Abstract
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.
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Acknowledgment
The authors would like to thank the Colombian center of excellence and Appropriation On Big data & data Analytics (CAOBA Alliance) for providing the funds for this study. Also, the authors would like to thank Bancolombia group, Eafit University and Icesi University, specially professors Diego Restrepo, and Juan Manuel Salamanca for their collaboration. Finally, the authors would like to thank the Colombian Administrative Department of Science Technology & Innovation (COLCIENCIAS), and the Colombian Ministry of ICT (MINTIC), both members of the CAOBA Alliance.
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Angée, S., Lozano-Argel, S.I., Montoya-Munera, E.N., Ospina-Arango, JD., Tabares-Betancur, M.S. (2018). Towards an Improved ASUM-DM Process Methodology for Cross-Disciplinary Multi-organization Big Data & Analytics Projects. In: Uden, L., Hadzima, B., Ting, IH. (eds) Knowledge Management in Organizations. KMO 2018. Communications in Computer and Information Science, vol 877. Springer, Cham. https://doi.org/10.1007/978-3-319-95204-8_51
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DOI: https://doi.org/10.1007/978-3-319-95204-8_51
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