Gartner-首席数据和分析官如何领导数据科学和机器学习领域的技能提升计划

GartnerResearch

HowCDAOsCanLead

UpskillingInitiatives

inDataScienceand

MachineLearning

PeterKrensky

15December2022

HowCDAOsCanLeadUpskillingInitiativesinDataScienceandMachineLearning

Published15December2022-IDG00780993-12minread

ByAnalyst(s):PeterKrensky

Initiatives:ChiefDataandAnalyticsOfficerLeadership

Asthehiringboomfordatasciencetalentcontinues,initiativestoupskillquantitativeprofessionalsremainjustasprominent.CDAOsshouldusethishigh-levelguidancetohelpdevelopin-housetalentandimprovedatascienceandmachinelearningliteracy.

Overview

KeyFindings

■Machinelearningliteracyremainslowinmanyorganizations;concertededucationandculturechangearenecessarybutdifficultduetothegapbetweendatascientists’technicalexpertiseandbusinessusers’domainknowledge.

■Abundanteducationalopportunitiescombinedwithtalentacquisitionandretentionchallengesmotivateorganizationstoupskilltheirdataprofessionalsatalllevelsofsophistication,especiallywithanaimtogrowtheircitizendatascientistpopulations.

■Anoverwhelmingnumberoftoolsandapproachesareavailabletoexpertandcitizendatascientists.CDAOsmustnavigateavastlandscapetomatchdiverseuserstoappropriatesolutionsandcorrespondingeducationalpaths.

Recommendations

CDAOsresponsibleforanalytics,BIanddatasciencesolutionsshould:

■Raiseoveralldatascienceandmachinelearningawareness,adoptionandliteracybyprovidingcentralizededucationalresourcesandshowcasingexistingusecasesandsuccessstories,bothinternalandexternal.

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■Identifycitizendatascience(CDS)candidatesintheirorganizationbycreatinganinventoryofin-houseskillsandambitions.MatchupskillingpathstothevariousbackgroundsandaspirationsofCDScandidates.Looktobuildinterconnectedcommunitiesofdatascientists,citizendatascientistsandotherMLpipelinestakeholders.

■Buildrepeatableandsustainableeducationprogramsbydesigningdifferentupskillingroadmapsforaverageconsumersofanalytics,CDScandidatesandexpertdatascientists.

StrategicPlanningAssumptions

By2024,75%oforganizationswillhaveestablishedacentralizeddataandanalytics(D&A)centerofexcellencetosupportfederatedD&Ainitiativesandprevententerprisefailure.

By2025,50%ofdatascientistactivitieswillbeautomatedbyartificialintelligence,easingtheacutetalentshortage.

Introduction

Thetalentgapindatasciencemayneverbefullyclosed—butitcanbenarrowed.ManyGartnerclientsstillreportdifficultyfindingandattractingtalent.Retainingproductivedatascientistsforlongtenuresisalsoamajorchallenge.CDAOsneedtolearnhowtobuilddevelopmentpathsforexpertsandsupportbuddingcitizendatascientistswiththerighttools,trainingandstructure(seeNote1forCDAOroledefinitionandNote2foradefinitionofcitizendatascientist).Evenorganizationsthatbuildhighvolumesofcomplexandaccuratemodelshavetodiligentlyfosterdataliteracyandproperadoptionofsolutions.Forexample,CDAOswhoinvestmuchmoreinresourcesandtalentare1.8xmoreeffectiveandsuccessfulwiththeirdataliteracyprograms.1Upskillingshouldbepromotedthroughouttheorganization,withtargetedtrainingforaselectgroupofindividuals—bothexpertsandnewCDScandidates—aswellasgeneraleducationforallconsumersofanalytics.

Thegreatestopportunityformostorganizationstogrowtheirtalentpoolfordatascienceandmachinelearningisthroughtheupskillingofcurrentstaff.

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Mostsuccessfulupskillingincorporatessomeformaleducationandtraining,butoftenthemostimpactfullearninghappens“onthejob”duringthecompletionofanewprojectortheassumptionofnewanalyticalduties.Manyself-identifieddatascientistshavefewerthanfiveyearsofexperienceworkingwithML.Leadersshouldexpectsomegrowingpainsandsteeplearningcurves.Timelinesfordevelopmentshouldbeflexiblewherepossible,andearlyprojectsshouldbelimitedinscopeandrisk.

