data.Datahasthegreatestvalueasitentersthedatapipelinewherereal-timeinteractionsgeneratemoreinsightsforactionablecorrectiveactions.Thesignificanceofthisconceptmaybeunderstoodbestthroughthelensofseveralusecases:

FIGURE 2 Time-to-ValueCounts

Source:Logtrust

FIGURE 1 AgeofDataCounts

Source:Logtrust

4

• Stock Trading: Whentradingstocks,beforeplacingabuyoraselltradersmustknowthepaststockperformance,andtheywillbemonitoringreal-timestockvariationsontheircomputerscreenstoseizeupontheidealmomenttoact.

• Cyber Security: ACyberSecurityteamdetectssuspiciousbehaviorfroma10-yeartenuredemployeewe’llcall“EdSnowstorm.”ThoughSnowstorm’sactivityshowsherecentlyinfiltratedcorporatedata,securityexpertsneedtoknowwhatothersuspiciousactionsheengagedinoverthepast10years.

• Automotive IoT: Autonomousvehiclesarenecessitatingcontinuousmonitoringofcarvelocitytelemetrycombinedwithscanningexternalobjectstotriggeravehicle’sbrakesystem.Respondingtosuchsafetyscenariosrequiresresponsetimeswithinmilliseconds.

• Industrial IoT: WhenindustrialIoTsensorsarecontrollingfurnacefunctions,millisecondscount.Additionally,IoTequipmentsensorsareincreasinglymonitoringtelemetryforengineandmachinehealthdiagnostics;dependinguponinvestmentsize,maintenancecosts,uptimedemands,etc.,responsetimeswithinsecondstohundredthsofasecondmaybenecessary.

• IT Operations: Proactivemonitoringofdatabasesmayrequireresponsetimesofminutestoseconds,butthismaybelesscriticalifITOperationsisperformingmonthlycompliancereports.However,ifanintrusionisdetected,thenresponsetimeswithinsecondsismandatory.

Asonecansee,responsetimevariesacrossallusecases,buttheTime-to-Valuerelationshipbetweentheageofdataanditsarrivaltimeremainscriticalacrossallcases.Essentially,time—andmoreimportantly,response time—matters.

Source:Logtrust

5

TheRiseoftheEventLake

Silos of Data and Multi Layers Lowering Value Density

TherearemultiplefactorsleadingtolowvaluedensityofFastDataandBigDataprojects:

– Silosofdataduetotoolvendorspossessingtheirowndatacollectorsandprocessorrepositories

– Layersuponlayersoftechnologies,fromcollectiontoingestiontocorrelationtovisualization,increasingthecostofownershipandthecosttosupportsuchadataservice24x7

– Longtime-to-insightsduetoprotractedandlengthytimeperiodsfordataexploration

Forexample,ifyouneedtoanalyzethelastfivesecondsoflogineventsandcompareittothelastfiveyearsoflogineventsfromanIPaddresstodetectanomalousdeviation,youwillneedto1.)traverseanHadooplayerinordertoaccessthefiveyearsofdataretained,2.)createschemas,3.)useSQLHadooptoqueryit,4.)useSpark,Cassandra,andKafkatocompareitwithreal-timedata-in-motion,and5.)usevisualanalyticstoolstosharetheresultswithstakeholders—allbeforeyoucanmakeadecision.

An Event Lake to Simplify and Increase Density

LogtrustEventLakeTMisaplatformthatsimplifiestheentirecommunicationparadigm—fromenterprisedata,exogenousdata,SmartCityIoT,andIndustrialIoT—bycollecting,ingesting,andprocessing“eventsofinterest”forusers,SIEMs,BI,andapplications,andenablesthebrokeringofinformationbetweenthesevariousentities.

Process Event Lake on Behalf of Subscribers

Withextremeingestionandprocessingcapabilities,LogtrustEventLakecanbecalleduponformassiveparallelqueriesandcomplexeventprocessingbyusers,SIEMs,BI,logmanagementtools,andIoTplatformsandreturnresultstoanysubscriberintheirpreferreddataformat.Subscribersneedknownothingaboutthedevices,servers,routers,orexogenousdatacomingfromsocialmedia.Theyonlyneedtobenotifiedand/orinputtheresultsproducedbyLogtrustEventLake.

AnidealusecaseforLogtrustEventLakeiscomplementingSIEMtoolssuchasHPArcsight,IBMQRadar,orSplunkES:LogtrustEventLakerelievestheloggersorindexersfromheavycomputeandoutputstheresultsofmassiveparallelqueriesinaCEFformatreadytobeconsumedbyeachSIEMtool.

Source:Logtrust

FIGURE 4 HighValueDataDensity

Source:Logtrust

FIGURE 3 LowValueDataDensity

Source:Logtrust

6

LogtrustEventLakeTMCapabilities

Extreme Performance

LogtrustEventLakeisahighlyscalableservicethatcaningest+150,000EPSpercore,performsearchesat+1,000,000EPSpercoreandachieveover+65,000EPSpercoreforcomplexeventprocessing.Inotherwords,withjustafewunitsof8-16computecores,youcaningest,query,andprocesstenstohundredsoftrillionsofeventspersecondandscalepurelylinearly.Thisalsomeansyoucanensureguaranteedresponsetimeonselectedqueriesifneeded.

Flat Ultra Low Latency (FULLTM) Queries

Regardlessofwhetherthedataarrivedfiveyearsagoorfiveseconds,Logtrust’sFULLTMqueriesdeliverthesamereal-timeresponsetime.ThislevelofextremelowlatencyoverhistoricaldataisalsopossiblebecausetheLogtrustEventLakemaintainsalleventsinan“alwayshotmode,”readytobequeriedbyanytool.

