Abstract
Organizational data science projects provide organizations with evidence-based business intelligence to improve their business processes (BPs). They require methodological guidance and tool support to deal with the complexity of the socio-technical system that supports the organization’s daily operations. This system is usually composed of distributed infrastructures integrating heterogeneous technologies enacting BPs and connecting devices, people, and data. Obtaining knowledge from this context is challenging since it requires a unified view capturing all the pieces of data consistently for applying both process mining and data mining techniques to get a complete understanding of the BPs execution. We have presented the PRICED framework in previous works, which defines a general strategy for performing data science projects. In this paper, we propose a methodology with phases, disciplines, activities, roles, and artifacts, providing guidance and support to navigate from getting the execution data, through its integration and quality assessment, to mining and analyzing it to find improvement opportunities.
Supported by project “Minería de procesos y datos para la mejora de procesos en las organizaciones” funded by Comisión Sectorial de Investigación Científica, Universidad de la República, Uruguay.
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Acknowledgement
We would like to thank students: Alexis Artus, Andrés Borges, Federico Pérez, Francisco Betancor, Fabián Gambetta, Juan Canaparo, Martín Rubio, for their work in the PRICED framework and prototypes.
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Delgado, A., Calegari, D., Marotta, A., González, L., Tansini, L. (2022). A Methodology for Organizational Data Science Towards Evidence-based Process Improvement. In: Fill, HG., van Sinderen, M., Maciaszek, L.A. (eds) Software Technologies. ICSOFT 2021. Communications in Computer and Information Science, vol 1622. Springer, Cham. https://doi.org/10.1007/978-3-031-11513-4_3
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