CreativeBusiness2016

Experiencingincreasingreturnswithcreativity

Largemarketsfornewbusinesstools

Creativebusinesswillusesmartbusinesstools

Smarttoolsareenteringallindustriestoempowercreativepeople

Creativepeopleandsmartertoolswillbepartofthenextinnovativecycle

Innovationwillinvolvethesynthesisofdifferentdevicesanddata

Businesstoolswillmovebeyondlogic

• Enterpriseapplicationsusedtobemostlyaboutbusinesslogic• Inthepriorgenerationofapplications,domainexpertsmappedoutkeybusinessprocessworkflowsandsoftwarewaswrittentocodifythem• Efficiencywasthegoalandautomationwasthemeans• Dataandanalyticsweretheprovinceofseparatesystemsandwereasecondarypriority• A newclassofbusinessapplicationsrootedindataattheircoremostlikelywillemerge

Datawillcauseincreasingreturnsinbusiness

• Applicationsgeneratedatathatarethecriticalinputtoadditionaldomain-specificalgorithms• Algorithmsformthecoreofnext-generationbusinessapplicationsthatgenerateevenmorerefineddata,whichfeedsadditionalalgorithms

CRM

• CRM• Manyoftheearlyexamplesofdata-firstapplicationshaveemergedintop-line-drivingsegmentssuchasSalesandMarketing.Thisistobeexpected—provablyincrementalrevenuemakesforthesimplestandmostcompellingofROIs.Lastyear’sDreamforce tradeshowwasfilledwithdatatalk,includingfromSalesforceitselfwithitsWaveAnalyticsannouncements.Meanwhileoutonthetradeshowfloor,alegionofsalesandmarketinganalyticsstartupsstrovetodifferentiatetheirwares.Therewillbealotofvaluecreatedhere—andprobablyalotofincumbentmarketcapdestroyedintheprocess.• Astrongcasecanalsobemadethatthedata-firstmodelwillhavethemostvalueinindustry-specificapplications.Veeva isthecanonicalexampleinCRM.Thecompanybuiltitsfootprint—anditsdataset—withastandardCRMapplicationforlifesciences.Itsubsequentlyrevealeddata-firstapplicationssuchasVeevaNetworkandOpenData.VeteransfromVeeva andCRMpioneerSiebelSystemshavenowteamedupatVlocity toexecuteasimilarstrategyinotherverticals.

ITOperations

• Withlotsofdata,complexoperationsandhighlytechnicalusers,ITisanaturalplacetolookforsignsofdata-firstapplications.• Oneinterestingexampleisfortheautonomousoperationoflarge-scalenetworks.Asamazingasthatsounds,suchanapproachisalreadyinproductionatthelargestwebscale giants.Theopportunityistoproductizethiscapabilityfortherestofthemarket.

Security

• Cybersecurityisalsoblessedwithampledata—toomuchinmanycases.Today’sSIEMproductsareeasilyoverwhelmedbythevolumeanddiversityofdatastreamingtowardsthem,andarenotwellequippedmathematicallytodetecttoday’ssophisticatedthreats.AnewclassofproductsfromcompaniessuchasSecuronix,Exabeam,Fortscale andCybereason ingestexistingdatastreamsandemploybigdataanalyticstoidentifyanomalousbehaviorsandcreatesomemeasureofpotentialriskinordertoimprovetheeffectivenessandproductivityofsecurityoperationspersonnel.• Anothergroupofcompaniesdistinguishthemselvesbybringingnewdatatobear.Theseincludeendpoint- anduser-monitoringtechnologies,whichthenfeedanalyticalsystemsforanomalyidentification.

HumanResources

• AttheotherendofthespectrumliesHR,witharelativelylowvolumeofdataandfarlesstechnicalusers.IncumbentHRIS,HumanCapitalManagement,RecruitingandLearningManagementSystemsareepitomesofbusiness-logic-firstapplications.Theymissopportunities tocapturerelevantdata,makelittleuseofthedatatheydohave,anddon’ttapmuchexternaldataatall.EvenrelativelyprogressivenewleadersinthisarenalikeWorkdayareonlynowbeginningtogetseriousaboutdata-drivenapproaches.Workdaydeservescreditforrealizingtheneed,but itwillstillbedifficultforittoinvertitsarchitecturetothedata-firstmodel.

• Meanwhilenewentrantsarechampioningadata-firstapproach.Googlehasbeenout infrontandhighlightskeylearningsfromitsownexperienceonitsre:Work site.Vendorsareemergingtoproductizerelatedconcepts, includingHiQ whichusesdatascienceandpublicinformationto identifyflightrisksinacompany’semployeebase.Kanjoya hasfollowedanunusualpathtodata-first,startinglifeasauniquesocialnetworkcalled“TheExperienceProject”.Ithasputthisnetworktounexpecteduseasanincredibletrainingdatasetforitsalgorithms.Thecompany’sapplicationsareabletoaddrichqualitativeanalysis,includingemotionsandthemes,totheclassic“ratethis1thru5”employeesurvey,potentiallyreinventingthefieldofemployeeengagement.