Pure Linear Scalability

Linearscalabilityenablesaradicalnewwayofconceptualizing—anddelivering—performance.Whereasotherplatformswould,atbest,giveyouanestimatedrangeofperformanceand,mostoften,anexponentialnumberofcoresandexpensivestorage,withLogtrustEventLake,youcancommittoaguaranteedresponsetimeonselectedqueriesandeventprocessing–atacostyoucanpredictandcontrol.

Always On Event Lake

AsraweventsflowintotheEventLake,theyarestoredinanimmutablemodeonAWSorAzureSOC2type2certifieddatacentersandfullyencryptedatRest.Allcomputedeventsaremaintainedina“hot”modereadilyavailabletobe1.)accessedbySIEMsorapplicationsorqueriedandvisualizedbyLogtrust’sintegrated“NoCode/LowCode”querybuilderand/or2.)visualizedwithreal-timeintegratedadvancedvisualizationsdashboardorexternalanalyticstoolssuchasTableauandPowerBuilder.

Source:Logtrust

FIGURE 5 LogtrustEventLakeTMCapabilities

Source:Logtrust

7

ExampleofLogtrustIoTEventLake

In2020GartnerpredictsthatIoTwillincludenearly25billiondevices.TofacetheonslaughtoftrillionsofeventspersecondsentbyconsumerorindustrialIoTdevices,LogtrustReal-timeCloudEventLakepre-computesallevents-of-interestandnotifiesusers,SIEMs,BIs,applications,andmicroservicesoftheresultstowhichtheyhavesubscribed.

LogtrustEventLakeprovidesmultilayersofabstractionsothatenterprisescanmakesenseofIoTdataandeventuallycollaborateinavirtualdatamodel.

Quicklyidentifyassetswithdata-in-motion,detectevents-of-interestaswellashiddendatarelationships,enablefastexplorationbylookingintothepastandcomparingwiththemostrecentevents.

Makesenseofvastvarietiesandtypesofeventssuchas:

– Dealingwithlargenumbersofeventsofthesametype

– Detectingafewnumberofeventsofafewtypes

– Ingestingeventratesoftrillionsofeventspersecond

– Detectingafewevents-of-interestoveraperiodicintervaloftime

Source:Logtrust

FIGURE 6 The“InternetofEvents”

Source:Logtrust

FIGURE 7 TheLogtrustEventLake

Source:Logtrust

8

Research From Gartner:

100DataandAnalyticsPredictionsThrough2020

Overthenextfewyears,analyticswillbepervasiveandmission-criticalfordecisionsandactionsacrossthebusiness.ThisresearchroundsupGartner’stop100predictionsthatarerelevanttoCIOs,CDOsandanalyticsleaders,andwillhelpthemtobuildtheirfuturestrategicplans.

Analysis

Dataandanalyticsarecentraltothesuccessofanybusinessfunctionorindustry.Theyhelptoprovidethemuchsought-aftercompetitivedifferentiation,operationaleffectivenessandanyearly-moveradvantagethatareessentialinthedigitalbusinessage.Thenumberofdatasourcesandusersisgrowing.Withthetransitiontoadigitalbusiness,algorithmsareshiftingthegears,andsmartautonomousmachinesandtheInternetofThings(IoT)arebecomingkeydrivers.Thiswillleadtoanewlyemerginginformationecosystem,ormesh,thatisofferingenterprisesanopportunitytoevolveandlead.Butwithincreasedentrypointsandopportunitiestothisecosystemcomescomplexity.Enterpriseswillneedanewfinancialdisciplinesuchasinfonomicstomanageandexploitinformationasanasset,andtoimprovetheyieldorreturnonthoseinformationinvestments.

Asaresult,foratleastthelastfiveyears,CIOshavebeenreportingthatoneoftheirhottestprioritiesforITinvestmentshasbeenbusinessintelligence(BI)andanalytics.ThispriorityislogicalandimportantasCEOsandboardsfocusongrowth,andunderstandmoreabouthowimprovingdecisionmaking—acrossallfacetsofthebusiness,spanningcustomers,operations,andperformance—iscritical.Withthenewdigitaltransformationunderway,andtheshiftto“algorithmicbusiness,”thistoppriorityisnotlikelytochange.

AsevidencedbythepervasivenesswithinourvastarrayofrecentlypublishedPredicts2016research,itisclearthatdataandanalyticsareincreasinglycriticalelementsacrossmostindustries,businessfunctionsandITdisciplines.Mostsignificantly,dataandanalyticsarekeytoasuccessfuldigitalbusiness.Thisexhaustivecollectionofover100dataandanalytics-relatedStrategicPlanningAssumptions(SPAs),orpredictions,through2020heraldsseveraltransformationsandchallengesaheadthatCIOsandITleadersshouldembraceandincludeintheirplanningtoformulatesuccessfulstrategies.

FIGURE 1 WordCloudof2016DataandAnalyticsPredictions

Source:Gartner(March2016)

9

Research Highlights

Core Information Predictions

Information Strategy

Informationstrategyisnotatechnology(orstackoftechnologies)thatanenterprisecaneasilyacquire.Itisalong-termcommitmenttotheexploitationofinformationforimprovedbusinessoutcomes.Infact,theemergenceofthechiefdataofficer(CDO)roleinmanyorganizations,andacrossallindustries,indicatesagrowingrecognitionofinformationasastrategicbusinessasset.Forenterprisestorealizethebenefitsoftreatinginformationasanactualenterpriseasset,thefollowingStrategicPlanningAssumptionsfrom“Predicts2016:InformationStrategy”shouldbeconsideredasanimportantpartofanoverallenterpriseinformationmanagementandbusinessstrategydevelopment.

