大数据分析机构战略和运作影响（第三、四章）翻译项目报告.pdf

'巧译专业学位硕女论文夫巧务祈／机我戌？和化巧恭第左、巧幸＾Ｉｎ种巧《振告张乃斌推巧巧巧ｔ巧巧巧巧巧专化巧巧ｔ巧译巧丈硏巧巧负１巧语缠巧论义巧巧財间：２０巧年４月论文答辩时阐：２０化年５月沦义＊今＊２０１制６１？，‘ SichuanInternationalStudiesUniversityATranslationProjectReportofBigDataandAnalytic：StrategicandOrganizationalImpacts（Chapter3and4）byZhangWanbinAthesissubmittedtotheGraduateSchoolinpartialfulfillmentoftherequirementsforthedegreeofMasterofTranslationandInterpretingunderthesupervisionofAssociateProfessorTangFangChongqing,P.R.ChinaMay2016 i 大数据分析：机构战略和运作影响(第三、四章)翻译报告摘要本文是一篇翻译项目报告。翻译项目的原文节选自文森佐·莫拉比托所著的《大数据分析：机构战略和运作影响》的第三章和第四章（BigDataandAnalytic：StrategicandOrganizationalImpacts）。原文第三章主要探讨大规模数字教育体系（如慕课）如何推动正规教育机构的远程教育工作，以及如何开展同伴之间相互学习的教育模式，并对两个案例进行分析解释，阐明了大数据分析在课程设计和推广方面发挥了基础性作用。原文第四章描述了大数据对商业模式图中各个要素的影响，同时也探讨大数据在实现大众化定制和产品服务个性化方面的潜能，对现有的商业模式进行更为具体的定义。翻译文本属于信息类文本，译者在翻译过程中遵循尤金·奈达的功能对等理论，旨在突出翻译的交际功能，消除误解，使译文文本符合译入语的要求。本翻译报告分析原文本的类型和特点，阐明翻译的指导理论，并以实例说明翻译过程中遇到的困难和相应的解决措施。翻译理论与翻译实践的结合，让译者得以全面提高翻译能力，并为今后的翻译实践打下坚实的基础。关键词：大数据；信息类文本；功能对等；交际功能；重组法ii ATranslationProjectReportofBigDataandAnalytic：StrategicandOrganizationalImpacts（Chapter3and4）AbstractThisisatranslationprojectreportonBigDataandAnalytic:StrategicandOrganizationalImpacts（Chapter3and4）byVincenzoMorabito,anAssociateProfessorattheManagement&TechnologyDepartmentofBocconiUniversityandaResearchScholaratboththeCenterforInformationSystemResearch,MITSloanSchoolofManagement(2006)andtheDecisionandInformationScienceDepartment,UniversityofFlorida.ChapterThreediscusseshowmassivedigitaleducationsystemslikeMOOCs,facilitatethedistancelearningaspectsofformaleducationinstitutionsandenableapeer-to-peerlearning,illustratedbytwocasesstudieswherebigdataandanalyticsplayedessentialrolesinthedesignanddeliveryofthecurricula.ChapterFourdescribestheimpactofbigdataoneachoftheelementsasidentifiedintheBusinessmodelcanvasanddiscussesthepotentialofbigdataformasscustomizationandpersonalizationofproductandservices,focusingonamorespecificdefinitionofthetermBusinessModelnowadays.Thetexttypeofthesourcetextisinformative.ThetranslatorstickstotheprincipleoffunctionalequivalenceproposedbyEugeneNidainthetranslationprocess,andputsmoreemphasisoncommunicativefunction,aimingtoclearupmisunderstandingsandmeettherequirementoftargetlanguage.Inthisreport,theauthoranalyzestexttypesandlinguisticfeaturesofthesourcetext,introducesaguidingtheoryforthetranslation,andillustratesdifficultiesencounteredduringtranslatingandtheircorrespondingsolutions.Bycombiningtranslationtheorieswithpractices,theauthorgainsanopportunitytocomprehensivelyimprovehistranslationcompetence,soastolayasolidfoundationforlattertranslationpractice.Keywords：bigdata;informativetext;functionalequivalence;communicativefunction;recastingiii AcknowledgementsIwouldliketoexpressmysinceregratitudetothosewhodirectlyorindirectlyhelpedmeintheprocessofwritingthistranslationprojectreport.ItappearsthatIhavebeenaccustomedtothelifeinSichuanInternationalStudiesUniversity,whichmakesmereluctanttoleavethiscampuswhereIhavelivedforalmosttwoyears.HereIwanttoespeciallyextendmyheart-feltthankstomysupervisor,MrsTang.Herprofoundknowledge,patience,andlogicalwayofthinkingwillhavebeenveryvaluabletome.Also,Ifeelverythankfultotheadviceshegavemeinlasttwoyears.Additionally,Iamgratefultoalltheteacherswhohavetaughtmeduringthetwoyears.Withtheirhelp,Ilearnedalot.Still,Iwanttoextendmythankstomyclassmates.ItiswiththemthatIhavespentthesetwoyearshappily.Lastbutnottheleast,Iwouldliketoexpressmygratitudetomyparents,whoalwayssupportmeunconditionally.iv CONTENTS摘要.............................................................................................................................iiAbstract.........................................................................................................................iiiAcknowledgements.......................................................................................................ivChapterOneIntroduction.............................................................................................11.1BackgroundoftheProject............................................................................11.2SignificanceoftheProject..........................................................................21.3StructureoftheReport................................................................................2ChapterTwoAnalysisoftheSourceText....................................................................42.1AbouttheAuthor..........................................................................................42.2IntroductiontotheSourceText....................................................................42.3AnalysisofItsTextTypeandFeatures.........................................................52.3.1AnalysisofTextType..........................................................................52.3.2AnalysisofTextFeatures.....................................................................6ChapterThreeTheoreticalBasis,TranslationDifficultiesandTheirSolutions...........83.1FunctionalEquivalenceTheory.....................................................................83.2EquivalenceontheLexicalLevel.................................................................93.2.1TranslationofSpecializedTerminology...............................................93.2.2TranslationofNon-SpecializedAbstractNouns.................................113.2.3Conversion..........................................................................................133.3EquivalenceontheSyntacticLevel.............................................................153.3.1Recasting.............................................................................................153.3.2Division...............................................................................................163.4EquivalenceontheStylisticLevel...............................................................18ChapterFourConclusion.............................................................................................204.1LessonsGained...........................................................................................204.2ProblemstoBeSolved................................................................................21References...................................................................................................................22AppendixSourceⅠText............................................................................................23AppendixⅡTargetText.............................................................................................59v ChapterOneIntroduction1.1BackgroundoftheProjectWithabillionplususersontheonlinesocialgraphdoingwhattheyliketodoandleavingadigitaltrail,andwithtrillionsofsensorsnowbeingconnectedintheso-calledInternetofThings,organizationsneedclarityandinsightsintowhatliesaheadindeployingthesecapabilities.Whileacademicscholarsarejustbeginningtoappreciatethepowerofbigdataanalyticsandnewmediatoopenupafascinatingarrayofquestionsfromahostofdisciplines,thepracticalapplicabilityofthisarenaisstilllacking.ThebookBigdataandanalyticscoversmultipledisciplinesrangingfromsociology,psychology,andethicstomarketing,statistics,andeconomics,aswellaslawandpublicpolicy.Ifharnessedcorrectlyithasthepotentialtosolveavarietyofbusinessandsocialproblems.ThisbookaimstodevelopthestrategicandorganizationalimpactsofBigDataandanalyticsfortoday’sdigitalbusinesscompetitionandinnovation.Writtenbyanacademic,thebookhasnonethelessthemaingoaltoprovideatoolboxsuitabletobeusefultobusinesspracticeandknow-how.TothisendVincenzo,aswhathedidinhisformerbookshasstructuredthecontentintothreepartsthatguidethereaderthroughhowtocontrolandgoverntheinnovationpotentialofBigDataandAnalytics.Accordingly,eachtopicconsideredinthisbookwillbeanalyzedinitstechnicalandmanagerialaspects,adoptingaclearandeasy-to-understandlanguage,inordertocapturetheinterestsoftopmanagersandgraduatestudents.Sothisbookisexpectedtobeaconsulttoolboxforbothmanagersandscholarstocopewithmanymodernchallengesinbusinesscircleandourlife.WiththeanalysisofBigData,thebookisaboldnewfrontierinmanydifferentareasanditissafetosaywehavedoneinadequateresearch,soitiswellworthytobetranslated.1 1.2SignificanceoftheProjectThebookhaseightchapters.ThetranslatorselectsthecoreinformationinrelationtobothMOOCseducationandbigdatadrivenbusinessmodelsasthesourcetextofthetranslationproject.AsforMOOCserviceproviders,theirprimeconcernisthequalityofproductsorservicesforitisthefoundationofgainingastrongfootholdintheindustry.Thus,thetranslationofthisprojectcanprovidesomenewperspectivesforthefieldsofMOOCindustry.Besides,withtheincreasingimportanceofMOOCeducationinavarietyofareas,suchasinglobaltradeandsoftwareindustry,universitiesofInternationalBusinessandEconomicsandotheruniversitiesinChinahavesetupcoursesonthisarea.Thus,thisprojectcanenlightenscholars,teachersandstudentswhoareinterestedinMOOCandbigdatadrivenbusinessmodelsandprovideawayforthemtogetfamiliarwiththelatestindustrialinformation.Furthermore,asapostgraduatemajoringinEnglishTranslation,selectinganacademicoriginaltextrelatedtoeducationandbusinesscanbebeneficialtothetranslator’stranslationcompetence.Inaddition,itcanmakethetranslatorknowsomeupdatedinformationandspecializedknowledgeofrelatedindustry.Hence,byacquiringsomeinformationaboutanemergingindustrylikeMOOC,itwilllayasolidfoundationforthetranslator’stranslationworkinthefuture.1.3StructureoftheReportThemaincontentsofthistranslationreportaredividedintofourchapters.Chapteroneisthedescriptionofthetranslationproject,includingthebackground,significanceandstructureoftheproject.ThetranslatordepictsthegeneralinformationanddevelopmentofMOOCandbigdatadrivenbusinessmodels.2 ChaptertwointroducesthebackgroundoftheST,includinginformationabouttheauthorandsourcetext,andanalysisofthetexttypeandlanguagefeaturesofthesourcetext.Chapterthreeismainlyconcernedwiththediscussionofequivalenceonthelexical,syntacticandstylisticlevelsinacademictextssupportedbyEugeneA.Nida’sdynamicequivalencetheory,andthreetranslationskills—conversion,divisionandrecasting,areintroducedtosolvethetranslationdifficulties.Chapterfouristheconclusionofthetranslationprojectreport,describingtheexperienceofthetranslatorandthelimitationsoftheprojectduringthetranslationprocess.3 ChapterTwoAnalysisoftheSourceText2.1AbouttheAuthorVincenzoMorabito,PhD,isAssociateProfessorattheManagement&TechnologyDepartment,UniversitàCommercialeLuigiBocconi(BocconiUniversity),Milan,Italy.HegainedhisdoctoratefromtheUniversitàCommercialeLuigiBocconiandwasaResearchScholaratboththeCenterforInformationSystemResearch,MITSloanSchoolofManagement(2006)andtheDecisionandInformationScienceDepartment,UniversityofFlorida(2005/2006).VincenzoMorabitoisinchargeofthecourseonBusinessOrganization,ManagementofInformationSystems,andInformationManagementforthevariousdegreeprogramsofBocconiUniversity.Hehasparticipatedinavarietyofresearchprojects,manyfinancedbytheItalianMinistryofUniversityandScientificResearch(Ministerodell"UniversitàedellaRicercaScientificaeTecnologica).2.2IntroductiontotheSourceTextThisnewly-publishedbookBigDataandAnalytics:StrategicandOrganizationalImpacthasnoChineseversionandisavailableonthemarketafteritspublishingdateofMarch3,2015.Itwasdividedintoninechapters,fromwhichthetranslatorchosechapterthreeandchapterfourastranslationproject.ChapterThreediscusseshowmassivedigitaleducationsystemslikeMOOCs,inspirethedistancelearningofformaleducationinstitutions,enableapeer-to-peerlearning,andexplaintwocasesstudieswherebigdataandanalyticsplayedessentialrolesinthedesignanddeliveryofthecurricula.ChapterFourdescribestheinfluenceofbigdataoneachoftheelementsasidentifiedintheBusinessmodelcanvasanddiscussesthepotentialofbigdataformasscustomizationandpersonalizationofproductandservices,focusing4 onamorespecificdefinitionofthetermBusinessModelnowadays.2.3AnalysisofItsTextTypeandFeatures2.3.1AnalysisofTextTypeThesourcetextofthisproject—thethirdandfourthchaptersofBigDataandAnalytic：StrategicandOrganizationalImpacts—belongstoinformativetext.Weneedtointroducethepaperfromaspecificperspective.Thispaperisaninformativetext,themainfunctionofwhichistoinformreadersofdetailedinformationandphenomenonintherealworld.Itfeaturescontent-centeredanddescribesalotoffacts.Hence,translatingthisbookshouldpaymoreattentiontoaccuracyandintegritywithoutredundancy.Functionalismservesasoneoftheimportanttranslationtheoriesschools,andcommunicativeapproachtotheanalysisoftranslationmoveawayfromthestaticlinguistictypologiesoftranslationshifts.Reiss,oneoftherepresentativefiguresoffunctionalism,viewsthetext,ratherthanthewordorsentence,asthelevelatwhichcommunicationisachievedandatwhichequivalencemustbesought(Reiss,1971,p.64).Reiss’sworkisimportantbecauseitmovestranslationtheorybeyondaconsiderationoflowerlinguisticlevelstowardsaconsiderationofthecommunicativepurposeoftranslation.Accordingtohertheory,texttypecanbedividedintothreetypesbytheirmainfunction,namelyinformative,expressive,andoperative.Vermeer’sskopostheoryinsiststhatthepurposeofthetranslationisthetoppriority,whichdeterminesthetranslationmethodsandstrategiesthataretobeemployedinordertoproduceafunctionallyadequateresult(Vermeer,1984,p.48).Histheoryexplainsthetheoreticalfoundationsandbasicprinciplesasageneraltheoryoftranslationandinterpretingor"translationalaction".Thesourcetextisaninformativetypeinvolvingbusinessmodelsandprofessionaltrendyknowledgeineducationnowadays.Therefore,intheprocessoftranslation,duetranslationstrategiesandtranslationmethodsshouldbeadoptedtomakethetranslationunderstandablefortargetedreaders.Thesetranslationstrategies5 andtranslationmethodsincludefunctionalequivalenceonthelexical,syntacticandstylisticlevel.2.3.2AnalysisofTextFeaturesThesourcetextiscontent-centeredandpresentsuswithmanyfactsandfigures.Inastructure-buildingwaythatismoreobjective,itrequirestobetranslatedwithoutredundancy.Forinstance,thetranslatorchoseanexamplebelowtodemonstratethischaracteristic.Example1:ST:Educationisakeyfactortoeconomicdevelopmentfornationsandsocialmobilityfortheindividual.Twofactorshaveaccentuatedtheneedforglobaldemandineducation.Theskillscurrentlyrequiredtocompeteinaglobalenvironmentarechangingduetoshiftsintheeconomicmodelandcompetitionfromemergingdevelopingmarkets.ICT’shavegivennationsandinstitutionstheopportunitytoresourcehighlyskilledpeopleanywhereintheworldandindividualstheopportunitytomoveupsocially.Thisjourneyisnotwithoutchallenges.Developingcountriesforexamplehavelowdigitaltechnologypenetration.Africa,theMiddleEast,andLatinAmerica/Caribbeanconstitutejust17.2%oftheworld’sInternetusers(MiniwattsMarketingGroup2014).Basictechnologyeducationandaffordabilityholdpeoplebackfromdevelopingnecessarytechnologicalskills.Ontheotherhand,initiativessuchasNegroponte’sOneLaptopPerChild(OLPC)projectwhichsoughttoprovideeachchildinthedevelopmentworldwithalow-budgetcomputerslowlychangethisdynamic.TT:信息通信技术为各国、各大学提供在全球范围内优化配置高技术人才资源的机会，并为个人提供社会地位攀升的机会。在这一进程中，我们也会遇到挑战。拿发展中国家来说，其数字技术的普及程度较低。在非洲、中东和拉丁美洲/加勒比海地区，他们的网民人数只占到了世界网民的17.2%（米尼沃茨营销集团2014年调查报告）。基础技术教育和支付能力制约着人们对必要技术工艺的改进工作。另一方面，如内格罗蓬特提出的“每个孩子一台笔记本电脑”项目6 试图为发展中国家的每个孩子提供一台低成本电脑，类似的倡议正缓慢地改变着这一进程。