Haydar Özler wrote this piece about his hands-on experience tips on team structure, skills, cross functionality, product backlog items, sprint lengths, difficulties and benefits when using scrum framework on a data project.
The author worked in an agile tribe composed of 7 teams and 58 people established to deliver AI functionality to existing products of the bank. One of the teams is architectural team and the others are delivery teams.
The article covers:
- Business case examples
- Team structure
- Product backlog items & what to present in sprint reviews?
- Sprint length
- Difficulties
Let’s accept it — scrum is a great way of working. Nevertheless, until all team members get used to its methodology, get ready for some challenges, e.g.: people who prefer working alone start complaining about the time spent for rituals and communications; it takes time to learn the art of creating thin vertical product backlog items for a data science project; writing acceptance criteria is also more complicated than software development cases, etc.
Excellent read accompanied by schemas and detailed explanations. Well done!
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