• By2020,10%oforganizationswillhaveahighlyprofitablebusinessunitspecificallyforproductizingandcommercializingtheirinformationassets.

• Through2019,90%oflargeorganizationswillhavehiredaCDO;ofthese,only50%willbehailedasuccess.

• By2020,50%ofinformationgovernanceinitiativeswillbeenactedwithpoliciesbasedonmetadataalone.

• By2020,theIoTanddigitalbusinesswilldriverequirementsin25%ofnewinformationgovernanceandmasterdatamanagementimplementations.

• Through2019,10%oforganizationswillhaveestablishedoperationalinformationstewardshipinline-of-businessfunctions.

Information Infrastructure

Informationinfrastructureismovingtowardacomplementaryenvironmentthatencouragessimultaneousdeploymenton-premisesandacrossmultiplecloudenvironments.Anincreasingpressuretomanagedatainmultipledeploymentmodels,whilealsooptimizingitsaccessandretrieval,ismounting.

Ourfivekeypredictionsforinformationinfrastructurein“Predicts2016:EvolvingInformationInfrastructureTechnologiesandApproachesBringNewChallenges”highlightthe

barrierstosuccessandthestepstoavoidthem.Usethemasguidepoststomaximizereturnfromyourinformationinfrastructuremodernizationefforts.

• By2018,30%oforganizationsmanagingtheirinformationinfrastructureinthepubliccloudwillbesubjecttocloudlock-in,makingmigrationtoanotherproviderdifficult.

• Through2018,80%ofdatalakeswillnotincludeeffectivemetadatamanagementcapabilities,makingtheminefficient.

• By2020,atleast75%ofmasterdatamanagementvendorswillsupportorrequirelower-costdatabasetechnology,reducingsellingpriceby30%.

• Through2019,one-thirdofIoTsolutionswillbeabandonedbeforedeploymentduetoinformationcapabilities(security,privacy,integration,metadata)builtontraditionaldesignandimplementationmethodologies.

• Through2018,70%ofHadoopdeploymentswillfailtomeetcostsavingsandrevenuegenerationobjectivesduetoskillsandintegrationchallenges.

Core Analytics Predictions

Advanced Analytics and Data Science

Advancedanalyticssolutionsarebecomingincreasinglypopularindrivingbusinessinnovationandexperimentation,andcreatingcompetitiveadvantage.Enterprisesnowseektoadoptadvancedanalyticsandadapttheirbusinessmodels,establishspecialistdatascienceteams,andrethinktheiroverallstrategiestokeeppacewiththecompetition.“Predicts2016:AdvancedAnalyticsAreattheBeatingHeartofAlgorithmicBusiness”offersadviceonoverallstrategy,approachandoperationaltransformationtoalgorithmicbusinessthatleadershipneedstobuildtoreapthebenefits.

• By2018,algorithmmarketplaceswillbecombinedwithPaaStoboostadvancedanalyticsandenablesecuresharingandmonetizationofrawdata.

• By2018,single-nodeanalyticswithSparkwillpredominateovermultinodeHadoop-basedarchitectures.

10

• Through2018,aminorityoforganizationswillhavearigorousapproachtodemonstratingthetrustworthinessoftheiranalyticsalgorithms.

• By2018,decisionoptimizationwillnolongerbeanichediscipline;itwillbecomeabestpracticeinleadingorganizationstoaddressawiderangeofcomplexbusinessdecisions.

• By2018,overhalfoflargeorganizationsgloballywillcompeteusingadvancedanalyticsandproprietaryalgorithms,causingthedisruptionofentireindustries.

Business Intelligence

In“Predicts2016:ChangesCominginHowWeBuyBusinessAnalyticsTechnology,”weofferadvicewithaspecificfocusonthebroader,moregeneral-purposebusinessanalyticsmarketmeantforwidespreadconsumptionandusagebymainstreamusers.Thefollowingpredictionssuggestchangestothebusinessintelligenceandanalyticsplatformmarketthatwillincludefurtherbundlingofnext-generationcapabilitiesalongwithamajoremphasisonproducttrialsinthevendorselectionprocess.

• By2018,mostofthestand-aloneself-servicedatapreparationvendorofferingseitherwillhaveexpandedintoend-to-endanalyticalplatforms,orwillhavebeenintegratedasfeaturesofexistinganalyticsplatforms.

• By2018,smart,governed,Hadoop-based,search-basedandvisual-baseddatadiscoverywillconvergeintoasinglesetofnext-generationdatadiscoverycapabilitiesascomponentsofamodernBIandanalyticsplatform.

• By2017,virtuallyallnewanalyticsoftwarepurchaseswillbeginasafreeorlow-costproofofconcept,enablingbuyerstotrythesoftwarebeforetheybuy.

• By2019,80%ofnewapplicationsusingIoTormachinedatawillanalyzedatainmotionaswellascollectthisinformationforanalysisofdataatrest.

Finally,“Predicts2016:AnalyticsStrategy”hasdeeperfocusontheroleofseniorleadership,suchasthechiefanalyticsofficer,wheretheywillmakeinvestments,andtheincreasingroleofnewdataserviceproviderproducts.

• By2020,only50%ofchiefanalyticsofficerswillhavesuccessfullycreatedanarrativethatlinksfinancialobjectivestobusinessintelligenceandanalyticsinitiativesandinvestments.

• By2020,predictiveandprescriptiveanalyticswillattract40%ofenterprises’net-newinvestmentinbusinessintelligenceandanalytics.