Asisseenfromabove,thesourcetextisveryobjective,anditexploitsstatisticsandfacts,suchasprecisefigure“17.2%”,professionalreport“MiniwattsMarketingGroup2014”and“Negroponte’sOneLaptopPerChild(OLPC)project”.Suchpersuasiveexamplesandnumberscanbelistedbelowwithalonglineinthispaper.Therefore,thetargettextshouldbetranslatedaccuratelyinaccordancewiththeoriginallinguisticstyleoftheST.Example2:ST:Coursesarenotfree,butaffordable.Udemyhosts16,000suchcoursesandengages3millionstudents.Itkeeps50%ofrevenuesfromeachcourseinexchangeformarketingandtechnicalsupportthroughtheplatform.TT:这些课程并不是免费的，不过人们还是能够负担得起。Udemy开设了16,000门这样的课程，参与的学生有300万人。他们把每门课程收入的50%用于平台的营销和技术保障。7 ChapterThreeTheoreticalBasis,TranslationDifficultiesandTheirSolutions3.1FunctionalEquivalenceTheoryTranslatingthisbookshouldpaymoreattentiontoaccuracyandintegritywithoutredundancy.AmericanscholarEugeneA.NidaputsforwardtheconceptsofformalequivalenceanddynamicequivalenceinhisbookTowardaScienceofTranslatingforthefirsttimewhichunderstandsthenature,purpose,taskandmethodoftranslationfromanewperspective,focusingonnamelytransmissionofinformationandreaders’responses.Later,inhisbookTheTheoryandPracticeofTranslation,dynamicequivalenceisdefined“intermsofthedegreetowhichthereceptorsofthemessageinthereceptorlanguagerespondtoitinsubstantiallythesamemannerasthereceptorsinthesourcelanguage(Nida&Taber,2004,p.24).”Itshowsthetranslationprocessreproducestheinformationofthesourcetextwiththemostequivalentexpressionsfromlinguisticstostyle.Healsoholdsthatagreattranslatorshouldkeepeyesonthecontentsofthesourceandtargettext,andtryhardtomakethereceptorofthetargettexthavethesameorsimilarresponsetothetranslatedversionasthereceptorofthesourcetexttotheoriginaltext.InNida’sdefinitionofdynamicequivalencequotedabove,anaturalandequivalenttranslationshouldmeetfourbasicrequirements:makingsense,conveyingthespiritandmanneroftheoriginal,havinganaturalandeasyformofexpression,andproducingasimilarresponse(Nida,2004,p.164).Itisobviousthatatcertainlevelstheconflictbetweencontentandform(ormeaningandmanner)willbeacute,andthatoneortheothermustgiveaway.Ingeneral,translatorsagreewiththatuntoldrequirement,andmeaningofwordsmusthavepriorityoverstyleshouldthereisnocompromisebetweenallsides.Duringthetranslationprocessofthesourcetext,thesefunctionalequivalenceonthelexical,syntacticandstylisticlevelbasedonNida’sfourbasicrequirements,8 shouldbeachievedandhelpreaderstobetterunderstandthetextwithoutconfusions.3.2EquivalenceontheLexicalLevelJustasmentionedinanalyzingthenatureandcharacteristicsofthesourcetext,thesourcetextisanacademicwork.Itslanguageisquiteformalandstandardized,andmanyspecializedterminologyareused.Besides,inordertomakethediscussionrigorous,thewritersprefertousesomecomplexsentences,whichcausessomedifficultiesintranslatingforthetranslator.InthefollowingChapter,thetranslator,undertheguidanceoftheprinciplesandstrategiesstatedabove,willparticularlydiscusssomespecificsolutionstotranslationdifficultiesencounteredintheprocessoftranslationandusesometranslationexamplestoillustratethemintermsofthespecializedterminologyandcomplexsentences.3.2.1TranslationofSpecializedTerminologyTheSThasmanyvocabulariesinthefieldofeducationandbusiness.Someofthemaretranslatedconventionally,whilesomeofthemhavenounifiedtranslations.Intheprocessoftranslation,thetranslatortrieseveryefforttoensuretheaccuracyandreadabilityofthetranslationmainlybylookingupallkindsofparalleltext.“Paralleltextreferstothelocaltextwiththesameorsimilarsubject,styleandfunctionindifferentlanguage......Broadlyspeaking,‘paralleltext’canbeunderstoodasanyreferencematerialswithcontentclosetotheoriginaltext.”(LiChangshuan,2009,p.91)Thetranslatorcanobtainprofessionalknowledge,refineexpression,offsetthelackoflinguisticcompetenceandspecializedknowledgebyreadingparalleltext,andguaranteethequalityoftheTT.Example3:ST：timebankTT：时间银行规则（如果帮助别人一次,你就会获得一定的时间美元）Theimplicationofthisterminologyishardtounderstandevenifyouseekhelp9 fromtheInternet.FromBaiduBaike,itrefersto“志愿者将参与公益服务的时间存进时间银行，当自己遭遇困难时就可以从中支取“被服务时间”.Well,it’sratherconfusing.ButyoumayrealizewhattimebankdoesifyouhavealookatthepicturebelowfromBaiduBaike.Thetranslatorhasresearchedforalongtimetomakesuretheaccuratemeaningandcontentoftimebank,soastobetterhandleproblemsinthisnewlytouchedfield.Example4:ST：‘bigbrother’capabilityofbigdataTT:大数据的“老大哥”能力Inthispaper,itmainlydiscussesthefunctionsofbigdata.“Bigbrother”refersto“老大哥”，andtherelevantcitationpicturedbelowisusedinChineseEditionofFinancialTimes.10 (RelevantinformationpicturedfromChineseEditionofFinancialTimes.)Asisseenfromabove,wecometoknowthatbigdataplaysadominantroleinourlifeanditevencontrolsusatgiventimeandplace.3.2.2TranslationofNon-SpecializedAbstractNounsMostacademictextsdescribeknowledgeandtheoryofacertainfieldbasedonfactsandresearches.Therefore,thelanguageusedisfairlyformaltohighlighttheexpertiseinacademicwriting.Andduetotheirobjectivityandlogicality,abstractnounsareapplicabletotheconveyanceofacademictexts.LianShuneng(1993,p.164)pointsoutthat,“Abstractnounscanhighlysummarizethenatureofthingsandexpresscomplicatedideasinaconciseform.”Inaddition,“abstractnounscanpresentthefactsinanobjectiveanddispassionatewaywithoutanysubjectivetones,andtheyarealsoconducivetorationalandscientificthinking”(LianShuneng,1993,p.164).Thus,whentranslatingabstractnouns,thetranslatorshouldapplythedynamicequivalencetheoryandtrytomakethereadersoftheTThavethesameresponseasthereadersoftheSTdo.Example5:ST:gamechanger.TT:行业机制颠覆者Itisclearthattheauthorchoosestheword“gamechanger”mainlytodescribethefactthatMOOCshavemadeasensationinthefieldofeducation.AccordingtoBingDictionary,“gamechanger”haseightmeanings.ItsmeaningsarepicturedbelowfromBaidu:11 WithsomanytranslationsavailableontheInternet,thetranslatordidnotchooseanyofthemeither.Instead,thetranslatorselectedthecorrespondingChineseversion“行业机制颠覆者”andignoredtheword“game”.Bytakingthewholesentenceandindustrybackgroundintoconsideration,thetranslatorbelievestheChineseversioncanpresenttheauthor’struepurposeanditbestsuitsthereadinghabitsofChinesereaders.12 Example6:ST:monetizationTT:变现FromWikipedia,thefreeencyclopedia,monetizationistheprocessofconvertingorestablishingsomethingintolegaltender.Theterm"monetization"mayalsobeusedinformallytorefertoexchangingpossessionsforcashorcashequivalents,includingsellingasecurityinterest,chargingfeesforsomethingusedtobefree,orattemptingtomakemoneyongoodsorservicesthatwerepreviouslyunprofitableorhadbeenconsideredtohavethepotentialtoearnprofits.OnMBAlib,monetizationrefersto“货币化”.RelevantinformationpicturedfromMBAlibisasfollows:Inaddition,thetranslatorreadanarticlenamed“2013这一年：移动互联网能否变现（Monetization）”fromChinesetechblogLeiphone.com.Accordingtothesesources,thetranslatorfinallychose“变现”asthefinalversion.“变现”isalsoatrendytopicformanystart-upsinChina,sothetranslatorthinksitshouldbethemostpropertranslation.3.2.3ConversionAswritingstylevariesgreatlybetweenEnglishandChinese,thetranslatoroftenthinksitnecessarytochangeacertainformofwordorphraseandmakethetargettextmorereadableandeasiertounderstand.Inthisproject,conversioniswidely13 employedtoachievetheeffect.Example7:ST:Asitstands,MOOCshaveafundamentalproblemofidentityandthereforeissuesaroundtheirmonetizationefforts.TT:就目前情况来看，社会认知度是慕课面临的最大难题，这个因素也使得慕课的变现尝试饱受争议。Here“monetization”isanoun,butistranslatedasaverb“变现”,becausethenoun“monetization”hasastrongtendencyforaction.Consideringthis,wechooseitsdynamicformtohelpreadersunderstandexplicitly.Example8:ST:Sofar,thereisalimitedimpetusforfundamentalstructuralchangesthatplacebigdatatothecoreofcompanyvaluecreationasopposedto‘nice-to-have’capabilities.TT:截止目前，人们对变革公司基本结构的动力还是比较有限的，他们并未将大数据置于企业价值创造的核心地位，相反倒更看中大数据“锦上添花”的功能。Inthissentencetranslation,theword“limited”isanattributethatmodifythenoun“impetus”,butherethetranslatordescribesitas“动力还是比较有限的”.Thetranslatorchangesitspartofspeechbyconsideringthemeaningofthissentence.Additionally,theword“changes”,anoun,isalsoregardedasaformofverbforithasastrongtendencyforaction.Thefinalversionofthissentencecanbeeasierforreaderstounderstand,trulyachievingthepurposeoffunctionalequivalenceandcommunicativefunctionoftranslation.Example9:ST:Itisworthnotingthatabusinesssectorconsistsofanumberofcompaniesorbusinessunitsoperatingeffectivelyunderthesameorsimilarbusinessmodels.14 TT:值得注意的是，一个商业部门是由许多拥有相同或相似商业模式的高效运营公司或企业单位构成。Herethetranslatorseesthattheoriginalsentencecanbetranslateddirectlywithouthavingtosplitit.Sosomethingweshoulddoistochangesomepartsofspeechofcertainwords.Inthissentence,“underthesameorsimilarbusinessmodels”refersto“拥有相同或相似商业模式”,and“unitsoperatingeffectively”istranslatedas“高效运营单位”.Bythismeans,wecanmakeourtranslationclosertoChinesewayofexpression.3.3EquivalenceontheSyntacticLevel3.3.1RecastingFangMengzhi(1999,p.96)suggeststhat“theconnectionandarrangementbetweensentencesshouldoftenbeconsidered.”AsEnglishisatree-likelanguage,subordinatesentenceisoftenputafteramainclause,that’stosay,theconclusionorresultliesinthebeginning,andthensubordinateclausesarefollowed.ButinChinese,theanalyticclausesareusuallyplacedbeforeaconclusiveremark.Intheprocessoftranslation,weneedtoreadjustthelongandcomplicatedsentencestomakeourtargettextmorereadableandacceptablefortheChinesereaders.Example10:ST:ItdiscusseshowMOOCsopennewincomestreamsfortraditionalinstitutionsthroughemploymentrecruitingservices,syndication,andsponsoring,aswellasbyadvertisingincome,sellingstudentinformationtopotentialemployersoradvertisers.TT:慕课通过雇佣服务、辛迪加组织形式、赞助活动和广告收入，以及把学生信息卖给潜在雇主、广告商等方式，为传统教育机构带来新的收入，而本15 章正是探讨慕课是如何做到这一点的。Inthissentencetranslation,weshouldfirstreadjusttheorderoftheST,andreconsiderhowtotranslateitinamorelogicway.Herethetranslatorfirstrendersthevariousmoney-makingchannelsfortraditionalinstitutionsincluding“雇佣服务、辛迪加组织形式、赞助活动和广告收入”,andthensummarizesthatthischapteraimstodiscusshowMOOCsopennewincomestreams.Example11：ST:Furthermore,thechapterhasanalyzedtheimpactofbigdataonrevenues,asithasfacilitatednewformsofvaluecreation.TT:此外，由于大数据促成了新的价值创造模式，本章为此分析了大数据对收入的影响。Thisexampleisatypicalsentencethatputstheconclusionfirst.Generally,themostimportantpartinEnglishisincludedinmainclause,andthesecondaryinformationisinsubordinateclauses.Inthesourcetext,thefirstpartanalyzestheimpactofbigdataonrevenues,andthendescribedthereasonwhyithasdonethejob.Therefore,inChineseversion,itisbetterforthetranslatortolistthereasonatfirst,andsubsequentlytheconclusionfollows.3.3.2DivisionNida(1982,p.16)holdsthat“oneofthemostimportantdifferencesbetweenEnglishandChineseisparataxisandhypotaxis.”Asanexplicitlanguage,Englishstressesitshypotaxisandcentersontheintegrityoftextstructure,usingformsincludingpreposition,relativepronounandconjunctiontoexpressmeaningaswellastopresenttheindications,whileChineseisanimplicitlanguage,aimingtoprioritizeitsparataxis,covertcoherence,andlaylessemphasisontextcohesion.Therefore,longandcomplicatedsentencesaswellascompositestructuresaremorefrequentlyusedwithafinelogicalorderbetweenallpartsinEnglish.However,sentencesin16 Chinesearerelativelyshorter,mostofwhicharewater-flowingsentence,compoundsentenceandloosesentence.SothedifferentthinkingpatternsandlanguagehabitsbetweenEnglishandChinesesuggestthatthetranslatorneedstodividethecomplexandlongsentencesintoindependentoneswhiletranslating.AstheprincipleofNida’sdynamicequivalencetheoryistoretaintheoriginalmessageintheTTratherthantheform,itisadvisableforthetranslatortosplitthecomplexsentences.Example12：ST:MOOCsisrootedwithintheideologyofopenaccesstoeducationwithoutdemographic,economic,andgeographicalconstraintswhichdatesbackintheearly20thcentury(YuanandPowell2013).TT:慕课根植于这样一种理念，即人们不受人口、经济、地理因素的影响，都能免费接受教育，这种理念最早起源于20世纪初期（袁、鲍威尔2013）。Inthisexample,thesentenceisalongintegratedstructure,soweshouldsplitthesentenceaccordingtoitssensegroupswhentranslatingfromEnglishtoChinese.Here“openaccesstoeducationwithoutdemographic,economic,andgeographicalconstraints”isusedtoexplain“theideology”,andthetranslatorclarifiedthemeaninginvolved.Bydoingso,thetargetChinesereaderscanwellacceptwhattheSTdescribes.Example13:ST:Thischapteroutlinestheconceptof‘bigdatadrivenbusinessmodel’andutilizesittodescribeasetofbusinessesthatrelyonbigdatatoachievetheirkeyvaluepropositionandtosubstantiallyaugmenttheirvaluepropositiontodifferentiatethemselvesinordertogaincompetitiveadvantage.TT:本章勾勒出“大数据驱动的商业模式”概念，并用这个概念描述一系列企业的做法。这些企业依靠大数据实现其主要价值主张，同时为了获得竞争优17 势，他们还大幅扩充各自的价值主张，以突显与众不同。Herethetranslatorsplitthislongcompoundsentenceintotwoparts.Thetranslatorfirstpresentedthebigdatadrivenbusinessmodel,withwhichtohelpdescribeasetofbusinesses,andthentolddetailedinformationaboutthesebusinesses.Inamorelogicandsensibleway,thereadercangainabetterunderstandingofthesourcetext.3.4EquivalenceontheStylisticLevelAccordingtoNida’sdynamicequivalencetheory,thethirdlevelofequivalenceshouldbeachievedinacademicwritingistheequivalenceonthestylisticlevel.Achievingtheequivalenceofstyleneedstopaycloseattentiontothechoiceofdictionandthelanguagefeaturesinthetranslatedtextsoastoconformtothestyleofacademicwriting.FangMengzhi(1999,p.60)summarizesthat“styleofwritingreferstothecombinationoflinguisticfeaturescausedbydifferenttypesofwriting.”Differentstylesofwritingrequiredifferentlinguisticfeatures.Thus,thestylisticfeatureofthetranslatedtextisalsoacrucialfactortomeasurethetranslationquality.Atthispoint,Nida(2004,p.13)pointsoutthat“thoughstyleissecondarytocontent,itisneverthelessimportant.Oneshouldnottranslatepoetryasthoughitwereprose.”Fromtheabovestatement,differentlinguisticstylesarebasedondifferenttexttypes.Thisisatranslationprojectofacademicwritingwiththefeaturesofobjectivity,concisenessandrigorousness.Therefore,itisrequiredthatthetranslatorshouldexpresstheoriginalmessageinaclear,conciseandformalwaytoensuretheacademicstyleinboththeSTandTT.Example14：ST:Clickandcollectseemstoworkatmanylevels,peopledonotneedtobesomewherewaitingfortheirparcel,theirparcelwaitsforthematasafepick-uppoint,companiesdon"tburdenthemselveswiththecostofunsuccessfuldeliveries,and18 citizensdonothavetoputupwiththeCO2emissionsofafleetmakingroundsdeliveringparcels.TT：“点击提货”似乎适用于不同程度的配送，人们不需要在某处等候自己的包裹，他们的包裹会放在安全的提货点，等着人们自己来取。各公司不会让客户承担无效配送所产生的费用，同时市民不必再忍受快递车队配送包裹时所排放的二氧化碳。InthistypeofST,weknowthetermsaremuchmoreprofessionalinbusinesssector,andweshouldmakeourtranslationprofessionalandunderstandable,mostimportantly,equivalenttoitsstylisticlevel.Suchtermslike“clickandcollect”,“pick-uppoint”and“unsuccessfuldeliveries”shouldberenderedaccuratelyandacademically.Example15:ST:Thereisalimitedimpetusforfundamentalstructuralchangesthatplacebigdatatothecoreofcompanyvaluecreationasopposedto‘nice-to-have’capabilities.TT:人们对变革公司基本结构的动力还是比较有限的，他们并未将大数据置于企业价值创造的核心地位，相反倒更看中大数据“锦上添花”的功能。Inthisproject,thissentenceisnotdifficulttounderstand,butitmattershowtokeepthelinguisticstyleoftheST.ThetranslatorshouldlaymoreemphasisontherigorousattitudeofsuchacademicworkandkeepitslinguisticstyleoftheST.Herethetranslatorhadconsideredtimeandagainhowtotranslate“‘nice-to-have’capabilities”andtrulyhadaheadacheaboutit.Afteralong-timereflection,aChineseidiom“锦上添花”popsupinthetranslator’sheadatlast.