Leveraging XP and CRISP-DM for Agile Data Science Projects
This addresses process integration challenges for Data Science teams in industry, but it is incremental as it adapts existing methodologies.
This study tackled the integration of eXtreme Programming (XP) and CRISP-DM in agile Data Science projects through a case study at e-commerce company Elo7, finding that 86% of the team frequently uses CRISP-DM and 71% adopts XP practices, demonstrating a feasible combination for structured collaboration.
This study explores the integration of eXtreme Programming (XP) and the Cross-Industry Standard Process for Data Mining (CRISP-DM) in agile Data Science projects. We conducted a case study at the e-commerce company Elo7 to answer the research question: How can the agility of the XP method be integrated with CRISP-DM in Data Science projects? Data was collected through interviews and questionnaires with a Data Science team consisting of data scientists, ML engineers, and data product managers. The results show that 86% of the team frequently or always applies CRISP-DM, while 71% adopt XP practices in their projects. Furthermore, the study demonstrates that it is possible to combine CRISP-DM with XP in Data Science projects, providing a structured and collaborative approach. Finally, the study generated improvement recommendations for the company.