• By2018,75%oftechnology-orientedbusinessintelligencecompetencycenterswillhaveevolvedintostrategy-orientedanalyticscentersofexcellencetofocusoninformationvaluegeneration.

• By2019,75%ofanalyticssolutionswillincorporate10ormoreexogenousdatasourcesfromsecond-partypartnersorthird-partyproviders.

• Through2020,over95%ofbusinessleaderswillcontinuetomakedecisionsusingintuition,insteadofprobabilitydistributions,andwillsignificantlyunderestimaterisksasaresult.

Information Technology Infrastructure Predictions

Smart Machines

BusinessandITleadersaresteppinguptoabroadrangeofopportunitiesenabledbysmartmachines,includingautonomousvehicles,smartvisionsystems,virtualcustomerassistants,smart(personal)agentsandnatural-languageprocessing.Gartnerbelievesthatthisnewgeneral-purposetechnologyisjustbeginninga75-yeartechnologycyclethatwillhavefar-reachingimplicationsforeveryindustry.In“Predicts2016:SmartMachines,”wereflectonthenear-termopportunities,andthepotentialburdensandrisksthatorganizationsfaceinexploitingsmartmachines.

• By2020,smartmachineswillbeatopfiveinvestmentpriorityformorethan30%ofCIOs.

• By2020,CFOswillneedtoaddressthevaluationsderivedbysmartmachinedataand“algorithmicbusiness.”

• Byyear-end2018,25%ofdurablegoodsmanufacturerswillutilizedatageneratedbysmartmachinesintheircustomer-facingsales,billingandserviceworkflows.

11

• Byyear-end2018,R&D-basedend-userapproachestosmartmachinedeploymentwillbethreetimesmorelikelytoproducebusinessvaluethanITproject-basedapproaches.

• By2018,morethan3millionworkersgloballywillbesupervisedbya“roboboss.”

• By2020,Microsoft’sstrategywillbecenteredaroundCortana,ratherthanWindows.

Internet of Things

TheIoTisemergingasakeyenablerofourdigitalfutureandglobalspendingonIoT—includingallhardware,softwareandservices—willincreaseinthenextfiveyears.However,thepathtobenefitscapturedfromIoTwillnotbeastraightline.Itwillhavemanytwistsandturnsascompaniespursuebigplans,hitroadblocks,learnandadjust.Somewillgiveup,whileotherswillfollowthroughandrealizethetransformationalpotentialtheIoTcanhaveintheirbecomingasuccessfuldigitalbusiness.

“Predicts2016:ChartingthePathtoIoTBusinessValue”

• Through2018,80%ofIoTimplementationswillsquandertransformationalopportunitiesbyfocusingonnarrowusecasesandanalytics.

• By2018,directmonetizationofIoTalgorithmswillreach$15billion.

“Predicts2016:UnexpectedImplicationsArisingFromtheInternetofThings”

• By2020,morethanhalfofmajornewbusinessprocessesandsystemswillincorporatesomeelement,largeorsmall,oftheIoT.

• Through2018,75%ofIoTprojectswilltakeuptotwiceaslongasplanned.

Mobile, Web and Portal

Mobiledevicesandapplicationsarebeingusedmorefrequentlytosupportbusiness-criticalapplications,requiringmorestringentmanageabilitytoensuresecureuseraccessandsystemavailability.ThefollowingresearchprovidesinsightforCIOs,ITleaders,applicationleadersandmobileappdevelopmentmanagersintowhatGartnerperceivesassomekeydevelopmentsoverthenextfewyearsformobiledevicesandapps.

“Predicts2016:MobileandWireless”

• By2018,5millionpeoplewillhaveenterprise-confidentialinformationontheirsmartwatches.

“Predicts2016:MobileAppsandDevelopment”

• By2018,65%ofenterpriseappswillincludedirectaccesstodocumentsandcontentfromenterprisecontentmanagementsystems,upfrom20%today.

• By2018,25%ofnewmobileappswilltalktoIoTdevices.

Security, Privacy and Identity Predictions

In2016andbeyond,achievingthreeimportantgoals—privacy,safetyandreliability—willrequirestrongplanningandexecutionintheareasofsecurity,privacyandidentitymanagementaspredictedbyGartner’sapplicationanddatasecurityanalysts.ITleadersshouldconsidertheseforward-lookingpredictionswhenallocatingresourcesandselectingproductsandservices.

“Predicts2016:ApplicationandDataSecurity”

• By2018,theneedtopreventdatabreachesfrompubliccloudswilldrive20%oforganizationstodevelopdatasecuritygovernanceprograms.

• By2018,40%ofenterpriseswillmanagedatalossbyleveragingcloudgatewaysandenterprisemobilitymanagement,bypassinglegacydatalosspreventioninfrastructure.

“Predicts2016:BusinessContinuityManagementandITServiceContinuityManagement”

• By2020,30%oforganizationstargetedbymajorcyberattackswillspendmorethantwomonthscleansingbackup,resultingindelayedrecoveries.

“Predicts2016:IdentityandAccessManagement”

• By2018,25%oforganizations—upfromlessthan5%today—willreducedataleakageincidentsby33%byreviewingprivilegedsessionactivity.

12

Enterprise Content Predictions

Enterprisesaremodernizingtheircontentmanagementinfrastructuresandapplicationstobettersupportdigitalworkplaceinitiatives.Atthesametime,emergingcontentmanagementtechnologiesandcapabilitiesprovideenterpriseswiththeopportunitytoleveragethetrendsassociatedwithcloud,mobileandsocial.In“Predicts2016:HaveContentYourWay,”weassistITleadersresponsiblefortheenterprisecontentstrategytoaddressnotonlyhowtomanagecontent,butalsohowtousecontentinwaysthatpromoteproductivity,efficiencyandbusinessopportunities.