“锦上添花”maybeisnotthebesttranslationversion,butthetranslatorbelievesitcanbeagoodfitforthelinguisticstyleofthisacademicwork.19 ChapterFourConclusion4.1LessonsGainedThroughthistranslationpractice,thetranslatorcouldhaveacertainunderstandingofMOOCresearchandbusinessmodelsabroadaswellastherelatedprofessionalvocabulariesandtheirmeanings.Inaddition,whendeterminingthenatureofthesourcetext,thetranslatorneededtohaveanoverviewofthetexttypetheoryanddecidedthemaintranslationstrategiesintheprocessoftranslation.InNewmark’stranslationtheory,whenitcomestothetranslationofaninformativetext,theTTreader-orientedcommunicativetranslationstrategy,whichusesacombinationofvarioustranslationskillstoensuretheaccuracyandreadabilityoftheTTwithanemphasisontransferringtheSTcontent,isrecommended.Thewholetranslationprocesscanbesummedupinthreepoints:understanding,expressionandproofreading.ForsmoothexpressionisbasedonthedepthoftheunderstandingoftheST,thetranslatorshouldfirstreadtheSTrepeatedlyinordertohaveathoroughunderstanding,thengraspitscentralidea,analyzetheoverallstructureandplanningoftheST,sortoutthewriter"slogicinferencelevel,checktherelevantbackgroundinformationandfinallytranslatetheST.Intheprocessoftranslation,rigorousandseriousattitudeisalsorequired,orotherwisethetranslationispronetosomelowlevelerrors.AlthoughtheTTisChinese,thetranslatorstillclearlyfeltthedeficiencyofChineseexpressionabilityintheprocessoftranslation,especiallythephraseandsentencearrangement.Therefore,itisimportanttoenhanceChineseproficiency.Proofreadingisanindispensablepartofthetranslation,includingthereviewoftranslatorandotherpeers.Althoughthetranslatorisverycarefulduringtranslation,intheTToccurinevitablysomeomissionsandinappropriatewordsandexpressions.ThetranslatorisexpectedtonotonlyavoidtheseerrorsbutalsocanfurtherdeepentheunderstandingoftheSLandelaborationoftheTL.Inaddition,translationalsorequiresteamwork.Thediscussionwithteacherandclassmatesishelpfulforthe20 translatortoresolvesomeproblemsintermsofcomprehensionandexpression.4.2ProblemstoBeSolvedInthetranslationprocess,thetranslatorhasfacedvariousproblemsandconfusions.Someofthemhavebeensolvedafterthetranslator’sexertions,whileothersstillremaintobesolved.Limitedbythetranslator’stranslationcompetence,relatedbackgroundknowledgeandsomemisinterpretationoftheST,somepartsoftheTTappeartobeawkward-soundingandstiff.Besides,vaguedescriptions,ambiguousreferenceintheSTmaycausesomesemanticfuzziness.Thetranslatorhopesthatthesequestionscanbedealtwithbyveteranexpertswhoarewillingtooffertheirinvaluableadvice,forexample:ST:Maintainfairaccessandreciprocityinthecommunitywiththeexchangeofvirtualtokensandrulesoftenenjoyedbytimebanksandreciprocalfeedbackonbothstudentperformanceandthequalityofthereviewer.TT:用兑换虚拟令牌的方式维护公平参与和互惠互利，坚持时间银行规则（如果帮助别人一次,你就会获得一定的时间美元），以及继续对学生表现和评论者素质进行信息反馈。Thetranslatorsufferedconfusionswhiletranslatingthissentence,andattemptstopolishthistranslationasmuchaspossible.21 ReferencesBassnett,Susan.(2010).Translationstudies(thirdedition)[M].Shanghai:ShanghaiForeignLanguageEducationPress.House,J.(1997).TranslationQualityAssessment:AModelRevisited.Tübingen:GunterNarr.Munday,Jeremy.(2001).IntroducingTranslationStudies:Theoriesandapplications.LondonandNewYork:Routledge.MarkShuttleworth&MoiraCowie.(2004).DictionaryofTranslationStudiesshanghai:ShanghaiForeignLanguageEducationPress.JefVerschueren.(1998).UnderstandingPragmatics.London:HodderArnold.Nida,EugeneA.(2001).Languagesandculture.Contextsintranslating.Shanghai:ShanghaiForeignLanguageEducationPress.Nida,E.A.(2004).Towardascienceoftranslating.Shanghai:ShanghaiForeignLanguageEducationPress.Newmark,Peter.(2001).ApproachestoTranslation.Shanghai:ShanghaiForeignLanguageEducationPress.冯庆华.(2010).实用翻译教程.上海：上海外语教育出版社.方梦之.(1999).翻译新论与实践.青岛:青岛出版社.柯平.(1993).英汉与汉英翻译教程[M].北京:北京大学出版社.连淑能.(2000).英汉对比研究（修订版）.北京：高等教育出版社.廖七一.(2009).当代西方翻译理论.江苏：译林出版社.陆谷孙.(2005).英汉大词典（第二版.上海：上海译文出版社.庄绎传.(2002).英汉翻译简明教程.北京：外语教学与研究出版社.22 AppendixISourceTextChapterThreeBigDataandEducation:MassiveDigitalEducationSystemsThischapterdiscusseshowmassivedigitaleducationsystemslikeMOOCs,facilitatethedistancelearningaspectsofformaleducationinstitutionsandenableapeer-to-peerlearning.ItdiscusseshowMOOCsopennewincomestreamsfortraditionalinstitutionsthroughemploymentrecruitingservices,syndication,andsponsoring,aswellasbyadvertisingincome,sellingstudentinformationtopotentialemployersoradvertisers.Tothisend,thischapterfirstexplainsMOOCeducationalmodelsandtherolethatbigdataandanalyticsareplayinginthiscontext,highlightinginstitutionaladvantages,opportunitiesandchallenges.Itthenexplainstwocasesstudieswherebigdataandanalyticsplayedessentialrolesinthedesignanddeliveryofthecurricula.3.1IntroductionDistancelearninghasalwaysbeendesignedforthosewhodidnothaveaccesstoformallearninginstitutions.OpenUniversitiesaroundtheworldhavemadesuchmarketstheirprimaryfocus.Pedagogicalmaterialwasadaptedtoreachstudentswithdifferentsocioeconomicbackgrounds,learninghabits,andstudyingpatterns,atremoteareasorevenclosetohomebutlimitedinfreetimeorincompatibleworkingscheduleswiththoseofinstitutions.Originallydistancelearningprogramsemployedavarietyofaudiovisual,informationdisseminationmeans,suchastelevisionprograms,CDsandthelikebuttherewasstilllackingstudent-tutorandpeer-groupinteraction.Itwasreallyonlinesocialplatformsthatcouldre-instillthesocialaspectoflearning(Marques2013).Overthepast20years,anacademicrevolutionhastakenplace,unprecedentedintheirglobalscopeanddiversityofinstitutionsandpeopletheyaffect,duetoadvancementsininformationandcommunicationstechnologies(ICTs)(Executive2014;YuanandPowell2013).Educationisakeyfactortoeconomicdevelopmentfornationsandsocialmobilityfortheindividual.Twofactorshaveaccentuatedtheneedforglobaldemandineducation.Theskillscurrentlyrequiredtocompeteinaglobalenvironmentarechangingduetoshiftsintheeconomicmodeland23 competitionfromemergingdevelopingmarkets.ICT’shavegivennationsandinstitutionstheopportunitytoresourcehighlyskilledpeopleanywhereintheworldandindividualstheopportunitytomoveupsocially.Thisjourneyisnotwithoutchallenges.Developingcountriesforexamplehavelowdigitaltechnologypenetration.Africa,theMiddleEast,andLatinAmerica/Caribbeanconstitutejust17.2%oftheworld’sInternetusers(MiniwattsMarketingGroup2014).Basictechnologyeducationandaffordabilityholdpeoplebackfromdevelopingnecessarytechnologicalskills.Ontheotherhand,initiativessuchasNegroponte’sOneLaptopPerChild(OLPC)projectwhichsoughttoprovideeachchildinthedevelopmentworldwithalow-budgetcomputerslowlychangethisdynamic.TheuseofICTtechnologiesineducationhascomeunderdifferentguises.Guri-Rosenblit(2010)mentionsmorethan20differentrelevantterms,eachconnotingslightdifferentimplementationsofafundamentallysamethingICT-enableddistancelearning:·Internetmediatedteaching,·technology-enhancedlearning,·web-basededucation,·onlineeducation,·computer-mediatedcommunication(CMC),·telematicsenvironments,·e-learning,·virtualclassrooms,·I-Campus,·electroniccommunication,·informationandcommunicationtechnologies(ICTs),·cyberspacelearningenvironments,·computer-driveninteractivecommunication,·openanddistancelearning(ODL),·distributedlearning,·blendedcourses,·electroniccoursematerials,24 ·hybridcourses,·digitaleducation,·mobilelearning,·technology-enhancedlearning.MassiveOpenOnlineCourses(MOOCs)haverecentlycometobeaddedtothislonglistofdifferentimplementationsofICT-enableddistancelearning.WhileMOOCsfacilitatedthedistancelearningaspectsofformaleducationinstitutions,theyalsointroducedanevenmorefundamentaleducationalchange;theyopenedupparticipationtoeducationbyenablingapeer-to-peerlearning.Theyofferthepossibilitytothousandsofpeopletovoluntarilyshareknowledgewitheachotherthroughopenaccesstocourses.Hence,theypracticallyprovidetoanyoneinterestedfast-track,accessible,flexible,freeoraffordablecourses,atdifferentlevelsfromhighereducationtovocationaltrainingcourses.Notonlythecoursesthemselvesaddtothecontentvarietyofbigdata,butbigdatacanhavealeadingroletoplayintheproductionanddeliveryofMOOCseducationalmaterial.Indoingso,theyareakeygamechangerintheeducationsectorwithpervasiveglobalimplications,aboutnations’economiccapabilities,internationalrelationsandglobalsocialmobilityandwelfare.ThischapterbeginsbydiscussingtheimplicationsofMOOCsintheeducationalsphereandtheroleofbigdatainthedesignandimplementationofMOOCs.Itisstructuredasfollows.3.1.1FromInstitutionalizedEducationtoMOOCsMOOCswereanoffshootofformaleducation.MOOCsisrootedwithintheideologyofopenaccesstoeducationwithoutdemographic,economic,andgeographicalconstraintswhichdatesbackintheearly20thcentury(YuanandPowell2013).In2001,MITestablishedtheOpenCoursewareinitiative(MassachusettsInstituteof25 Technology2014).Theprioritywastodisseminateknowledgefarandwidewherepeoplecaninteracttolearnandsupporteachotherintheirjourneytoknowledge.By2007,MIThasputalltheircoursesonlineandavailabletothepublic.IntheoriginalannouncementoftheprogramtoNewYorkTimes,MIT’spresidentDrVesthadpredictedIalsosuspect,inthiscountryandthroughouttheworld,alotofreallybright,precocioushighschoolstudentswillfindthisagreatplayground.[⋯]therewillprobablybealotofusesthatwillreallysurpriseusandthatwecan’treallypredict(Goldberg2001).Indeed,opencoursewarewasasteppingstonetoamuchlargerscalechange.SincethenwehaveaproliferationofMOOCplatformsandanumberofhighlyregardedinstitutionsthathaveembarkedontheverysamejourney.Coursera(2014),forexample,establishedin2013isapartnershipbetweenStanfordUniversityand61well-regardeduniversities.EdX(2014)isestablishedbyMassachusettsInstituteofTechnology,ÉcolePolytechniqueFédéraledeLausanne,TheHongKongUniversityofScienceandTechnology.Udacity(2014),Peer2Peer(P2P)University,andFutureLearn(theUKOpenUniversity’sMOOCplatform)areotherrelatedplatformsarejustbutafewotheroffshootsofinstitution-ledMOOCs.Howeveranumberofcommunity-based,openlearningplatformshavestartedtopop-upwithsignificantdifferencesintheirpedagogicapproachandphilosophy.Peer2PeerUniversity(2014),forexample,isagrassrootsinitiative.ItcreatesamodelforlifelonglearningbyfacilitatinglearnerstointerrogateInternetmaterialtoco-createtheircurriculumandindoingsotakingresponsibilityfortheirownandtheirteamslearning.AccordingtothewebsiteofPeer2PeerUniversity,openness,communityandpeerlearningarethekeydriversandnothingfallsbeyondtheagencyofthelearner.Eventhetechnologyandprocessoflearningcanbeopentochangeshouldthelearnerswishto.Theideaistoleavebehindthehierarchicalandroleseparationofacademicsandstudents.Materialssuchasreadings,videosandpaneldiscussionsseektoprovokediscovery,ratherthantransmitknowledge.Theyaredesignedinself-containedmodulestobeconsumedondemand,ratherthanfollowadiscipline.Theirmottostirawayfrominstitutionalconventions,tocreateanenvironmentof“Nojudgment”26 whereyou“Putinwhatyouwant”and“Takewhatyoucan”anenvironmentwhereeducatorsandlearnersacceptthattheywillexperimentandindoingso“[they]willtakerisks.[they]willlearntogether.Thingswillbemessy.Thingswillbefun.”(Bergeretal.2014).Othercourses,suchaslearningaprogramminglanguagemightneedmoredirectionandstructurefromexperts,butemphasisisstilloninteraction,self-learningresponsibilityandself-pacing.Udemy(2014)isanotheronlinelearningplatform,hostingavarietyofcourses,frompracticalonessuchasphotography,languages,andtestpreparationtomoreacademicones,suchasphilosophy,humanitiesandscience.Coursesarenotfree,butaffordable.Udemyhosts16,000suchcoursesandengages3millionstudents.Itkeeps50%ofrevenuesfromeachcourseinexchangeformarketingandtechnicalsupportthroughtheplatform.ThedifferencesbetweenMOOCsbusinessmodelsaresignificantandsoistheinvestinterestinthem,presentingthefundingmodelsofthekeyMOOCsplayersinthemarketnowadays.MOOCsareinsearchforbusinessmodelvalidityandsustainability.ForCourseraforexample,thebusinessprinciplesresemblethoseofAmazon.Createahubofsupplierstoattractalargenumberofstudentswhowillbeattractedinfindingwhatevertheywantinoneplace.Forothers,suchasSimonNelsonofFutureLearn(Wilby2014),thenameofthegameremainstobeseen,asthepotentialofthetechnologyhasnotbeenenvisionedletalonerealized.UdacityfocusesonemploymentandemploytrainingteamingupwithcompaniessuchasGoogle,AT&Ttoofferdegreesthatareofinterest(Udacity2014).EdXissteeringitsmodelawayfromcourseprovisiontobecomingthemanagerofthetechnologyplatformhostingMOOCs,andprovidingtheirplatformmanagementexpertiseasaservicetouniversities.Notonlyistheirmarketfocusdifferent,butsoistheirfundingmodelstoo.VentureCapitalists,philanthropistsandnationalandeducationalinstitutionsareallhappytopartwiththeirfundstosupporttheMOOCsagenda.Coursera(2014)hasacceptedsome$65million,whileUdacity(2014)another$15millionfromventurecapitalists,andthisbearsariskintermsoftheopennessoftheirbusinessmodelsastheirinvestorsmaydemandthecommercializationofcourses27 forbusinessprofit(Watters2012;TheEconomistexplains2014).3.2MOOCEducationalModelClusters3.2.1University-LedMOOCsUniversityledMOOCs,alsocharacterizedasxMOOCs,representthelatestinnovationinformaleducation.AccreditedinstitutionsarenowacceptingMOOCsaspartofadegree,yetawhollyfreeonlinedegreeisnotineffect.Tuitionfeesaredrivendownbecause,whilestudentsstillhavetopaytocertifyfortheirdegree,theydon’tpayfortheoperationalcostsofrunningtheacademicprocess,suchassupervisionexpenses,classroomandfacilitiesmaintenanceandthelike.WhenMOOCsareabletoprovidefreeaccreditedonlinedegrees,theUniversitybusinessmodelwillhavefundamentallychanged(Dellarocas2013).Afeasibleeconomicoptionforuniversitiesisvirtualizingtraditionalteaching,byrecordingoflivelecturesandpresentations.Nothingnewheree-LecturesarethendeliveredovertheWebusingaLearningManagementSystem(LMS).Whileattendancelevelsmaydrop,studentperformanceisnotnecessarilycompromised(Ottmann2013).Inaddition,reachingstudentpopulationsaroundtheglobeisnotonlyflatteringforacademics.butalsoagreatwaytodecreasetheirfacetofaceteachingload.MOOCsarecertainlyattractivetobothacademicsanduniversitymanagementduetotheirabilitytoattractventurecapitalandgrantmoney,oftenlinkedtoacademicrewardsystems.Anothersourceofpotentialincomeisadvertising.Thisisperhapsthereasonwhyprestigiousinstitutionshavejoinedforcestooffertheirmodulesanddegreesfromasingleplatform.ThelikesofHarvard,MIT,Cornell,Berkeley,andothersfoundedthenon-profitedXtooffertheoptionto“⋯takegreatcoursesfromtheworld’sbestuniversities”orto“Taketheworld’sbestcoursesonlineforfree”(Ottmann2013).Afterall,thereseemstobeagapinthemarketforlowcosteducationalprovision.AccordingtoLaurillardetal.