Analysis

RaiseMachineLearningLiteracyAmongConsumersandPromote

CollaborationWithDataScientists

Raisingthelevelofdiscoursearounddatascienceandmachinelearningisthefirststeptowardupskillingyourworkforce.Beginbyensuringthatallline-of-business(LOB)leadersanddecisionmakershaveaclearunderstandingofhowdatascientistscreatevalue.Thiscanbedonethroughsimpleworkshopsorexerciseswheredatascientistsand/orotherparticipantsbegintoquestionsomeexistingKPIs,dataormetrics.Aspiringmodelbuildersandheavyconsumersshouldhaveafoundationalunderstandingofthemachinelearninglifecycle—particularlydatapreparation,featureengineering,testingandtraining,anddeployment(seeFigure1).Emphasizetoconsumersthatanalyticsconsumersarethekeytogeneratingvaluefromtheworkofdatascienceteamsandgivingfeedbackforthefutureprojectsandmodeliterationsthatwillgothroughthiscycle.

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Figure1.TheMachineLearningLifeCycle

Helpenthusiasticindividualsbecomefamiliarwiththebasicsofseveralmachinelearningtechniques,suchasregression,clusteringandclassification.Encourageorrequiredatascientiststoregularlyholdopensessionstodiscussacurrentproject(inlayperson’sterms)orintroduceanaspectofdatasciencetheyarepassionateabout.Considergamificationofdevelopmentthatencouragesupskillingindividualstoattendregulartraining,engagenewsubjectsorenterintohealthycompetitionwithpeers.

CreateaSkillsInventorytoIdentifyIn-HouseCDSCandidatesandFosterInterconnectedDataScienceCommunities

Talentalignment,careerdevelopmentandretainingtalentarethemajorleadershipdemandsneededforsustainingsuccessfulupskillinginitiatives.TheroleofthecitizendatascientistpresentsuniquedemandsforCDAOsintermsoftalentrecognitionanddevelopment.CDAOsneedtounderstandwhattheCDSpersonais(seeFigure2)andrecognizetheskillsandprofilesthatmakeforgoodCDScandidates.

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Figure2.BreakingDowntheCDSPersona

Talktopotentialcandidatestogaugetheirinterestandaspirationsaroundcareersindatascience.Getcandidatestocompleteself-evaluationsoftheirbackgroundssothatadetailedinventoryofskillscanbeestablished.Thisinventorywillbeinvaluablefordesigningtrainingprogramsandmakingtechnologyinvestments.Themostpromisingcandidatesforupskillingoftenhaveeducationalandprofessionalbackgroundsinphysics,chemistry,biology,actuarialscience,computerscience,engineering,finance,economicsandmathematics.

UsethatdatafromtheinventorytoidentifycommunitieswithinITandbusinessunitsandacrossbusinessfunctions.Supportandgrowthesecommunitiesviaformalmentorship,structuredcollaborationandstrategicsoftwareinvestments.Lookforleadersandpractitionersthatwillkeepthesecommunitiesactiveandtalkingtoeachother.Formoreonspreadingupskillinginitiativesthroughcommunities,seeUseSocialInfluencerPrinciplestoBuildCommunitiesandIncreaseDataandAnalyticsAdoption.

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DesignUpskillingRoadmapsforExpertCDSCandidatesandAnalyticsConsumersandSupporttheContinuousDevelopmentofExperts

Conductfoundationalupskillinginitiativesarounddatascienceandmachinelearningwithintwogroupssimultaneously:CDScandidatesandanalyticsconsumers(seeFigure3).

Figure3.SimultaneousUpskillingRoadmap

ThePathforCDSCandidates

TheCDScandidategroupwillembarkonastructuredupskillingprogramacrossthreestages.

Stage1:ApproachSelectionandFormalTraining

Regardlessoftheselectedapproach,allCDScandidatesshouldsetoutwithaclearvisionoftheskillstheywillneedtoacquireandhone.CDAOsshouldtasktheirteamstoworkwithHRtodesigncareerpathsforcitizendatascientists,includingdifferentspecializations.