• By2018,atleast50%oftheleadingenterprisecontentmanagementvendorswillrearchitecttheirofferingsintonewcloud-basedplatforms.

• By2018,15%ofworkerswillrelyonproactiveservicestodiscover,organizeandcontextualizeinformation.

• By2018,machine-generated,dynamicmetadatawillbeintegraltodiscovering50%ofnewdigitalbusinessrevenuestreams.

• By2018,federation,governanceandback-endintegrationwithmultiplecontentrepositorieswillberequiredfor70%ofbusinessenterprisefilesynchronizationandsharingdeployments.

• By2018,20%ofallbusinesscontentwillbeauthoredbymachines.

Digital Business/Commerce and Business Function Predictions

Digital Business

Digitalbusinessisthecreationofnewbusinessdesignsbyblurringthedigitalandphysicalworlds.ThefollowingpredictionsoffertohelpCIOs,digitalbusinessleadersandITleadersmovefrom“digitaldreams”to“digitalreality,”andtakealeadershippositionwithinanewworldofvaluedeliveredbyintegratingpeople,businessandthings.

“Predicts2016:TheOpportunitiesforIntegrationinDigitalBusinessAreExpanding”

• By2019,two-thirdsofenterpriseswillincludebothdataandapplicationintegrationcapabilitieswhenselectinganewintegrationtechnologyprovider.

“Predicts2016:ITServicesInnovationsforDigitalServices”

• By2020,contextualpredictivedatastreams—andtheproprietaryalgorithmsbehindthem—willbeatopthreeserviceproviderdifferentiator.

“Predicts2016:DigitalBusinessUprootsTraditionalRetailRevenueGeneration”

• By2018,retailersengagedinIoTpartnershipswithmajormanufacturerswilltakesignificantmarketsharefromcompetitorsduetodirectconnectionswithconsumerlives.

• By2018,largeTier1multichannelretailersthathavenotmadeatleastonesignificant“techquisition”willlosetheirleadingmarketsharepositionduetodigitalbusinessdisruption.

• By2018,CIOsofatleasttwooftheworld’slargestmultichannelretailerswillbesuedfordatabreaches.

• By2020,merchantleaderswillbealgorithms,promptingthetop10retailerstocutuptoone-thirdofheadquartersmerchandisingstaff.

Digital Commerce

Spendingondigitalcommerceinitiativescontinuestogrowandvendorsarestrugglingtokeepupwithdemandbyutilizingmoreappsandanalytics,investingincommerceinnovation,andexpandingdigitalcommercetobusinessbuyers.Our“Predicts2016:PredictiveTechnologiesandNewSalesChannelsWillEscalateGrowth”suggestsintensecompetitionbetweendigitalcommercesellers,whichwillincreasetheurgencyoftheneedforadvancedusesofdata,alongwiththesearchforviablenewsaleschannels.

• By2020,smartpersonalizationenginesusedtorecognizecustomerintentwillenabledigitalbusinessestoincreasetheirprofitsbyupto15%.

• By2018,40%ofB2Bdigitalcommercesiteswillusepriceoptimizationalgorithmsandconfigure/price/quotetoolstodynamicallycalculateanddeliverproductpricing.

13

Digital Marketing

Marketingtechnologiesaregettingsmarterandpromisetofundamentallyimprovecoremarketingactivities.Technologywillsoonbecomesointelligentthatitwillperformtasksthathavealwaysrequireddirecthumaninvolvement.Intelligenttechnologieswilldomorethanautomaterepetitiveoperations.Theywillinvestigate,evaluateandmakedecisionsonbehalfofbothmarketersandconsumers.In“Predicts2016:IntelligentMarketingTechnologyWillBringGenerationalChange,”weadvisedigitalmarketershowtoseizetheopportunitiesanewgenerationofmarketingtechnologywillcreate.

• By2018,machineswillauthor20%ofbusinesscontent.

• By2018,customerdigitalassistantswillrecognizeindividualsbyfaceandvoiceacrosschannelsandpartners.

• By2018,60%ofsurveyswillbereplacedbyalgorithms.

CRM Sales

“Predicts2016:CRMSales”envisageshowsalesorganizationswillbeusingdataandanalyticstobecomesmarterandbetter—muchfaster.ITleaderssupportingsalesshouldfocusonimprovinguserexperienceforsalespeopleandpartnerstoboostadoptionanddataquality.Betterdatawillleadtobetteruseofpredictiveanalyticsforsalesorganizations.ITleaderswillalsodiscoverhowsmartmachineswillbecomethenext-generationsalespeople.

• By2018,manualdataentrybysalespeopleforsalesforceautomationsystemswillbereducedby50%duetoadoptionofmobilesalesproductivitytools.

• By2018,smartmachineswillhavecontactedandinitiatedasalewithmorethan5millionconsumersinNorthAmericaandWesternEurope.

Customer Service

TherearegreatexpectationsfromtheemergingcustomerserviceandsupportbusinessapplicationsnowmaturingfromCRMsoftwarevendors.Yetenormousgapsexistwithinenterprisesbetween

whatITleadersareabletodeliver,andwhatthecustomerserviceandcustomerexperienceleadersareapprovedtoreceive.“Predicts2016:CRMCustomerServiceandSupport”isforward-lookingresearchmeanttoshedlightonhowthefuturecustomerserviceorganizationwilluseandrespondtotechnologyinnovationtoimprovecustomerprocesses.

• Byyear-end2018,atleastonelargeCRMsoftwarevendorwilloffera“customerengagementhub”solutiontoitsclients.