(2010),professoroflearningwithdigitaltechnologiesattheInstituteofEducation,UniversityofLondon,studentloansarehigherthancreditcardloansintheUS,while40%ofstudentdebtwillneverberepaidintheUK,andby2025demandwilldoubletoapproximately200millionstudentsperyearas28 emergingeconomiesdemandaccesstohighereducation.Perhapsthisalsoindicatesadeepershiftawayfromgovernmentbudgetstomoreself-sustainableformsoffundingortobetterlinksbetweentertiaryeducationandprivatecapital,orevenperhapsashiftinthemissionandstudentreachofuniversitiestoexploitnewtechnologiesinordertoeducatetheglobalpopulationand,inthatway,createcommonworldviews.Thesameprocessofstudent-expertcanbeobservedinMOOCswithmorepracticalcontentwhereprofessionalsorexpertsprovidetrainingandskillsdevelopmenttonovices.ManyofthecoursesinCoursera(2014),areofthistype.3.2.2Peer-to-PeerMOOCsUniversity-led,xMOOCsdifferfrompeer-to-peerdrivenMOOCs,alsoreferredtoascMOOCs[i.e.connectivistMOOCs(YuanandPowell2013)].Theydifferinphilosophy,deliveryandintentionandacertain‘war’oflearningideologyandpracticeiscurrentlyplayedout.ThequickestwayperhapstounderstandcMOOCsistojuxtaposethemagainsttheirmoretraditionalcounterparts.Table3.1outlinessomeofthebasicdifferences.29 Table3.1xMOOCsversuscMOOCsInsteadofrelyingontraditionaleducationmodels,P2PorcMOOCsseektomotivatelearningcollaborationsamongststudents.Insomeways,studentsarenotstudentsinthattherearenotteacherseither.cMOOCSarecommunitiesoflearningoflike-mindedpeopletakingresponsibilityforandorchestratingtheirownlearningprocess.Indoingso,theyco-definetheirlearningobjectives,createcontentthroughresearch,sharethemes,givefeedback,insights,ideasandsupport.Whileacoregrouptendstotakemoreofthecoordinationactivities,coursegoalsandobjectivesarefluidanddeterminedinresponsetothecommunity.Theideaoflearningthroughresearchisnotforeigntoformaleducation.Thereareplentyof30 MastersinResearchusuallyasteppingstonetoaPhD,and,ofcourse,Doctorateprograms,thepinnacleofformaleducation,allusingresearchasthemeansorpathtoachievingknowledge.However,achievingadegreeisasolitaryprocesswherethestudenttakesaccountabilityforhisorherownresearchtowhichtheypresenttosomeacademicauthorityinexchangeforacertificationoftheirability.cMOOCssofardonotworklikethis,atleastnotyet.Whyisthisimportantfromabigdatapointofview?Forstarts,asthereisnocurriculum,informationfromanysourcecanbecombinedtoimprovelearning.Anythingfromgovernmentinformationtodatageneratedbytheinternetofthings,socialmedia,academicjournals,observationsfromvirtualgames,countriesstatisticsbureauscanbeblended,orinthespeakofbigdata,mashedtocreatenewknowledge.Suchblendingmightbecriticizedorsupportedbyone’slearninggroupbutinevitablythereisnoacademicauthoritytobindthatsearchtospecificsources,ideasorframeworks.Theworldofinternetisthelearners’oyster(Yeageretal.2013).Atthemoment,mostcMOOcsareteachersandacademicsinterestedinlearningperse.P2PU(2014)issuchaninstitution,whereateamofexpertshascometogethertoexplorenewideasincurriculumdevelopmentanddesign,research,knowledgesharing,etc.andbydoingsodevelopourknowledgeaboutlearning!Willsuchformgomainstream?Itisremainedtobeseen.3.3TheRoleofBigDataandAnalyticsThissectionseekstounderstandtheroleofbigdatainthisnewfieldofeducation.ItshouldbeevidenttothereaderbynowthatcMOOCsmakeuseofmashingdisparatesourcesofopeninformation,whetherbigorsmall,todiscoverandcreatenewknowledge.ButwhatistherolebigdatainxMOOCs?Muchlikeanyotherbusiness,MOOCsneedtodevelopmarketstrategiesforattractingnewstudents.Hence,bigdatacanhelpwiththemarketanalyticswithintheacademicsphere.Indeed,suchmarketresearchservicesforacademicinstitutionshasnowreserveditsownterm:Academicanalytics(GarcíaandSecades2013).AcademicAnalyticsutilizeslargedatasets,statisticaltechniques,andpredictivemodelingtoofferbusinessintelligencetoacademicinstitutionstoimproveontheircustomerexperience.Forexample,educationsystems,especiallyMOOCswith31 varyingdegreesofstudentengagementandcompletionrate,willrequireacarefulmanagementofresourcesaswellasseekcrosssellingopportunitiesofmorepremiumproducts.ThiswillbeparticularlytrueasxMOOCswilltrytocapitalizeontheirlargestudentbasistosatisfytheirinvestors.Suchpredictiveanalyticsarelikelytobeintegratedwithworkflowsystemstoautomateoratleastsemi-automateadministrationprocesses.Forexample,academicanalyticscanalsoinformadecision-supportsystemandaworkflowsystemtoautomatetheadmissionsprocess.Theycanalsobeusedtosupportstudentsthroughoutthelearningprocess.Utilizingexistingknowledgeandmodelsrelatingtostudenteffortandsuccess,theycanmonitorstudents’interactionwiththesystem,alertingadministratorswhenstudentengagementpatternschangeinordertoinitiatecommunicationwiththestudent,orindeedsendapredetermineddefaultemailcommunication(Campbelletal.2007).Anothermorefundamentaluseofbigdataanalyticsisitsuseasalearningtool.ThisparticularuseofbigdataanalyticshasbeendefinedasLearningAnalytics(Fournieretal.2011).Learninganalyticsutilizeindividuallearner-produceddataagainstknownmodelsaboutsuccessfullearnerinteractionsandbehaviors.Peopleinthelearninganalyticscommunityproposesixcriticaldimensions,bothsoftandhard,alongfordesigningsuchlearningmodels.Softissuesrelatetoassumptionsabouthumansorthesociety,e.g.,competencesorethics.Hardchallengesontheotherhandrelatetodataandalgorithms.Inthatsense,Learninganalyticscanbedefinedas⋯theareaofdiscoveryanalyticswhichmodelofintelligentandlearner-produceddatatodiscoverinformationandsocialconnectionsforpredictingandadvisingpeople’slearning.(Siemens2010).Suchanalyticshaveappealtoanumberofeducationalstakeholders.Forexample,individuallearnerscanbeassistedinreflectingontheirachievementsandpatternsofbehaviorinrelationtoothers,academicscanhaveearlywarningsaboutstudentsrequiringextrasupportandattentionandplanrelevantinterventions;managementandcourseleaderscanbeassistedindevelopingandmarketingattractiveacademicprogramsandcurriculaandpromotethem(Campbelletal.2007).3.4InstitutionalAdvantagesandOpportunitiesfromMOOCs32 AccordingtoGlobalIndustryAnalysts,theglobale-learningmarketwillreach$107billionby2015.Inevitablyinstitutionswanttoleveragetheiroperationstocapturemarketshare.MOOCsopennewincomestreamsfortraditionalinstitutionsbynotonlyrelyingonstateeducationsubsidiesandstudenttuition,butthroughemploymentrecruitingservices,syndication,andsponsoring,aswellasbyadvertisingincome,sellingstudentinformationtopotentialemployersoradvertisers.Traditionalinstitutions,particularlyuniversities,aspiretotransformhighereducationbyexpandingacademicaccessonanunprecedentedscaleasmeanstocutcostsinalreadyunderfundedpublicuniversities.Whileothersproposevariousformsof‘pay-as-you-learn’teachingprovision.OneschoolofthoughtproposesthatMOOCswasanexperimentthathasservedtheirpurposeasadriverforinstitutionstotakeamorestrategicapproachtoonlinelearning,whichcreatenewopportunitiesforuniversities,by·Utilizingopennessasanonlinelearningapproachthroughtheuseofonlinecommunities,includingmodelsforscalableprovisionthatmaygeneraterevenues,andgoesbeyondinstitutionalboundaries.·InventingnewBusinessmodels,suchasapplyingtheconceptsoffreemiumandpremiumoffersintoonlinelearning,providinginstitutionswithnewwaysofthinkingaboutmarketingandincomegeneration.·EnablingServiceDisaggregationthatincludesunbundlingandre-bundlingofcoursesanddeliveryrelatedservicestoofferpremiumeducationalservicessuchaspayingforassessmentand/orteachingsupport.·Experimentingwithnewapproachesforteachingandlearning,byutilizingnewtechnologies.·CreatingneweducationmodelsthatcaterforLearnerswithvariousstudyingpatternsacrosstheglobe.·Developinginternalcapabilitybyreviewingtheirtechnicalinfrastructure,academicandsupportstaffworkingpractices(Yuanetal.2014).Itisalsoimportantforacademicinstitutionsatalllevelstoincreaselearningattainment33 withoutincreasingtheircosts.LearningAnalytics,alongwitheducationaldataminingandteachinganalyticscanallbeseenasthreeaspectsofthesamesolutiontoraisingtheeducationstandardsoftheyouthwithoutnecessarilyincreasingthenumberofeducatorsrequired,thusmakinginstitutionsmorecostefficient.Learninganalyticsfocusoncapturingstudentbehaviorandcorrelatingittoachievinglearningobjectives,educationaldataminingseektodesignpredictiveanalyticsmodelsforstudentattainmentwhileteachinganalyticshelpseducatortranslatesuchfindingsintobettercoursedesignandstudentsupportproceduresandinterventions.Astimegoesbymoreandmoreonlinetoolsarebeingdevelopedinthisarea.AccordingtoCharltonandMavrikis(2013),theCourseResourceAppraisalModel(CRAM),forexample,allowseducatorstounderstandtheimpactofdifferentdeliverymodes(face-to-face,blendedlearningandlarger-scaledelivery)onstudentlearningandhenceeaseofftransitionorimprovecoursedesign.Theauthors(CharltonandMavrikis2013)alsoarguethattheMaths-Whizzcanimprovemathsscoresbyonlinetutoringstudentsandassessingscorelevels.BlikBookfocusesonmodelingclassengagementandsubstituteslecturers’studentsupportengagementbyassistingstudentsfindrelevantmaterial.Table3.2providesabriefoverviewofsomeofthemostcommonlyusedlearninganalytictoolscurrentlyavailableandtheirimpactonlearningaspresentedinCharltonandMavrikis(2013).34 Akeyissuethatalleducationalsystemshavetosolveinsocietytodayisyoungpeople’smotivationtolearn.Likemanyotherfields,educationmayneedtoseekoutsidetheirconventionalboundariesforsolutions.Oneareaofinterestformotivatingandmaintainingstudentengagementmaycomefromgamification,whichutilizedsoundgametheoryandgamemechanics,asalsomentionedinchapterone.Itisapotentiallypowerfulmethodfordrivinguserengagement,andnotveryfarfromeducationalsimulations(seeforexample,virtonomics(2014)).3.5InstitutionalChallengesfromMOOCsAsitstands,MOOCshaveafundamentalproblemofidentityandthereforeissuesaroundtheirmonetizationefforts.MostMOOCstart-upsdonotappeartohaveclearbusinessmodelsandlikemostventuresnowadaysstriveforachievingmarketshareandworryaboutprofitabilitylater.ThisisparticularlytrueofxMOOCs,suchasCoursera,whoseektoincreasethroughput.Onthebasisofregistrationnumbers,xMOOCsseekrevenuesforcertificationfees;recruitmentservices,35 sponsorships.Yet,manyinstitutionsdonottrustthequalityofMOOCdeliveryyet;atleastnotenoughtooffertheirinstitutionaldegrees.Thus,MOOCsarestillseenasaninterestingbrandingandmarketingactivityatpresent(Yuan2013).IndeedthequalityoflearningthroughMOOCsisuncertain.Tostartwiththereishighdropoutrate,oftenreportedashighas90%(Liyanagunawardenaetal.2013).Fortraditionalinstitutionsthisratewouldbealarming,ifnotfatalfortheirgoingconcern.Somesuggest,however,thatthesameyardsticksforevaluatingMOOCscannotholdtrue,simplyduetothenatureandstageofadoptionofthenewtechnology.Moreoverqualityissuesneedtobeovercome,asisacertainattachmentto‘oldways’ofteachingandoldwaysofevaluatingteaching.Nevertheless,certainincidentsmayharmtherelationshipbetweenMOOCs,academics,andstudents.InFebruary2013,forexample,theCoursera/GeorgiaTechcourse“FundamentalsofOnlineEducation”wascancelled,makingstudentslosealltheironlinecontributions(Morrison2013).Inthesameyear,UCIrvineprofessorRichardMcKenzieabandonedhiseconomicsMOOCcourseinthemiddleduetodisagreementoverhowtobestconductthecourse.Michiganprofessor,GautamKaulcausedwaveswithhisCoursera-runfinanceclassbyrefusingtogivestudentsthecorrectanswerstoassignments,“toavoidpreparingnewsetsofquestionswithmultipleversionstoallow[students]toattempteachonemorethanonce.Handingoutanswerswillforceustodothat.”KarenHead,GeorgiaInstituteofTechnologyprofessor,raisedconcernsaroundthetechnicalfunctionalityofMOOCstoserveherpedagogicalrequirements(Watters2013).Qualityalsosufferswhenstudentsuccessratesaremeasured.Forexample,while74%ofstudentsintraditionalclassespassed,only51%ofthoseattendedUdacity’sMOOCsequivalentprogramsucceeded.Toaddressqualityissues,forexample,Udacityhadturnedtocorporatetraining(Watters2013).AnotherthreatremainshiddeninthepotentialsuccessofMOOCs;thatofpedagogicalhegemony.Whenandifeducationisconcentratedinthehandsoffewglobalinstitutionsanddrivenbyfewacademicswhoareabletoreachouttolargenumbersofstudentsglobally,thenwemightbecreatingaworldwhereknowledgeisfoundedonthesameprinciples,ideasand36 viewpoints,intheexclusionofothers.Thiswillnotonlycompromisethefinancialgoingconcernofinstitutionsbutthepluralityofknowledgebasesandhencethedemocratizationofideas.ThisisofcoursenotanissueforcollaborativelearningenvironmentssuchascMOOCs,asthelearningagendaisnotcontrolledbyanauthoritybutco-createdonthewaybyparticipants.cMOOCs,despitebeingafundamentaldeparturefromexisting,instructor-led,educationalmodels,andindeedbetterpositionedtousebigdataasatoolforknowledgediscoveryandintegration,facetheirownchallenges.Theyrequireadegreeofstudentmaturity,motivationandresponsibilityinorderforstudentstotakeresponsibilityfortheirownlearningandforthatofothers,thus,undertakingthelevelofresearchandknowledgesharingrequiredandexpectedbyothers.Thismaynotbefeasibleatallagesoratleastadifferentkindofsocializationandupbringingmustprecedeitforprimaryandhighschoolstudentstobeabletoengageatthatlevel,andnationalcurriculamandatemustbeabletoopenuptobecomecompatible.Eveninadulteducationcontext,peoplemayfindthemselvesinthemidstofdifficultgroupdynamicssuchasconflict,differentlevelsofcommitment.Certainskillsneedtobedevelopedsuchasconflictresolution,teambuilding,andcreativethinking.Moreover,evenwebsearchisbiased.ThetopologyoftheWeballowsustoseeonlyfewofthebilliondocumentsonatopic,andevenwhentheyfindlesspopularinformationtheyneedthecriticalskillstoevaluateit(Barabási2003).Boyd(2010)emphasizedthatinformationbrokers,suchasGoogle,filterinformationwithoutnecessarilytakingacriticalpointofview,andhopedthatsocialmediacanplayamediatingroletobalancethisout(Kop2011).Anewperspectiveseemstoemerge,seekingtheintegrationofinstruction-ledmodesoflearningwithmoresocialformsofpeertopeerlearningtoprovideamoreholisticexperience(seeFig.3.3)(Crosslin2014).Suchsolutionmightbeasaferoption,butlacksimaginationandstillrecyclesoldnotionsofeducating.Perhapseducatorsneedtolookintootheraspectsofsociallifetodesigneducationalproducts.WealsoneedtostartdifferentiatingMOOCsbasedontheiraudienceandpurpose,whichmightbe37 aviciouscycleaspeoplehavenotdecidedonthebusinessmodelforMOOCs.3.6CaseStudiesHarvardXwasformallylaunchedinOctober2012andisco-hostedinEdXplatformalongwithMIT,DukeUniversityandotherinstitutions.Sinceitslaunchithad1,331,043registrantsin195countrieswithvariousdegreesofcertificationattainment(HarvardUniversity2014).Initsfirstyearalone,over500,000studentshaveregisteredforHarvardXcourses,whichislargerthanthenumberofHarvardgraduatesinits377-yearhistory.AsideUSthataccountsfor36%ofenrollment,therestofitsstudentcohortcomesfrom204differentcountries.Amongstthem,NigeriaisthemostenrolledcountryofAfrica,SpainisthefirstinEurope,Indiaisthehighest-enrolledcountryinAsiawithalmost50,000students,andBrazilinSouthAmerica.ApotentialbarriertoglobalparticipationinenrolmentmaybelanguageasmoststudentsfromglobalHarvardXenrolmentcomesfromEnglishspeakingcountries(HarvardUniversity2014).InChina,forexample,only23,548studentsenrolledinspiteofitshugepopulationof1.3billion.Interestinglycertificationattainmentdatadonotfollowthepatternofenrolment.Despiteofaccountingforlessthan2%ofenrolmenteach,BurkinaFaso,Greece,andGeorgiaarethecountrieswiththehighestratesofcertificateattainment,indicatingperhapsdifferencesintheculture,educationalstructureandconditionsorpersonalmotivationsforattendingthecourse.However,certificationismorelikelyamongregistrantswhoenrollnearthelaunchdates,butviewinglikelihoodisstablethroughtherunofthecourses,indicatingperhapsapremeditationonachievingaparticulareducationalgoal(HarvardUniversity2014).Itisimportantalsotonotethatthemedianageofstudentsis28andmostofthemalreadyhaveaBachelorsoraMaster’sdegree.SoHarvardXcourseseemstoactasaneducational‘top-up’,ratherthanauniversitysubstitute,andappealmoretothealreadyeducated,matureuniversitystudentsasopposedtotheuneducatedmassesitsproponentshavebeenadvertising.PointofAttention:MOOCsarenotsubstitutesforconventionaluniversitiesorotherlearninginstitutions.However,theuseofbigdataanalyticsbothindesigningcurriculaandindelivering38 MOOCscanprovidenewmodesforlearning,meritedtheirownattention.Theultimatequestionis“DoMOOCswork?”Harvardrepresentativesanswerthequestionwithapoignantclarification:“Workforwhat?”