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/10.48009/2_iis_2018_149-154
IssuesinInformationSystems
Volume19,Issue2,pp.149-154,2018
FOURIT/ISPILLARSFORARTIFICIALINTELLIGENCEMACHINELEARNING/DEEPLEARNINGAPPLICATIONS
RobertE.Samuel,
Robert.Samuel@
GeraldCormier,
gcormier@
ShannonFascendini,
Shannon.Fascendini@
ChristinaM.Stubanas,
tinastubanas@
KatherineA.Yacko,
kyacko1@
ABSTRACT
ArtificialIntelligenceMachineLearning/DeepLearning(AIML/DL)technologyadoptionforbusinessapplicationsisimpactingmanyInformationTechnology/InformationSystems(IT/IS)roles.Bridgingthedividebetweendata,insight,andactionrequiresrevisitingthedevelopmentoperatingmodels.TheevolutionoffourkeyIT/ISpillarswillbenecessarytosuccessfullyimplementtoday’sAIML/DLbusinesssolutions.BasedonqualitativeresearchwithinaFortune50U.S.-basedhealthcarecompany,thispaperassessestheIT/ISpillarsforroledefinition,requiredtechnicalskills,andbehavioralcompetencies.TheevolutionofthesepillarscouldinfluencehowindividualslearnandpreparefortherolesasAIapplicationsgainbusinessadoptionandacceptance.
Keywords:ArtificialIntelligence,MachineLearning,DeepLearning,IT/ISRoles
INTRODUCTION
McKinseyGlobalInstitutebroughtnationalspotlighttotheterm“bigdata”andtheexponentialgrowthofdatainmajorindustries(Manyika,et.al.,2011).Autonomousvehicles,advancedhealthcareresearchandpervasivevirtualassistants,socialmediaanalytics,andInternetofThings(IoT)areafewusecaseswheredatascienceandmachinelearningarebeingrapidlyadopted.Theadoptionofartificialintelligencemachinelearning/deeplearning(AIML/DL)applicationsincorporationsisforcinginformationtechnology/informationsystems(IT/IS)practitionerstoreassessthepillarsnecessarytosuccessfullyimplementbusinesssolutions(BanavarandCooper,2016)(Samuel,et.al.,2017).TheITindustryhasexperiencedcomputingerashiftsbeginningwiththefirsteraoftabulatingmachinestotheseconderaofprogrammablecomputerstotoday’seraofAIML/DL.Tabulatingmachineswereheavilyfocusedontheapplicationofmathematics.Programmablecomputerswerecodedwith“if,then,else”instructionstoproduceadeterministicoutput.Today’sAIML/DLapplicationsareabouttheprobabilisticoutcomesbasedoncontext(BanavarandCooper,2016).
AliteraturereviewindicatesthattheindustryremainsfragmentedontheexactclassificationofAI(Harper,2017)(Chen,2017)(Skansi,2018).Skansi(2018)furtherelaboratesthattherearetwomajorindustrysocietiesthatprovideaformalAIclassificationusedtoclassifyresearchpapers:theAmericanMathematicalSociety(AMS)andtheAssociationforComputingMachinery(ACM).TheAMSmaintainstheMathematicsSubjectClassification2010whichdividesAIintothefollowingsubfields:
General,
Learningandadaptivesystems,
Patternrecognitionandspeechrecognition,
Theoremproving,
Problemsolving,
Logicinartificialintelligence,
Knowledgerepresentation,
Languagesandsoftwaresystems,
Reasoningunderuncertainty,
Robotics,
IssuesinInformationSystems
Volume19,Issue2,pp.149-154,2018
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150
Agenttechnology,
Machinevisionandsceneunderstanding,and
Naturallanguageprocessing.
TheACMclassificationforAIprovidestheirsubclassesaswell(NotethatACMidentifiesmachinelearningisaparallelcategorytoAI,notsubordinatedtoit).Thesubclassesare:
Naturallanguageprocessing,
Knowledgerepresentationandreasoning,
Planningandscheduling,
Searchmethodologies,
Controlmethods,
Philosophical/theoreticalfoundationsofAI,
Distributedartificialintelligence,and
Computervision.
Skansi(2018)concludedfromthesetwoclassificationsthatthereareafewbroadfieldsofAIthatcansummarizedas:
Knowledgerepresentationandreasoning,
Naturallanguageprocessing,
MachineLearning,
Planning,
Multi-agentsystems,
Computervision,
Robotics,
Philosophicalaspects.
