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Towards an improved asum-dm process methodology for cross-disciplinary multi-organization geographically-distributed big data & analytics projects

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dc.contributor.advisorTabares Betancur, Marta Silvia
dc.contributor.authorAngee Agudelo, Santiago
dc.coverage.spatialMedellín de: Lat: 06 15 00 N degrees minutes Lat: 6.2500 decimal degrees Long: 075 36 00 W degrees minutes Long: -75.6000 decimal degreeseng
dc.creator.emailsangeea@eafit.edu.cospa
dc.date.accessioned2018-11-27T17:39:34Z
dc.date.available2018-11-27T17:39:34Z
dc.date.issued2018
dc.description.abstractIn recent years, the big data & analytics projects developed in big enterprises or excellence centers have special conditions like being cross-disciplinary, having participants geographically distant one another, and the participation of several organizations. This has caused that traditional methodologies used to undertake data analytics, like CRISP-DM or other emerging methodologies, be not sufficient to perform an appropriate project management. This proposal uses Design Science Research Methodology (DSRM) to identify a problem, define the objectives for a solution, design, develop and show the usage of an ASUM-DM based big data & analytics process methodology for cross-disciplinary, multi-organization, geographically-distributed work teams. The results generated are a big data & analytics project management process methodology and a gap analysis applied on three enterprise-university use cases, showing how the proposed methodology can help address the big data characteristics of a project, and coordinate and integrate several multi-organization, geographically-distributed, cross-disciplinary work teams. This process methodology is expected to ease practitioners and researchers the implementation and management of big data & analytics projects with the participation of several cross-disciplinary work teams, and geographicallydistributed organizations.spa
dc.description.degreelevelMaestríaspa
dc.description.degreenameMagíster en Ingenieríaspa
dc.format.mimetypeapplication/pdf
dc.identifier.ddc006.312 A581
dc.identifier.instnameinstname:Universidad EAFIT
dc.identifier.reponamereponame:Repositorio Institucional Universidad EAFIT
dc.identifier.repourlrepourl:https://repository.eafit.edu.co
dc.identifier.urihttps://hdl.handle.net/10784/13221
dc.language.isoeng
dc.publisherUniversidad EAFITspa
dc.publisher.facultyEscuela de Ingenieríaspa
dc.publisher.placeMedellínspa
dc.publisher.programMaestría en Ingenieríaspa
dc.rights.accessrightsinfo:eu-repo/semantics/closedAccess
dc.rights.coarhttp://purl.org/coar/access_right/c_14cb
dc.rights.localAcceso metadatos
dc.subjectProcesosspa
dc.subject.keywordBig dataspa
dc.subject.keywordAnalyticsspa
dc.subject.keywordProcess methodologyspa
dc.subject.keywordProject managementspa
dc.subject.keywordCRISP-DMspa
dc.subject.keywordASUMDMspa
dc.subject.keywordMultidisciplinaryspa
dc.subject.keywordCross-disciplinaryspa
dc.subject.keywordGeographically-distributedspa
dc.subject.keywordMulti-organizationspa
dc.subject.lembBig dataspa
dc.subject.lembMinería de datosspa
dc.titleTowards an improved asum-dm process methodology for cross-disciplinary multi-organization geographically-distributed big data & analytics projects
dc.typeinfo:eu-repo/semantics/masterThesis
dc.type.coarhttp://purl.org/coar/resource_type/c_bdcc
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aa
dc.type.localTesis de Maestríaspa
dc.type.redcolhttp://purl.org/redcol/resource_type/TM
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublication

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