.Sofar,thepresumptionthatMOOCswillenhance,replaceanddisruptexistingmodelsofhighereducation,wasbasedontheassumptionthatstudentswillutilizeMOOCSasasubstituteforconventionaleducation.Theyhaven’t.MOOCsseemtobeopen,virtualspacesforlearningexperimentation.Forexample,registrantactivitywithincoursesisdiverse.Certificationattainmentisonepossiblelearningpathwayandthereisanindicationthattheymightbenefitfromsynchronouscourseschedulesandthecohortsthattheybuild.Most,simplywatchvideosorreadtext.Someregistrantsdipintoacoupleofsessionsinthefirsttwoweeksandthenabandoniteithertoregistertoothercoursesorsignupproperlyonalaterinstanceofthecourse.Othersfocusonself-assessmenttotestthemselves.MOOCssuccesswasallegedlybasedontheiradherencetowell-establishededucationalandlearningmodels.Forthesemodelstoberelevant,howevertothenewcontext,similarmotivations,interactions,norms,expectations,shouldbepresent;somethingnotnecessarilytruewhencomparingMOOCstoconventionaleducationsettings.Forexample,openenrolmentperiodsandunrestricteduseofcourseresourcesraiseimportantquestionsforanalysisanddesign.Perhaps,weneedtoexamineMOOCsintheirownrightandnotassubstituteofuniversitiesorotherinstitutions.Tothisend,newmetrics,farbeyondgradesandcoursecertification,wouldbenecessarytocapturethediversityofusagepatternsandgoals.ThesecondcasestudywediscussinthisChapteristheoneofLivemocha,whichisafreeonlinelanguagelearningcommunity,providinginstructionalmaterialsin38languagesandaplatformforspeakerstointeractwithandhelpeachotherlearnnewlanguages.Ithasapproximately12millionregisteredmembersfrom196countries,withover400,000usersvisitingthesitedaily(Livemocha2014).Itwasfoundedin2007,byShirishNadkarniandKrishnanSeshadrinathanandisbasedontheideathatyoubestlearnalanguagewhenyouinteractwithnativespeakers.By2010,itwasannouncedasoneofthetop50websitesby,theNewYorkTimesandtheFinancialTimes,andTimemagazine(2010).39 Livemocha’sonlinesociallanguagelearningplatformhasfollowedaclassicdisruptiveinnovationpaththroughbusinessmodelinnovation,totransferonlinetheprocessoflearningaforeignlanguageinaforeigncountry.Thebusinessmodelwassimple.Engagenativespeakerswhowanttolearnanotherlanguageforfreeandarewillingtohelpotherstolearntheirownlanguage;hence,nativespeakersalternatebetweenbeingstudentsandtutors.Offeraplatformoftoolsandtechniques(suchasflashcards,quizzes)tohelpstudentswithlearningandmemorizing.Maintainfairaccessandreciprocityinthecommunitywiththeexchangeofvirtualtokensandrulesoftenenjoyedbytimebanksandreciprocalfeedbackonbothstudentperformanceandthequalityofthereviewer(Gartner2013).PointofAttention:Technology-enabledlearningcommunitieswillgeneratenewtypesofunstructuredbigdatarelatingtoeducationandlearning.Newnormsincommunityeducationcanprovidenewprinciplestomodellearninganalytics,basedonprinciplesofreciprocityandmutualbenefitandgamification.Incomecomesmainlyfromadvertising,withapproximately700,000recurringuserseachmonthandapproximately1.85millionusersreachedwithinthefirst15monthsfromitslaunch.Inthenextphase,LivemochapairedwithPearsonstoprovideapremiumproductthatbundledformallanguagelearningmethodswithsocialmediainteractionforafee.Thecommunityremainedloyaltoitscustomerbase,stillprovidingfreecourses,whichhelpeditgrowitscommunitybaseto5millionusersbyJanuary2010.Finally,thecommunityengagedaccreditedteachersonapayasyougobasis,whereregisteredaccreditedteachersgotpaidforeveryinteractiontheyhadwithastudent.Dedicatedstudentsweregivenaccesstothepremiercontentusingtheirvirtualtokens(Gartner2013).3.7RecommendationsforInstitutionsInstitutionswillingtoconsiderMOOCsatthecurrentstagehavetokeepenvisioningthepossibilitiesofMOOCsnotasasinglebusinessmodel,butasasetofbusinessmodelscentered40 aroundthenotionsofglobalonlineaccess,freeorrelativelylowfee,edification.Theuseofthetermedificationasopposedtoeducationisusedheretoconnotebothengagementsineducation:(i)thoseforobtainingformalqualificationsforemployabilitypurposes,and(ii)thoseforcontinuousdevelopmentthroughthepursuitofpersonalinterests.Withthisinmind,interestedinstitutionalpartiesshouldkeepthefollowinginmindduringtheirsearchforapositioningintheMOOCmarket.·DifferentstrandsofMOOCsareverylikelytodevelopdependingontheirutilisationofbigdata.·Bigdataanalyticscanbeutilisedasamarketingtooltotargetpotentiallearners.·Bigdatacanbeutilisedasalearningtooltoassisthelp—learning,byprofilingstudentslearningpatternsandtoalerttheeducationinstitutionwhenhumaninterventionisrequiredtomotivateorsupportstudentlearning.·WithincMOOCs,bigdataanalyticscanbeutilisedasatooltosourceandintegratelearningrelevantlearningmaterial.·xMOOCsarelikelytotransformintotheMOOCequivalentofvocationaleducation,bigdataanalyticscanplayacrucialroleinmatchingindustryrequiredemploymentskillsandcurriculumdevelopment.3.8SummaryWhileMOOCsfacilitatedthedistancelearningaspectsofformaleducationinstitutions,theyalsointroducedanevenmorefundamentaleducationalchange;theyopenedupparticipationtoeducationbyenablingapeer-to-peerlearning.MOOCsopennewincomestreamsfortraditionalinstitutionsbynotonlyrelyingonstateeducationsubsidiesandstudenttuition,butthoughemploymentrecruitingservices,syndication,andsponsoring,aswellasbyadvertisingincome,sellingstudentinformationtopotentialemployersoradvertisers.Bigdataanalyticscanenablethepersonalizationoftheonlinelearningprocessthatwasmissinginpreviousonlineinstructionmethodsandfacilitatethistohappenataglobalscale.Usedasapedagogicaltoolinlearninganalytics,bigdataalongwitheducationaldataminingandteachinganalyticscanallbeseenasthreeaspectsofthesamesolutiontoraisingtheeducationstandardsoftheyouthwithout41 necessarilyincreasingthenumberofeducatorsrequired,thusmakinginstitutionsmorecostefficient.Utilizingexistingknowledgeandeducationmodelsrelatingtostudenteffortandsuccess,institutionscanusebigdatatechnologiestomonitorstudents’interactionwiththesystem,alertingadministratorswhenstudentengagementpatternschangeinordertoinitiatecommunicationwiththestudent,orindeedsendapredetermineddefaultemailcommunication.Bigdataintheformoflearninganalyticsfocusoncapturingstudentbehaviorandcorrelatingittoachievinglearningobjectives,educationaldataminingseektodesignpredictiveanalyticsmodelsforstudentattainmentwhileteachinganalyticshelpseducatortranslatesuchfindingsintobettercoursedesignandstudentsupportproceduresandinterventions.Therealityhoweverisquitedifferent,MOOCsplatformsdonotsharesomeofthebasicassumptionsaboutstudentmotivationandinstitutionalnorms,expectations,andobligations,sopreexistingeducationalmodelsarenotagoodfitfortheiranalysis.Moreover,astheLivemochacasesuggested,MOOCscouldutilizegamificationorotherlearningfromonlinecommunitiesandworldstoensurecontinuousengagementandcommitment.ChapterFourBigDataDrivenBusinessModelsThischapteroutlinestheconceptof‘bigdatadrivenbusinessmodel’andutilizesittodescribeasetofbusinessesthatrelyonbigdatatoachievetheirkeyvaluepropositionandtosubstantiallyaugmenttheirvaluepropositiontodifferentiatethemselvesinordertogaincompetitiveadvantage.ItdescribestheimpactofbigdataoneachoftheelementsasidentifiedintheBusinessmodelcanvas.Alsothechapterdiscussesthepotentialofbigdataformasscustomizationandpersonalizationofproductandservices,asavaluepropositioninitsownright,onB2BandB2Clogisticsaswellasforcustomerrelationshipmanagementandcustomerservice.Italsotouchesuponhowbigdatahasfacilitatedashiftinourconceptionsofutilityasopposedtoresourcesasthebasisforsocio-economicvaluecreation.Also,thechapterexploresthisissueabitfurtherbyunderstandingtheimplicationsofbigdataonpartnerships,monetization,andtheopportunitiesandchallengesitraisesforaccounting,budgetingandperformancemetrics.Inconclusion,thechapteracknowledgesthesynergisticpotentialwithotheremergingtechnologies42 suchas3Dprinting,Robots,Drones,self-drivingcars,andthelike.4.1IntroductionThefocusofthischaptermeritsamorespecificdefinitionofthetermBusinessModel.BusinessmodelsareanaturalfitwithIT-relatedbusinessinnovations.Althoughrootedintransactioncosteconomics,itwasreallyanInformationCommunicationTechnology(ICT)relatedphenomenon.ICTmadeitfeasibleandcosteffectiveforbusinessestocollaboratewithvaluenetworksinordertocompete(Donetal.2000;AmitandZott2001).Bundlingofproductandservicesbecameverypopularduringthe90sblurringtheboundariesbetweenindustries.So,whatisabusinessmodel?Amongthediverseavailabledefinitions(AmitandZott2001),abusinessmodelcanbeseenasoutliningthearchitecturallogicofbusinesselements,suchasabusinessstructure,businessprocesses,infrastructure,andsystemsaswellasfinanceoptions,i.e.howtheyallfittogethertocoordinatevaluecreation.Itdescribeswhobuysthecompany’sproductsandservicesandwhy,howacompanyorganizesandwhichresourcestheyutilizeinordertofinance,produceanddelivertheirofferings,whattheypayfordoingsoandhowtheygettheirmoneyin.Muchlikeanymodel(Baden-FullerandMorgan2010),businessmodelsareabstractionsofreallifeandinthisparticularcase,businessmodelshavebeenusedtodescribevariousbusinessphenomena.Accordingto(Osterwalderetal.2005)peoplehaveusedthetermlooselytodescribeallrealworldbusinesses(e.g.thecapitalistmodel),orparticulartypesofbusinesswithcommoncharacteristics(e.g.theauctionmodel),oraveryparticularrealworldbusinessmodel(e.g.theApplemodel).Furthermore,accordingto(OsterwalderandPigneur2010),businessmodelscompriseninefundamentalelementsillustratedinTable4.1.Whileinthepast,organizationsreliedonmanagersintuitiontofillinthegapsofscarceincomplete,poorinformationtomakebusinessdecisions,theproliferationofdatageneratedeverydaythroughsocialmedia,cloudcomputing,andmobilephonesandsoontheInternetofThings(IoTs)givemanagersanewheadache,moreinformationthatwhattheyknowwhattodoabout.43 Thiscombinationofdigitalintensity,connectivity,andbigdataprovidesacontextofnetworkedabundance(Bharadwajetal.2013).Thepurposeofthisbriefintroductionoftheconceptwastwofold.Firstitwasnecessaryinordertoclarifyhowwearegoingtousetheterminthischapter.Wearegoingtousetheterm‘bigdatadrivenbusinessmodels’,describingasetofbusinesseswhichrelyonbigdatatoachievetheirkeyvaluepropositionandtosubstantiallyaugmenttheirvaluepropositiontodifferentiatethemselvesinordertogaincompetitiveadvantage.Second,itwouldbeusefultoestablishontheoutsetthescopeofthischapter.Mostbigdatadrivenbusinessmodelsarecurrentlyaugmentationsofexistingvaluepropositions(Hagenetal.2013).Bigdatadrivenbusinessmodelsarecurrentlyindevelopment.Mostbusinesseshaveintroducedbigdataintheirprojectsportfolioonthebasisofefficiency,whichbydefaultmeansdoingthesamething,withlessmoney,andevenfeweroneffectiveness,i.e.doingthingsbetter(Hagenetal.2013).Itwillbesometimebeforetruevisionariesintroducebusinessmodelswhoseuniquesellingpointwillrelyonbigdata,andTable4.1Ninefundamentalelementsofbusinessmodels(OsterwalderandPigneur2010)Bigdatawillhavebigimplicationsforvariousfields(Hagenetal.2013).Yet,suchchangesseektocomplementtoday’ssectorsandbusinessmodels.Itisworthnotingthatabusinesssectorconsistsofanumberofcompaniesorbusinessunitsoperatingeffectivelyunderthesameorsimilarbusinessmodels.Table4.2showssomeoftheindicatedchanges.Sofar,thereisalimitedimpetusforfundamentalstructuralchangesthatplacebigdatatothecoreofcompanyvaluecreationasopposedto‘nice-to-have’capabilities.Inthatsense,the44 suggestedchangesseektoenhanceexistingbusinessmodelsratherthandisruptthem.Forexample,bigdatahasbeenutilizedtoimproveexistingbusinessmodelsasshowninTable4.3.Theseincrementalchangeswillbringsignificantvaluetoexistingorganizationsandchangeourcustomerexpectations,whichleaveanalyticslaggingcompaniesbehindinthecompetitiongame.Thenagainsuchmodelsarenotfundamentallybig-datadriven,butbigdataenabled.Fundamentalchangescanbeenvisionedbycombiningtheanalyticalpowerofbigdataanalyticswithnewproductiontechnologiesandnewconceptsaboutproductionbusinessvalue.Forexample,whatbusinessmodelscanevolvebycombiningthepowerofbigdataanalytics,withotheremergingtechnologies,suchastheonesshowninTable4.4.45 Willsocialservicesandprimarycarebecomeobsoletebysmarthomesandhealthtrackingdevicesprovidingreal-timeinformationaboutthehealthstatusofanindividual,whileartificiallyintelligentsocialroboticsdecideandadministerappropriateregularcaretotheelderly?Willbitcoinbecomepartofthemixoforasubstituteforexistingfinanceoptions?Canweorganizecentralizedfarmingbyutilizingsmartassets,dronesandswarmroboticstotendtoagriculturalproduction.Willthedistinctionbetweenpublicandprivatetransportsystemsdisappearbymergingtocreateanew,ondemand,door-to-doorself-driventransportsystem?Tosomeoftheseideasmayseemfarfetch,butforotherssomeoftheseareexpectedtobecomemainstreaminthenext15−20years.Forexample,podbasedinnercitytransportisalreadyarealityinMasdar,AbuDhabi.Real-time,bigdataanalyticscantransformproductionprocesses.Forexample,bigdataanalyticsisutilizedbyGeneralElectricasan‘inprocess’monitoringmechanismtoqualitycontrolhighlysensitiveandveryexpensiveindustrial3Dprintingprocessesofaerospacecomponentswherestructuralintegrityiscriticaltosafety(Gereports2013).Forexample,IBM’sDeepThunderprogramisorientedtowardsprecisionagriculturethatcombinesmicroclimatepredictivemodelswithremotedronemonitoringtooptimizeagriculturalprocesses,suchasweeding,spraying,wateringandharvestingcrops.Wecontinuethischapterbyunderstandingthefundamentalimplicationsofbigdataoneachofthenineidentifiedelementscomprisingabusinessmodel.46 4.2ImplicationsofBigDataforCustomerSegmentationBigdatahasgiventhenotionof‘masscustomization’anewleaseoflife.Bigdatacangivecompaniestheabilitytotargeteachcustomerindividuallybasedontheirpreferencesandpurchasinghabits,byintegratingpersonalinformationaboutwebsitebrowsing,purchasehistories,physicalposition,responsetoincentives,aswellasdemographicinformationsuchasworkhistory,groupmembershipandpeople’sviewsandopinionsbasedonsocialinfluenceandsentimentdata.Thisdataenableeverfinertargetingofcontent,offers,productsandservices,whichcandeliverrealandsubstantialreturns,byincreasingtheprobabilityofafinalpurchase.Whilemasscustomizationisnowenabledbybigdata,twoevenmoredisruptiveadvancementscanbefacilitatedbynewtechnologies.Makingcustomizableproducts,whichcanself-customizetosuitthepreferenceoftheirusers.Asproductscomewithembeddedintelligenceandmachine-to-machinecollaborationcapabilities,theywillbeabletoundertakeresponsibilityforservingtheirusers.Forexample,aself-drivingcarcoulddoubleupaspersonalassistantmodifyingappointmentschedules.Whilebigdataisdiscussedprimarilyasabusinesstool,thereisnoreasonwhybigdatacannotbeutilizedbyconsumersthemselvestosearchandprocureproductsandservices.CognitiveCodeforexamplehasinventedadvancedconversationalartificialintelligencethatcaninteractinnaturallanguagewithhumanbeingswhileinteractincodewithmachinesprovidingoutputsandfeedbackinnaturallanguage.Theperfectpersonalassistantfortheelectronicage!ItisapitythatcompaniessuchasCognitiveCodestillorienttheirbusinessmodeltocompanies,suchascallcenters,asopposedtothepublic.Yet,Idoubtitwilltakelongforconsumerelectronicsgiants,suchasiPhonetobuildinsuchacapability.4.3ImplicationsofBigDataasaValuePropositionTherearefourbigdata‘things’thatcompaniescansellforprofit:rawbigdatabases,bigdataanalyticsservices,bigdataexperts,andbigdatatechnologies.AccordingtoForbesetal.