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Formaltrainingcanbeacourse,bootcamportooltrainingprogram.CDAOsandCDScandidatesinterestedinpursuingupskillingthroughfreestudyshouldexplorethemassiveopenonlinecourses(MOOCs)offeredbyorganizationssuchasCoursera,DataCamp,edX,UdacityandUdemy.CDAOsshouldensurethatthenumeroustutorialsavailableonYouTube,KaggleLearnandelsewhereareinvestigated.LeadersandCDScandidatesshouldinvestigatelocalin-personoptionsaswell.Whenoverseeingafreestudyprogram,developclearincentivesandmilestonesforCDScandidatesinordertoaccuratelymeasuretheirprogress.Classroomlearningwillserveasafoundationforextensiveproject-basedlearning.

Stage2:ExperimentationandPrototyping

OnceCDScandidatescompletetheirformaltraining,theyshouldbeginexperimentingwiththeirnewskillsanddesigningprototypes.LeadersshouldprovidesandboxenvironmentsfornewMLpractitionerswhereuserscanworkwithdesensitizeddatafortrialanderror.Wheneverpossible,placecitizendatascientistsunderthementorshipofdatascientistswhocanreviewtheirworkandprovidefeedback.LeadersshouldensureCDScandidatesalso:

■Beginhoningthecommunicationskillsnecessaryforsuccessfuldatascienceandevangelizethemethodologyandpotentialoftheirworkwithmachinelearning.

■Establishregularcontactandcollaborationwithanalyticsconsumersandexpertdatascientists.

■Examineanalyticsconsumers’suggestionsfornewmodels.

■Sharetheirdomainexpertisewiththeirdatascienceteammates.

■Initiateareverseknowledgetransfertofillgapsamongeventhemosttenureddatascientists.

Stage3:DeliveryandOperationalization

ThethirdstageofCDSupskillingisthedeliveryandoperationalizationofnewmodels.TheemergingrolesofchiefdatascientistandMLengineershouldtakeanactiveroleinshepherdingcitizendatascienceprojectsfromexperimentationtoproduction.Citizendatascientistsshouldalsopromoteutilizationamonganalyticsconsumersandworktoestablishcontinuousfeedbacktoimproveexistingmodelsandexchangeideasfornewones.

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Gartnerhaspublishedresearchthatfurtherexploresbestpracticesformanagingcitizendatascientistsandcommonpitfallstoavoid(seeLessonsFromDataScientistsonTheirEducationandCareerDevelopment).

ThePathforAnalyticsConsumers

Thesecondgrouprequiringsimultaneousstructuredupskillingisconsumersofanalyticsthroughouttheorganization—againacrossthreestages.

Stage1:IntroductiontoDataScienceandMachineLearning

Begintheupskillingroadmapfortheaverageconsumerwithintroductory-leveleducationondatascienceandmachinelearning.Thisshouldcomeintheformofanexternalguestspeakerordedicatedtimewithaninternaldatascienceprofessional.Showconsumersinternalcasestudieswithmeasurablebusinessvalueandrecruitthemasenthusiasticstakeholdersinyourentiredatascienceinitiative.CDAOsshouldviewthisphaseasakeyopportunitytoestablishnewworkingrelationshipsbetweenthedatascienceteamanddifferentLOBs.

Stage2:BrainstormIdeasonMetricsandAnalytics

Followupthisintroductorytrainingwithbrainstormingsessionsonmetricsthatanalyticsconsumerswouldliketobetterpredictandoptimize.Thisinturnshouldleadtoproposalsforinvestmentsinpackagedapplicationswiththehighesteaseofimplementationanduse.

Atthisstage,putanalyticsconsumersincontactwithCDScandidatestosharetheirideasandhearwhatmaybepossibleuponthecompletionoftheupskillinginitiative.Analyticsconsumerscanhelpbrainstormideas,andthetwogroupscanthencollaborateonfeasibilityandprioritization.

Stage3:TesttheFunctionalityoftheModels

Finally,analyticsconsumersshouldreceiveandutilizethefirstroundofmodelscreatedbycitizendatascientists.CDAOsshouldtasktheirteammemberstomonitortheconsumptionofnewmodelstoensureactiveparticipationattheLOBlevel.Analyticsconsumersshouldofferfeedbacktodatascienceteamsonthestrengthsandshortcomingsofnewmodelsandparticipateinbrainstormingforthenextroundofmodeldevelopment.

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DriveUpskillingofExpertDataScientists

Successfuldatasciencerequiresconstantlearningatalllevels—especiallyamongexperts.Encourageandsupporttheircontinuedknowledgedevelopmentinallareasofthefast-movingAIspace.Createspaceindatascienceprofessionals’schedulesforindependentlearning.

