Publicación: Towards an improved asum-dm process methodology for cross-disciplinary multi-organization geographically-distributed big data & analytics projects
Fecha
2018
Autores
Angee Agudelo, Santiago
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Editor
Universidad EAFIT
Resumen
In 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.