(2014),manycompaniesaregearedtowardssellingtheircustomer47 dataasameanstocomplementcompanyrevenues.Whilethismaybareareputationalriskforsmallcompanies,largeorganizationssuchasmobilecompanies,banks,andairlinesthatcollecthugeamountsofinformationaboutourwhereaboutsandshoppingpatternsareunfazedbysuchrisks.However,rawdataisnotofmuchuseunlessyouanalyzeforapurpose.Banksaremovingawayfromsellingrawdatatodevelopinghighervaluebigdataanalyticsservicesbyrecruitingbigdataanalyticsexpertstomine,analyzeandsynthesizedataintoconsultancyservices.BigdataconsultancyhasbecomethemusthaveserviceforBusinessIntelligenceconsultancyproviders.Withbigdataanalyticsbeingharnessedineveryaspectofbusinessactivity,fromstrategydevelopmenttoHumanResources(HR),andwithbigdataanalystsbeingascarceresource,theopportunityforconsultancyworkishigh.Infact,itwouldbeimpossibleforaconsultancycompanytosurvive,withoutofferingbigdataconsultancyservices.Hence,allprestigiousconsultancycompaniesaswellassmallerboutiqueones,offerclientsbigdataanalyticsservices.Anotherbigdatabusinessisrelatedtodatascientistrecruitmentservices.Recruitingbusinessescannotshyawayfromtheneedtorecruitanddeploydatascientisttalent.Yet,bigdataanalystsarecurrentlyascarceresource.Sonewbusinessmodelsaredevelopingtoservetheneedsofcompanies.Kaggle(2014)forexample,usesahackathontypecompetitionmodelforundertakingpredictivemodelingtodatascienceprojectsbutalsodoublesasalearningcommunityforthosewhowanttoattaindatascienceexpertise.4.4ImplicationsofBigDataforChannelsWithonlineshoppingtakentonewheights,business-to-consumer(B2C)deliveryoptionsareopenfordiscussion.Aconstraintinachievinghighoperationalefficiencyinadistributionnetworkoccursatthe“lastmile”,i.e.deliveryataspecificdestination.ClickandcollectdeliverysystemsseemstobethehottesttrendinB2Clogistics,forexample,inatownasLondonintheUnitedKingdom(Butler2014).Clickandcollectseemstoworkatmanylevels,peopledonotneedtobesomewherewaitingfortheirparcel,theirparcelwaitsforthematasafepick-uppoint,companiesdon’tburdenthemselveswiththecostofunsuccessfuldeliveries,andcitizensdonothavetoput48 upwiththeCO2emissionsofafleetmakingroundsdeliveringparcels.Yet,thereisscopeforapplyingbigdatatechniquestodrivecostsdownevenfurther,whileincreaseflexibilityforcustomers.Onewayistoutilizereal-timeoptimizationondeliveryroutingtomaximizingtheroutingschedulesofconventionaldeliveryfleet.Anotherapproachwouldbetoutilizecombinatorialoptimizationtodeliverproducttopeopleonthego.Combinatorialoptimizationcanprocess,forexample,real-timeinformationaboutrecipient’spositionanddirection,thus,deliveriescanbeschedulesdynamically.Thisinvolveslocatingthepathsofbothrecipientsanddeliveryvehiclesinordertore-routevehiclesonthegotothenextbestpointtomeetrecipients.Currently,thesequencingofdeliveriesiscarriedoutmanuallybydriversfollowingeachdelivery.Abigdatadrivensequencingsoftwarecanrapidlyprocessinformationaboutdeliveries,currenttrafficconditions,loadingbayavailability,recipientavailabilitytoavoidunsuccessfuldeliveries,therebyminimizingcosts.As,bigdatahavealsofacilitatedatrendtowards“crowdsourcing”everything,whynotutilizethecrowdtodeliverpackagestoo?Eveninbusiness-to-business(B2B)logistics,bigdatapredictivemodelingcanalsotakejust-in-timeoperationalefficienciestoanewlevel,reducingthespacerequiredtostoregoodswhileensuringbusinesscontinuity.Predictivemodelingcannowtakeintoaccountnotonlycustomerpurchasingpatternsandfeedback,butalsoanincreasingnumberofdisruptionsduetocivilunrest,naturaldisasters,orevensuddeneconomicdevelopments.Bigdatatechnologyandanalyticscankeepaneyeandkeeptrackofdevelopmentsinanyofthecriticalriskfactorsthatcancompromisethegoingconcernoftheorganizationfromavarietyofsources(e.g.,socialmedia,blogs,weatherforecasts,newssites,stocktrackers,andthelike).Itcanthenalertandinstigateriskmanagementscenarios,sucharefulfillinganorderviaanothercountryorrouteaddingtothecompany’sresilienceandflexibility.4.5TheImpactofBigDataonCustomerRelationshipsProfilingcustomersutilizingbigdatacapabilitiestoincreasesaleshavebeenattheforefrontofadoptingthenewtechnologies.Buthowaboutcustomerservice?Withsomuchinformationaboutcustomers,surelycompaniescanimprovecustomerservicetoincreasecustomerintimacy49 andloyalty.Oneofthekeychallengesforcustomerserviceoperatorshasbeenthelackofcomprehensivecustomerinformationattheirfingertips.Unavailabledataonthefrontlinemakecustomerservicerepresentativesunableorslowtorespondtocustomerissuesandrequestsandofferalternativeoptions.CustomerRelationshipManagement(CRM)projectshavemadebiginvestmentstowardsaddressingthematter,yettheyhavebeencostly,highmaintenanceprojects.Moreover,standardizedsiloedsupplychainoperationsmadedealingwithexceptioncasesanoperationalnightmare,withsuchcasesfallingusuallybetweentwostoolsorrequiringexpensivededicatedteamstohandlethemappropriately.Bigdatatechnologies,suchasHadoop,canrelateisolateddatasilosviahyperfastin-memoryanalyticsplatforms,providingrepresentativeswithreal-timeinformationtoassistcustomers.However,howcompaniesrelatethistoimprovecustomerserviceasopposedtosalesisnotyetclear.Bigdatacustomerrelationshipprojectsarelaggingbehind,butthisisexpected.Customerrelationshipprojectsareconsideredcostlycomparedtotheirrevenuegeneratingpotentialsandtendtobeinstigatedlaterinthematuritylifecyclewhenthemarketbeginstoplateauandcompaniesseektomaximizevalueelicitedfrombusinessassets.Banksarelikelytobethefirsttojumpintothisbandwagon,notonlybecausetheyhavebudgettodoso,butalsobecausetheyneedtorebuildthetrustofthedisenchantedpublicfollowingtheglobalfinancialcrisis.Trendstowardsrebuildingstrongpersonalrelationshipcanbeseeninretailbanks’advertisingthemes.Banorte,aMexicanbank,focusesstronglyonits13millioncustomerbaseandcapitalizesonnewbigdatatechnologiestodesignbankingservices.Bigdataenabledmarketingautomationcanhelpthebankserviceindividualcustomerneedswhilekeepthecostsofmarketing,creatingapersonalizedexperienceatareturnoninvestment.Also,harnessingthepowerofautomatedpersonalizedcustomerservice,requiresbusinessprocessreengineeringthroughouttheentireorganizationandrethinkingofthecustomerjourney(Wagle2014).Bigdatawillalsoexpandtheuseofautomatedcallcentersusingnaturallanguagerecognitionpatternsandresponse,thus,makingArtificialIntelligence(AI)becomingincreasingly50 moresociableintermsofrespondingtocustomerqueriesusingvoice.Hence,companieswithlargecallcentersarelikelytoadoptthisnext-generationAIenabledandbigdatainformedspeechsolutionstodrivecostsdownaspartofbigdataorientedbusinessmodels.4.6TheImpactofBigDataonRevenueStreamSocialmediahasenabledpeer-to-peertransactionsand,ineffect,collaborativeconsumption.NowhotelscompetewithAirbnb,apeertopeerplatform,forholidaymakers’cash(Riedy2014).Airbnbsupportstherentalofsparebedroomspacetoprivatetravelers.Establishedin2008,Airbnblistsmorethan500,000propertiesworldwideandhashostedmorethan8.5millionpeopleincreasingthevalueofthecompanyatUS$2.5billion(Riedy2014).Thefinancialcrisishaschangedsocialvaluesaboutre-use,providingimpetusfortheriseofasharingeconomyemergesasapotentialthreattoestablishedbusinesses,asconsumersaresharingthingstheyalreadyhave,thentheydon’tneedtobuysomethingneworsooften.Blahblahcars(2014),enablescarsharingwithsomeonewhogoestotheirpointofdestination,bymatchmakingpeoplewithcarswithpeopleinneedforaride.bigdatacanmakethesetransactionssafer,real-timeandefficient.Forexample,carsharingdoesnotmeanyouhitchingaridewithacompletestranger.Bigdatatechnologycanadvancethesafetyofcarsharing,viacarmonitoring,passengeranddriverhealthmonitoring,andthelike,offeringprecautionarymeasuresanddeterrentsofcommonsocialrisks.Intransactioncosteconomicsterms,the‘bigbrother’capabilityofbigdatacansubstituteinstitutionsrolestomonitorand‘police’adherencetorulesandregulations.Anotherdisruptivechangeintherevenuestreamiselectroniccurrencies.Whatwillthecompanyofthefuturebepaidwith?LeHong,researchvicepresidentandGartnerFellow,alsopointedtothecryptocurrencyBitcoin(Nakamoto2008),asamajordisruptiveforce:Typicallyonlynationalgovernmentscouldissuecurrency,butwe’reseeingpeopleputmoretrustinaprivatelyfundedstartupthaninmostWesterngovernments(Turner2013).Ofcourse,theregulatoryenvironmentforsuchcurrenciesisstilluncertainandtheriskscurrentlynotassessedinasystematicway.AccordingtoWhite&Case(DuplatandVercauteren51 2014),theinternetenvironmentwherevirtualmoneyisheldandtradedisstillrisky,whentradingplatformordigitalwalletcouldbehacked,makingownerslosetheirvirtualmoneyandsystemsreliability,particularlywithregardtotheriskoffraud,actuallynotproperlyassessed.Inaddition,fluctuationsinthevirtualcurrencyexchangeratecanresultinsubstantialfinanciallosses,andthereisnolegalguaranteethatvirtualcurrencycanbeexchangedatanytimeforitsoriginalvalue.Finally,virtualcurrencyisnotlegaltender:no-oneisobligedtoacceptpaymentwithvirtualcurrency.Whilesuchfearsarecurrentlyjustified,thefutureofBitcoinpaymentsdependsonpeople’sacceptanceandtherearestrongproponentsforitsfuture.AccordingtoprofessorCampellHarveyofDukeUniversity(Card2014),Bitcoin,likeallcryptocurrency,bearsfewerrisksthandebitorcreditcardfraudandhacking,andminimalcostoftransactingcomparedtotraditionalmethodsadoptedbytraditionalinstitutions.Cryptocurrencyreliesonthepowerofcomputerstoenablerealtimeexchangeofownership,verificationofownership,aswellastheabilitytoalgorithmicallydesignconditionalcontracts(Card2014).Inaddition,cryptocurrency-basedtransactionsrequiretriangulationofdatatoensuresecurity,privacy,andtrustwithoutusingcentralizedinstitutions.Alloftheaboveissueswillrequirethenumbercrunchinganduserprofilingpowerofbigdataanalytics,whichwillhaveabigroletoplayinanalyzingthehugeamountsofdatathatBitcoinandcryptocurrencysystemswillbegenerating.Companieswillbeabletoissueandguaranteetheirowncurrencies,whichcanthenbespentinassociatedproviders.Accordingly,onecanonlyimaginethepowerofretailsupplierssuchasAmazonandGoogle.Forexample,GoogleVentureshasputmoneyintoRipple,theopencurrencyexchangecalledRipple,whichallowsuserscantradeanycurrencywithoutrequiringanybrokerorthird-partytofacilitatethetrades.4.7TheImpactofBigDataonKeyResourcesandKeyActivitiesTraditionallycapitalresourcesaremanmadeassetsorganizedtogenerateincome.Therearecertainunderlyingassumptionsinthisdefinitionthatarelikelytobechallengedinthefuture.First,resourcespresumeownershipbythecompany,andthereforetheiruseanddisposalatcompanywill.Second,resourcesdonotgivevalueunlesstheyareused;henceitisutilitythatprovides52 value,thus,neitherresourcenorownershipperse.Third,togeneratevalue,theutilizationofresourcesneedtobeorganizedintoperformkeybusinessprocessesthatdelivervalue,intheformofaproductorservicedesirabletoamarket.Bigdatacanrevolutionizealloftheaboveforbusinessmodelinnovation.Tostartwith,bigdatacanrevolutionizeemploymentrelationships,asmoreandmorecompaniescanrelyoncrowdsourcing,thus,outsourcingafunctiononceperformedbyemployeestoanundefined(andgenerallylarge)networkofpeopleintheformofanopencall.Thiscantaketheformofpeer-productionwhenthejobisperformedcollaboratively.Crowdsourcinggrowingpopularityisduetoitscostefficienciesanditsglobalreachpotential.Asorganizationsrelymoreandmoreoncrowdsourcing,theywillrequireastrongcoordinationmechanism,capabletogatherandreconcileoftendiverseinformationandviews.Afterall,businessesneedtobeabletojudgedataquality,findwaystoovercomegeographicaldifferencesandapplytoorganizationalgoals.Actually,crowdsourcingandbigdatacanrevolutionizepublicservice.TheUnitedNations(UN),forexample,hasstartedutilizingMindjet’sSpigitEngage,abigdataandCrowdsourcingplatformtodevisebestpracticeintacklingrefugeeissues,suchas,e.g.,learninganewlanguageinordertoabsorbnewculture,findingjobs,andgainingaccesstoinformationandvitalsocialservices.Also,usinggamification,SpigitEngageplatformallowsthegrassrootsideastorisetothetopavoidingstatusquobureaucraticbarriersandgatekeeping(Spigitengage2014).Theaboveissueshavealsobigimplicationsaboutmanagementstructuresandmanagers.Iftheendtoenddecisionmakingprocesscanbecoordinatedbybigdataalgorithms,andsoisthemonitoringtheimplementationprocessesaswellasthedynamicallocationofpeopleandresources,whatisthepurposeofexpensiveorganizationalstructures?Thismayseemfarfetchedfromtoday’srealitywhereformalorganizationsarestillthedominantworkorganizationstructure,butflexingtheboundarieshasalreadystartedandthetrendseemirreversible.Anotherresourceimpactedbybigdataisthecapitalitself,i.e.thefinancingofbusinessventures.Socialmediaandbigdatahaveenabledthemergingofsalesandfinancingthroughcrowdfunding.AquickstudyofKickstarterprojectsandassociatedpledgeswilldemonstratethat53 mostpeopleeffectivelypre-buytheproductorservice(Jeffries2013).Inthatsense,consumersactasfinanciersoftheirwantedproducts,andatthesametimeasearlyadoptersofit.Takeforexample,CoolestCooler(aportablecooler,integratingacooler,ablenderforoutdoorpartyoccasion),themostfundedKickstarterprojectwhoreachedfinancingheightsofmorethan$13million.Morethan57,000outofits62,642backerswerepeoplewhowishedtopre-purchasetheproduct,creatingthemarketandtherevenueforthecompanywayaheadofitsproduction.Finally,bigdatanotonlyposeenormouschallengesandtechnologyinvestmentstoaccess,process,andanalyzethemassiveamountsofdata,butrequirescompaniestoalsomakecorrespondingchangesonbusinessprocessestocapitaliseonit.Forexample,Applecapitalisedonmarketandsupplychaindatawhentheyimprovedsupplychainmanagementpracticestobecometwiceasfastastheaveragecompanyintheelectronicsmarket(Bharadwaj2013).4.8TheImpactofBigDataonKeyPartnershipsWhilewealreadyseepartnershipsintravelandhospitalitytoscaleoperationbysharingreservationsystems,loyaltyprograms,andcross-sellingopportunities,wewillincreasinglyseetheformationofdifferentfirmstopulltogethertheirstrengths,organizedthroughplug-and-playarchitecturestocreatecompetitiveadvantage(Bharadwajetal.2013).Byenablingreal-timecoordinationmonitoringandfeedback,bigdatacanprovideasenseofsecuritytothoseinvolvedinsupplychaincollaborations.Previously,supplychainrisksweremoderatedbypolicies/proceduresandcontractualagreementsthatactedasdeterrentandprovidedsomesecurityassurancesagainstnegligence,deceptionanddeceptivepractices.Real-timeinformationanalysisofsupplychaincoordinationdatacouldflagissuesaheadoftime,highlightunderlyingreasonsfordelaysandoffercontingencyplanningalternatives.Furtherdevelopmentsinadvancedpredictivebigdataanalyticsarelikelytoleadtothedevelopmentofcomplexdecisionmakingscenariosbasedonavarietyofstresstests,inmultiplemarketconditions,against,numerouskeyperformanceindicators.Suchbusinesssimulations,canbeusedtodesignandredesignsupplychainoperations(Grovesetal.2014).Forexample,by54 simulatingdifferentmarketandcompetitorscenarios,bigdatacanassessbusinessagilityandresilienceofagivensupplychain.Pricevolatilityinthecommoditiesandcurrencyfluctuationshasmadelong-termprocurementariskybusiness.Forexample,organizationsbufferthemselvesfromtherisksoflatedeliveryandinabilitytofulfillordersbyplanningforaminimumsafetystock,whilepricecompetitiononscarceresourcesandrawmaterialoftenleadstobiddingformorequantitiestoensuresufficientorders.Allegedlysuchriskscanbereducedbybigdataanalyticsbyimprovingpricepredictionsorbidefficiency.TheInternetofThings(IoTs)willalsoimprovethequalityofinformationaboutthelocationofphysicalthingsinthesupplychain.Yanetal.