• Byyear-end2018,useofan“intentarbitrationsystem”thatweighsenterprisegoalsagainstcustomerexpectationswillbeacentraltechnologycomponentfor4%ofinnovativeenterprises.

• By2018,6billion“things”willrequestsupport.

• Byyear-end2018,25%ofcustomerserviceandsupportoperationswillintegratevirtualcustomerassistanttechnologyacrossengagementchannels.

Workforce and Human Capital Management

Thedigitalworkplaceisabusinessstrategytopromoteemployeeagilityandengagementthroughamoreconsumerizedworkenvironment.In“Predicts2016:DigitalDexterityDrivesCompetitiveAdvantageintheDigitalWorkplace,”Gartneremphasizesthesuggestionthattheabilitytopromotedigitaldexterityintheworkforcewillbeacriticalsourceofcompetitiveadvantage,basedonthesimplenotionthatanengaged,digitallyliterateworkforcecapableofseizingtechnologicaladvantagewilldrivebetterbusinessoutcomes.

• By2020,15%oflargeenterpriseswillregularlyassessanddevelopthedigitaldexterityoftheirworkforce.

• By2020,thegreatestsourceofcompetitiveadvantagefor30%oforganizationswillcomefromtheworkforce’sabilitytocreativelyexploitdigitaltechnologies.

• By2018,15%ofenterpriseswillpromoteanentrepreneurialculturebyinterconnectinginnovation,hackathonandcitizendevelopmentefforts.

14

Humancapitalmanagement(HCM)applicationsenableenterprisepeoplemanagementprocessesincludingcoreHRdatamanagement,payroll,talentmanagement,workforcemanagement,integratedHRservicedeliveryandworkforceanalytics.TheStrategicPlanningAssumptionsfrom“Predicts2016:HCMApplicationsTransformtoSupporttheEmergingDigitalWorkplace”highlightthechangestoHCMbeingdrivenbytheemergingdigitalworkplace,andaworkforceinvestmentstrategythatenablesnew,moreeffectivewaysofworking,raisesemployeeengagementandagility,andexploitsconsumer-orientedstylesandtechnologies.

• By2018,thefirstvirtualcareercoachwillemerge,providingjust-in-timeadvicetoemployeestoimproveperformance.

• By2018,morethan80%oforganizationswillleverageuser-generatedcontentaspartoftheircorporatelearningstrategy.

IT Operations, Procurement and Asset Management

In“Predicts2016:ITProcurementWillTransformIntoTechnologyProcurementforDigitalSuccess,”wehighlightthatITprocurementmustevolvebeyonditsfocusoncostandrisktoseekoutandacquiregreatervaluefromtechnologiesthatsupportbusinessgrowthandinnovation.

• By2018,25%oftechnologyprocurementteamswillprioritizeriskreduction,innovationandbusinessgrowthabovecostsavingsmetrics.

• Analyzingandcommunicatingcommercialalternativeswilloverridenegotiatingcontracttermsasthetoptechnologyprocurementskillby2020.

• As“things”starttopurchase,procurementautomationwilleliminatehumaninterventionin15%ofdigitaltechnologyspendingby2019.

• By2019,annualmaintenancepricingforperpetualsoftwarelicenseswillbecomemoreexpensivethanthesubscriptionpriceforequivalentfunctionality.

“Predicts2016:ITOperationsManagement”researchprovidesguidanceforinfrastructureandoperationsleadersthatareunderpressuretoquicklyevolvetheirpeople,processesand

technologiestomeetfuturebusinessrequirementsandend-userexpectations.

• By2018,ITservicesupportmanagementtoolswilleliminatetheneedforITILFoundationtraining.

• By2020,eightofthetop12publiclytradedIToperationsmanagementvendorswillrespondtopressurefromactivistinvestorstosellallorpartsoftheirbusinesses.

• By2020,20%ofclientswillbeusingDevOpstosupporttraditionalITinitiatives,upfromfewerthan5%today.

• By2018,50%oflarge-enterpriseinfrastructureandoperationsorganizationswillofferwalk-upservicesupport,upfrom30%today.

Supply Chain Planning

In“Predicts2016:ReimagineSCPCapabilitiestoSurvive,”weprovidesupplychainandITleaderswithtargetedadviceonhowtheymustreimaginewhatsupplychainplanningtechnologytheywillneedtosupporttheirorganizationsoverthenextfouryears.

• By2018,80%oforganizationswillconcludethattheircurrentdescriptiveanalyticssolutionswillnotsupporttheirsupplychainrealities.

• By2018,25%ofcompanieswillhavedeployeddemand-sensingandshort-termresponseplanningtechnologiestoenableresponsivesupplychains.

Industry Predictions

Government

“Predicts2016:GovernmentContinuestoAdapttotheDigitalEra”centersonITmanagementpracticesthatareadaptingtotheacceleratedrateofsocietalandtechnologicalchangesassociatedwithdigitaltransformation.Expectationsforperformanceandvalueforgovernmentwillrisetooffermoredataandinteractionsdigitally.

• By2018,morethan50%oftheTier1supportservicesatgovernmentcontactcenterswillbeprovidedbyvirtualpersonalassistants.

• By2018,morethan25%ofgovernmentagencieswilladopt“bringyourownalgorithm”policiestointegratemultiplelayersofknowledgetoboostworkforce-ledinnovation.

15

Energy and Utilities

Gartner’spredictionsforenergyandutilitiesenterprisesin2016areallaconsequenceoftheemergenceofdigitalbusiness.Thiswillhaveamajorimpact,notonlyontheITextensionsandapplicationportfoliosmanagedbycompaniesinthesesectors,butalsoonthecorebusinessprocesseswithintheenergyandutilitiesindustriesandtheemergenceofnewecosystemsinthesector.