ThisresearchusedtheSkansibroadfieldsofAIasabasisfordiscussionandinterviews.ThesefieldsprovidethebackgroundtoassesstheIT/ISpillarsforAIML/DLbusinessapplicationdevelopment.TheidentifiedIT/ISpillarsare:
DataArchitecture-theprimaryroletogatherhighlevelbusinessneedsandrequirements(ofteninpartnershipwithadataanalyst)anddesignthesolution.Typically,thearchitectisageneralistbutoftenhasdepthofknowledgeinparticulartechnologydomain(Forrester,2017).
DataEngineering-theprimaryroletodeployaruntimeimplementationoftheapplicationandmonitorapplicationperformance,reliability,andstability.Theengineerisoftenaspecialistinaparticularoperations-basedtechnologystack(Forrester,2017)
DataScience–theprimaryroleistofollow“asetoffundamentalprinciplesthatsupportandguidetheprincipledextractionofinformationandknowledgefromdata”(ProvostandFawcett,2013,p.52).
DataAnalysis–theprimaryroleisjudgethevalueofgeneratedinsightsandtoensureapplicationsaddressimportantbusinessproblems(ProvostandFawcett,2013).DataAnalystsdobasicdescriptivestatistics,datavisualization,datasourceassessments,andcommunicatedatapointsforconclusions.
ThepotentialofAIML/DLdependsontheavailabilityoftalentandtechnologytoharnessitsvalue(Boisvert,et.al.,2017).AsbusinessAIapplicationsareincreasinginfrequencyandcomplexity,IT/ISskillsareevolving.ForresterResearch(Goetz,2017)discoveredthatenterpriseswanttogetvalueoutoftheirdatafasterandatscale,howevertechnologyalonecan’tsolvethischallengeandaddingroleswiththesoleresponsibilityofactivatingdataisnecessary.DavenportandPatil’sHarvardBusinessReviewarticle(2012)identifieddatascientistsasanimportantandemergingrolethathaslittleconsensusonwheretherolefitsinanorganization,thevaluetotheorganization,ortheprocessofdiscovery.Asaresult,itwasnecessaryfordatascientisttocrafttheirowntoolsandresearchapproaches.WilliamChen(2017),aDataScientistatQuora,summarizesthefiveskillsandcompetenciesthatheseesisimportantforadatascientistrole.Thisincludes1)programming(augments,largedatasets,createtools);2)quantitativeanalysis(experimentaldesignandanalysis,modelingofcomplexeconomicorgrowthsystems,machinelearning);3)productintuition(generatinghypothesis,definingmetrics,debugginganalysis);4)communication(communicatinginsights,
datavisualizationandpresentation,generalcommunication);and5)teamwork(beingselfless,constantiteration,sharingknowledge).Theauthorsnoticethatthreeofthefivecompetenciesarenottechnicalskills.
Inrecentyears,theemphasishasbeenonestablishingandcultivatingthedatascientistrolewithincorporations.ThefocusonthedatascientistrolehasresultedinabottleneckwhenthereareinadequateresourcestoefficientlybuildtheAIapplications.Forrester(2017)discoveredtheneedtogrowtheroleofdataengineerequallytothedatascientistroletosuccessfullydeployAIML/DLapplicationsbystating:
“Asorganizationsbegandevelopingdatascientists,theyexpectedthemtocarrytheloadofdevelopingdatalakesanddatapipelinesaswellascreatingsophisticatedanalytics.Butindividualswithcomputerscienceandstatisticalskillsarerare.Atoneaerospacemanufacturer,dataengineerstakeonthetaskstosource,wrangle,anddemocratizedataindatalakes,allowingdatascientiststofocusoninsightcreation.”
Theycontinuebystatingthatdatadevelopmentrolesarehighlyoutsourcedandtransient.Thistransientnatureoftheseengineerandscientistrolesmeansthatknowledgefromdevelopmentprojectsarelostwhenresourcesarereassigned.Dataarchitectsareoftennecessaryfordesignworkforplatformsandtoprovidedataaccesstoadhocrequests.Therefore,asdataengineeringevolves,dataarchitectureisbecomingapeerandguidestheinvestmentsrequiredfordatasourcesandserviceswhileprovidingtheplatform,frameworks,andreferencearchitectures.
Theroleofadataanalystcontinuestoevolve.NicholasChamandy(2018),theScientificDirectoratLyft,recentpublishedanarticlestatingthathave“maintainedafairlystrongsemanticdistinctionbetweenthetworoles:analystsextractinsightsfromdata,trackthehealthofourbusinessanddrivebetterdecision-making;scientistsbuildthemathematicalmodelsandalgorithmsthatpowerthecorecomponentsofourproduct.”ThisfurtherhighlightstheindustrychallengesatdefiningthepillarsandrolesofAIML/DL.
