将XP与CRISP-DM结合，提升数据科学项目敏捷性与协作效率
Leveraging XP and CRISP-DM for Agile Data Science Projects
- 融合敏捷开发的XP与结构化流程的CRISP-DM，构建协同工作框架
- 86%团队常使用CRISP-DM，71%实践XP方法，验证整合可行性
- 适合希望优化数据科学流程的电商或技术团队参考
本研究探讨了在敏捷数据科学项目中整合极端编程（XP）与跨行业数据挖掘标准流程（CRISP-DM）的可行性。以电商平台Elo7为案例，通过访谈和问卷收集数据科学团队（含数据科学家、机器学习工程师、数据产品经理）的实际经验。结果显示，86%的团队频繁或始终采用CRISP-DM，71%在项目中应用XP实践。研究证明，两者可有效结合，形成结构化且协作性强的工作模式，并为公司提出改进建议。
原文摘要 · Abstract (English)
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.
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