• Otherenterpriseapplicationsegmentscontainanalogousopportunities. Publicandinternaldataonproductsandcomponents canbebroughttobearonthebillofmaterials,breathingnewlifeintosupplychainandproduct lifecyclemanagement.Customersupport canbeacceleratedandimprovedwithdataaswell,aphenomenon alreadyinclearviewintheformofNimbleStorage’sInfoSight offering.TheInternetofThingscreatesapathtobringthiskindofintelligent,automatedsupport toafarwiderrangeofsectors,includingconsumerproductsofallkinds.TheIoT tetherenablesbothrichdatacaptureaswellasproactivecontactwiththeuser,andservesasapowerfulaccelerantofthevirtuousdatacycle.

MachinelearningwillsupportnewapplicationsMachinesaregoodat:• 1.“Fuzzy”problemswithunstructureddata.Unliketraditionalmodelsthatdirectlymapinputstooutputs,machineintelligenceapproachescanperformprobabilisticandsometimesnon-deterministicassessments.Forexample,amodelmaymakeaprobabilistic“bestguess”attheanswertoaquestion,whereboththequestionandthedatamaybeunstructuredandsomewhatambiguous.IBM’sWatsonisagoodexampleofthisapproach(moreonthisbelowtoo).

• 2.Changingconditionsovertime.The“learning”aspectofmachineintelligencestemsfromamodel’suseofpreviousdatatoimprovetheperformanceoffuturepredictions.Unliketraditionalapproaches,wherecertainassumptionsmaybe“hardcoded”intothemodel,atruemachineintelligencemodelwillhavesignificantdegreesoffreedomtoadapttochangingconditionsandtolearnnewbehaviors.Thisisanalogoustothewayanintelligentcreaturecanadapttoitsenvironment.DeepMind isagoodexampleofthis.

• 3.Largeanddynamicdatasets. LikeotherautomatedITsolutions,machineintelligencecanbehighlyscalableifimplementedwell.Thismakestheapproachsuitedtodatasetsthataretoolargeortoodynamicforhumanstobe“intheloop.”Thisisparticularlyvaluablewhencombinedwiththepointsabove,asinsomeusecasestasksthatusedtorequireahumantomakeasubjectivedeterminationcannowbefullyautomatedatdigitalspeedsandscale.

Examplesofmachineintelligence• Netflix’srecommendation engine:Netflix’sengineisactuallyalinearcombinationoftwomodels,oneofwhichisamachineintelligencemodel.Themachine

intelligencemodelusediscalledaRestrictedBoltzmanMachine,andisessentiallyatwo-layergraphmodel.Thismodelusesasetofvariablestocharacterizeeachuser.Expectedmovieratingsarethenafunctionofthesevariables.ForcaseswhereauserhasnotyetwatchedamovieonNetflix,theirexpectedratingforthemovieisinferredfromtheirpersonalvariables,whichareinturn inferredfromothermoviestheyhavewatchedandrated.Asimplifiedhill-climbingmodelisalsousedtoimprovethequalityofforecastedratingsovertimebasedonfeedback.Thismodelison thesimplerendofthemachineintelligencespectrumwehavedefinedhere—itdoesuseamulti-layermodel,anditdoes improvewithadditionalexposure todata.However, themodelisonlya two-layerone,andisoperatingonahighlydefinedsetofinputsandoutputs.Netflix’sengineisanexampleofcriteria#2and#3above.

• DeepMind: DeepMind isaprogramthatcanlearntoplayAtarivideogamesthatithasneverseenbefore.Overaperiodofhours,itwentfromnot knowinghowtoplayBreakouttosettingaworldrecord.Thevideoisprettyamazingifyouhaven’tseenit.DeepMind isprogramedwithagoal—forexampletoincreaseitsscoreinagame—andthenexperimentswithdifferentinputstofindagloballyoptimalsolutiontoachievethegoal.Thisapplicationisa particularlygoodexampleofcriteria#2above—theprogramstartswithnoknowledgeofthegameitisplaying.Itacquiresknowledgeover timethroughtrialanderror.Ifthegamechanges(forexample,someoneinsertsanewcartridge),theprogramrespondsbylearningthenewgame.ThisisclearlyverydifferentthanatraditionalprogramlikeDeepBlue,whichwasprogramedtoplaychessbutcouldneverlearntoplaycheckers.

• IBM’sWatson:Watsonisamachine-intelligencemodelthatcananswernaturallanguagequestionswithnaturallanguageanswers.Giventheunstructurednatureofboththequestionandthedataset,Watsonusesaprobabilisticapproachandsuggeststhemostlikelyanswerbasedon itsanalysis.Watsonwasabletobeatthebesthumanplayersatthegameshow Jeopardy.Itisaparticularlygoodexampleofcriterion#1above.

• Spiderbook: Spiderbook isastartupthatusesamachine-intelligencemodeltosuggestsalesleadsbasedonacombinationofinformationaboutyourbusinessandascanoftheentireinternet.Spiderbook startswithunstructuredinputsabout theproductsyourcompanysells,thetypesofcustomersyousellto,andwhoyourcompetitorsare.Itthenusesamulti-layermodeltomakeaprobabilisticassessmentofwhoyourmostlikelynextcustomersare, basedonpubliclyavailabledata.Earlycustomershavereportedextremelyhighaccuracyofthesepredictions,withthemodelinmanycasesexceedingtheaccuracyoftrainedsalesdevelopmentreps.Spiderbook isanexampleofallthreecriteriaabove.

Businessapplicationswillsynthesizeallavailabledata

Applicationswillcollapseontoeachotherforbusinesssingularity