This is the finale to another series that I’ve followed closely. Part 1
outlined why Agile works great in an analytics context and Part 2
outlined why sometimes it’s not a perfect fit.
This final post talks about the adjustments that the author (the head of analytics at Harry’s) suggests to standard Scrum when applying it to analytics. The bullet points:
- Time-bound spikes for research
- Build in slack time for exploration
- Acceptance Criteria includes “write the next story”
- Peer-review instead of sprint-review
I’m definitely going to think hard about operationalizing some of these practices in our own Agile workflow.