2021-04-122018-01-011865092918650937WOS;000558244200050SCOPUS;2-s2.0-85051970837http://hdl.handle.net/10784/28767The 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.enghttps://v2.sherpa.ac.uk/id/publication/issn/1865-0929info:eu-repo/semantics/conferencePaperDataminingKnowledgemanagementProjectmanagementSoftwareprototypingAnalyticsASUM-DMBankingsectorsCRISP-DMCross-disciplinaryDataminingprocessNontrivialproblemsProjectmanagersBigdata2021-04-12Angée, S.Lozano-Argel, S.I.Montoya-Munera, E.N.Ospina-Arango, J.-D.Tabares-Betancur, M.S.10.1007/978-3-319-95204-8_51