“Predicts2016:UpstreamOilandGas”

• By2020,40%offieldassetswillbemonitoredandmanagedbyinteractionswithvirtual3Dmodels.

• By2020,morethan50%ofwellboredrillingactivitiesonelectricrigswillbecontrolledprimarilybyalgorithmsandsecondarilybyhumanexpertise.

• By2020,50%ofupstreamoilandgasCIOswillbeaccountableforintegratinginformationmanagementacrossIT,operationaltechnologyandupstream-modelingdomains.

“Predicts2016:UtilitiesGetReadytoTransformWhilePerforming”

• By2019,morethan40%ofutilityCIOswillmanagebimodalITorganizations.

Manufacturing

Manufacturersincreasinglyadoptdigitalbusiness—includingthedigitalthreadamongdesign,manufacturingandservice,andsoftwareaspartofmanufacturedproducts—theimpactonprocesses,practices,organizationsandsupportingtechnologiesispervasive.Thefollowingpredictionsanticipatethedigitalbusinessdisruptionsthatposethemostsignificantchallengestomanufacturersandtheirsupplychains.

“Predicts2016:DigitalBusinessUnlocksInnovationandOperationalEffectivenessforConsumerGoodsManufacturers”

• By2018,expertuseofbigdata/analyticswillresultina10%increaseinconsumergoodsmanufacturernewproductsuccessrate.

• By2019,1%ofconsumableproductswillbemanufacturedinthehomevialow-cost3Dprintersandotherfood-fabricationmachines.

“Predicts2016:DigitalBusinessWillDisruptProductDesign,ManufacturingandPLM”

• By2018,50%ofalldurablegoodswillberemotelyconfigurableusingembeddedIoT(thisdoesnotincludeprimarymetalsorfabricatedmetalparts).

• By2019,80%ofdurablegoodsmanufacturersinvestinginIoTecosystemsasanintegralpartoftheirbusinesseswillemploydatascientistsorcontractthird-partyserviceswithintegralrolesinnewproductdevelopment.

“Predicts2016:OpportunitiesAboundfortheFactoryoftheFuturetoReachItsPotential”

• Through2019,15%ofmanufacturerswillusesmartadvisorstoorchestratecontinuousimprovementprogramsandcurtailfutureprogramhiring.

“Predicts2016:TheRiseofDigitalR&DWithoutBorders”

• By2018,15%ofmanufacturerswilluseturnkeysolutionstotranslatescientificinformationintoengineering-basedactionsfornewproductdevelopment.

Life Sciences

Industryfluidityisimpactingthelifescienceindustry,andnewapproachestodeliveringpatient-centricandoutcome-focusedsolutionsenabledbydigitaltechnologyarerapidlyoccurring.Thefollowingpredictionsandrecommendationsguidecompaniesintheindustryonhowtobealeaderduringtheseturbulenttimes.

“Predicts2016:DigitalGeneratesBusinessValueOpportunitiesinLifeScience”

• By2019,50%ofthetop100lifesciencecompanieswillhaveinitiatedatleastoneclinicaltrialinvolvingwearabledevices.

• By2019,30%ofthetop100lifescienceR&DITorganizationswillhavesuccessfullymovedbigdataprojectsfromproofofconceptandpilotsintoproduction.

16

• By2019,only25%oflifescienceorganizationssellingintoEuropewillbetrulycompliantwiththeidentificationofmedicinalproduct(IDMP)standard,althoughinvestmentsinturnkeyregulatoryinformationmanagementsystem(RIMS)solutionswillhavebeenmade.

Education

Significantchangestotheglobaleducationlandscapehavetakenshapein2015,andspotlightnewandinterestingtrendsfor2016andbeyond.“Predicts2016:BuildingtheFoundationfortheDigitalizationofEducation”isfocusedonseveralStrategicPlanningAssumptions,eachuniquelycontributingtothefoundationneededtocreatethedigitalizededucationenvironmentsofthefuture.Organizationsandinstitutionswillrequirenewstrategiestoleverageexistingandnewtechnologiestomaximizebenefitstotheorganizationinfreshandinnovativeways.

• By2020,atleast10%ofhighereducationinstitutionswillusesmartmachinestoimprovestudentsuccess.

• By2020,one-thirdofinstitutionswillsupportuniversityadmissionwithacombinationofpointsolutions,CRMandbusinessprocessoutsourcing,ratherthanthestudentinformationsystem.

• By2020,atleast50%ofK-12organizationswillbeusingsometypeofdigitalcontentmanagement.

• By2018,atleast30%ofhighereducationinstitutionsgloballywillhavealearninganalyticsstrategytoimprovestudentoutcomes.

Evidence

Gartner’sStrategicPlanningAssumptions(SPAs),orpredictions,areconceivedthroughouttheyearbyGartneranalystsbasedonhundredsofclientandvendorinteractions,primaryandsecondaryresearch,andincollaborationwithanalystswithintheirownareasandacrossresearchagendas.Gartner’syear-endcollectionof“Predicts”researchnotesgathersandelaboratesfurtheronthesepredictions.

Source:GartnerResearchNoteG00301430,DouglasLaney,AnkushJain,24March2016

17

AboutLogtrust

Logtrustunderstandsthatbusinessesmust“bereal-timeorbeobsolete.”FoundedinMay2011byateamofprofessionalswithover15yearsofexperienceinFastDataandBigDatamanagementandprotectionforfinancialservices,LogtrustisaReal-TimeBigData-in-MotionplatformofferingFastData,BigDataanalyticsthroughasolutionthatenablesreal-timeanalyticsforoperations,fraud,security,marketing,IoTandotheraspectsofbusiness.Logtrustprovidestheabilitytoingest,storeandanalyzemassive,variedanddynamicdatasetsathighspeedthroughitsflexiblecloud,on-premiseandhybriddeployments.