RESEARCHMETHODOLOGY
Thereisalimitedvolumeofacademicpeer-reviewedliteraturethataddresseshowAIML/DLisimpactingIT/ISpillars.Theauthorsfoundtheclearmajorityofmaterialpublishedisintradejournals.Eveninthetradejournals,thetopicisaddressedwithsignificantvariance.Whilethefieldofmachinelearning,deeplearningandartificialintelligencespansthreedecades,theacademicresearchregardingIT/ISpillarsandindustryrolesisrathersparse.
UsingthefourIT/ISpillarsasaframework,theresearchercentereduponthefollowingresearchquestions:
R1:HowhastheIT/ISpillarsdefinitionevolvedwithrespecttothedevelopmentofML/DLbusinessapplications?
R2:WhichskillsaligntotheIT/ISpillarsforthedevelopmentofML/DLbusinessapplications?
R3:WhichcompetenciesaligntotheIT/ISpillarsforthedevelopmentofML/DLbusinessapplications?
Toaddresstheseresearchquestions,theauthorsusedaqualitativeopinion-basedresearchmethodologyapproachusinginformalindividualinterviewsofsubjectmatterexpertpractitionersandmanagers.Areviewofpeer-reviewedscholarlypublicationswasusedtodeterminetheboundaries(skills,competencies,technologymaturity)oftheproblemspaceandIT/ISpillarframework.Theinterviewquestionsderivedfromarticlesthatarewithinthecomputerscience,softwareandsystemsengineering,informationscience,andartificialintelligencedomains.Theinterviewquestionswereassembledbasedonkeydomaintopicsandissuesobservedduringthereviewofexistingpeer-reviewscholarlypublications.Thequestionswerestructuredtoinquireontheparticipant’sunderstandingofhowthepillarsandroleshaveevolvedoverthepastfiveyears.TheinterviewsinvolvedaconveniencesamplesizeofnineindustrypractitionersataU.S.basedFortune50company.TheindustryexpertsspannedthefourIT/ISpillarsofdataarchitecture,dataengineering,datascienceanddataanalysis.TheinterviewswereconductedduringtheMarchand
April2018timelineviaphone,in-person,andemailcommunication.Interviewdatawasnormalizedandgroupedintocategoriesforcommonalityandroleassociation.
RESULTS
Thefirstgeneralobservationisthatthereareavarietyofrolesineachpillarthatessentiallyhavethesame,orsimilar,definition,technicalskillsandcompetencies.Dependingontheindustry,theserolescouldgobyadifferenttitle(e.g.InformationArchitect,MachineLearningEngineer,DataSpecialist,DatabaseAnalyst,BigDataAnalyst).Regardlessofthetitle,it’simportanttounderstandthekeydistinctionsbetweeneachroleandhowtheyfittogetheracrossthepillarstomakebetterdata-drivenbusinessapplications.Thedatavolume,veracity,andvariabilityareincreasingandbecomingmoreprominentwithinthesystemsinwhichusersinteracteveryday.
Analysisoftheinterviewsresultedinthefollowingobservationsacrossthefourpillars:
DataArchitecture–TraditionallydataarchitectsarenotincludedintheearlyphaseofdevelopmentAIML/DLbusinessapplications.IndustrychangesarerequiringdataarchitectstohaveabroadunderstandingofAIML/DLtechnologiesandassistinprovidingthe“holistic”viewoftheproblemdomain.Thisrolerequiresincreasedcompetenciesforteamworkandfacilitationalongwithtechnicalskills.
DataEngineering–Thisroleisbecomingmoreimportantandmoreinclusiveofskillspreviouslyheldbythedatascientist.Thedataengineerhastheprimaryroleofdatapreparationandmanipulation,fromingestiontoformatting/transformationtostoragefortheconsumptionbythedatascientist.OfthefourIT/ISpillars,thedataengineerhashadthemostimpactonamountoftechnicalskillsrequiredtobesuccessful.
DataScience–Withtheevolutionofthedataengineer,thedatascientisthasbeenaffordedmoretimetofocusondiscoveringinsights.Thereislessfocusonthetoolsneededforthedatapreparation.Thetechnicalskills/competencieshavenotbeensignificantlyimpacted,buttheirproductivityhasshiftedgiventhelesstimeneededfordatapreparation.
DataAnalysis–Thisrolewashistoricallyalignedtocorporatebusinessintelligencereportinghasexpandedtoincludebusinessacumenanddatasemanticunderstanding.Thisrolehasincreasedemphasisonthevisualizationofthedataforbusinessunderstanding.Thedataanalystrolewillneedtoexpandintonewwaysofdeliveringinsightsthatgobeyondthecurrentdeliverablesofreports,dashboards,andmessagingalerts.
