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As data science becomes necessary for organizations to keep up in the marketplace delivery teams are encountering a new problem: a lack of standardized approaches to deliver data science projects. Teams often default to using practices that are not repeatable or sustainable. This then leads to low project maturity, characterized by a lack of continuous improvement and adequate feedback Without consistent feedback loops, organizations are potentially overlooking value that can be delivered to end users and leaving valuable insights on the table.
If data science is not as “mature” as development in its methodology, how can we leverage the principles of Agility and other complementary practices to identify an approach that fits for data?
In this session we will:
• Discuss how team communication and product development has been impacted by incorporating data science teams
• Give a brief overview Agility, and Agile practices to level set on approaches that are potentially useful to data science
• Generate ideas on how to leverage Agility and complementary practices to mature data science delivery
Topic: Other (Agility and Data Science/Engineering)
Target Audience: Intermediate
Keywords: Data science, data engineering, Agile, project management, Kanban, Agilists, Project Managers, Data Scientists, Data Engineers, Tech Lead