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Towards an improved ASUM-DM process methodology for cross-disciplinary multi-organization big data & analytics projects
(Springer Verlag, 2018-01-01) Angée, S.; Lozano-Argel, S.I.; Montoya-Munera, E.N.; Ospina-Arango, J.-D.; Tabares-Betancur, M.S.; Universidad EAFIT. Departamento de Ingeniería de Sistemas; I+D+I en Tecnologías de la Información y las Comunicaciones
The development of big data & analytics projects with the participation of several corporate divisions and research groups within and among organizations is a non-trivial problem and requires well-defined roles and processes. Since there is no accepted standard for the implementation of big data & analytics projects, project managers have to either adapt an existing data mining process methodology or create a new one. This work presents a use case for a big data & analytics project for the banking sector. The authors found out that an adaptation of ASUM-DM, a refined CRISP-DM, with the addition of big data analysis, application prototyping, and prototype evaluation, plus a strong project management work with an emphasis in communications proved the best solution to develop a cross-disciplinary, multi-organization, geographically-distributed big data & analytics project. © 2018, Springer Nature Switzerland AG.