(2014)envisionaCloudofthingssupplychainsolutiontofacilitateresourcesharingandparticipantcollaborationinthewholesupplychainlifecycle,wheresupplychainconditionperception,heterogeneousnetworkaccessconvergence,andresource“servitization”co-exist.4.9TheImpactofBigDataonCostStructuresWithcrowdfunding,crowdsourcing,andbigdata-drivensupplychainjoinventuresdismantlingthetraditionalboundariesoforganizations,thedefinitionandboundariesofcosteconomieschangedramatically.Ontheotherhand,asdata,informationandknowledgebecomethefocalpointforcollaborationatalllevels,accountingforintellectualpropertyrights,data,andintangibleassetswilllikelytobecomeafocalpoint(ViscusiandBatini2014).Morethan20%oflargecompaniesalreadyclassdataasanassetontheirbalancesheets.Datavaluationislikelytobecomeakeyaccountingskillinthefuture.Valuingdatatodatehavebeenimpossibleasintangibleassetstendtogethiddeninexistingreportingandgovernancesystemsoncedevelopedfortheindustrialage.Evenwhenvisible,datausuallygetsanarbitraryvaluation.Calculatingdepreciationisdifficult,andinformationvelocitymakessuchcalculationsevenmoredifficultandunreliable.Inaddition,likeinmostassets,thevalueofdatadependsonitsutilization.Tomovetowardsacostingmodelofdataresources,newmethodologiesneedtobedevelopedandagreedamongstaccountingandfinanceprofessionalsthatdeterminekeyassumptionsaboutwhatdataisofvalue(Chua2013).Whatmattersfromanaccountingpointofviewistoassessthemonetaryvalueorprofitan55 organizationcreatesoutoftheinternetofeverythingandbigdata.Todoso,managementaccountantsshouldcombineinternalfinancialdataandperformanceanalysiswithoveralleconomicperformancemetrics.Theyshouldalsobeabletocomparethesetosomereferentorganizationalgroupswithinthesector.Inaddition,thesewillbeaffectbypolitical,economicsocialandtechnologicalinfluencesontheirindustryandonthebusiness.Yet,nowadaysalltheseassumptionsareopentonegotiationandredefinition.Technologytrendsredefineindustryboundaries,companies’strategictrajectoryandcapabilitiesagainstotherorganizations.Forexample,FacebookcompetewithGoogleforadvertisingrevenues,eventhoughwhattheyoffertousersarecompletelydifferentvaluepropositions(Dias2013).Insummary,inthesamewaydefiningnewmarketshasbeenthekeydriverfortech-drivenbusinessmodels;developmentsintheareaofcostandmanagementaccountingwilllikelyfollow.Bigdatadrivenbusinessmodelsarestillintheirinfancy,hencecosteconomicsarestillinembryonicstages.4.10OrganizationalAdvantagesandOpportunitiesThebigdataeraisanopenminefieldforallwhoarewillingtounlearntheiroldwaysandexperimentwithnewones.AccordingtoMcKinsey,somesectorsorindeedsomeplayersmaybemoreadvantagedoverotherstomakingsuchmoves.Forexample,companiesthosehaveinvesteddeeplyinITandhavelargedatapoolstoexploitaswellasthoseininformationindustries,whohavetheexpertisetoperformsophisticatedanalytictechniques,arelikelytobethefirsttocreatebusinessvalue.Ontheothersideofthespectrum,publicsectororganizationsareinneedforradicalchangeandcanmorereadilyopenuptheirinformationresourcestoleveragethevalueofpublicservicestocitizens.Theyaremorelikelyaswelltofindlargepoolsofvolunteerswillingtohelpwithcrowdsourcingprojects.Maturesectorscharacterizedbyalargenumberofcompetingorganizationsarelikelytostaybehind,duetoego-drivenleadership,strategicmyopia,technophobia,andconflictingviewsaboutthefutureoftheindustryandsectorinfighting.Arehoweversuchsectorsthatcouldmostbenefitfromsharingcompanyinformation,identifyingnewmarketopportunitiesandadopting56 ‘collaborate-to-compete’networkingpartnerships.Theextenttowhichsectorleadersandassociationsarereadytoinvestintheseopportunitieswilldefinethefutureoftheirindustry.Companiesshouldbeabletoreflectonwhetherandhowtheycancreatenewbusinessmodelsbasedonbigdata.TheInternetofThings(IoTs)willintensifyinformationabundanceandraisemorepointsforreflection,suchas,e.g.,howdoweconvertthedatageneratedorcapturedbyIoTsintoknowledgetoprovideamoreconvenientenvironmenttopeople.Tobeabletodevisenewbusinessmodels,companiesshouldopenuptonewideasandmakesuretheycanquicklyassimilatethemwhereverthesecomefrom,(grassrootsinitiatives,openinnovationcompetitions).Theyshouldalsobepreparedtomodifyorfundamentallychangetheirinternalstructurestodeploynewproductsandservices.Thisrequiresa‘u-turn’inmentalityandcultureformostorganizations(Tsaietal.2014).Tsaietal.(2014)identifyanumberofIoTsapplicationsthatwillchangeexistingsectorsandservices.Thesehavebeenlooselycategorizedinto:(1)smartcities,(2)smartenvironment,(3)smartwater,(4)smartmetering,(5)securityandemergencies,(6)retail,(7)logistics,(8)industrialcontrol,(10)smartagriculture,(11)smartanimalfarming,(12)homeautomation,and(13)eHealth.4.11OrganizationalChallengesandThreatsThereareanumberofchallengeswithrespecttoredesigningabigdatadrivenbusinessmodel.Technologyassessmentcapability,technologymaturitylevels,evolvinglegalframeworksaroundprivacyandintellectualproperty,dataanalysttalentscarcity,technologyinvestmentcapability,internaldataavailabilityandquality,interoperability,securityissues,limitedbusinessprocessunderstandingandassociatedmetrics,datahoardingandoldfashionedhierarchicalleadership:thesearejustfewofthebarrierstobigdatadrivenapproachestobusinessmodelredesign.Thesebarriershavebeenreferredtoalsoinprevioussectionsandchapters.Inthissection,itwouldbeappropriatetorefertochallengeswithrespecttoadoptingabigdatadrivenbusinessmodelinnovation(Osterwalderetal.2005).4.11.1CreativityandInnovationCapabilityDeficit57 Thebigdatainnovationerasuggestthatmanagersneedtothink“laterally”outsidethebox.Manyestablishedorganizationshavepromotedtodirectorpositionsgoodadministratorswhocanmaintainorderandefficiency.Formanysmallandmediumorganizationsthetopmanagementteamisburdenedwiththeday-to-dayoperationofthebusiness,leavinglittletime,“headspace”andenergytorenewtheirskillsandkeepupwithdevelopmentsoutsidetheirimmediatefield.Managementstructuresoftenlackboththepersonalcreativityandinnovationskillsandtherightattitudetofostercreativityandinnovation.4.12SummaryThischapterhasdiscussedbusinessmodelsasthearchitecturallogicthatidentifyhowbusinesselements(suchasabusinessstructure,businessprocesses,infrastructure,andsystems)fittogethertocoordinatevaluecreation(Osterwalderetal.2005).ItthenwentontodescribetheimpactofbigdataoneachoftheelementsasidentifiedintheBusinessmodelcanvasproposedbyOsterwalderandPigneur(2010).Inparticular,ithasdiscussedthenewimpetusthatbigdataandIoTstechnologiesgivetomasscustomizationandpersonalizationofproductandservices.Furthermore,thechapterhasinvestigatedbigdataasavaluepropositioninitsownright.ThechapterhasalsotoucheduponbigdatasolutionsforB2BandB2Clogisticsaswellasforcustomerrelationshipmanagementandcustomerservice.Furthermore,thechapterhasanalyzedtheimpactofbigdataonrevenues,asithasfacilitatednewformsofvaluecreation.Ithasalsodiscussedtheopportunitiesandchallengesitraisesforaccounting,budgetingandperformancemetrics.Finally,thechapterconcludesacknowledgingthatwhilesofarbigdatahasbeenusedtoimproveexistingbusinessmodels,farmorefuturisticscenarioswillemergeincombinationwithotheremergingtechnologies.58 AppendixIITheTargetText第三章大数据和教育：大规模数字教育体系摘要：本章探讨大规模数字教育体系（如慕课）如何推动正规教育机构的远程教育工作，以及开展同伴之间相互学习的教育模式。本章还探讨慕课是如何通过聘请服务、辛迪加（企业联合组织）、赞助和广告收入以及向潜在的用人单位或广告商出售学生信息等方式为传统教育机构打开新的赚钱门路。对此，本章首先会对慕课的教育模式和大数据分析在这一背景下所发挥的作用进行阐释，并强调学院教育模式的优点和其面临的机遇、挑战。本章随后会对两个案例进行分析解释，在这两个案例中，大数据分析在课程设计和推广方面发挥了基础性作用。3.1简介一直以来，远程教育都是服务于那些未能走进正式学府的人群。全球范围内的函授大学已把他们的主要精力放在了这一市场领域。接受远程教育的学生，其社会经济背景、学习习惯和学习模式都不尽相同，有的人生活在偏远地区，有的人虽说在离家近的地方工作，但他们的空闲时间有限，还有的人因工作的日程安排而无法接受大学的函授课程。为了适应不同类型的学生，函授课程的教材也做出了改编。起初，远程教育的课程采用一系列试听教学的信息传播手段，如电视节目，音像光盘等，但这样的课程仍缺少学生与教师、学生与学生之间的互动。在线社交平台确实可以重新强调教育的社会性（马奎斯2013年调查报告）。在过去的二十年里，得益于突飞猛进的信息通信技术，全球爆发了前所未有的学术革命，各类大学和人群也受到了这一革命的冲击（《管理者》2014年调查报告；袁、鲍威尔2013年调查报告）。教育是促进国家经济发展、推动个体社会流动的关键因素。两大因素着重强调了全球教育需求的必要性。由于经济模式发生变化、新兴市场参与竞争，当前全球竞争所需的技术正在变革。信息通信技术为各国、各大学提供在全球范围内优化配置高技术人才资源的机会，并为个人提供社会地位攀升的机会。在这一进程中，我们也会遇到挑战。拿发展中国家来说，其数字技术的普及程度较低。在非洲、中东和拉丁美洲/加勒比海地区，他们的网民人数只占到了世界网民的17.2%（米尼沃茨营销集团2014年调查报告）。基础技术教育和支付能力制约着人们对必要技术工艺的改进工作。另一方面，如内格罗蓬特提出的“每个孩子一台笔记本电脑”项目试图为发展中国家的每个孩子提供一台低成本电脑，类似的倡59 议正缓慢地改变着这一进程。信息通信技术在教育领域的应用已呈现出分门别类的特点，古丽-罗森布里特（2010）提到了20多条既有联系又有差异的术语，每一条都暗含了它们略有差别地实施基于同一信息通信技术的远程教育。·网络教学·技术辅助学习·网络教育·在线教育·电脑中介传播·远程信息处理环境·在线学习·虚拟课堂·信息化校园·电子通信·信息通信技术·网络学习环境·电脑互动交流·开放式远程教育·分散式学习·混合课程·电子教材·混编课程·数字教育·移动学习·技术辅助教育在这个术语长名单中，基于信息通信技术的远程教育有着不同的实施方案。目前，慕课也被收入该名单。慕课推动了正规教育机构的远程教育工作，同时也引入了一种更为彻底的教育改革；它们利用学生之间互帮互助的学习形式，开创了学生参与教育的新模式；它们能让成千上万的学生通过公开课主动地与他人分享知识。因此，它们几乎为每一个学习者提供了他们感兴趣的快速课程、无障碍课程、弹性课程、免费或负担得起的课程。这些取自高等60 教育阶段到职业培训阶段的课程，其难易程度不一。大规模开放式在线课程增加了大数据的类型，同样地，大数据对制作和运送该类课程的教材发挥着主导作用。在这种情况下，慕课成为了教育领域重要的‘变革者’，并在全球范围内产生普遍影响，它关乎各国（地区）经济实力、国际关系和全球性社会流动以及福利事业。本章开篇便探讨慕课对教育领域的影响，同时还探讨大数据在设计、实施慕课方面所发挥的作用。章节结构如下所示：3.1.1从机构化教育到慕课的演变慕课根植于这样一种理念，即人们不受人口、经济、地理因素的影响，都能免费接受教育，这种理念最早起源于20世纪初期（袁、鲍威尔2013）。2001年，麻省理工学院推出公开课方案（麻省理工学院2014）。其首要目的是广泛传播知识，让人们在求知路上互帮互助。截止2007年，麻省理工学院已把所有课程放在网络上，而且这些课程向公众开放。在刊登于《纽约时报》的一份最初项目声明中，麻省理工学院院长维斯特博士就预言他们将对外开放所有课程。表3.1慕课和开放式教育时间轴。改编自袁、鲍威尔（2013）我还认为，放眼国内乃至全世界，许多确实聪明的早熟高中生将会发现这里是学习天堂。公开课程可能很有益处，可能真的会令我们吃惊，我们真的不好预言它们将会带来什么样的益处（戈尔德贝尔格2001）。事实上，公开课是推动更大规模改革的垫脚石。届时我们会拥有不断壮大的慕课平台和许多走上慕课这条道路的著名院校或机构。例如，Coursera（2014）是斯坦福大学和61所著名院校于2013年共同创建的大规模公开在线课程。EdX（2014）是麻省理工学院、洛桑理工联邦学院和香港科技大学联合创建的大规模开放在线课堂平台。Udacity（在线大学）（2014），P2PUniversity（端对端虚拟大学）和FutureLearn（未来学习是英国开放大学创建的慕课平台）都是其他类似的平台，相对于院校主导的慕课平台，它们只是衍生平台。但是，许多基于社区的开放式学习平台开始出现，而且它们的教学方法和理念大为不同。例如，P2PUniversity（2014）主要基于学生的主动性，它通过让学习者审视网络材料来共同打造自己的课程，这么做的话，学习者就能对自己和团队的学习负责，从而创造出一种终生学习的模式。据P2PUniversity的网站称，开放、社区和同伴学习是主要推动因素，他们肯定会把握61 住这三个因素。要是学习者想做出改变的话，技术和学习过程甚至都能改变。人们的想法是放弃学校与学生之间的等级管理和角色分离。诸如阅读、视频和小组讨论的材料寻求有所发现，而不是用于传播知识。人们根据自身需求，独立设计这样的材料，并未遵循学科特点。他们的箴言搅乱了传统教学，创造出一种“无评价”的环境氛围。在这种氛围下，你可以随意添加和获取材料。教育家和学习者承认他们将对此进行实验，如果他们这么做的话，“他们将承担风险，他们将共同学习。事情将会变得一团糟，也将会很有趣。”（伯杰等人2014）其他课程，如学习一个编程语言，可能需要专家更多的指导和框架介绍，但重点仍在于互动、自学的责任心和自我控速。Udemy（2014）是另一个网上学习平台，开设了多种多样的课程，包括摄影、语言等实际操作性课程、考试准备课程和诸如哲学、人文学科及理科等更为理论性的课程。这些课程并不是免费的，但价格还是能够承受的。Udemy开设了16000门这样的课程，参与学习的学生有300万人。他们把每门课程收入的50%用于平台的市场营销和技术支持。慕课商业模式之间的差异是巨大的，对它们的投资差异也是巨大的，正如表3.2所示，当前市场上实施慕课项目机构的融资模式。慕课寻求商业模式的有效性和可持续性。例如Coursera，他们的商业原则类似于亚马逊的商业原则。通过创建一个供应商中心来吸引大量学生，让他们在其中一个地方找到他们想要的。对其他人来说，如未来学习的西蒙·尼尔森（威尔比2014），问题的实质还尚不可知，因为技术的潜力无法预见，更不要说能够感知了。Udacity把主要精力放在就业和培训上，他们和谷歌、美国电话电报等公司进行培训合作，并提供人们感兴趣的学位证书。EdX正把他们的模式从课程设置上转移，他们想成为开设慕课这一技术平台的管理者，并将自己的平台管理经验当作一种服务提供给各个大学。不光他们的市场定位不同，他们的融资模式也各不相同。风险投资家、慈善家和国家教育机构都乐于投资，以支持慕课项目。Coursera（2014）接受了大约6500万美元的投资，而Udacity（2014）从风险投资家那里获得了1500万美元的投资。就其商业模式的开放性特点来说，他们都承担了风险，因为他们的投资者可能会为了商业利润，要求公开课走向商业化道路（沃特斯2012；《经济学人解释》2014）。3.2慕课教育模式的种类3.2.1名校带动的慕课名校带动的慕课也叫作X-慕课，代表着正规教育的最新创举。世界上公认的大学目前62 同意利用慕课来授予学位，但完全免费的网络学位并不实际。虽说学生仍得花钱来获取学位，不过学费也降低了，因为他们不用支付学术过程中所需的运营成本，比如管理费用、教室和设备维护等等。当慕课能够提供公认、免费的网络学位时，大学商业模式将发生根本性的变化（德拉诺卡斯2013）。对于各个大学来说，利用光盘记录现场授课和陈述，促进传统教学虚拟化，这是一项经济可行的方案。网络授课已不是什么新鲜事儿，它利用网页的学习管理系统来授课。尽管游客量可能会减少，不过学生表现不一定会打折扣（奥特曼2013）。另外，参与学习的全球学生人数不只是附庸风雅，还大大减轻了他们面对面的教学负担。慕课当然也吸引着学者和高校管理人士，因为慕课能吸引风险投资和奖学金，这些资金通常和学术奖励制度挂钩。另一个潜在的收入来源是广告收入，这可能是各名校联合起来、在这一平台上提供他们的教学模块和学位的原因。像哈佛、麻省理工、康奈尔和伯克利以及其他非营利性机构edX向外界提供“世界最好大学的精品课程”或者“免费的世界精品网络课程”（奥特曼2013）。总之，为了实现低成本的教育支出，市场上似乎存有分歧。据伦敦大学教育学院的数字技术教授罗瑞兰德等人（2010年）研究显示，在美国，学生贷款数目要高于信用卡贷款数目，而英国40%的学生债务将不用偿还。随着新兴经济体对高等教育的需求，2025年以后，高等教育的需求也将增加一倍，每年大约会有2亿学生接受高等教育。也许这也显示出一个更深层次的转变，即不靠政府的预算支出，转而寻求更为自主的融资形式，或建立高等教育和私人资本之间更好的联系，甚至改变教育任务和大学生，从而探索新技术，并为全球人口提供教育，依这种做法，我们就能建立共同的世界观。在实用内容居多的慕课中，专业人士或专家向初学者提供培训和技术完善的课程，我们可以观察到这一过程。Coursera（2014）中的许多课程都属于这种类型。3.2.2端对端慕课端对端慕课与大学带动的慕课（X-慕课）有所不同，也被称作C-慕课，即关连性慕课（袁、鲍威尔2013）。这两种慕课形式在理念、传播方式和意图上有所不同，它们在学术思想和实践上的某些冲突已结束。或许理解C-慕课最快的办法是将它们和更为传统的X-慕课作对比。表3.1罗列了它们之间的一些根本差异。表3.1X-慕课与C-慕课的比较X-慕课C-慕课63 向广大/新的听众传播课程内创建一个学习社区，便于未来容；提供资格证书；试验新课继续共同学习最终目标程；接受更多北美顶级高校的课程或提供免费的教育教师做出决定的教育理论传统的教师中心论：教师向学社区论：学习过程重在学习者生传授知识之间的合作创设课程内容、教学方法和评学习者参与并推动课程内容、教师的作用估模式学习目标的创设，形成新知识等学习者领悟知识，参与小组讨学习者共同创建教学方法和学习者的角色定位论学习，应对各种考试和评估评估模式活动学习者学习教师制定的课程学习者创建与课程主题相关学习者如何建立新的知识框内容，并加以运用，解决教师的课题；分享信息和知识；互架设定的问题或课题帮互助教师进行衡量评价学习者分享他们的学习过程，如何评估学习并进行自我评价教师创设课程内容学生核心创设内容目标；所有课程内容的创设主体参与者创设课程内容学习者以小组形式进行基于学习者探索、分享相关的课程互动方式课程内容的评价活动内容，实现学习目标课程过程和课程目标的灵活学习过程依照教学大纲和课学习目标主要由课程的核心度程计划参与者设定，面对社区，每周进行回顾来源:克劳利等(2013)除了依赖于传统的教育模式，P2P或C-慕课试图激发学生的合作意识。在某种形式上，学生并不是单纯意义上的学生，因为没有老师存在。C-慕课是这样的学习社区，志趣相投的人汇聚在这里，他们对自己的学习过程负责，而且自己规划学习过程。这样做的话，他们共64 同确定了自己的学习目的，通过探索创设课程内容，分享课程主题，给予反馈、见解、主意和支持。虽说一个核心团队往往采取较多的协调性行动，不过课程目标和目的是不固定的，我们得根据社区特点做出决定。探究学习的理念对正规教育来说并不陌生。很多硕士在做研究，他们的研究通常是获得博士学位的奠基石，当然博士项目（正规教育的研究顶峰）全都利用研究来获取知识。但是，获取学位是一个孤独旅程，学生要对自己的研究负责，并将研究成果交给某个学术权威，从而换取和自己能力匹配的资格证书。C-慕课目前不是这样运作，至少现在还不是。从大数据角度来看，这一点为什么重要呢？首先，由于没有课程，任何来源的信息都能整合利用，以便改善学习。任何事物，不管是政府信息，还是物联网、社会媒体、学术期刊和观察虚拟游戏以及国家统计局所得出的数据，这些都能混杂在一起，或就大数据来说，这些都能捣在一起，产生新知识。这样的混杂可能引人诟病，或者受到某个学习团队的支持，但不可避免的是，没有学术权威会把这类研究和其具体来源、观点或框架联系在一起。世界处于互联网时代，学习者可以随意地汲取知识（耶格尔等2013）。一时间，教师和学者对学习大多数C-慕课感兴趣。P2PU（2014）就是这类机构，其中一个专家团队就聚在一起，共同探索课程开放和设计、研究项目及知识分享等新理念。他们这么做能提升我们对学习的认知。这种形式能成为主流吗？目前还有待于观察。3.3大数据分析的作用这部分试图了解大数据在这一教育新领域中发挥的作用。如今，读者应该清楚慕课利用不同的开放信息（或大或小）来发现和创造新知识。但大数据在X-慕课中发挥了什么作用？很大程度上，就如同其他商业一般，慕课需要开发吸引新学生的市场策略。因此，在学术领域，大数据可以利用市场分析来提供帮助。事实上，为学术机构提供的这类市场调研服务已有了自己的术语名称：学术分析（加西亚和斯喀斯2013）。学术分析利用大数据集、统计方法和预测建模向学术机构提供商业智能，从而改善顾客体验。例如，教育系统，特别是学生参与程度不同和完成率不同的慕课，需得小心地管理资源，同时还要寻求交叉销售更多优质产品的机会。由于X-慕课会尝试利用大规模的学生基础来满足其投资者，这个情况将变得尤为真实。这样的预测分析可能会与工作流系统整合，促使管理进程自动化或半自动化。例如，学术分析也能让决策支持系统和工作流系统自动开展招生工作。在学习过程中，学术分析也能用于支持学生。通过利用现有知识和与学生努力及成功相关的模式，学术分析能掌控学生与系统的互动，为了和学生进行主动沟通，或者进行预定的邮件沟通，在学生参65 与类型发生改变时提醒管理者（坎贝尔等2007）。大数据分析的另一个基本功能是用作学习工具。大数据分析的这一特殊功能已被称作学习分析（福尼尔等人2011）。学习分析利用学习者自我生成的个人数据，与已知的学生成功互动及行为模式形成对比。处于学习分析社区的人群提出六大维度，软硬兼顾，旨在设计这样的学习模式。软问题与人们的猜想、社会、能力或者伦理道德相关。硬挑战则与数据和运算相关。从这个角度来说，学习分析可以这样下定义：发现分析，在这种模式下，智能数据和学习者自我生成的数据用于发现信息和社会关系，以便对人们的学习进行预测和建议。（西门子2010）这类分析已得到许多教育主体的喜爱。例如，个体学习者能在反思自己的成就和对待他人的行为模式上得到帮助;关于学生需要更多的帮助和注意，学者能有早期预警，并规划相应的干涉措施;管理人士和课程领导在对有吸引力的学术项目和课程进行开发和市场宣传方面能得到帮助，并推动这些项目和课程的发展（坎贝尔等人2007）。3.4慕课带来的机构优势和机遇根据全球产业分析，全球在线学习市场到2015年将带来1070亿美元的收益。不可避免的是，各大机构希望利用自己的操作来占领市场份额。慕课为传统教育机构打开了新的赚钱门路，他们不光依赖于政府的教育资助和学费，也靠就业招聘服务、辛迪加（企业联合组织）、赞助和广告收入以及向潜在的用人单位或广告商出售学生信息来获取利益。传统机构，特别是资金短缺的公立大学，渴望通过大规模扩招来减少他们的办学成本。而其他机构提出各种“付费学习”的教学规定。有个学派认为，慕课是一次实验，其目的是推动各机构形成一个更具策略性的网络学习方法，而这为大学创造了新的机会。·在利用在线社区的过程中，将开放式学习看作在线学习的一个方法，而在线社区包括可延伸的模式，这些模式可能带来收入，可能超越机构界限。·创造新的商业模式，如将免费增值和有奖销售的观念应用到在线学习中，为各机构提供市场营销和创收的新思路。·促成包括课程重新分类和交付服务的服务崩溃，以便提供优质的教育服务，诸如对评估服务或教学支持付费。·利用新技术，对新的教学方法进行试验。·创建新的教育模式，使其适应全球各种类型的学习者。·通过观察他们的技术基础设施、学者和员工的工作实践，提高机构内部能力（袁等66 人2014）。提高各级学术机构的学习效果，同时减少他们的费用，这也很重要。学习分析、教育数据挖掘和教学分析都能用于提高青年的教育标准，而且不需要增加教育者人数，因此能使各机构更加注重成本效益。学习分析重在捕获学生行为，并根据其行为来实现学习目标；教育数据挖掘试图设计学生素质的预测分析模式；而教学分析帮助教育者将这些发现转变为更好的课程设计和学生支持程序及干预措施。随着时间的推移，越来越多的在线工具得到开发。根据查尔顿和马夫里斯基（2013），例如课程资源评价模型允许教育者理解不同授课方式（面对面授课、混合学习和大规模授课）对学生学习的影响，以便减轻知识传递的难度或改善课程设计。作者们（查尔顿和马夫里斯基2013）也认为，通过在线辅导学生和评估分数水平，Maths-Whizz（在线学习平台）能够提高数学成绩。BlikBook（在线学习平台）重在对课堂参与建模，并帮助学生找到对应材料，替代学生被动参与的模式。表3.2简要概括了一些当前极为常用的学习分析工具，并提到了它们对学习产生的影响，而查尔顿和马夫里斯基（2013）也提到了该影响。表3.2学习分析工具和数据工具和平台学习分析视角教育数据挖掘视角教学分析视角珍查巴尔Jenzabar强大的整体分析和使学习分析和统计模式该工具为讲师提供视（在线学习平台）用大规模机构数据的用于生成预测分析觉再现，协助员工找系统方法到需要帮助的学生以及他们为什么需要帮助课程资源评价模式提供教学的视觉再现辅助教学分析活动的影响教学的自动化视分类系统和关系觉模型Maths-Whizz（在线学对学生学习或数学学使用静态相对学习点使用视觉模型来提供习平台）习有不同见解的特定的分析群集模型来判教师信息，并将其作域分析定数学年龄和背景数为教学的一部分据BlikBook（在线学习社会同龄群体的交流学生建模和社区档案对教师和同龄人领导67 平台）方法及团队问题的解的学生团队进行的学决方法生反馈MiGen（在线学习平社会分组和对学生的基于规则的分析和其不断地对教师教学进台）结果预测他分析任务进展的计行视觉分析，在任务算方法进展和协作活动的过程中采取直接介入方式改编自查尔顿和马夫里斯基（2013）年轻人的学习动机是所有教育系统目前必须解决的一个社会主要问题。就像其他领域一样，教育界可能需要在传统界限外寻求解决方案。游戏化机制可能是一个令人感兴趣的话题，它利用纯粹的游戏理论和游戏机制，激发并维持学生的学习参与度，这在第一章中也有提及。游戏化机制是一种潜在的有效方法，它能提高用户的参与度，类似于教育模拟现实（例子见于虚拟经济2014）。3.5慕课带来的机构挑战就目前情况来看，社会认知度是慕课面临的最大困扰，这个因素也使得其变现的尝试饱受争议。大多数新兴的慕课公司似乎没有明确的商业模式，他们和时下的大多数企业一样，拼命追逐市场份额，并担忧企业未来的营收状况。X-慕课就是这样一个尤为真实的例子，如Coursera（课程时代）试图增加课程量。X-慕课的基础是注册人数，它寻求认证费收入、招聘服务和赞助等。不过，许多院校仍不相信慕课的授课质量，至少还不足已颁发他们的学位证书。因此，慕课在当前仍被看作有趣的品牌和营销活动（袁2013）。实际上，慕课学习的质量是无法确定的。首先，它有着较高辍学率，据称高达90%。对传统院校来说，这个辍学率可谓触目惊心，尽管没有对其运营产生致命影响。但是，许多人认为评价慕课的统一标准并不适用，原因仅在于新技术的性质和接受阶段不同。另外，质量问题需要解决，这和教学及评价教学的传统方式息息相关。然而，一些事件可能破坏了慕课、学术机构和学生三者之间的关系。例如，在2013年二月，课程时代/佐治亚理工学院课程“在线教育的基本原理”遭到取消，学生所有的课程投入都打了水漂（莫里森2013）。同年，加州大学教授理查德·麦肯齐因对开展课程的最佳方式不满而中途取消68 其经济课程。密歇根大学教授高塔姆·考尔在课程时代的平台上开设金融课程，因拒绝提供学生作业的正确答案而引起风波。“他这么做是为了避免准备新的问题，让学生重复思考那些老套路的问题。他会提供很多答案，这迫使我们重复去做。”佐治亚理工学院教授凯伦·黑德开始关注慕课的技术功能，以便实现其教学要求（沃特斯2013）。在衡量学生成功率时，也面临着质量问题。例如，接受传统教学课程的学生，其通过率为总人数的74%，而参加Udacity（在线大学）同类慕课项目的学生，其成功率只有51%。例如，为了解决质量问题，在线大学转而进行企业培训（沃特斯2013）。慕课成功路上的另一个潜在威胁是教学霸权。