Commonareasofstudyanddevelopmentforexpertdatascientistsinclude:

■Domainknowledge

■Communicationskills

■Strategicandexecutiveskills

■Deeplearning

■Reinforcementlearning

■Augmenteddatascienceandmachinelearning

■InternetofThingsandedgecomputing

■Computervision

■Naturallanguageprocessing

■Machinelearningoperations(MLOps)

■AItrust,fairnessandexplainability

■Digitalethics

InadditiontohardskillsandhottopicsinAI,expertdatascientistsespeciallybenefitfromleadershipandhumanskillsdevelopment.Datascientists,particularlyjuniordatascientists,tendtohavelessstrengthandexperienceinareassuchaspresentationsskills,mentorship,collaborationandprojectprioritization.Formoreonthistopic,seeAnExecutiveLeader’sGuidetoStaffingEffectiveDataScienceTeams.

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CDAOswhohavetenureddatascientistsshouldcollaboratewiththeseindividualstounderstandtheirambitions,thendesignappropriatecareerpaths.Doyourdatascientistswanttobepurepractitionersordevelopintomorehybridroles?Seniororchiefdatascientistscanstepintoaroleprimarilyfocusedonmanagementandarenolongerday-to-daypractitioners,whichmaynotappealtomanyexpertdatascientists.ReservesuchaleadershiproleformaturedatascienceoperationsthathavemultiplejuniordatascientistsandanestablishedCDSinitiative.Formoreonthistopic,seeRolesandSkillstoSupportAdvancedAnalyticsandAIInitiatives.

Evidence

GartnerChiefDataOfficerAgendaSurveyfor2022:ThisstudywasconductedtoexploreandtrackthebusinessimpactoftheCDOroleand/ortheofficeoftheCDOandthebestpracticestocreateadata-drivenorganization.TheresearchwasconductedonlinefromSeptemberthroughNovember2021among496respondentsfromacrosstheworld.Respondentswererequiredtobethehighestleveldataandanalyticsleaderintheorganization:chiefdataofficer,chiefanalyticsofficer,themostseniorleaderinITwithdataandanalyticsresponsibilities,orabusinessexecutivesuchaschiefdigitalofficerorotherbusinessexecutivewithdataandanalyticsresponsibilities.Thesurveysamplewasgleanedfromavarietyofsources(includingLinkedIn),withthegreatestnumbercomingfromaGartner-curatedlistofmorethan4,519CDOsandotherhigh-leveldataandanalyticsleaders.ThestudywasdevelopedcollaborativelybyGartnerD&AanalystsandthePrimaryResearchTeam(SeeCDAOAgenda2022:FocusonValue,TalentandCulturetoPullAhead).

Disclaimer:Resultsofthisstudydonotrepresentglobalfindingsorthemarketasawholebutreflectsentimentoftherespondentsandcompaniessurveyed.

Note1:CDAORoleDefinition

Chiefdataandanalyticsofficer(CDAO)referstothebusinessleadershiprolethathastheprimaryenterpriseaccountabilityforvaluecreationbymeansoftheorganization’sdataandanalyticsassets,andthedataandanalyticsecosystem.Equivalenttitlesforthisrolearechiefdataofficer,chiefanalyticsofficer(iftheCDAOroleorequivalentisnotintheenterprise),chief/headofdataandanalyticsandothervariations.

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Note2:CitizenDataScientist

Acitizendatascientistisapersonwhocreatesorgeneratesmodelsthatusepredictiveorprescriptiveanalytics,butwhoseprimaryjobfunctionisoutsidethefieldofstatisticsandanalytics.Thepersonisnottypicallyamemberofananalyticsteam(forexample,ananalyticscenterofexcellence)anddoesnotnecessarilyhaveajobdescriptionthatlistsanalyticsashisorherprimaryrole.ThispersonistypicallyinalineofbusinessoutsideITandoutsideabusinessintelligence(BI)team.However,anITorBIprofessionalmaybeacitizendatascientistiftheirworkonanalyticsisonlyasecondaryrole.Citizendatascientistsare“powerusers”whoareabletousesimpleandmoderatelysophisticatedanalyticsapplicationsthatwouldpreviouslyhaverequiredmoreexpertise.

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