Overall,manytechnicalskillsandcompetenciesdocumentedinliteraturealignstotheresearchfindings.Thisresearchdiscoveredthatduetothesacristyandhighersalariesofexperienceddatascientists,theroleofdataengineerisincreasingthroughouttheindustrytohelpaugment,andsometimesreplace,thedatapreparationtasksforthedatascientists.ThisfindingalignswiththeresearchperformedbyForrester(2017).
InterviewdatacollectedassociatedwiththeresearchquestionR1identifiedthatthepillardefinitionshaveevolvedasshowninTable1.WithrespecttoresearchquestionR2,thetechnicalskillsforeachpillarisshowninTable2.Table3highlightsthepillarcompetenciesthataddressesresearchquestionR3.
Table1.IT/ISPillarsRevisedRoleDefinitions
Table2.TechnicalSkillsforIT/ISPillars
Table3.CompetencySkillsforIT/ISPillars
Cao(2016)identifiedtherevolutionofdatascienceandanalyticsbythethreekeyindicatorsof1)adisciplinaryparadigmshift;2)atechnologicaltransformation;and3)innovativedataproducts.TheauthorsagreewiththisobservationandextendsittoincludetheevolutionoftheIT/ISpillars.Caoandthisresearchalignwiththefindingsthatmostdatascientistssimplyconductnormaldataengineeringanddescriptiveanalytics.Anorganizationrequiresdifferentroles,skillsandcompetenciesaccordingtothematuritylevelofbusinessapplicationsthroughtheeffectiveusetheIT/ISpillars.
Additionalsuggestedresearchcouldbecomparingthesefindingstothecurrentcareerpathsandavailableresourcesforindividualbecomeeducated.Sagheb-Tehrani(2015)concludedthat“thecollegecurriculumininformationsystems(IS)isrevisitedandoftenchangedininstitutionsforhighereducationtoreflectthechangesinthefield.ItisimportanttomakenecessarychangestotheIScurriculumtomakeprogramschallengingandtobetterpreparegraduatesfortoday’sjobmarket.”TheauthorsbelievethatitispossibletocapturetheevolutionoftheIT/ISpillarsforAIML/DLintheredesignofcollegecurriculums.Theavailabilityofundergraduatecurriculumsforthedataarchitectroleisparticularlysparse(Aasheim,et.al.,2015).Otherthanundergraduatecurriculums,theIT/ISindustryhasanopportunitytoincreaseunderstandingthroughamoreformalizedassociationtothepillarsandroles.
SUMMARY
ThisresearchindicatesthatAIML/DLtechnologyisimpactingmanyIT/ISroleswhendevelopingbusinesssolutions.Theinitialheavyemphasisonthedatascientistroleisresultinginaresourceconstraintthatcanbeaugmentedbythedataengineer,dataarchitectanddataanalyst.TheevolutionoffourkeyIT/ISpillars(forroledefinition,requiredtechnicalskills,andcompetencies)willbenecessarytosuccessfullyimplementtoday’sAIML/DLbusinesssolutions.TheevolutionofthesepillarscouldinfluencecurriculumchangesforuniversityIT/ISprogramsasAIapplicationsgainbusinessadoptionandacceptance.
ThefourIT/ISpillarswillneedtoexpandandevolvetheirtechnicalskillsandcompetenciestoincludeInternetofTechnology(IoT)dataandeventdriventechnologies.IoTwillimpactorganizationswithnewdatasources,tools,andarchitecturesforanalysisandinsights.Eventdrivenconceptsandtechnologieswillhelpmanageandthrottlemultiple
datastreamstodeliveradditionalinsights.TheauthorsrecommendfurtherresearchinrespecttoIoTandeventdriventechnologyastherelatetoAIML/DLtechnologies.
REFERENCES
Aaschiem,C.L.,Williams,C.,Rutner,P.,&Gardiner,A.(2015).DataAnalyticsvs.DataScience:AStudyofSimilaritiesandDifferencesinUndergraduateProgramsBasedonCourseDescriptions.JournalofInformationSystemsEducation,26(2),103-115.
BanavarG.,&CooperM.(2016).TuringLecture2017CognitiveComputing.ITNOW2016;58(4):62-63.doi:10.1093/itnow/bww117
Boisvert,D.,Topi,H.,Harris,M.D.,&Yohannan,K.(2017).ExploringtheLandscapeofDataScience.
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