当教育资源集中在少数全球机构手中时，或由那些能在全球范围内吸引众多学生的少数学院来支配时，我们也许创造了一个这样的世界：知识由相同的原则、理念和观点构成，其他不同的原则、理念或观点都不存在。这不仅危害到机构的金融运营状况，也危害到知识库的多元化发展和思想观念的民主化进程。当然，诸如C-慕课这样的合作学习环境不会面临这个问题，因为学习议程不受官方机构控制，由参与者共同制定。尽管C-慕课与以教师为主导的现行教育模式有着根本性区别，另外C-慕课定位清晰，利用大数据这一工具来挖掘和整合知识，但它依然面临着挑战。C-慕课需要学生具备一定的成熟度、动机和责任感，以便学生对自己和他人的学习负责，进而开展一定程度的研究和提供他人所需的知识共享。这可能不适用于所有的年龄层次，不过在推行C-慕课之前，必须对中小学学生进行一种不同的社会推广和教育，让他们能够参与进来。同时，国家课程要求必须开放，而且要更具兼容性。甚至在成人教育的环境下，人们发现自己处于困难的群体动态之中，诸如冲突和不同程度的承诺。诸如解决冲突、组建团队和创新思维的某些技能需要提升。另外，就连网页搜索都有失偏颇。与搜索主题相关的信息文件数不胜数，但因网页的拓扑结构，我们能看到的也只是极少的一部分。甚至当网页搜索到冷门信息时，网页还需要关键技术来对其进行评估（巴拉巴斯2003）。博伊德（2010）强调，像谷歌这样的信息中介不需要持重要观点，就能过滤信息，他希望社会媒体也能发挥这样的中介作用，在报道中不要夹带自己的观点（科普2011）。一个新视角似乎产生，它试图整合以指导为主的学习模式，搭配更多学生之间互相学习的社会形式，从而提供更丰富的完整体验（见表3.3）（克罗斯林2014）。这个办法或许是个更安全的选择，但仍缺乏想象力和沿用旧的教育观念。或许教育者需要关注社会生活的其他方面来设计教育产品。我们也需要根据受众和目的69 来区分慕课，如果人们还未决定慕课的商业模式，这可能导致一场恶性循环。3.6个案研究HarvardX项目于2012年十月正式实施，并和麻省理工学院、杜克大学等其他院校一道，在EdX平台上联合开办其课程。自从项目实施之日起，该项目便提供不同程度的学位认证，拥有1331043名注册用户，他们来自于全球195个国家（哈佛大学2014）。仅在项目实施的第一年，选择HarvardX课程的注册用户就超过了50万，这个人数多于哈佛建校377年以来的毕业生。美国本土的注册用户占到了36%，除此之外，其他学生都来自于204个不同的国家。在这些注册用户中，非洲要属尼日利亚的注册用户最多；欧洲要属西班牙的注册用户最多；印度拥有5万注册用户，在亚洲排第一；南美洲要属巴西的注册用户最多。制约该课程全球参与度的潜在障碍可能是语言，因为大多数参加HarvardX课程的学生来自于说英语的国家（哈佛大学2014）。例如，在中国，尽管它拥有13亿人口，但参见HarvardX课程的学生只有23548人。有趣的是，证书认证数据和招生模式并不搭调。布基纳法索、希腊和格鲁吉亚的注册用户尽管都低于总用户的2%，但他们的证书结业率是最高的，这可能显示出文化、教育结构和教育状况的差异，也可能显示出个人参与课程的动机差异。但是，在那些注册用户当中，谁在项目开始实施的那段日子里率先参与课程的学习，那么他就更容易取得证书。不过，在课程的开展过程中，获取证书的概率比较稳定，这或许显示出一个预先设置的特定教育目标（哈佛大学2014）。另一个需要着重强调的是，学生的中值年龄为28岁，他们中的大多数人已获得了学士或硕士学位。因此HarvardX课程似乎作为教育的一种补充，而不是要替代大学课程。这类课程更受成熟大学生的喜爱，而此前课程的推广者一直向未受过教育的民众宣传这类课程，他们对此表现出的兴趣也不大。关注点：慕课并不能替代传统大学或其他学习机构。但是，在设计课程和开展慕课的过程中使用大数据分析能提供新的学习模式，这点值得关注。终极问题：“慕课有用吗？”哈佛大学的代表用尖锐的澄清方式回答道：“针对的对象是什么？”目前存在着这样一个假设，即慕课将增加、替代和打破高等教育的现有模式，该假设基于一种设想：学生将慕课看做传统教育的替代品。事实上，学生并未这么看待。慕课看上去是开放、虚拟的学习实验场所。例如，参与课程的注册活动多种多样。获取证书可能是他们的学习目的。有迹象表明，他们可能会从同步课程表和组建的团队中受益。大多数学生仅观看视频或者阅读文本。许多注册用户在前两周的时间里比较投入，随后便开始厌倦，70 要么注册其他课程，要么适当地签选后续的课程实例。其他注册用户着重进行自我评估和检测。据称，慕课的成功是基于其遵循已确立的教育和学习模式。但是，对于这些相应的模式来说，新环境下的类似动机、互动、标准和期望应该提出来。当慕课与传统教育套路作比较时，许多事物不一定可靠。例如，开放入学期间和不受限制地使用课程资源对分析和设计提出了重要问题。或许我们需要对慕课自身进行检查，不把慕课看作大学或其他机构的替代品。为此，新标准绝不是分数和课程证书，它有必要捕获使用类型和目标的多样性。Livemocha是免费的在线语言学习社区，为用户互动和相互学习新语言提供38种语言教材和在线平台。本章探讨的第二个案例针对的就是Livemocha课程。Livemocha有大约1200万注册用户，他们来自于196个国家，每天官网的访问量超过40万次（Livemocha2014）。2007年，纳徳卡尼和克里斯南创建了Livemocha，他们基于这样一种理念：当你和当地人进行语言交流时，你处于最佳的语言学习状态。到了2010年，《纽约时报》、《金融时报》和《时代杂志》（2010）将其列入世界50大网站。通过商业模式创新，Livemocha的在线社交语言平台遵循了一个经典的颠覆性创新道路，改变了在国外学习外语的网络过程。它的商业模式很简单，即让那些想要免费学习他国语言、同时又愿意教授本国语言的人参与进来。因此，当地民众在学生和教师两种角色上进行转换。提供一个工具和技术（如抽认卡、小测验）平台来帮助学生学习和记忆。用兑换虚拟令牌的方式维护公平参与和互惠互利，坚持时间银行规则（如果帮助别人一次,你就会获得一定的时间美元），以及继续对学生表现和评论者素质进行信息反馈（加特纳2013）。关注点：技术支撑的学习社区将产生非结构化教学大数据的新类型。社区教育的新标准能为学习分析模型提供新原则，而学习分析模型是基于互惠互利和游戏化原则。Livemocha的收入主要来源于广告，每月大约有70万重复用户，在其实施慕课项目的头15个月里，用户大约有185万。下个阶段，Livemocha和皮尔森公司进行合作，以提供一个优质产品。该收费产品提供正式的语言学习方法和社交媒体互动。社区依然忠实于消费者群体，仍会继续提供免费课程，而2010年1月社区发展到500万用户正是得益于免费课程的开展。最终，社区在即用即付的基础上使用那些合格教师。在社区中，注册的合格教师每和一名学生进行互动，他就能获得相应的报酬。优秀学生可以利用他们的虚拟令牌来接受优质的课程内容（加特纳2013）。71 3.7给各机构的建议在当前阶段愿意考虑慕课的机构必须设想这样的可能，即慕课不是一种单一的商业模式，而是一整套完整的商业模式，且这些模式以全球在线访问、免费或相对低价和启迪熏陶等观念为中心。没有使用“教育”这个词，而是使用“启迪熏陶”，在这里暗含着两种教育活动：第一是以就业为目的、获取正式文凭证书的教育活动；第二是通过追逐个人兴趣爱好而不断自我提升的教育活动。出于这种考虑，感兴趣的各机构在寻求慕课的市场定位过程中，应牢记以下几点：·开发不同类型的慕课，这取决于大数据的使用情况。·大数据分析用做市场工具，以寻求潜在的学生群体。·大数据可以用做学习工具，通过分析学生学习类型来帮助其学习。当需要人为介入来激发或支持学生学习时，要提醒教育机构。·在C-慕课中，大数据分析用作搜索和整合相关学习材料的工具。·X-慕课有可能变成类似职业教育的慕课类型，大数据分析在匹配行业所需的就业技能和课程开发上起着关键作用。3.8总结慕课推动了正规教育机构的远程教育工作，也引进了一场更为彻底的教育变革。慕课利用同伴之间相互学习的模式，提高了外界对教育的参与度。慕课不仅依赖于国家的教育补助和学生学费，还通过聘请服务、辛迪加（企业联合组织）、赞助和广告收入以及向潜在的用人单位或广告商出售学生信息等方式为传统教育机构打开新的赚钱门路。大数据分析推动了在线学习过程的个性化，而以前的在线教学方法不具备这个功能，同时在线学习过程的个性化趋势也在全球范围内出现。大数据作为学习分析的教学工具，同教育数据挖掘和教学分析一道，都能用于提高青年的教育标准，而且不需要增加教育者人数，因此能使各机构更加注重成本效益。通过利用现有知识和与学生努力及成功相关的教育模式，各机构可以利用大数据技术来监控学生与系统的互动。为了和学生进行主动沟通，或者进行预定的邮件沟通，在学生参与类型发生改变时提醒管理者。从大数据的形式来看，它是一种学习分析，重在捕获学生的行为，并根据其行为来实现学习目标；教育数据挖掘试图设计学生素质的预测分析模式；而教学分析帮助教育者将这些发现转变为更好的课程设计、学生支持程序和干预措施。然而，现实是完全不同的，开展慕课的各个平台并未对学生学习动机、机构标准、期望和义务形成某些基本的认知共识，因此现存已久的教育模式和他们的分析并不搭调。另外，72 正如Livemocha的案例所示，慕课可以利用游戏化机制，或者借鉴其他在线社区和世界各地的做法，确保学生持续参与和承诺的履行。第四章大数据驱动型商业模式本章勾勒出“大数据驱动的商业模式”概念，并用这个概念描述一系列企业的做法。这些企业依靠大数据来实现其主要价值主张，同时为了获得竞争优势，他们还大大扩充了自己的价值主张，以突显与众不同。本章描述了大数据对商业模式图中各个要素的影响，同时也探讨大数据在实现大众化定制和产品服务个性化方面的潜能，并将这一潜能作为自身的价值主张，另外在B2B（企业对企业的电子商务模式）和B2C（企业对消费者的电子商务模式）物流及客户关系管理和客户服务方面也探讨了大数据所具备的的潜能。本章还谈到了大数据如何推动实用观念的转变，而作为社会经济价值创造的资源基础则没有改变。另外，本章通过了解大数据对合作关系、市场货币化和在财务、预算和绩效指标上面临的机遇和挑战的影响，对这一问题进行了深度剖析。最后，本章承认了诸如3D打印、机器人技术、无人机和自动驾驶汽车等其他新兴科技的协同效应。4.1简介本章的重点是对商业模式这个术语进行更为具体的定义。商业模式和信息技术相关的商业创新天然匹配。虽然商业模式源于交易成本经济学，但它确实是一种与信息传播技术相关的现象。信息传播技术让商业模式变得切实可行、更具成本效益，促使企业和价值网络进行合作，以便更好地参与竞争（唐等人2000，阿米特、邹特2001）。在90年代，产品和服务的捆绑极为盛行，让行业之间的界限变得模糊起来。因此，商业模式的定义是什么？在现有的各种定义中（阿米特、邹特2001），商业模式可以概括为商业要素的建造逻辑和要素如何组合来协调价值创造，商业要素包括商业结构、商业过程、商业设施和商业体系以及金融选择等。商业模式能描述公司的各种情况，包括产品和服务的购买者、他们的购买原因、公司如何组织运营、公司使用哪些资源来投资、生产和交付产品、他们为此所付出的代价以及如何实现投资回报等。和其他模式大致一样（巴登·富勒、摩根2010），商业模式也是抽象的现实生活，而73 且在这种特殊的情况下，商业模式已用于描述各种商业现象。根据奥斯特瓦德等人2005年的调查报告，人们已广泛使用这个术语“商业模式”来描述现实世界的所有商业（如资本主义模式），或描述具有共同特点的特定商业类型（如拍卖模式)，或描述一个现实世界里极为特殊的商业模式（如苹果模式）。此外，根据奥斯特瓦德、皮尼厄2010年的调查报告，商业模式由表4.1所示的九大要素组成。在过去，各公司依赖管理者的直觉来填补信息严重不足的空白，以便做出商业决定。每天，社交媒体、云计算、手机和物联网带来的数据扩散成了管理者的新难题，他们不知道如何处理这些新增信息。数字强度、连通性和大数据共同构成了一个多样网络的背景（巴拉德瓦杰等人2013）。简要介绍这个概念的目的有两个。第一，为了阐明如何在本章使用这个术语，有必要对商业模式进行介绍。我们将使用“大数据推动的商业模式”这一术语，并描述一系列企业的做法。这些企业依靠大数据来实现他们的主要价值主张，为了获得竞争优势，他们还大大增加了自己的价值主张，让自己变得与众不同。第二，这一概念在本章的开篇部分便很有用处。大数据推动的大多数商业模式是现有价值主张的延伸（哈根等人2013）。大数据推动的商业模式目前正在完善过程中。在追求效率的前提下，大多数公司已在他们的项目组合中引进大数据，在默认的情况下，这意味着花更少的钱来做同一件事，或把事情做得更好，即使效率低了一点（哈根等人2013）。真正有远见的人会引进这样的商业模式，即其独特卖点将依赖于大数据和社会、商业和技术领域内其他创新活动所推广的价值主张，对于这一天的到来，我们还要等上一段时间。因此，在本章中，我们会给出一些预测，并对这些情况进行描述。表4.1商业模式的九大要素（奥斯特瓦德、皮尼厄2010）1公司服务的客户群体2为客户提供解决方案的价值主张3公司沟通、分配和销售完成的渠道4公司建立、维持的客户关系5源自客户的公司收益6所需的关键资源或资金7主要活动，如所需的任务、过程和行为74 8对上述某些要素承担责任的主要合作形式9成本结构，如维持和改善上述所有要素的成本大数据将对各个领域产生重大影响（哈根等人2013）。不过，这样的变化试图完善如今的部门结构和商业模式。值得注意的是，一个商业部门是由许多拥有相同或相似商业模式的高效运营公司或企业单位构成。表4.2列出了一些显现的变化。截止目前，人们对变革公司基本结构的动力还比较有限，他们并未将大数据置于企业价值创造的核心地位，相反倒更看中大数据“锦上添花”的功能。从这个角度来说，人们提出的结构性变化试图巩固现有的商业模式，而不是破坏现有商业模式。例如，正如表4.3所示，人们使用大数据来改善现有的商业模式。表4.2当前大数据巩固的现有商业模式1对客户进行分析和提供个性化的客户服务，改善客户关系2利用明确的众包理念或者分析社交媒体的意见和反馈，开拓产品/服务创新3通过分析大量数据的来源，并及时过滤为可执行的解决方案，以形成商业抉择，最终实现改进决策工作的目的。4在提供实时跟踪反馈的同时，开展实时操作和利用智能资产，二者可以相互协调，以便共同执行一个商业过程或立即改变商业过程。表4.3行业部门利用大数据来改变商业模式和改善他们在许多领域的表现零售业制造业客户关系管理欺诈检测和预防产品研发过程和质量分析存储位置和布局优化供应链工程分析配送优化动态定价预见性维护金融服务业传媒和电信行业程式交易欺诈检测网络优化生产预防风险分析业务组合分析客户评分欺诈预防广告及公共关系能源需求信号情感分析智能电网操作模型目标广告客户获取探索电力线路传感器75 政府卫生保健和生命科学市场管理计量经济学药物基因学药学研究武器系统和反恐卫生信息学生物信息学临床成果研究改编自哈根等人（2013）这些渐进式变化将给当前机构带来重要的价值，同时会改变客户期望，而这将使大数据分析滞后的公司落于竞争下风。从根本上来说，这些商业模式不是大数据驱动的，而是大数据促成的。通过利用大数据分析的能力、新生产技术和企业生产价值的新理念，我们可以预想到商业模式的根本性变化。例如，利用大数据分析的能力和诸如表4.4所示的其他新兴技术，未来的商业模式会如何演变呢？人工智能的社会机器人在决定和管理老年人的常规护理工作时，智能家居和提供个体实时健康信息的健康跟踪设备在未来会不会代替社会服务和基层护理？比特币会不会成为现有金融期权组合的一部分，或成为现有金融期权的替代品？利用灵活的资金投资、无人机和机器人系统来从事农业生产，我们能不能组织集约化农业生产？将公共交通系统与私人交通系统合并，派生出一个新型、市场所需的上门自驾交通系统，要是这么做的话，公共交通系统与私人交通系统的不同点会不会消失？有些想法可能不好实现，但对某些人来说，这些想法有望成为未来15-20年内的主流思想。例如，在阿布扎比的马斯达尔，Pod型市内交通已成为现实。实时大数据分析可以改变生产过程。例如，美国通用电气公司将大数据分析看做一个“过程”监管机制，该机制对质量控制的高敏感度和极为昂贵的工业航空部件3D打印流程进行监控，其中航空部件的结构完整性对航空安全至关重要（通用电气公司2013年报告）。另外，美国国际商用机器公司（IBM）开展的“深雷计划”针对的是精细农业，而精细农业利用微气候预测模型和远程无人机监控来优化除草、施药、灌溉和收割等农业生产过程。本章后续内容是了解大数据对商业模式九大要素的基本影响。表4.4与大数据分析相适应的新兴技术1家用3D打印技术2健康和健身监视器3眼球和手势跟踪设备76 4无人机5自动驾驶汽车6比特币7机器人8智能家居4.2大数据对客户分类的影响大数据为大众化定制提供了新的含义。各公司依靠大数据，能够在客户喜好和购买习惯的基础上，通过整合个人的网页浏览信息、购买记录、具体位置和促销反应以及工作经历、成员关系和人们基于社会影响、信心数据的观点等人口统计信息，实现对每个客户的定位。这个数据能够更好地促进内容、产品和服务的市场定位，并通过提高购买下单的成功率来获取实质性收益。虽然大众化定制是由大数据促成的，但新技术能够推动两个更具突破性的改进工作。制造可定制的产品来满足客户的喜好。由于产品附带嵌入式智能功能和机器之间的协作功能，它们能承担服务客户的责任。例如，一辆自动驾驶汽车可以适应私人助理修改的预约时间表。人们在谈及大数据时，首先将其看做一个商业工具，现在没有任何理由能够解释顾客为什么不自己利用大数据来搜索产品和服务。例如，认知代码公司已研发了先进的人工智能对话技术，该技术以代码的形式和机器互动，而机器能提供自然语言的输出和反馈，届时人们就能和机器进行语言互动。这可谓是电子时代最佳的私人助理！遗憾的是，认知代码这样的公司仍将自己的商业模式定位在公司电话服务中心上，而不是定位于公共服务中心。不过，我认为，像苹果这样的电子消费产品巨头，他们要具备这种能力还需要较长的一段时间。4.3大数据作为价值主张所产生的影响为了获取利润，各个公司可以出售四种与大数据相关的“事物”，即原始数据库、大数据分析服务、大数据专家和大数据技术。据福布斯等2014年的调查报告显示，许多公司将出售客户数据作为增加公司收入的一种手段。对小公司来说，这么做会面临名誉受损的风险，不过对于移动电话公司、银行和航空公司等大机构来说，尽管他们收集了大量客户的真实信息和购物模式，但并不担忧这些风险。但是，原始数据并没有太大用处，除非你对它进行某种意图的分析。目前，各银行雇佣77 大数据分析专家来对咨询服务的数据进行挖掘、分析和综合，不再为了改善高价值的大数据分析服务而出售原始数据。大数据咨询服务已成为商业智能咨询提供商必不可少的服务。由于商业活动的各个方面，包括从战略发展到人力资源，都利用了大数据分析，同时大数据分析又作为一种稀缺资源，咨询工作的机会还是很多。实际上，如果一个咨询公司不提供大数据咨询服务，那么它就不可能生存发展。因此，所有知名的咨询公司和小型的咨询精品店都为客户提供大数据分析服务。另一个大数据商业模式和数据科学家的招聘服务相关。招聘企业不能回避自身对数据科学人才的引用需求。但是，大数据分析师在目前是稀缺资源。因此，新的商业模式的作用是服务于公司的需求。例如，卡歌网（2014）使用编程马拉松式的竞争模式，对数据科学项目进行预测建模，同时还将这一模式看做一个学习社区，专门服务于那些想要获得数据科学专业知识的人群。4.4大数据对公司商业渠道的影响随着网上购物屡创佳绩，企业-消费者（B2C）配送选项得到公开讨论。在分配网络中，制约高效率运作的因素出现在最后环节，即在特点目的地的配送环节。例如，在英国伦敦的一个小镇，“点击提货”配送系统似乎是B2C物流配送最火爆的潮流趋势（巴特勒2014）。“点击提货”似乎适用于不同程度的配送，人们不需要在某处等候自己的包裹，他们的包裹会放在安全的提货点，等着人们来取。各公司不会让客户承担无效配送的费用，同时市民不必再忍受运输队来回配送包裹时所排放的二氧化碳。不过，在增加客户灵活性的同时，利用大数据技术来大大减少成本费用，这还是值得商榷的。其中一个方法是实时优化配送路线，对传统运输队伍的路线计划进行最优处理。另一个方法是利用组合优化策略来向繁忙的用户配送产品。例如，组合优化策略能够处理用户所在的位置及方向等实时信息，配送者能够据此做出灵活的安排。组合优化策略包括确定客户和配送车辆的位置，以便配送车辆改变线路，前往下一个最佳的客户取件地点。目前，配送司机自己制定快递的配送顺序，并且一件一件的配送快递。大数据驱动的测序软件能够对配送者、当前交通状况、装货区情况、客户情况（避免无效配送）进行重复处理，从而将成本压到最低。大数据也推动了众包潮流，那么为什么不利用众包配送包裹呢？甚至在企业对企业(B2B)物流中，大数据预测建模也能将即时操作效率提高到一个新的高度，在确保业务连续性的同时压缩储存货物的空间。预测建模不仅能考虑到客户购买模式78 和反馈，还能考虑到国内动乱、自然灾害甚至是突发的经济发展问题所造成的越来越多的破坏。大数据技术和分析能注意和记录任何一个关键性风险因素的发展过程，而这些风险因素能从多种渠道（如社交媒体、博客、天气预报、新闻网站、股票追踪器等）危害到公司的持续经营。接着它会警示和煽动风险管理场景，这些场景通过其他国家或彰显公司柔韧性的路线来完成订单。4.5大数据对客户关系的影响利用大数据提高销量的功能来分析客户，这已成为采用新技术的中心工作。但是客户服务如何呢？各公司掌握了大量客户信息，肯定能改善客户服务，提高客户亲密度和忠实度。客户服务运营商面临的一个主要挑战是缺乏对客户信息的全面了解。由于前沿数据不可用，客户服务代表不能回应客户的问题及要求或迟于回应，也不能及时提供替代方案。客户关系管理项目为解决此事投资重金，即使这些项目是昂贵的高维护项目。另外，标准化的封闭式供应链操作让异常情况的处理大打折扣，这些异常情况往往让人得不偿失，或需要高价的专业团队来妥善处理。大数据技术，如大数据分析，能通过超高速内存分析平台与孤立的数据仓库建立联系，并为客户服务代表提供实时信息，从而帮助客户。公司建立这种联系是为了改善客户服务，而不是为了提高销量，但是目前还不清楚公司会通过什么方式建立这种联系。大数据客户关系项目正处于发展滞后阶段，不过人们对该项目抱有期望。相比客户关系项目的潜在收益，人们认为该项目的成本高昂。当市场开始处于稳定发展阶段，各公司寻求商业资产的价值最大化时，届时处于“生命周期成熟阶段”的客户关系项目就能得以实施。银行可能率先引领这个潮流，不仅因为他们有资金预算，还因为他们需要重建公众的信任，而公众在经历了全球金融危机后不再抱有幻想，变得日益清醒。在零售银行的广告主题中，我们可以看到重建强大人际关系的趋势。墨西哥北方银行（Banorte）强烈关注其拥有的1300万客户群，并利用大数据技术来策划银行服务项目。大数据推动的营销自动化不仅能帮助银行满足客户的个性需求，同时还能维持营销成本，并在投资回报的过程中创造个性化体验。同样地，要想利用自动、个性化的客户服务，就需要对整个公司的业务流程进行重组，并重新考虑客户行程（韦格尔2014）。自动呼叫中心使用自然语言识别和响应模式，而大数据也将扩大该呼叫中心的使用范围，就语音回复客户问题而言，人工智能技术变得越来越社会化。因此，配备大型呼叫中心的公司可能采用由新一代人工智能技术和大数据推动的语音解决方案，从而降低成本，而这79 也是基于大数据的商业模式所要求的。4.6大数据对收入来源的影响社交媒体推动了对等交易和实际意义上的协同消费。现在，各酒店为了赚取度假者的钱财，正同对等平台空中食宿（Airbnb）竞争（瑞迪2014）。空中食宿向私人旅行者出租备用卧室。空中食宿创建于2008年，该平台登记了全球超过50万处房舍，招待的旅客人数超过850万，公司的市场价值也提高到了25亿美元（瑞迪2014年）。金融危机改变了重复使用的社会价值，并为分享经济的兴起提供动力。由于消费者分享彼此拥有的东西，他们就不需要购买新的物品，分享经济就成了实体企业的一个潜在威胁。Blahblahcars网站（2014）通过撮合汽车司机和有搭乘需求的客人，实现了拼车。大数据能让这些交易变得更安全、实时和高效。例如，拼车并不是意味着你要和一个完全陌生的人一同搭车。大数据技术能够利用汽车监控、乘客和司机的健康监测等方式来改善拼车的安全性，并提供预防措施和避免常见的社会风险。在交易成本经济学的术语中，大数据的“老大哥”能力能够替代机构的作用，对规章制度进行监控和严格执行。电子货币是收入来源的另一个突破性变革。未来的公司会用什么样的支付货币呢？研发副总裁、加特纳同事乐鸿也指出加密货币比特币是一个主要的突破性货币：通常说来，只有政府能够发行货币，但是在大多数西方国家，人们明显信任私人企业，而不信任政府（特纳2013）。当然，电子货币的监管环境仍然不确定，也不能系统地评价其风险。据美国伟凯律师事务所（狄勃拉、韦科特朗2014）透露，存放和交易虚拟货币的网络环境仍然面临着风险，因为交易平台或电子钱包可能遭到黑客入侵，用户会丢失虚拟货币，其交易系统的可靠性也就荡然无存，特别是针对欺诈风险而言，目前还不能对这种风险进行正确评估。另外，虚拟货币汇率的波动导致了大量的经济损失。没有任何法律条文确保电子货币能在任意时间、以原始价值进行兑换。最终，虚拟货币不是法定货币：用虚拟货币来支付，任何人都不愿接受。虽然目前的这些担忧合情合理，但比特币支付的未来前景取决于人们对它的认可度，目前已有坚定的支持者看好比特币的前景。据杜克大学教授坎贝尔·哈维透露，比特币和所有加密货币一样，同借记卡或信用卡欺诈、入侵相比，其承担的风险更小；同时与传统机构采取的传统方法相比，其交易成本最低。加密货币依赖于计算机技术来实现产权的实时交易、产权证明以及利用规则制定暂行合同（卡德2014）。另外，在不借助集中式机构的情况下，基于加密货币的交易需要校正数据，80 以确保安全、隐私和信任。以上所有问题将要进行数值运算和大数据的用户分析，这将对比特币和加密货币系统产生的海量数据发挥重要作用。各公司在未来能够发行和担保他们的虚拟货币，随后再和相关供应商进行虚拟货币的交易。因此，人们只能想象亚马逊和谷歌等零售供应商的实力。例如，谷歌风投已将资金投进Ripple（世界第一家开放式支付网络），该支付网络允许用户交易任一币种，不需要任何代理或第三方机构来促成交易。4.7大数据对关键资源和主要活动的影响传统意义上的资本资源是用于创收的人造资产。定义中的某些基本假设可能在未来遭到挑战。第一，公司掌握着资源的所有权，因此公司能够随意使用和处理资源。第二，资源只有得到利用，它们才能产生价值，因此资源的实用性创造了资源的价值，所以并不是资源或所有权本身创造了价值。第三，为了产生价值，需要对资源的利用加以组织，从而执行关键的业务流程。而关键的业务流程是以市场青睐的产品或服务形式传递价值。大数据能变革上述三个观点，促进商业模式创新。首先，随着越来越多的公司依赖众包，他们把员工先前的工作外包给公开招募的未知（通常人数众多）人群，大数据得以变革雇佣关系。当一份工作需要协作完成时，可以利用“群众协作生产”这一形式。众包越来越受欢迎，得益于其成本效率和覆盖全球的潜力。随着各公司越来越多地依仗众包，他们将要建立一个强大的协调机制，以收集和兼容多样化的信息和观点。毕竟，各公司要能判断数据质量，寻求解决地理差异和实现机构目标的方法。事实上，众包和大数据能够变革公共服务。例如，联合国已开始利用一个大数据和众包平台Mindjet’sSpigitEngage来制定如何处理难民问题和学习一门新语言的最优方案，目的是吸收新文化、找工作、接受信息和重要的社会服务。SpigitEngage平台同样利用了游戏化机制，能使草根理念上升为上层理念，还避免现存的官僚壁垒和把关行为（Spigitengage2014）。上述问题也对管理结构和管理者产生重大影响。如果大数据运算能够协调端对端决策过程，那么它也能协调监控实施过程和人力资源的动态分配。昂贵的机构框架是出于什么目的呢？从目前的现实来看，提这个问题似乎有些牵强，因为正式的公司机构仍主导着工作组织结构，不过其界限的模糊已开始出现，这种趋势看上去不可逆转。大数据影响的另一个资源是资金，即企业的融资。社交媒体和大数据利用众筹，促成了销售和融资的合并。对Kickstarter（在线募资网站）项目和相应的筹资需要进行快速研究将展示这样的现象，即大多数人有效地对产品或服务进行提前购买。从这个角度来说，消费者既是所需产品的投资者，又是这些产品的前期使用者。例如，CoolestCooler（便携式小冰箱，81 内设一个冷却器和一个搅拌器，适用于户外聚会场所）是Kickstarter最烧钱的项目，其融资金额超过了1300万美元。在投资该项目的62642人中，有超过57000人希望提前购买这款产品，他们提前为公司开辟市场和创造收入。最后，大数据不仅对获取、处理和分析大量数据带来巨大挑战和技术投资，也要求各公司对商业过程做出相应的改变，并对这些变化加以利用。例如，为了比电子市场同类行业的运营快一倍，苹果公司在改善供应链管理实践时，利用市场和供应链数据（巴拉德瓦杰2013）。4.8大数据对关键性合作关系的影响我们已看到旅游和酒店行业的合伙企业通过共享预订系统、忠诚计划和交叉销售机会进行规模经营，我们还将看到不同的公司为整合他们的优势而合并，并利用即插即用的组织架构来创造竞争优势（巴拉德瓦杰等人2013）。大数据促成了实时协调监控和反馈，为那些参与供应链协作的人提供安全感。方针政策/规章和合同协议作为一种威慑，在应对疏忽、欺骗和欺诈行为上能够提供一定的安全保证，并缓和了供应链风险。对供应链协调数据进行实时信息分析能提前标记问题，揭示延迟的深层原因和提供应急计划的替代方案。先进的大数据预测分析会进一步发展，可能导致复杂决策场景的发展变化，而这些场景基于各种各样的压力测试，并在多种市场状况下和许多关键绩效指标作对比。这类商业模拟能用于反复设计供应链运作（格罗夫斯等人2014）。例如，大数据通过模拟不同的市场环境和竞争对手情况，能够评估某一供应链的业务敏捷性和弹性。商品和货币的价格波动使长期采购变得有风险。例如，为了确保充足的订单，针对稀有资源和原材料的价格战通常导致更大的收购量，不过各机构通过规划最低安全库存来避免出货延迟和无法满足订单的风险。据称，利用大数据改善价格预测或收购效率能够减少这类风险。物联网也将改善供应链中实物位置信息的质量。扬等人2014年的调查报告提出了云供应链解决方案，并在整个供应链循环周期里促进资源共享和用户协作。在供应链循环周期里，供应链信息感知、异构网络访问汇流和资源的“服务化”共存。4.9大数据对成本结构的影响随着众筹、众包和大数据驱动的供应链参与企业解除机构的传统界限，成本经济的定义和界限发生了显著变化。另外，随着数据、信息和知识成为各级协作的焦点，知识产权、数82 据和无形资产可能成为未来的焦点（维斯库斯、巴蒂尼2014）。超过20%的大型公司已将数据作为公司负债表的一种资产。数据价值评价可能成为未来的关键性会计技能。由于无形资产往往隐藏于现有的报告和工业时代的制度模式中，现在还无法评估数据日期。即使看得见无形资产，数据还是常常得到一个随意的估值。很难计算折旧，而且信息速度让这种计算更为困难、更不靠谱。另外，和大多数资产一样，数据价值取决于数据的实用性。为了建立数据资源的成本模型，我们需要改进新方法，同时会计和金融专业人士得认同这些新方法，因为他们确定着关键假设：什么类型的数据有价值？（蔡氏2013）。从会计学的角度来说，重要的是评估货币价值或企业从物联网和大数据中创造的利润。如果要这么做，管理会计师应该将公司内部的金融数据、工作业绩分析和公司整体的经济业绩指标这三个因素结合起来。他们也应该和某些参照性机构进行对比，看看这三个因素的差异。另外，政治、经济社会和技术对他们行业的影响将制约这三个因素。不过，目前的所有这些假设都会得到公开地协商和重新定义。技术趋势重新定义了产业边界、公司的战略轨迹和对抗其他机构的能力。例如，脸谱网同谷歌公司竞争广告收入，尽管两家公司提供给用户的是两种完全不同的价值主张（迪亚斯2013）。概括起来，以同样的方式定义新市场成了技术驱动型商业模式的关键驱动因素。成本和管理会计领域的发展动态或许将成为下一个驱动因素；大数据驱动型商业模式仍处于开始阶段，因此成本经济学还仍处于酝酿阶段。4.10机构优势和机遇对于所有愿意抛开传统做法、放手尝试新法的人来说，大数据时代就是一个开放的雷区。据麦肯锡公司称，在做出这些改变时，一些部门或市场参与者可能更有优势。例如，巨额投资信息技术行业的公司拥有大型可利用的数据池，还有那些能够执行复杂分析技术的信息产业公司，这些公司有可能率先创造商业价值。从另一个角度来说，公共部门需要巨变，为了利用公共服务的价值，他们更愿意公开自己的信息资源。公共部门也更有可能找到大量协助其众包项目的志愿者。成熟的行业部门，其特征是存在大量的竞争公司，由于机构的自我领导、战略短视、技术恐惧和对行业未来、部门内斗的矛盾观点，他们可能停留在传统的思维上。但是，这些部门可能是共享公司信息、确定新的市场机会和采取“协作-竞争”的网络合作关系的最大受益者。部门领导和公司投资这些机遇的意愿强度将确定行业的未来。83 各公司应考虑他们是否以及如何创造新的基于大数据的商业模式。物联网将增加信息流量，提出更多的考虑因素，比如我们如何将生成的数据或物联网捕获的数据转换为知识，从而为人们提供一个更为便利的环境氛围。为了设计出新的商业模式，各公司应接纳新观点，确保自己快速地吸收各类观点（来源于基层项目和开放的创新竞赛）。各公司也应做好修改或彻底改变公司内部结构的准备，从而部署新产品和服务。对于大多数公司来说，其思维和文化层面需要来个大转变（蔡等人2014）。蔡等人2014年的调查报告发现许多物联网应用程序将改变现有的部门和服务。这些程序大致可以分类为：（1）智能城市（2）智能环境（3）智能饮水（4）智能测量（5）安全与突发事件（6）零售（7）物流（8）工业控制（10）智能农业（11）智能畜牧业（12）家庭自动化（13）电子健康。4.11机构挑战和威胁在设计大数据驱动型商业模式上，我们面临着许多挑战。设计大数据驱动型商业模式的几个障碍：技术评估能力、技术成熟度、隐私和知识产权的法律条文、稀缺的数据分析师人才、技术投资能力、内部数据的可用性和质量、互操作性、安全事宜、有限的业务流程理解和相关的指标、数据收集和老式的等级领导制。本书之前的章节部分也有提及这些障碍。本章恰当地利用这些障碍，来说明采用大数据驱动型商业模式创新所面临的挑战（奥斯特瓦德等人2005）。4.11.1创造力和创新能力不足大数据创新时代要求管理者进行创造性思维。许多公司已把那些能维持公司秩序和效率的优秀管理者升为主管。对于许多中小企业来说，高层管理团队都要受日常商业运作所累，没有时间、空间和精力去更新他们的技能和跟上同行业的发展动向。管理结构通常缺乏个人创造力和创新能力，也缺乏培养创造力与创新能力的正确态度。4.12总结本章以建筑逻辑探讨商业模式，并确定如何搭配商业元素（如商业结构、商业过程、基础设施和商业系统等）来协调价值创造（奥斯特瓦德等人2005）。本章接着描述了大数据对奥斯特瓦德和皮尼厄（2010）提出的商业模式图中各个商业元素的影响。需要特别注意的是，本章探讨了大数据和物联网对产品和服务的大众化定制及个性化产生了新的促进作用。另外，本章将大数据当做一种价值主张来研究。本章还谈及了B2B和B2C物流、客户关系84 管理和客户服务的大数据解决方案。此外，随着大数据促进了价值创造新形式的产生，本章分析了大数据对收入的影响。本章还探讨了大数据给财务会计、预算和绩效指标带来的机遇和挑战。最后，本章做出结论，承认了这么一点：虽然到目前为止，大数据已用于改进现有的商业模式，但未来更多的商业模式将伴随着其他新兴技术的产生而出现。85'