RecognizedasaGartnerCoolVendor2016andbyCIOReviewasoneoftheTop100MostPromisingBigDataSolutionProvidersof2016,ourMissionistodemocratizereal-timeBigDatatoolsforcompaniesofanysizeandsector,allowingthemtomaximizetheirbusinessvalueviasecurityintelligence,infrastructure,monitoring,compliance,customerbehavior,analytics,andbusinessmonitoringsolutions.FundedbyAtlanticBridge(ABVen),aglobaltechnologyfundspecializedinacceleratingthescaleupoftechnologycompaniesintheU.S.andChinesemarkets,andInvestingProfitWisely(IPW),aventurefundspecializedintheinternationalizationofcompanies,Logtrusthasasenioradvisoryboardwithextensiveindustryexperienceandprovenleadershipingrowingcompaniesinternationally.LogtrustislocatedattheepicenterofSiliconValleyinSunnyvale,CA,andfurtherservesitsglobalclientsthroughofficesinBoston,Philadelphia,NewYork,andMadrid.Tolearnmore,visitwww.logtrust.com,emailinfo@logtrust.comorphone+1866242-1700or+34913088331.

What Makes Us Unique

Becauseeverysecondcountsandtime-to-insightmatters,Logtrusthasdesigneda“timemachine”thatingeststime-serieslogsinreal-timeandcorrelateswithultra-low-latencyqueries.Businessescantravelbackintimeatlightningspeedandfastforwardtothepresenttogainreal-timedatainsights.OurindustryfirstFastDataandBigDataplatformusesaFULLTM(Flat-Ultra-Low-Latency)elasticarchitecturecapableofprocessingover150,000eventspersecondpercoreorqueryingover1millioneventspersecondpercore.Thisenablescustomerstoperformreal-timedata-in-motionanalyticsandhistoricaldataqueriestogaininsightas-data-come—deliveringlivedataexplorationandadvancedvisualizationonanAlwaysHot,Always-Onarchitecture.MajorEuropeantelecommunicationproviders,cybersecurityserviceproviders,andbanksareusingtheLogtrustplatformforreal-timeQoSTVstreamsmonitoring,real-timeadaptivedefenseagainstintrusion,andreal-timeapplications-to-networkmonitoring.

Logtrust,LogtrustEventLake,andFULL(Flat-Ultra-Low-Latency)aretrademarksofLogtrust,Inc.,intheUnitedStates,Spain,andothercountries.Othernamesmaybetrademarksoftheirrespectiveowners.

TheGartnerCoolVendorLogoisatrademarkandservicemarkofGartner,Inc.,and/oritsaffiliates,andisusedhereinwithpermission.Allrightsreserved.Gartnerdoesnotendorseanyvendor,productorservicedepictedinitsresearchpublications,anddoesnotadvisetechnologyuserstoselectonlythosevendorswiththehighestratingsorotherdesignation.GartnerresearchpublicationsconsistoftheopinionsofGartner’sresearchorganizationandshouldnotbeconstruedasstatementsoffact.Gartnerdisclaimsallwarranties,expressedorimplied,withrespecttothisresearch,includinganywarrantiesofmerchantabilityorfitnessforaparticularpurpose.

IncreasingtheValueDensityofDatawithLogtrustEventLakeTMispublishedbyLogtrust.EditorialcontentsuppliedbyLogtrustisindependentofGartneranalysis.AllGartnerresearchisusedwithGartner’spermission,andwasoriginallypublishedaspartofGartner’ssyndicatedresearchserviceavailabletoallentitledGartnerclients.©2016Gartner,Inc.and/oritsaffiliates.Allrightsreserved.TheuseofGartnerresearchinthispublicationdoesnotindicateGartner’sendorsementofLogtrust’sproductsand/orstrategies.ReproductionordistributionofthispublicationinanyformwithoutGartner’spriorwrittenpermissionisforbidden.Theinformationcontainedhereinhasbeenobtainedfromsourcesbelievedtobereliable.Gartnerdisclaimsallwarrantiesastotheaccuracy,completenessoradequacyofsuchinformation.Theopinionsexpressedhereinaresubjecttochangewithoutnotice.AlthoughGartnerresearchmayincludeadiscussionofrelatedlegalissues,Gartnerdoesnotprovidelegaladviceorservicesanditsresearchshouldnotbeconstruedorusedassuch.Gartnerisapubliccompany,anditsshareholdersmayincludefirmsandfundsthathavefinancialinterestsinentitiescoveredinGartnerresearch.Gartner’sBoardofDirectorsmayincludeseniormanagersofthesefirmsorfunds.Gartnerresearchisproducedindependentlybyitsresearchorganizationwithoutinputorinfluencefromthesefirms,fundsortheirmanagers.ForfurtherinformationontheindependenceandintegrityofGartnerresearch,see“GuidingPrinciplesonIndependenceandObjectivity”onitswebsite.

Today,datascientists

use“brontobytes”

(10e27)tomeasure

volumeofsensordata

generatedbyIoTand

yetthe“business

relevantfactsperGB”

or“valuedensity”of

FastDataandBigData

islightduetothelack

ofanenterprise-grade,

costefficientreal-time

data-in-motion

platform.Logtrust

addressesthis

fundamentalchallenge.

EricTran-Le–GlobalCMOLogtrust